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1#
2# LSST Data Management System
3# Copyright 2008-2017 AURA/LSST.
4#
5# This product includes software developed by the
6# LSST Project (http://www.lsst.org/).
7#
8# This program is free software: you can redistribute it and/or modify
9# it under the terms of the GNU General Public License as published by
10# the Free Software Foundation, either version 3 of the License, or
11# (at your option) any later version.
12#
13# This program is distributed in the hope that it will be useful,
14# but WITHOUT ANY WARRANTY; without even the implied warranty of
15# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
16# GNU General Public License for more details.
17#
18# You should have received a copy of the LSST License Statement and
19# the GNU General Public License along with this program. If not,
20# see <https://www.lsstcorp.org/LegalNotices/>.
21#
22"""
23Define dataset class for MeasurePhotonTransferCurve task
24"""
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 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.
145 Returns
146 -------
147 `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
148 Output dataset from MeasurePhotonTransferCurveTask.
149 """
151 _OBSTYPE = 'PTC'
152 _SCHEMA = 'Gen3 Photon Transfer Curve'
153 _VERSION = 1.0
155 def __init__(self, ampNames=[], ptcFitType=None, covMatrixSide=1, **kwargs):
157 self.ptcFitType = ptcFitType
158 self.ampNames = ampNames
159 self.covMatrixSide = covMatrixSide
161 self.badAmps = [np.nan]
163 self.inputExpIdPairs = {ampName: [] for ampName in ampNames}
164 self.expIdMask = {ampName: [] for ampName in ampNames}
165 self.rawExpTimes = {ampName: [] for ampName in ampNames}
166 self.rawMeans = {ampName: [] for ampName in ampNames}
167 self.rawVars = {ampName: [] for ampName in ampNames}
168 self.photoCharge = {ampName: [] for ampName in ampNames}
170 self.gain = {ampName: np.nan for ampName in ampNames}
171 self.gainErr = {ampName: np.nan for ampName in ampNames}
172 self.noise = {ampName: np.nan for ampName in ampNames}
173 self.noiseErr = {ampName: np.nan for ampName in ampNames}
175 self.ptcFitPars = {ampName: [] for ampName in ampNames}
176 self.ptcFitParsError = {ampName: [] for ampName in ampNames}
177 self.ptcFitChiSq = {ampName: np.nan for ampName in ampNames}
179 self.covariances = {ampName: [] for ampName in ampNames}
180 self.covariancesModel = {ampName: [] for ampName in ampNames}
181 self.covariancesSqrtWeights = {ampName: [] for ampName in ampNames}
182 self.aMatrix = {ampName: np.nan for ampName in ampNames}
183 self.bMatrix = {ampName: np.nan for ampName in ampNames}
184 self.covariancesModelNoB = {ampName: [] for ampName in ampNames}
185 self.aMatrixNoB = {ampName: np.nan for ampName in ampNames}
187 self.finalVars = {ampName: [] for ampName in ampNames}
188 self.finalModelVars = {ampName: [] for ampName in ampNames}
189 self.finalMeans = {ampName: [] for ampName in ampNames}
191 super().__init__(**kwargs)
192 self.requiredAttributes.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'])
200 def __eq__(self, other):
201 """Calibration equivalence
202 """
203 if not isinstance(other, self.__class__):
204 return False
206 for attr in self._requiredAttributes:
207 attrSelf = getattr(self, attr)
208 attrOther = getattr(other, attr)
209 if isinstance(attrSelf, dict) and isinstance(attrOther, dict):
210 for ampName in attrSelf:
211 if not np.allclose(attrSelf[ampName], attrOther[ampName], equal_nan=True):
212 return False
213 else:
214 if attrSelf != attrOther:
215 return False
216 return True
218 def setAmpValues(self, ampName, inputExpIdPair=[(np.nan, np.nan)], expIdMask=[np.nan],
219 rawExpTime=[np.nan], rawMean=[np.nan], rawVar=[np.nan], photoCharge=[np.nan],
220 gain=np.nan, gainErr=np.nan, noise=np.nan, noiseErr=np.nan, ptcFitPars=[np.nan],
221 ptcFitParsError=[np.nan], ptcFitChiSq=np.nan, covArray=[], covArrayModel=[],
222 covSqrtWeights=[], aMatrix=[], bMatrix=[], covArrayModelNoB=[], aMatrixNoB=[],
223 finalVar=[np.nan], finalModelVar=[np.nan], finalMean=[np.nan]):
224 """Function to initialize an amp of a PhotonTransferCurveDataset.
226 Notes
227 -----
228 The parameters are all documented in `init`.
229 """
230 nanMatrix = np.full((self.covMatrixSide, self.covMatrixSide), np.nan)
231 if len(covArray) == 0:
232 covArray = [nanMatrix]
233 if len(covArrayModel) == 0:
234 covArrayModel = [nanMatrix]
235 if len(covSqrtWeights) == 0:
236 covSqrtWeights = [nanMatrix]
237 if len(covArrayModelNoB) == 0:
238 covArrayModelNoB = [nanMatrix]
239 if len(aMatrix) == 0:
240 aMatrix = nanMatrix
241 if len(bMatrix) == 0:
242 bMatrix = nanMatrix
243 if len(aMatrixNoB) == 0:
244 aMatrixNoB = nanMatrix
246 self.inputExpIdPairs[ampName] = inputExpIdPair
247 self.expIdMask[ampName] = expIdMask
248 self.rawExpTimes[ampName] = rawExpTime
249 self.rawMeans[ampName] = rawMean
250 self.rawVars[ampName] = rawVar
251 self.photoCharge[ampName] = photoCharge
252 self.gain[ampName] = gain
253 self.gainErr[ampName] = gainErr
254 self.noise[ampName] = noise
255 self.noiseErr[ampName] = noiseErr
256 self.ptcFitPars[ampName] = ptcFitPars
257 self.ptcFitParsError[ampName] = ptcFitParsError
258 self.ptcFitChiSq[ampName]
259 self.covariances[ampName] = covArray
260 self.covariancesSqrtWeights[ampName] = covSqrtWeights
261 self.covariancesModel[ampName] = covArrayModel
262 self.covariancesModelNoB[ampName] = covArrayModelNoB
263 self.aMatrix[ampName] = aMatrix
264 self.bMatrix[ampName] = bMatrix
265 self.aMatrixNoB[ampName] = aMatrixNoB
266 self.ptcFitPars[ampName] = ptcFitPars
267 self.ptcFitParsError[ampName] = ptcFitParsError
268 self.ptcFitChiSq[ampName] = ptcFitChiSq
269 self.finalVars[ampName] = finalVar
270 self.finalModelVars[ampName] = finalModelVar
271 self.finalMeans[ampName] = finalMean
273 def updateMetadata(self, setDate=False, **kwargs):
274 """Update calibration metadata.
275 This calls the base class's method after ensuring the required
276 calibration keywords will be saved.
277 Parameters
278 ----------
279 setDate : `bool`, optional
280 Update the CALIBDATE fields in the metadata to the current
281 time. Defaults to False.
282 kwargs :
283 Other keyword parameters to set in the metadata.
284 """
285 kwargs['PTC_FIT_TYPE'] = self.ptcFitType
287 super().updateMetadata(setDate=setDate, **kwargs)
289 @classmethod
290 def fromDict(cls, dictionary):
291 """Construct a calibration from a dictionary of properties.
292 Must be implemented by the specific calibration subclasses.
293 Parameters
294 ----------
295 dictionary : `dict`
296 Dictionary of properties.
297 Returns
298 -------
299 calib : `lsst.ip.isr.CalibType`
300 Constructed calibration.
301 Raises
302 ------
303 RuntimeError :
304 Raised if the supplied dictionary is for a different
305 calibration.
306 """
307 calib = cls()
308 if calib._OBSTYPE != dictionary['metadata']['OBSTYPE']:
309 raise RuntimeError(f"Incorrect Photon Transfer Curve dataset supplied. "
310 f"Expected {calib._OBSTYPE}, found {dictionary['metadata']['OBSTYPE']}")
311 calib.setMetadata(dictionary['metadata'])
312 calib.ptcFitType = dictionary['ptcFitType']
313 calib.covMatrixSide = dictionary['covMatrixSide']
314 calib.badAmps = np.array(dictionary['badAmps'], 'str').tolist()
315 # The cov matrices are square
316 covMatrixSide = calib.covMatrixSide
317 # Number of final signal levels
318 covDimensionsProduct = len(np.array(list(dictionary['covariances'].values())[0]).ravel())
319 nSignalPoints = int(covDimensionsProduct/(covMatrixSide*covMatrixSide))
321 for ampName in dictionary['ampNames']:
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.covariances[ampName] = np.array(dictionary['covariances'][ampName]).reshape(
336 (nSignalPoints, covMatrixSide, covMatrixSide)).tolist()
337 calib.covariancesModel[ampName] = np.array(
338 dictionary['covariancesModel'][ampName]).reshape(
339 (nSignalPoints, covMatrixSide, covMatrixSide)).tolist()
340 calib.covariancesSqrtWeights[ampName] = np.array(
341 dictionary['covariancesSqrtWeights'][ampName]).reshape(
342 (nSignalPoints, covMatrixSide, covMatrixSide)).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)).tolist()
350 calib.aMatrixNoB[ampName] = np.array(
351 dictionary['aMatrixNoB'][ampName]).reshape((covMatrixSide, covMatrixSide)).tolist()
352 calib.finalVars[ampName] = np.array(dictionary['finalVars'][ampName]).tolist()
353 calib.finalModelVars[ampName] = np.array(dictionary['finalModelVars'][ampName]).tolist()
354 calib.finalMeans[ampName] = np.array(dictionary['finalMeans'][ampName]).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['covariances'] = self.covariances
391 outDict['covariancesModel'] = self.covariancesModel
392 outDict['covariancesSqrtWeights'] = self.covariancesSqrtWeights
393 outDict['aMatrix'] = self.aMatrix
394 outDict['bMatrix'] = self.bMatrix
395 outDict['covariancesModelNoB'] = self.covariancesModelNoB
396 outDict['aMatrixNoB'] = self.aMatrixNoB
397 outDict['finalVars'] = self.finalVars
398 outDict['finalModelVars'] = self.finalModelVars
399 outDict['finalMeans'] = self.finalMeans
400 outDict['photoCharge'] = self.photoCharge
402 return outDict
404 @classmethod
405 def fromTable(cls, tableList):
406 """Construct calibration from a list of tables.
407 This method uses the `fromDict` method to create the
408 calibration, after constructing an appropriate dictionary from
409 the input tables.
410 Parameters
411 ----------
412 tableList : `list` [`lsst.afw.table.Table`]
413 List of tables to use to construct the datasetPtc.
414 Returns
415 -------
416 calib : `lsst.cp.pipe.`
417 The calibration defined in the tables.
418 """
419 ptcTable = tableList[0]
421 metadata = ptcTable.meta
422 inDict = dict()
423 inDict['metadata'] = metadata
424 inDict['ampNames'] = []
425 inDict['ptcFitType'] = []
426 inDict['covMatrixSide'] = []
427 inDict['inputExpIdPairs'] = dict()
428 inDict['expIdMask'] = dict()
429 inDict['rawExpTimes'] = dict()
430 inDict['rawMeans'] = dict()
431 inDict['rawVars'] = dict()
432 inDict['gain'] = dict()
433 inDict['gainErr'] = dict()
434 inDict['noise'] = dict()
435 inDict['noiseErr'] = dict()
436 inDict['ptcFitPars'] = dict()
437 inDict['ptcFitParsError'] = dict()
438 inDict['ptcFitChiSq'] = dict()
439 inDict['covariances'] = dict()
440 inDict['covariancesModel'] = dict()
441 inDict['covariancesSqrtWeights'] = dict()
442 inDict['aMatrix'] = dict()
443 inDict['bMatrix'] = dict()
444 inDict['covariancesModelNoB'] = dict()
445 inDict['aMatrixNoB'] = dict()
446 inDict['finalVars'] = dict()
447 inDict['finalModelVars'] = dict()
448 inDict['finalMeans'] = dict()
449 inDict['badAmps'] = []
450 inDict['photoCharge'] = dict()
452 for record in ptcTable:
453 ampName = record['AMPLIFIER_NAME']
455 inDict['ptcFitType'] = record['PTC_FIT_TYPE']
456 inDict['covMatrixSide'] = record['COV_MATRIX_SIDE']
457 inDict['ampNames'].append(ampName)
458 inDict['inputExpIdPairs'][ampName] = record['INPUT_EXP_ID_PAIRS']
459 inDict['expIdMask'][ampName] = record['EXP_ID_MASK']
460 inDict['rawExpTimes'][ampName] = record['RAW_EXP_TIMES']
461 inDict['rawMeans'][ampName] = record['RAW_MEANS']
462 inDict['rawVars'][ampName] = record['RAW_VARS']
463 inDict['gain'][ampName] = record['GAIN']
464 inDict['gainErr'][ampName] = record['GAIN_ERR']
465 inDict['noise'][ampName] = record['NOISE']
466 inDict['noiseErr'][ampName] = record['NOISE_ERR']
467 inDict['ptcFitPars'][ampName] = record['PTC_FIT_PARS']
468 inDict['ptcFitParsError'][ampName] = record['PTC_FIT_PARS_ERROR']
469 inDict['ptcFitChiSq'][ampName] = record['PTC_FIT_CHI_SQ']
470 inDict['covariances'][ampName] = record['COVARIANCES']
471 inDict['covariancesModel'][ampName] = record['COVARIANCES_MODEL']
472 inDict['covariancesSqrtWeights'][ampName] = record['COVARIANCES_SQRT_WEIGHTS']
473 inDict['aMatrix'][ampName] = record['A_MATRIX']
474 inDict['bMatrix'][ampName] = record['B_MATRIX']
475 inDict['covariancesModelNoB'][ampName] = record['COVARIANCES_MODEL_NO_B']
476 inDict['aMatrixNoB'][ampName] = record['A_MATRIX_NO_B']
477 inDict['finalVars'][ampName] = record['FINAL_VARS']
478 inDict['finalModelVars'][ampName] = record['FINAL_MODEL_VARS']
479 inDict['finalMeans'][ampName] = record['FINAL_MEANS']
480 inDict['badAmps'] = record['BAD_AMPS']
481 inDict['photoCharge'][ampName] = record['PHOTO_CHARGE']
482 return cls().fromDict(inDict)
484 def toTable(self):
485 """Construct a list of tables containing the information in this
486 calibration.
488 The list of tables should create an identical calibration
489 after being passed to this class's fromTable method.
490 Returns
491 -------
492 tableList : `list` [`astropy.table.Table`]
493 List of tables containing the linearity calibration
494 information.
495 """
496 tableList = []
497 self.updateMetadata()
498 nPoints = []
499 for i, ampName in enumerate(self.ampNames):
500 nPoints.append(len(list(self.covariances.values())[i]))
501 nSignalPoints = max(nPoints)
502 nPadPoints = {}
503 for i, ampName in enumerate(self.ampNames):
504 nPadPoints[ampName] = nSignalPoints - len(list(self.covariances.values())[i])
505 covMatrixSide = self.covMatrixSide
507 catalog = Table([{'AMPLIFIER_NAME': ampName,
508 'PTC_FIT_TYPE': self.ptcFitType,
509 'COV_MATRIX_SIDE': self.covMatrixSide,
510 'INPUT_EXP_ID_PAIRS': self.inputExpIdPairs[ampName]
511 if len(self.expIdMask[ampName]) else np.nan,
512 'EXP_ID_MASK': self.expIdMask[ampName]
513 if len(self.expIdMask[ampName]) else np.nan,
514 'RAW_EXP_TIMES': np.array(self.rawExpTimes[ampName]).tolist()
515 if len(self.rawExpTimes[ampName]) else np.nan,
516 'RAW_MEANS': np.array(self.rawMeans[ampName]).tolist()
517 if len(self.rawMeans[ampName]) else np.nan,
518 'RAW_VARS': np.array(self.rawVars[ampName]).tolist()
519 if len(self.rawVars[ampName]) else np.nan,
520 'GAIN': self.gain[ampName],
521 'GAIN_ERR': self.gainErr[ampName],
522 'NOISE': self.noise[ampName],
523 'NOISE_ERR': self.noiseErr[ampName],
524 'PTC_FIT_PARS': np.array(self.ptcFitPars[ampName]).tolist(),
525 'PTC_FIT_PARS_ERROR': np.array(self.ptcFitParsError[ampName]).tolist(),
526 'PTC_FIT_CHI_SQ': self.ptcFitChiSq[ampName],
527 'COVARIANCES': np.pad(np.array(self.covariances[ampName]),
528 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
529 'constant', constant_values=np.nan).reshape(
530 nSignalPoints*covMatrixSide**2).tolist(),
531 'COVARIANCES_MODEL': np.pad(np.array(self.covariancesModel[ampName]),
532 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
533 'constant', constant_values=np.nan).reshape(
534 nSignalPoints*covMatrixSide**2).tolist(),
535 'COVARIANCES_SQRT_WEIGHTS': np.pad(np.array(self.covariancesSqrtWeights[ampName]),
536 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
537 'constant', constant_values=0.0).reshape(
538 nSignalPoints*covMatrixSide**2).tolist(),
539 'A_MATRIX': np.array(self.aMatrix[ampName]).reshape(covMatrixSide**2).tolist(),
540 'B_MATRIX': np.array(self.bMatrix[ampName]).reshape(covMatrixSide**2).tolist(),
541 'COVARIANCES_MODEL_NO_B':
542 np.pad(np.array(self.covariancesModelNoB[ampName]),
543 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
544 'constant', constant_values=np.nan).reshape(
545 nSignalPoints*covMatrixSide**2).tolist(),
546 'A_MATRIX_NO_B': np.array(self.aMatrixNoB[ampName]).reshape(
547 covMatrixSide**2).tolist(),
548 'FINAL_VARS': np.pad(np.array(self.finalVars[ampName]), (0, nPadPoints[ampName]),
549 'constant', constant_values=np.nan).tolist(),
550 'FINAL_MODEL_VARS': np.pad(np.array(self.finalModelVars[ampName]),
551 (0, nPadPoints[ampName]),
552 'constant', constant_values=np.nan).tolist(),
553 'FINAL_MEANS': np.pad(np.array(self.finalMeans[ampName]),
554 (0, nPadPoints[ampName]),
555 'constant', constant_values=np.nan).tolist(),
556 'BAD_AMPS': np.array(self.badAmps).tolist() if len(self.badAmps) else np.nan,
557 'PHOTO_CHARGE': np.array(self.photoCharge[ampName]).tolist(),
558 } for ampName in self.ampNames])
559 inMeta = self.getMetadata().toDict()
560 outMeta = {k: v for k, v in inMeta.items() if v is not None}
561 outMeta.update({k: "" for k, v in inMeta.items() if v is None})
562 catalog.meta = outMeta
563 tableList.append(catalog)
565 return(tableList)
567 def getExpIdsUsed(self, ampName):
568 """Get the exposures used, i.e. not discarded, for a given amp.
569 If no mask has been created yet, all exposures are returned.
570 """
571 if len(self.expIdMask[ampName]) == 0:
572 return self.inputExpIdPairs[ampName]
574 # if the mask exists it had better be the same length as the expIdPairs
575 assert len(self.expIdMask[ampName]) == len(self.inputExpIdPairs[ampName])
577 pairs = self.inputExpIdPairs[ampName]
578 mask = self.expIdMask[ampName]
579 # cast to bool required because numpy
580 return [(exp1, exp2) for ((exp1, exp2), m) in zip(pairs, mask) if bool(m) is True]
582 def getGoodAmps(self):
583 return [amp for amp in self.ampNames if amp not in self.badAmps]