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1# This file is part of cp_pipe.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <https://www.gnu.org/licenses/>.
21#
23__all__ = ['MeasurePhotonTransferCurveTask',
24 'MeasurePhotonTransferCurveTaskConfig',
25 'PhotonTransferCurveDataset']
27import numpy as np
28import matplotlib.pyplot as plt
29from sqlite3 import OperationalError
30from collections import Counter
31from dataclasses import dataclass
33import lsst.afw.math as afwMath
34import lsst.pex.config as pexConfig
35import lsst.pipe.base as pipeBase
36from .utils import (NonexistentDatasetTaskDataIdContainer, PairedVisitListTaskRunner,
37 checkExpLengthEqual, fitLeastSq, fitBootstrap, funcPolynomial, funcAstier)
38from scipy.optimize import least_squares
40from lsst.ip.isr.linearize import Linearizer
41import datetime
43from .astierCovPtcUtils import (fftSize, CovFft, computeCovDirect, fitData)
46class MeasurePhotonTransferCurveTaskConfig(pexConfig.Config):
47 """Config class for photon transfer curve measurement task"""
48 ccdKey = pexConfig.Field(
49 dtype=str,
50 doc="The key by which to pull a detector from a dataId, e.g. 'ccd' or 'detector'.",
51 default='ccd',
52 )
53 ptcFitType = pexConfig.ChoiceField(
54 dtype=str,
55 doc="Fit PTC to approximation in Astier+19 (Equation 16) or to a polynomial.",
56 default="POLYNOMIAL",
57 allowed={
58 "POLYNOMIAL": "n-degree polynomial (use 'polynomialFitDegree' to set 'n').",
59 "EXPAPPROXIMATION": "Approximation in Astier+19 (Eq. 16).",
60 "FULLCOVARIANCE": "Full covariances model in Astier+19 (Eq. 20)"
61 }
62 )
63 maximumRangeCovariancesAstier = pexConfig.Field(
64 dtype=int,
65 doc="Maximum range of covariances as in Astier+19",
66 default=8,
67 )
68 covAstierRealSpace = pexConfig.Field(
69 dtype=bool,
70 doc="Calculate covariances in real space or via FFT? (see appendix A of Astier+19).",
71 default=False,
72 )
73 polynomialFitDegree = pexConfig.Field(
74 dtype=int,
75 doc="Degree of polynomial to fit the PTC, when 'ptcFitType'=POLYNOMIAL.",
76 default=3,
77 )
78 doCreateLinearizer = pexConfig.Field(
79 dtype=bool,
80 doc="Calculate non-linearity and persist linearizer?",
81 default=False,
82 )
83 linearizerType = pexConfig.ChoiceField(
84 dtype=str,
85 doc="Linearizer type, if doCreateLinearizer=True",
86 default="LINEARIZEPOLYNOMIAL",
87 allowed={
88 "LINEARIZEPOLYNOMIAL": "n-degree polynomial (use 'polynomialFitDegreeNonLinearity' to set 'n').",
89 "LINEARIZESQUARED": "c0 quadratic coefficient derived from coefficients of polynomiual fit",
90 "LOOKUPTABLE": "Loouk table formed from linear part of polynomial fit."
91 }
92 )
93 polynomialFitDegreeNonLinearity = pexConfig.Field(
94 dtype=int,
95 doc="If doCreateLinearizer, degree of polynomial to fit the meanSignal vs exposureTime" +
96 " curve to produce the table for LinearizeLookupTable.",
97 default=3,
98 )
99 binSize = pexConfig.Field(
100 dtype=int,
101 doc="Bin the image by this factor in both dimensions.",
102 default=1,
103 )
104 minMeanSignal = pexConfig.Field(
105 dtype=float,
106 doc="Minimum value (inclusive) of mean signal (in DN) above which to consider.",
107 default=0,
108 )
109 maxMeanSignal = pexConfig.Field(
110 dtype=float,
111 doc="Maximum value (inclusive) of mean signal (in DN) below which to consider.",
112 default=9e6,
113 )
114 initialNonLinearityExclusionThresholdPositive = pexConfig.RangeField(
115 dtype=float,
116 doc="Initially exclude data points with a variance that are more than a factor of this from being"
117 " linear in the positive direction, from the PTC fit. Note that these points will also be"
118 " excluded from the non-linearity fit. This is done before the iterative outlier rejection,"
119 " to allow an accurate determination of the sigmas for said iterative fit.",
120 default=0.12,
121 min=0.0,
122 max=1.0,
123 )
124 initialNonLinearityExclusionThresholdNegative = pexConfig.RangeField(
125 dtype=float,
126 doc="Initially exclude data points with a variance that are more than a factor of this from being"
127 " linear in the negative direction, from the PTC fit. Note that these points will also be"
128 " excluded from the non-linearity fit. This is done before the iterative outlier rejection,"
129 " to allow an accurate determination of the sigmas for said iterative fit.",
130 default=0.25,
131 min=0.0,
132 max=1.0,
133 )
134 sigmaCutPtcOutliers = pexConfig.Field(
135 dtype=float,
136 doc="Sigma cut for outlier rejection in PTC.",
137 default=5.0,
138 )
139 nSigmaClipPtc = pexConfig.Field(
140 dtype=float,
141 doc="Sigma cut for afwMath.StatisticsControl()",
142 default=5.5,
143 )
144 nIterSigmaClipPtc = pexConfig.Field(
145 dtype=int,
146 doc="Number of sigma-clipping iterations for afwMath.StatisticsControl()",
147 default=1,
148 )
149 maxIterationsPtcOutliers = pexConfig.Field(
150 dtype=int,
151 doc="Maximum number of iterations for outlier rejection in PTC.",
152 default=2,
153 )
154 doFitBootstrap = pexConfig.Field(
155 dtype=bool,
156 doc="Use bootstrap for the PTC fit parameters and errors?.",
157 default=False,
158 )
159 maxAduForLookupTableLinearizer = pexConfig.Field(
160 dtype=int,
161 doc="Maximum DN value for the LookupTable linearizer.",
162 default=2**18,
163 )
164 instrumentName = pexConfig.Field(
165 dtype=str,
166 doc="Instrument name.",
167 default='',
168 )
171@dataclass
172class LinearityResidualsAndLinearizersDataset:
173 """A simple class to hold the output from the
174 `calculateLinearityResidualAndLinearizers` function.
175 """
176 # Normalized coefficients for polynomial NL correction
177 polynomialLinearizerCoefficients: list
178 # Normalized coefficient for quadratic polynomial NL correction (c0)
179 quadraticPolynomialLinearizerCoefficient: float
180 # LUT array row for the amplifier at hand
181 linearizerTableRow: list
182 meanSignalVsTimePolyFitPars: list
183 meanSignalVsTimePolyFitParsErr: list
184 meanSignalVsTimePolyFitReducedChiSq: float
187class PhotonTransferCurveDataset:
188 """A simple class to hold the output data from the PTC task.
190 The dataset is made up of a dictionary for each item, keyed by the
191 amplifiers' names, which much be supplied at construction time.
193 New items cannot be added to the class to save accidentally saving to the
194 wrong property, and the class can be frozen if desired.
196 inputVisitPairs records the visits used to produce the data.
197 When fitPtc() or fitCovariancesAstier() is run, a mask is built up, which is by definition
198 always the same length as inputVisitPairs, rawExpTimes, rawMeans
199 and rawVars, and is a list of bools, which are incrementally set to False
200 as points are discarded from the fits.
202 PTC fit parameters for polynomials are stored in a list in ascending order
203 of polynomial term, i.e. par[0]*x^0 + par[1]*x + par[2]*x^2 etc
204 with the length of the list corresponding to the order of the polynomial
205 plus one.
207 Parameters
208 ----------
209 ampNames : `list`
210 List with the names of the amplifiers of the detector at hand.
212 ptcFitType : `str`
213 Type of model fitted to the PTC: "POLYNOMIAL", "EXPAPPROXIMATION", or "FULLCOVARIANCE".
215 Returns
216 -------
217 `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
218 Output dataset from MeasurePhotonTransferCurveTask.
219 """
221 def __init__(self, ampNames, ptcFitType):
222 # add items to __dict__ directly because __setattr__ is overridden
224 # instance variables
225 self.__dict__["ptcFitType"] = ptcFitType
226 self.__dict__["ampNames"] = ampNames
227 self.__dict__["badAmps"] = []
229 # raw data variables
230 # visitMask is the mask produced after outlier rejection. The mask produced by "FULLCOVARIANCE"
231 # may differ from the one produced in the other two PTC fit types.
232 self.__dict__["inputVisitPairs"] = {ampName: [] for ampName in ampNames}
233 self.__dict__["visitMask"] = {ampName: [] for ampName in ampNames}
234 self.__dict__["rawExpTimes"] = {ampName: [] for ampName in ampNames}
235 self.__dict__["rawMeans"] = {ampName: [] for ampName in ampNames}
236 self.__dict__["rawVars"] = {ampName: [] for ampName in ampNames}
238 # Gain and noise
239 self.__dict__["gain"] = {ampName: -1. for ampName in ampNames}
240 self.__dict__["gainErr"] = {ampName: -1. for ampName in ampNames}
241 self.__dict__["noise"] = {ampName: -1. for ampName in ampNames}
242 self.__dict__["noiseErr"] = {ampName: -1. for ampName in ampNames}
244 # if ptcFitTye in ["POLYNOMIAL", "EXPAPPROXIMATION"]
245 # fit information
246 self.__dict__["ptcFitPars"] = {ampName: [] for ampName in ampNames}
247 self.__dict__["ptcFitParsError"] = {ampName: [] for ampName in ampNames}
248 self.__dict__["ptcFitReducedChiSquared"] = {ampName: [] for ampName in ampNames}
250 # if ptcFitTye in ["FULLCOVARIANCE"]
251 # "covariancesTuple" is a numpy recarray with entries of the form
252 # ['mu', 'i', 'j', 'var', 'cov', 'npix', 'ext', 'expTime', 'ampName']
253 # "covariancesFits" has CovFit objects that fit the measured covariances to Eq. 20 of Astier+19.
254 # In "covariancesFitsWithNoB", "b"=0 in the model described by Eq. 20 of Astier+19.
255 self.__dict__["covariancesTuple"] = {ampName: [] for ampName in ampNames}
256 self.__dict__["covariancesFitsWithNoB"] = {ampName: [] for ampName in ampNames}
257 self.__dict__["covariancesFits"] = {ampName: [] for ampName in ampNames}
258 self.__dict__["aMatrix"] = {ampName: [] for ampName in ampNames}
259 self.__dict__["bMatrix"] = {ampName: [] for ampName in ampNames}
261 # "final" means that the "raw" vectors above had "visitMask" applied.
262 self.__dict__["finalVars"] = {ampName: [] for ampName in ampNames}
263 self.__dict__["finalModelVars"] = {ampName: [] for ampName in ampNames}
264 self.__dict__["finalMeans"] = {ampName: [] for ampName in ampNames}
266 def __setattr__(self, attribute, value):
267 """Protect class attributes"""
268 if attribute not in self.__dict__:
269 raise AttributeError(f"{attribute} is not already a member of PhotonTransferCurveDataset, which"
270 " does not support setting of new attributes.")
271 else:
272 self.__dict__[attribute] = value
274 def getVisitsUsed(self, ampName):
275 """Get the visits used, i.e. not discarded, for a given amp.
277 If no mask has been created yet, all visits are returned.
278 """
279 if len(self.visitMask[ampName]) == 0:
280 return self.inputVisitPairs[ampName]
282 # if the mask exists it had better be the same length as the visitPairs
283 assert len(self.visitMask[ampName]) == len(self.inputVisitPairs[ampName])
285 pairs = self.inputVisitPairs[ampName]
286 mask = self.visitMask[ampName]
287 # cast to bool required because numpy
288 return [(v1, v2) for ((v1, v2), m) in zip(pairs, mask) if bool(m) is True]
290 def getGoodAmps(self):
291 return [amp for amp in self.ampNames if amp not in self.badAmps]
294class MeasurePhotonTransferCurveTask(pipeBase.CmdLineTask):
295 """A class to calculate, fit, and plot a PTC from a set of flat pairs.
297 The Photon Transfer Curve (var(signal) vs mean(signal)) is a standard tool
298 used in astronomical detectors characterization (e.g., Janesick 2001,
299 Janesick 2007). If ptcFitType is "EXPAPPROXIMATION" or "POLYNOMIAL", this task calculates the
300 PTC from a series of pairs of flat-field images; each pair taken at identical exposure
301 times. The difference image of each pair is formed to eliminate fixed pattern noise,
302 and then the variance of the difference image and the mean of the average image
303 are used to produce the PTC. An n-degree polynomial or the approximation in Equation
304 16 of Astier+19 ("The Shape of the Photon Transfer Curve of CCD sensors",
305 arXiv:1905.08677) can be fitted to the PTC curve. These models include
306 parameters such as the gain (e/DN) and readout noise.
308 Linearizers to correct for signal-chain non-linearity are also calculated.
309 The `Linearizer` class, in general, can support per-amp linearizers, but in this
310 task this is not supported.
312 If ptcFitType is "FULLCOVARIANCE", the covariances of the difference images are calculated via the
313 DFT methods described in Astier+19 and the variances for the PTC are given by the cov[0,0] elements
314 at each signal level. The full model in Equation 20 of Astier+19 is fit to the PTC to get the gain
315 and the noise.
317 Parameters
318 ----------
320 *args: `list`
321 Positional arguments passed to the Task constructor. None used at this
322 time.
323 **kwargs: `dict`
324 Keyword arguments passed on to the Task constructor. None used at this
325 time.
327 """
329 RunnerClass = PairedVisitListTaskRunner
330 ConfigClass = MeasurePhotonTransferCurveTaskConfig
331 _DefaultName = "measurePhotonTransferCurve"
333 def __init__(self, *args, **kwargs):
334 pipeBase.CmdLineTask.__init__(self, *args, **kwargs)
335 plt.interactive(False) # stop windows popping up when plotting. When headless, use 'agg' backend too
336 self.config.validate()
337 self.config.freeze()
339 @classmethod
340 def _makeArgumentParser(cls):
341 """Augment argument parser for the MeasurePhotonTransferCurveTask."""
342 parser = pipeBase.ArgumentParser(name=cls._DefaultName)
343 parser.add_argument("--visit-pairs", dest="visitPairs", nargs="*",
344 help="Visit pairs to use. Each pair must be of the form INT,INT e.g. 123,456")
345 parser.add_id_argument("--id", datasetType="photonTransferCurveDataset",
346 ContainerClass=NonexistentDatasetTaskDataIdContainer,
347 help="The ccds to use, e.g. --id ccd=0..100")
348 return parser
350 @pipeBase.timeMethod
351 def runDataRef(self, dataRef, visitPairs):
352 """Run the Photon Transfer Curve (PTC) measurement task.
354 For a dataRef (which is each detector here),
355 and given a list of visit pairs (postISR) at different exposure times,
356 measure the PTC.
358 Parameters
359 ----------
360 dataRef : list of lsst.daf.persistence.ButlerDataRef
361 dataRef for the detector for the visits to be fit.
363 visitPairs : `iterable` of `tuple` of `int`
364 Pairs of visit numbers to be processed together
365 """
367 # setup necessary objects
368 detNum = dataRef.dataId[self.config.ccdKey]
369 detector = dataRef.get('camera')[dataRef.dataId[self.config.ccdKey]]
370 # expand some missing fields that we need for lsstCam. This is a work-around
371 # for Gen2 problems that I (RHL) don't feel like solving. The calibs pipelines
372 # (which inherit from CalibTask) use addMissingKeys() to do basically the same thing
373 #
374 # Basically, the butler's trying to look up the fields in `raw_visit` which won't work
375 for name in dataRef.getButler().getKeys('bias'):
376 if name not in dataRef.dataId:
377 try:
378 dataRef.dataId[name] = \
379 dataRef.getButler().queryMetadata('raw', [name], detector=detNum)[0]
380 except OperationalError:
381 pass
383 amps = detector.getAmplifiers()
384 ampNames = [amp.getName() for amp in amps]
385 datasetPtc = PhotonTransferCurveDataset(ampNames, self.config.ptcFitType)
386 self.log.info('Measuring PTC using %s visits for detector %s' % (visitPairs, detector.getId()))
388 tupleRecords = []
389 allTags = []
390 for (v1, v2) in visitPairs:
391 # Get postISR exposures.
392 dataRef.dataId['expId'] = v1
393 exp1 = dataRef.get("postISRCCD", immediate=True)
394 dataRef.dataId['expId'] = v2
395 exp2 = dataRef.get("postISRCCD", immediate=True)
396 del dataRef.dataId['expId']
398 checkExpLengthEqual(exp1, exp2, v1, v2, raiseWithMessage=True)
399 expTime = exp1.getInfo().getVisitInfo().getExposureTime()
401 tupleRows = []
402 for ampNumber, amp in enumerate(detector):
403 ampName = amp.getName()
404 # covAstier: (i, j, var (cov[0,0]), cov, npix)
405 doRealSpace = self.config.covAstierRealSpace
406 muDiff, varDiff, covAstier = self.measureMeanVarCov(exp1, exp2, region=amp.getBBox(),
407 covAstierRealSpace=doRealSpace)
409 datasetPtc.rawExpTimes[ampName].append(expTime)
410 datasetPtc.rawMeans[ampName].append(muDiff)
411 datasetPtc.rawVars[ampName].append(varDiff)
412 datasetPtc.inputVisitPairs[ampName].append((v1, v2))
414 tupleRows += [(muDiff, ) + covRow + (ampNumber, expTime, ampName) for covRow in covAstier]
415 tags = ['mu', 'i', 'j', 'var', 'cov', 'npix', 'ext', 'expTime', 'ampName']
416 allTags += tags
417 tupleRecords += tupleRows
418 covariancesWithTags = np.core.records.fromrecords(tupleRecords, names=allTags)
420 if self.config.ptcFitType in ["FULLCOVARIANCE", ]:
421 # Calculate covariances and fit them, including the PTC, to Astier+19 full model (Eq. 20)
422 datasetPtc = self.fitCovariancesAstier(datasetPtc, covariancesWithTags)
423 elif self.config.ptcFitType in ["EXPAPPROXIMATION", "POLYNOMIAL"]:
424 # Fit the PTC to a polynomial or to Astier+19 exponential approximation (Eq. 16)
425 # Fill up PhotonTransferCurveDataset object.
426 datasetPtc = self.fitPtc(datasetPtc, self.config.ptcFitType)
428 # Fit a poynomial to calculate non-linearity and persist linearizer.
429 if self.config.doCreateLinearizer:
430 numberAmps = len(amps)
431 numberAduValues = self.config.maxAduForLookupTableLinearizer
432 lookupTableArray = np.zeros((numberAmps, numberAduValues), dtype=np.float32)
434 # Fit (non)linearity of signal vs time curve.
435 # Fill up PhotonTransferCurveDataset object.
436 # Fill up array for LUT linearizer (tableArray).
437 # Produce coefficients for Polynomial ans Squared linearizers.
438 # Build linearizer objects.
439 linearizer = self.fitNonLinearityAndBuildLinearizers(datasetPtc, detector,
440 tableArray=lookupTableArray,
441 log=self.log)
443 if self.config.linearizerType == "LINEARIZEPOLYNOMIAL":
444 linDataType = 'linearizePolynomial'
445 linMsg = "polynomial (coefficients for a polynomial correction)."
446 elif self.config.linearizerType == "LINEARIZESQUARED":
447 linDataType = 'linearizePolynomial'
448 linMsg = "squared (c0, derived from k_i coefficients of a polynomial fit)."
449 elif self.config.linearizerType == "LOOKUPTABLE":
450 linDataType = 'linearizePolynomial'
451 linMsg = "lookup table (linear component of polynomial fit)."
452 else:
453 raise RuntimeError("Invalid config.linearizerType {selg.config.linearizerType}. "
454 "Supported: 'LOOKUPTABLE', 'LINEARIZESQUARED', or 'LINEARIZEPOLYNOMIAL'")
456 butler = dataRef.getButler()
457 self.log.info(f"Writing linearizer: \n {linMsg}")
459 detName = detector.getName()
460 now = datetime.datetime.utcnow()
461 calibDate = now.strftime("%Y-%m-%d")
463 butler.put(linearizer, datasetType=linDataType, dataId={'detector': detNum,
464 'detectorName': detName, 'calibDate': calibDate})
466 self.log.info(f"Writing PTC data to {dataRef.getUri(write=True)}")
467 dataRef.put(datasetPtc, datasetType="photonTransferCurveDataset")
469 return pipeBase.Struct(exitStatus=0)
471 def fitCovariancesAstier(self, dataset, covariancesWithTagsArray):
472 """Fit measured flat covariances to full model in Astier+19.
474 Parameters
475 ----------
476 dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
477 The dataset containing information such as the means, variances and exposure times.
479 covariancesWithTagsArray : `numpy.recarray`
480 Tuple with at least (mu, cov, var, i, j, npix), where:
481 mu : 0.5*(m1 + m2), where:
482 mu1: mean value of flat1
483 mu2: mean value of flat2
484 cov: covariance value at lag(i, j)
485 var: variance(covariance value at lag(0, 0))
486 i: lag dimension
487 j: lag dimension
488 npix: number of pixels used for covariance calculation.
490 Returns
491 -------
492 dataset: `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
493 This is the same dataset as the input paramter, however, it has been modified
494 to include information such as the fit vectors and the fit parameters. See
495 the class `PhotonTransferCurveDatase`.
496 """
498 covFits, covFitsNoB = fitData(covariancesWithTagsArray, maxMu=self.config.maxMeanSignal,
499 r=self.config.maximumRangeCovariancesAstier)
501 dataset.covariancesTuple = covariancesWithTagsArray
502 dataset.covariancesFits = covFits
503 dataset.covariancesFitsWithNoB = covFitsNoB
504 dataset = self.getOutputPtcDataCovAstier(dataset, covFits)
506 return dataset
508 def getOutputPtcDataCovAstier(self, dataset, covFits):
509 """Get output data for PhotonTransferCurveCovAstierDataset from CovFit objects.
511 Parameters
512 ----------
513 dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
514 The dataset containing information such as the means, variances and exposure times.
516 covFits: `dict`
517 Dictionary of CovFit objects, with amp names as keys.
519 Returns
520 -------
521 dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
522 This is the same dataset as the input paramter, however, it has been modified
523 to include extra information such as the mask 1D array, gains, reoudout noise, measured signal,
524 measured variance, modeled variance, a, and b coefficient matrices (see Astier+19) per amplifier.
525 See the class `PhotonTransferCurveDatase`.
526 """
528 for i, amp in enumerate(covFits):
529 fit = covFits[amp]
530 meanVecFinal, varVecFinal, varVecModel, wc = fit.getNormalizedFitData(0, 0, divideByMu=False)
531 gain = fit.getGain()
532 dataset.visitMask[amp] = fit.getMaskVar()
533 dataset.gain[amp] = gain
534 dataset.gainErr[amp] = fit.getGainErr()
535 dataset.noise[amp] = np.sqrt(np.fabs(fit.getRon()))
536 dataset.noiseErr[amp] = fit.getRonErr()
537 dataset.finalVars[amp].append(varVecFinal/(gain**2))
538 dataset.finalModelVars[amp].append(varVecModel/(gain**2))
539 dataset.finalMeans[amp].append(meanVecFinal/gain)
540 dataset.aMatrix[amp].append(fit.getA())
541 dataset.bMatrix[amp].append(fit.getB())
543 return dataset
545 def measureMeanVarCov(self, exposure1, exposure2, region=None, covAstierRealSpace=False):
546 """Calculate the mean of each of two exposures and the variance and covariance of their difference.
548 The variance is calculated via afwMath, and the covariance via the methods in Astier+19 (appendix A).
549 In theory, var = covariance[0,0]. This should be validated, and in the future, we may decide to just
550 keep one (covariance).
552 Parameters
553 ----------
554 exposure1 : `lsst.afw.image.exposure.exposure.ExposureF`
555 First exposure of flat field pair.
557 exposure2 : `lsst.afw.image.exposure.exposure.ExposureF`
558 Second exposure of flat field pair.
560 region : `lsst.geom.Box2I`, optional
561 Region of each exposure where to perform the calculations (e.g, an amplifier).
563 covAstierRealSpace : `bool`, optional
564 Should the covariannces in Astier+19 be calculated in real space or via FFT?
565 See Appendix A of Astier+19.
567 Returns
568 -------
569 mu : `float`
570 0.5*(mu1 + mu2), where mu1, and mu2 are the clipped means of the regions in
571 both exposures.
573 varDiff : `float`
574 Half of the clipped variance of the difference of the regions inthe two input
575 exposures.
577 covDiffAstier : `list`
578 List with tuples of the form (dx, dy, var, cov, npix), where:
579 dx : `int`
580 Lag in x
581 dy : `int`
582 Lag in y
583 var : `float`
584 Variance at (dx, dy).
585 cov : `float`
586 Covariance at (dx, dy).
587 nPix : `int`
588 Number of pixel pairs used to evaluate var and cov.
589 """
591 if region is not None:
592 im1Area = exposure1.maskedImage[region]
593 im2Area = exposure2.maskedImage[region]
594 else:
595 im1Area = exposure1.maskedImage
596 im2Area = exposure2.maskedImage
598 im1Area = afwMath.binImage(im1Area, self.config.binSize)
599 im2Area = afwMath.binImage(im2Area, self.config.binSize)
601 statsCtrl = afwMath.StatisticsControl()
602 statsCtrl.setNumSigmaClip(self.config.nSigmaClipPtc)
603 statsCtrl.setNumIter(self.config.nIterSigmaClipPtc)
604 # Clipped mean of images; then average of mean.
605 mu1 = afwMath.makeStatistics(im1Area, afwMath.MEANCLIP, statsCtrl).getValue()
606 mu2 = afwMath.makeStatistics(im2Area, afwMath.MEANCLIP, statsCtrl).getValue()
607 mu = 0.5*(mu1 + mu2)
609 # Take difference of pairs
610 # symmetric formula: diff = (mu2*im1-mu1*im2)/(0.5*(mu1+mu2))
611 temp = im2Area.clone()
612 temp *= mu1
613 diffIm = im1Area.clone()
614 diffIm *= mu2
615 diffIm -= temp
616 diffIm /= mu
618 varDiff = 0.5*(afwMath.makeStatistics(diffIm, afwMath.VARIANCECLIP, statsCtrl).getValue())
620 # Get the mask and identify good pixels as '1', and the rest as '0'.
621 w1 = np.where(im1Area.getMask().getArray() == 0, 1, 0)
622 w2 = np.where(im2Area.getMask().getArray() == 0, 1, 0)
624 w12 = w1*w2
625 wDiff = np.where(diffIm.getMask().getArray() == 0, 1, 0)
626 w = w12*wDiff
628 maxRangeCov = self.config.maximumRangeCovariancesAstier
629 if covAstierRealSpace:
630 covDiffAstier = computeCovDirect(diffIm.getImage().getArray(), w, maxRangeCov)
631 else:
632 shapeDiff = diffIm.getImage().getArray().shape
633 fftShape = (fftSize(shapeDiff[0] + maxRangeCov), fftSize(shapeDiff[1]+maxRangeCov))
634 c = CovFft(diffIm.getImage().getArray(), w, fftShape, maxRangeCov)
635 covDiffAstier = c.reportCovFft(maxRangeCov)
637 return mu, varDiff, covDiffAstier
639 def computeCovDirect(self, diffImage, weightImage, maxRange):
640 """Compute covariances of diffImage in real space.
642 For lags larger than ~25, it is slower than the FFT way.
643 Taken from https://github.com/PierreAstier/bfptc/
645 Parameters
646 ----------
647 diffImage : `numpy.array`
648 Image to compute the covariance of.
650 weightImage : `numpy.array`
651 Weight image of diffImage (1's and 0's for good and bad pixels, respectively).
653 maxRange : `int`
654 Last index of the covariance to be computed.
656 Returns
657 -------
658 outList : `list`
659 List with tuples of the form (dx, dy, var, cov, npix), where:
660 dx : `int`
661 Lag in x
662 dy : `int`
663 Lag in y
664 var : `float`
665 Variance at (dx, dy).
666 cov : `float`
667 Covariance at (dx, dy).
668 nPix : `int`
669 Number of pixel pairs used to evaluate var and cov.
670 """
671 outList = []
672 var = 0
673 # (dy,dx) = (0,0) has to be first
674 for dy in range(maxRange + 1):
675 for dx in range(0, maxRange + 1):
676 if (dx*dy > 0):
677 cov1, nPix1 = self.covDirectValue(diffImage, weightImage, dx, dy)
678 cov2, nPix2 = self.covDirectValue(diffImage, weightImage, dx, -dy)
679 cov = 0.5*(cov1 + cov2)
680 nPix = nPix1 + nPix2
681 else:
682 cov, nPix = self.covDirectValue(diffImage, weightImage, dx, dy)
683 if (dx == 0 and dy == 0):
684 var = cov
685 outList.append((dx, dy, var, cov, nPix))
687 return outList
689 def covDirectValue(self, diffImage, weightImage, dx, dy):
690 """Compute covariances of diffImage in real space at lag (dx, dy).
692 Taken from https://github.com/PierreAstier/bfptc/ (c.f., appendix of Astier+19).
694 Parameters
695 ----------
696 diffImage : `numpy.array`
697 Image to compute the covariance of.
699 weightImage : `numpy.array`
700 Weight image of diffImage (1's and 0's for good and bad pixels, respectively).
702 dx : `int`
703 Lag in x.
705 dy : `int`
706 Lag in y.
708 Returns
709 -------
710 cov : `float`
711 Covariance at (dx, dy)
713 nPix : `int`
714 Number of pixel pairs used to evaluate var and cov.
715 """
716 (nCols, nRows) = diffImage.shape
717 # switching both signs does not change anything:
718 # it just swaps im1 and im2 below
719 if (dx < 0):
720 (dx, dy) = (-dx, -dy)
721 # now, we have dx >0. We have to distinguish two cases
722 # depending on the sign of dy
723 if dy >= 0:
724 im1 = diffImage[dy:, dx:]
725 w1 = weightImage[dy:, dx:]
726 im2 = diffImage[:nCols - dy, :nRows - dx]
727 w2 = weightImage[:nCols - dy, :nRows - dx]
728 else:
729 im1 = diffImage[:nCols + dy, dx:]
730 w1 = weightImage[:nCols + dy, dx:]
731 im2 = diffImage[-dy:, :nRows - dx]
732 w2 = weightImage[-dy:, :nRows - dx]
733 # use the same mask for all 3 calculations
734 wAll = w1*w2
735 # do not use mean() because weightImage=0 pixels would then count
736 nPix = wAll.sum()
737 im1TimesW = im1*wAll
738 s1 = im1TimesW.sum()/nPix
739 s2 = (im2*wAll).sum()/nPix
740 p = (im1TimesW*im2).sum()/nPix
741 cov = p - s1*s2
743 return cov, nPix
745 def fitNonLinearityAndBuildLinearizers(self, datasetPtc, detector, tableArray=None, log=None):
746 """Fit non-linearity function and build linearizer objects.
748 Parameters
749 ----------
750 datasePtct : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
751 The dataset containing information such as the means, variances and exposure times.
752 nLinearity
754 detector : `lsst.afw.cameraGeom.Detector`
755 Detector object.
757 tableArray : `np.array`, optional
758 Optional. Look-up table array with size rows=nAmps and columns=DN values.
759 It will be modified in-place if supplied.
761 log : `lsst.log.Log`, optional
762 Logger to handle messages.
764 Returns
765 -------
766 linearizer : `lsst.ip.isr.Linearizer`
767 Linearizer object
768 """
770 # Fit NonLinearity
771 datasetNonLinearity = self.fitNonLinearity(datasetPtc, tableArray=tableArray)
773 # Produce linearizer
774 now = datetime.datetime.utcnow()
775 calibDate = now.strftime("%Y-%m-%d")
776 linType = self.config.linearizerType
778 if linType == "LOOKUPTABLE":
779 tableArray = tableArray
780 else:
781 tableArray = None
783 linearizer = self.buildLinearizerObject(datasetNonLinearity, detector, calibDate, linType,
784 instruName=self.config.instrumentName,
785 tableArray=tableArray,
786 log=log)
788 return linearizer
790 def fitNonLinearity(self, datasetPtc, tableArray=None):
791 """Fit a polynomial to signal vs effective time curve to calculate linearity and residuals.
793 Parameters
794 ----------
795 datasetPtc : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
796 The dataset containing the means, variances and exposure times.
798 tableArray : `np.array`
799 Optional. Look-up table array with size rows=nAmps and columns=DN values.
800 It will be modified in-place if supplied.
802 Returns
803 -------
804 datasetNonLinearity : `dict`
805 Dictionary of `lsst.cp.pipe.ptc.LinearityResidualsAndLinearizersDataset`
806 dataclasses. Each one holds the output of `calculateLinearityResidualAndLinearizers` per
807 amplifier.
808 """
809 datasetNonLinearity = {ampName: [] for ampName in datasetPtc.ampNames}
810 for i, ampName in enumerate(datasetPtc.ampNames):
811 # If a mask is not found, use all points.
812 if (len(datasetPtc.visitMask[ampName]) == 0):
813 self.log.warn(f"Mask not found for {ampName} in non-linearity fit. Using all points.")
814 mask = np.repeat(True, len(datasetPtc.rawExpTimes[ampName]))
815 else:
816 mask = datasetPtc.visitMask[ampName]
818 timeVecFinal = np.array(datasetPtc.rawExpTimes[ampName])[mask]
819 meanVecFinal = np.array(datasetPtc.rawMeans[ampName])[mask]
821 # Non-linearity residuals (NL of mean vs time curve): percentage, and fit to a quadratic function
822 # In this case, len(parsIniNonLinearity) = 3 indicates that we want a quadratic fit
823 datasetLinRes = self.calculateLinearityResidualAndLinearizers(timeVecFinal, meanVecFinal)
825 # LinearizerLookupTable
826 if tableArray is not None:
827 tableArray[i, :] = datasetLinRes.linearizerTableRow
829 datasetNonLinearity[ampName] = datasetLinRes
831 return datasetNonLinearity
833 def calculateLinearityResidualAndLinearizers(self, exposureTimeVector, meanSignalVector):
834 """Calculate linearity residual and fit an n-order polynomial to the mean vs time curve
835 to produce corrections (deviation from linear part of polynomial) for a particular amplifier
836 to populate LinearizeLookupTable.
837 Use the coefficients of this fit to calculate the correction coefficients for LinearizePolynomial
838 and LinearizeSquared."
840 Parameters
841 ---------
843 exposureTimeVector: `list` of `float`
844 List of exposure times for each flat pair
846 meanSignalVector: `list` of `float`
847 List of mean signal from diference image of flat pairs
849 Returns
850 -------
851 dataset : `lsst.cp.pipe.ptc.LinearityResidualsAndLinearizersDataset`
852 The dataset containing the fit parameters, the NL correction coefficients, and the
853 LUT row for the amplifier at hand.
855 Notes
856 -----
857 datase members:
859 dataset.polynomialLinearizerCoefficients : `list` of `float`
860 Coefficients for LinearizePolynomial, where corrImage = uncorrImage + sum_i c_i uncorrImage^(2 +
861 i).
862 c_(j-2) = -k_j/(k_1^j) with units DN^(1-j) (c.f., Eq. 37 of 2003.05978). The units of k_j are
863 DN/t^j, and they are fit from meanSignalVector = k0 + k1*exposureTimeVector +
864 k2*exposureTimeVector^2 + ... + kn*exposureTimeVector^n, with
865 n = "polynomialFitDegreeNonLinearity". k_0 and k_1 and degenerate with bias level and gain,
866 and are not used by the non-linearity correction. Therefore, j = 2...n in the above expression
867 (see `LinearizePolynomial` class in `linearize.py`.)
869 dataset.quadraticPolynomialLinearizerCoefficient : `float`
870 Coefficient for LinearizeSquared, where corrImage = uncorrImage + c0*uncorrImage^2.
871 c0 = -k2/(k1^2), where k1 and k2 are fit from
872 meanSignalVector = k0 + k1*exposureTimeVector + k2*exposureTimeVector^2 +...
873 + kn*exposureTimeVector^n, with n = "polynomialFitDegreeNonLinearity".
875 dataset.linearizerTableRow : `list` of `float`
876 One dimensional array with deviation from linear part of n-order polynomial fit
877 to mean vs time curve. This array will be one row (for the particular amplifier at hand)
878 of the table array for LinearizeLookupTable.
880 dataset.meanSignalVsTimePolyFitPars : `list` of `float`
881 Parameters from n-order polynomial fit to meanSignalVector vs exposureTimeVector.
883 dataset.meanSignalVsTimePolyFitParsErr : `list` of `float`
884 Parameters from n-order polynomial fit to meanSignalVector vs exposureTimeVector.
886 dataset.meanSignalVsTimePolyFitReducedChiSq : `float`
887 Reduced unweighted chi squared from polynomial fit to meanSignalVector vs exposureTimeVector.
888 """
890 # Lookup table linearizer
891 parsIniNonLinearity = self._initialParsForPolynomial(self.config.polynomialFitDegreeNonLinearity + 1)
892 if self.config.doFitBootstrap:
893 parsFit, parsFitErr, reducedChiSquaredNonLinearityFit = fitBootstrap(parsIniNonLinearity,
894 exposureTimeVector,
895 meanSignalVector,
896 funcPolynomial)
897 else:
898 parsFit, parsFitErr, reducedChiSquaredNonLinearityFit = fitLeastSq(parsIniNonLinearity,
899 exposureTimeVector,
900 meanSignalVector,
901 funcPolynomial)
903 # LinearizeLookupTable:
904 # Use linear part to get time at wich signal is maxAduForLookupTableLinearizer DN
905 tMax = (self.config.maxAduForLookupTableLinearizer - parsFit[0])/parsFit[1]
906 timeRange = np.linspace(0, tMax, self.config.maxAduForLookupTableLinearizer)
907 signalIdeal = parsFit[0] + parsFit[1]*timeRange
908 signalUncorrected = funcPolynomial(parsFit, timeRange)
909 linearizerTableRow = signalIdeal - signalUncorrected # LinearizerLookupTable has corrections
910 # LinearizePolynomial and LinearizeSquared:
911 # Check that magnitude of higher order (>= 3) coefficents of the polyFit are small,
912 # i.e., less than threshold = 1e-10 (typical quadratic and cubic coefficents are ~1e-6
913 # and ~1e-12).
914 k1 = parsFit[1]
915 polynomialLinearizerCoefficients = []
916 for i, coefficient in enumerate(parsFit):
917 c = -coefficient/(k1**i)
918 polynomialLinearizerCoefficients.append(c)
919 if np.fabs(c) > 1e-10:
920 msg = f"Coefficient {c} in polynomial fit larger than threshold 1e-10."
921 self.log.warn(msg)
922 # Coefficient for LinearizedSquared. Called "c0" in linearize.py
923 c0 = polynomialLinearizerCoefficients[2]
925 dataset = LinearityResidualsAndLinearizersDataset([], None, [], [], [], None)
926 dataset.polynomialLinearizerCoefficients = polynomialLinearizerCoefficients
927 dataset.quadraticPolynomialLinearizerCoefficient = c0
928 dataset.linearizerTableRow = linearizerTableRow
929 dataset.meanSignalVsTimePolyFitPars = parsFit
930 dataset.meanSignalVsTimePolyFitParsErr = parsFitErr
931 dataset.meanSignalVsTimePolyFitReducedChiSq = reducedChiSquaredNonLinearityFit
933 return dataset
935 def buildLinearizerObject(self, datasetNonLinearity, detector, calibDate, linearizerType, instruName='',
936 tableArray=None, log=None):
937 """Build linearizer object to persist.
939 Parameters
940 ----------
941 datasetNonLinearity : `dict`
942 Dictionary of `lsst.cp.pipe.ptc.LinearityResidualsAndLinearizersDataset` objects.
944 detector : `lsst.afw.cameraGeom.Detector`
945 Detector object
947 calibDate : `datetime.datetime`
948 Calibration date
950 linearizerType : `str`
951 'LOOKUPTABLE', 'LINEARIZESQUARED', or 'LINEARIZEPOLYNOMIAL'
953 instruName : `str`, optional
954 Instrument name
956 tableArray : `np.array`, optional
957 Look-up table array with size rows=nAmps and columns=DN values
959 log : `lsst.log.Log`, optional
960 Logger to handle messages
962 Returns
963 -------
964 linearizer : `lsst.ip.isr.Linearizer`
965 Linearizer object
966 """
967 detName = detector.getName()
968 detNum = detector.getId()
969 if linearizerType == "LOOKUPTABLE":
970 if tableArray is not None:
971 linearizer = Linearizer(detector=detector, table=tableArray, log=log)
972 else:
973 raise RuntimeError("tableArray must be provided when creating a LookupTable linearizer")
974 elif linearizerType in ("LINEARIZESQUARED", "LINEARIZEPOLYNOMIAL"):
975 linearizer = Linearizer(log=log)
976 else:
977 raise RuntimeError("Invalid linearizerType {linearizerType} to build a Linearizer object. "
978 "Supported: 'LOOKUPTABLE', 'LINEARIZESQUARED', or 'LINEARIZEPOLYNOMIAL'")
979 for i, amp in enumerate(detector.getAmplifiers()):
980 ampName = amp.getName()
981 datasetNonLinAmp = datasetNonLinearity[ampName]
982 if linearizerType == "LOOKUPTABLE":
983 linearizer.linearityCoeffs[ampName] = [i, 0]
984 linearizer.linearityType[ampName] = "LookupTable"
985 elif linearizerType == "LINEARIZESQUARED":
986 linearizer.fitParams[ampName] = datasetNonLinAmp.meanSignalVsTimePolyFitPars
987 linearizer.fitParamsErr[ampName] = datasetNonLinAmp.meanSignalVsTimePolyFitParsErr
988 linearizer.linearityFitReducedChiSquared[ampName] = (
989 datasetNonLinAmp.meanSignalVsTimePolyFitReducedChiSq)
990 linearizer.linearityCoeffs[ampName] = [
991 datasetNonLinAmp.quadraticPolynomialLinearizerCoefficient]
992 linearizer.linearityType[ampName] = "Squared"
993 elif linearizerType == "LINEARIZEPOLYNOMIAL":
994 linearizer.fitParams[ampName] = datasetNonLinAmp.meanSignalVsTimePolyFitPars
995 linearizer.fitParamsErr[ampName] = datasetNonLinAmp.meanSignalVsTimePolyFitParsErr
996 linearizer.linearityFitReducedChiSquared[ampName] = (
997 datasetNonLinAmp.meanSignalVsTimePolyFitReducedChiSq)
998 # Slice correction coefficients (starting at 2) for polynomial linearizer
999 # (and squared linearizer above). The first and second are reduntant with
1000 # the bias and gain, respectively, and are not used by LinearizerPolynomial.
1001 polyLinCoeffs = np.array(datasetNonLinAmp.polynomialLinearizerCoefficients[2:])
1002 linearizer.linearityCoeffs[ampName] = polyLinCoeffs
1003 linearizer.linearityType[ampName] = "Polynomial"
1004 linearizer.linearityBBox[ampName] = amp.getBBox()
1005 linearizer.validate()
1006 calibId = f"detectorName={detName} detector={detNum} calibDate={calibDate} ccd={detNum} filter=NONE"
1008 try:
1009 raftName = detName.split("_")[0]
1010 calibId += f" raftName={raftName}"
1011 except Exception:
1012 raftname = "NONE"
1013 calibId += f" raftName={raftname}"
1015 serial = detector.getSerial()
1016 linearizer.updateMetadata(instrumentName=instruName, detectorId=f"{detNum}",
1017 calibId=calibId, serial=serial, detectorName=f"{detName}")
1019 return linearizer
1021 @staticmethod
1022 def _initialParsForPolynomial(order):
1023 assert(order >= 2)
1024 pars = np.zeros(order, dtype=np.float)
1025 pars[0] = 10
1026 pars[1] = 1
1027 pars[2:] = 0.0001
1028 return pars
1030 @staticmethod
1031 def _boundsForPolynomial(initialPars):
1032 lowers = [np.NINF for p in initialPars]
1033 uppers = [np.inf for p in initialPars]
1034 lowers[1] = 0 # no negative gains
1035 return (lowers, uppers)
1037 @staticmethod
1038 def _boundsForAstier(initialPars):
1039 lowers = [np.NINF for p in initialPars]
1040 uppers = [np.inf for p in initialPars]
1041 return (lowers, uppers)
1043 @staticmethod
1044 def _getInitialGoodPoints(means, variances, maxDeviationPositive, maxDeviationNegative):
1045 """Return a boolean array to mask bad points.
1047 A linear function has a constant ratio, so find the median
1048 value of the ratios, and exclude the points that deviate
1049 from that by more than a factor of maxDeviationPositive/negative.
1050 Asymmetric deviations are supported as we expect the PTC to turn
1051 down as the flux increases, but sometimes it anomalously turns
1052 upwards just before turning over, which ruins the fits, so it
1053 is wise to be stricter about restricting positive outliers than
1054 negative ones.
1056 Too high and points that are so bad that fit will fail will be included
1057 Too low and the non-linear points will be excluded, biasing the NL fit."""
1058 ratios = [b/a for (a, b) in zip(means, variances)]
1059 medianRatio = np.median(ratios)
1060 ratioDeviations = [(r/medianRatio)-1 for r in ratios]
1062 # so that it doesn't matter if the deviation is expressed as positive or negative
1063 maxDeviationPositive = abs(maxDeviationPositive)
1064 maxDeviationNegative = -1. * abs(maxDeviationNegative)
1066 goodPoints = np.array([True if (r < maxDeviationPositive and r > maxDeviationNegative)
1067 else False for r in ratioDeviations])
1068 return goodPoints
1070 def _makeZeroSafe(self, array, warn=True, substituteValue=1e-9):
1071 """"""
1072 nBad = Counter(array)[0]
1073 if nBad == 0:
1074 return array
1076 if warn:
1077 msg = f"Found {nBad} zeros in array at elements {[x for x in np.where(array==0)[0]]}"
1078 self.log.warn(msg)
1080 array[array == 0] = substituteValue
1081 return array
1083 def fitPtc(self, dataset, ptcFitType):
1084 """Fit the photon transfer curve to a polynimial or to Astier+19 approximation.
1086 Fit the photon transfer curve with either a polynomial of the order
1087 specified in the task config, or using the Astier approximation.
1089 Sigma clipping is performed iteratively for the fit, as well as an
1090 initial clipping of data points that are more than
1091 config.initialNonLinearityExclusionThreshold away from lying on a
1092 straight line. This other step is necessary because the photon transfer
1093 curve turns over catastrophically at very high flux (because saturation
1094 drops the variance to ~0) and these far outliers cause the initial fit
1095 to fail, meaning the sigma cannot be calculated to perform the
1096 sigma-clipping.
1098 Parameters
1099 ----------
1100 dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
1101 The dataset containing the means, variances and exposure times
1103 ptcFitType : `str`
1104 Fit a 'POLYNOMIAL' (degree: 'polynomialFitDegree') or
1105 'EXPAPPROXIMATION' (Eq. 16 of Astier+19) to the PTC
1107 Returns
1108 -------
1109 dataset: `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
1110 This is the same dataset as the input paramter, however, it has been modified
1111 to include information such as the fit vectors and the fit parameters. See
1112 the class `PhotonTransferCurveDatase`.
1113 """
1115 def errFunc(p, x, y):
1116 return ptcFunc(p, x) - y
1118 sigmaCutPtcOutliers = self.config.sigmaCutPtcOutliers
1119 maxIterationsPtcOutliers = self.config.maxIterationsPtcOutliers
1121 for i, ampName in enumerate(dataset.ampNames):
1122 timeVecOriginal = np.array(dataset.rawExpTimes[ampName])
1123 meanVecOriginal = np.array(dataset.rawMeans[ampName])
1124 varVecOriginal = np.array(dataset.rawVars[ampName])
1125 varVecOriginal = self._makeZeroSafe(varVecOriginal)
1127 mask = ((meanVecOriginal >= self.config.minMeanSignal) &
1128 (meanVecOriginal <= self.config.maxMeanSignal))
1130 goodPoints = self._getInitialGoodPoints(meanVecOriginal, varVecOriginal,
1131 self.config.initialNonLinearityExclusionThresholdPositive,
1132 self.config.initialNonLinearityExclusionThresholdNegative)
1133 mask = mask & goodPoints
1135 if ptcFitType == 'EXPAPPROXIMATION':
1136 ptcFunc = funcAstier
1137 parsIniPtc = [-1e-9, 1.0, 10.] # a00, gain, noise
1138 bounds = self._boundsForAstier(parsIniPtc)
1139 if ptcFitType == 'POLYNOMIAL':
1140 ptcFunc = funcPolynomial
1141 parsIniPtc = self._initialParsForPolynomial(self.config.polynomialFitDegree + 1)
1142 bounds = self._boundsForPolynomial(parsIniPtc)
1144 # Before bootstrap fit, do an iterative fit to get rid of outliers
1145 count = 1
1146 while count <= maxIterationsPtcOutliers:
1147 # Note that application of the mask actually shrinks the array
1148 # to size rather than setting elements to zero (as we want) so
1149 # always update mask itself and re-apply to the original data
1150 meanTempVec = meanVecOriginal[mask]
1151 varTempVec = varVecOriginal[mask]
1152 res = least_squares(errFunc, parsIniPtc, bounds=bounds, args=(meanTempVec, varTempVec))
1153 pars = res.x
1155 # change this to the original from the temp because the masks are ANDed
1156 # meaning once a point is masked it's always masked, and the masks must
1157 # always be the same length for broadcasting
1158 sigResids = (varVecOriginal - ptcFunc(pars, meanVecOriginal))/np.sqrt(varVecOriginal)
1159 newMask = np.array([True if np.abs(r) < sigmaCutPtcOutliers else False for r in sigResids])
1160 mask = mask & newMask
1162 nDroppedTotal = Counter(mask)[False]
1163 self.log.debug(f"Iteration {count}: discarded {nDroppedTotal} points in total for {ampName}")
1164 count += 1
1165 # objects should never shrink
1166 assert (len(mask) == len(timeVecOriginal) == len(meanVecOriginal) == len(varVecOriginal))
1168 dataset.visitMask[ampName] = mask # store the final mask
1169 parsIniPtc = pars
1170 meanVecFinal = meanVecOriginal[mask]
1171 varVecFinal = varVecOriginal[mask]
1173 if Counter(mask)[False] > 0:
1174 self.log.info((f"Number of points discarded in PTC of amplifier {ampName}:" +
1175 f" {Counter(mask)[False]} out of {len(meanVecOriginal)}"))
1177 if (len(meanVecFinal) < len(parsIniPtc)):
1178 msg = (f"\nSERIOUS: Not enough data points ({len(meanVecFinal)}) compared to the number of"
1179 f"parameters of the PTC model({len(parsIniPtc)}). Setting {ampName} to BAD.")
1180 self.log.warn(msg)
1181 # The first and second parameters of initial fit are discarded (bias and gain)
1182 # for the final NL coefficients
1183 dataset.badAmps.append(ampName)
1184 dataset.gain[ampName] = np.nan
1185 dataset.gainErr[ampName] = np.nan
1186 dataset.noise[ampName] = np.nan
1187 dataset.noiseErr[ampName] = np.nan
1188 dataset.ptcFitPars[ampName] = np.nan
1189 dataset.ptcFitParsError[ampName] = np.nan
1190 dataset.ptcFitReducedChiSquared[ampName] = np.nan
1191 continue
1193 # Fit the PTC
1194 if self.config.doFitBootstrap:
1195 parsFit, parsFitErr, reducedChiSqPtc = fitBootstrap(parsIniPtc, meanVecFinal,
1196 varVecFinal, ptcFunc)
1197 else:
1198 parsFit, parsFitErr, reducedChiSqPtc = fitLeastSq(parsIniPtc, meanVecFinal,
1199 varVecFinal, ptcFunc)
1200 dataset.ptcFitPars[ampName] = parsFit
1201 dataset.ptcFitParsError[ampName] = parsFitErr
1202 dataset.ptcFitReducedChiSquared[ampName] = reducedChiSqPtc
1204 if ptcFitType == 'EXPAPPROXIMATION':
1205 ptcGain = parsFit[1]
1206 ptcGainErr = parsFitErr[1]
1207 ptcNoise = np.sqrt(np.fabs(parsFit[2]))
1208 ptcNoiseErr = 0.5*(parsFitErr[2]/np.fabs(parsFit[2]))*np.sqrt(np.fabs(parsFit[2]))
1209 if ptcFitType == 'POLYNOMIAL':
1210 ptcGain = 1./parsFit[1]
1211 ptcGainErr = np.fabs(1./parsFit[1])*(parsFitErr[1]/parsFit[1])
1212 ptcNoise = np.sqrt(np.fabs(parsFit[0]))*ptcGain
1213 ptcNoiseErr = (0.5*(parsFitErr[0]/np.fabs(parsFit[0]))*(np.sqrt(np.fabs(parsFit[0]))))*ptcGain
1214 dataset.gain[ampName] = ptcGain
1215 dataset.gainErr[ampName] = ptcGainErr
1216 dataset.noise[ampName] = ptcNoise
1217 dataset.noiseErr[ampName] = ptcNoiseErr
1218 if not len(dataset.ptcFitType) == 0:
1219 dataset.ptcFitType = ptcFitType
1221 return dataset