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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 maskNameList = pexConfig.ListField(
140 dtype=str,
141 doc="Mask list to exclude from statistics calculations.",
142 default=['DETECTED', 'BAD', 'NO_DATA'],
143 )
144 nSigmaClipPtc = pexConfig.Field(
145 dtype=float,
146 doc="Sigma cut for afwMath.StatisticsControl()",
147 default=5.5,
148 )
149 nIterSigmaClipPtc = pexConfig.Field(
150 dtype=int,
151 doc="Number of sigma-clipping iterations for afwMath.StatisticsControl()",
152 default=1,
153 )
154 maxIterationsPtcOutliers = pexConfig.Field(
155 dtype=int,
156 doc="Maximum number of iterations for outlier rejection in PTC.",
157 default=2,
158 )
159 doFitBootstrap = pexConfig.Field(
160 dtype=bool,
161 doc="Use bootstrap for the PTC fit parameters and errors?.",
162 default=False,
163 )
164 maxAduForLookupTableLinearizer = pexConfig.Field(
165 dtype=int,
166 doc="Maximum DN value for the LookupTable linearizer.",
167 default=2**18,
168 )
169 instrumentName = pexConfig.Field(
170 dtype=str,
171 doc="Instrument name.",
172 default='',
173 )
176@dataclass
177class LinearityResidualsAndLinearizersDataset:
178 """A simple class to hold the output from the
179 `calculateLinearityResidualAndLinearizers` function.
180 """
181 # Normalized coefficients for polynomial NL correction
182 polynomialLinearizerCoefficients: list
183 # Normalized coefficient for quadratic polynomial NL correction (c0)
184 quadraticPolynomialLinearizerCoefficient: float
185 # LUT array row for the amplifier at hand
186 linearizerTableRow: list
187 meanSignalVsTimePolyFitPars: list
188 meanSignalVsTimePolyFitParsErr: list
189 meanSignalVsTimePolyFitReducedChiSq: float
192class PhotonTransferCurveDataset:
193 """A simple class to hold the output data from the PTC task.
195 The dataset is made up of a dictionary for each item, keyed by the
196 amplifiers' names, which much be supplied at construction time.
198 New items cannot be added to the class to save accidentally saving to the
199 wrong property, and the class can be frozen if desired.
201 inputVisitPairs records the visits used to produce the data.
202 When fitPtc() or fitCovariancesAstier() is run, a mask is built up, which is by definition
203 always the same length as inputVisitPairs, rawExpTimes, rawMeans
204 and rawVars, and is a list of bools, which are incrementally set to False
205 as points are discarded from the fits.
207 PTC fit parameters for polynomials are stored in a list in ascending order
208 of polynomial term, i.e. par[0]*x^0 + par[1]*x + par[2]*x^2 etc
209 with the length of the list corresponding to the order of the polynomial
210 plus one.
212 Parameters
213 ----------
214 ampNames : `list`
215 List with the names of the amplifiers of the detector at hand.
217 ptcFitType : `str`
218 Type of model fitted to the PTC: "POLYNOMIAL", "EXPAPPROXIMATION", or "FULLCOVARIANCE".
220 Returns
221 -------
222 `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
223 Output dataset from MeasurePhotonTransferCurveTask.
224 """
226 def __init__(self, ampNames, ptcFitType):
227 # add items to __dict__ directly because __setattr__ is overridden
229 # instance variables
230 self.__dict__["ptcFitType"] = ptcFitType
231 self.__dict__["ampNames"] = ampNames
232 self.__dict__["badAmps"] = []
234 # raw data variables
235 # visitMask is the mask produced after outlier rejection. The mask produced by "FULLCOVARIANCE"
236 # may differ from the one produced in the other two PTC fit types.
237 self.__dict__["inputVisitPairs"] = {ampName: [] for ampName in ampNames}
238 self.__dict__["visitMask"] = {ampName: [] for ampName in ampNames}
239 self.__dict__["rawExpTimes"] = {ampName: [] for ampName in ampNames}
240 self.__dict__["rawMeans"] = {ampName: [] for ampName in ampNames}
241 self.__dict__["rawVars"] = {ampName: [] for ampName in ampNames}
243 # Gain and noise
244 self.__dict__["gain"] = {ampName: -1. for ampName in ampNames}
245 self.__dict__["gainErr"] = {ampName: -1. for ampName in ampNames}
246 self.__dict__["noise"] = {ampName: -1. for ampName in ampNames}
247 self.__dict__["noiseErr"] = {ampName: -1. for ampName in ampNames}
249 # if ptcFitTye in ["POLYNOMIAL", "EXPAPPROXIMATION"]
250 # fit information
251 self.__dict__["ptcFitPars"] = {ampName: [] for ampName in ampNames}
252 self.__dict__["ptcFitParsError"] = {ampName: [] for ampName in ampNames}
253 self.__dict__["ptcFitReducedChiSquared"] = {ampName: [] for ampName in ampNames}
255 # if ptcFitTye in ["FULLCOVARIANCE"]
256 # "covariancesTuple" is a numpy recarray with entries of the form
257 # ['mu', 'i', 'j', 'var', 'cov', 'npix', 'ext', 'expTime', 'ampName']
258 # "covariancesFits" has CovFit objects that fit the measured covariances to Eq. 20 of Astier+19.
259 # In "covariancesFitsWithNoB", "b"=0 in the model described by Eq. 20 of Astier+19.
260 self.__dict__["covariancesTuple"] = {ampName: [] for ampName in ampNames}
261 self.__dict__["covariancesFitsWithNoB"] = {ampName: [] for ampName in ampNames}
262 self.__dict__["covariancesFits"] = {ampName: [] for ampName in ampNames}
263 self.__dict__["aMatrix"] = {ampName: [] for ampName in ampNames}
264 self.__dict__["bMatrix"] = {ampName: [] for ampName in ampNames}
266 # "final" means that the "raw" vectors above had "visitMask" applied.
267 self.__dict__["finalVars"] = {ampName: [] for ampName in ampNames}
268 self.__dict__["finalModelVars"] = {ampName: [] for ampName in ampNames}
269 self.__dict__["finalMeans"] = {ampName: [] for ampName in ampNames}
271 def __setattr__(self, attribute, value):
272 """Protect class attributes"""
273 if attribute not in self.__dict__:
274 raise AttributeError(f"{attribute} is not already a member of PhotonTransferCurveDataset, which"
275 " does not support setting of new attributes.")
276 else:
277 self.__dict__[attribute] = value
279 def getVisitsUsed(self, ampName):
280 """Get the visits used, i.e. not discarded, for a given amp.
282 If no mask has been created yet, all visits are returned.
283 """
284 if len(self.visitMask[ampName]) == 0:
285 return self.inputVisitPairs[ampName]
287 # if the mask exists it had better be the same length as the visitPairs
288 assert len(self.visitMask[ampName]) == len(self.inputVisitPairs[ampName])
290 pairs = self.inputVisitPairs[ampName]
291 mask = self.visitMask[ampName]
292 # cast to bool required because numpy
293 return [(v1, v2) for ((v1, v2), m) in zip(pairs, mask) if bool(m) is True]
295 def getGoodAmps(self):
296 return [amp for amp in self.ampNames if amp not in self.badAmps]
299class MeasurePhotonTransferCurveTask(pipeBase.CmdLineTask):
300 """A class to calculate, fit, and plot a PTC from a set of flat pairs.
302 The Photon Transfer Curve (var(signal) vs mean(signal)) is a standard tool
303 used in astronomical detectors characterization (e.g., Janesick 2001,
304 Janesick 2007). If ptcFitType is "EXPAPPROXIMATION" or "POLYNOMIAL", this task calculates the
305 PTC from a series of pairs of flat-field images; each pair taken at identical exposure
306 times. The difference image of each pair is formed to eliminate fixed pattern noise,
307 and then the variance of the difference image and the mean of the average image
308 are used to produce the PTC. An n-degree polynomial or the approximation in Equation
309 16 of Astier+19 ("The Shape of the Photon Transfer Curve of CCD sensors",
310 arXiv:1905.08677) can be fitted to the PTC curve. These models include
311 parameters such as the gain (e/DN) and readout noise.
313 Linearizers to correct for signal-chain non-linearity are also calculated.
314 The `Linearizer` class, in general, can support per-amp linearizers, but in this
315 task this is not supported.
317 If ptcFitType is "FULLCOVARIANCE", the covariances of the difference images are calculated via the
318 DFT methods described in Astier+19 and the variances for the PTC are given by the cov[0,0] elements
319 at each signal level. The full model in Equation 20 of Astier+19 is fit to the PTC to get the gain
320 and the noise.
322 Parameters
323 ----------
325 *args: `list`
326 Positional arguments passed to the Task constructor. None used at this
327 time.
328 **kwargs: `dict`
329 Keyword arguments passed on to the Task constructor. None used at this
330 time.
332 """
334 RunnerClass = PairedVisitListTaskRunner
335 ConfigClass = MeasurePhotonTransferCurveTaskConfig
336 _DefaultName = "measurePhotonTransferCurve"
338 def __init__(self, *args, **kwargs):
339 pipeBase.CmdLineTask.__init__(self, *args, **kwargs)
340 plt.interactive(False) # stop windows popping up when plotting. When headless, use 'agg' backend too
341 self.config.validate()
342 self.config.freeze()
344 @classmethod
345 def _makeArgumentParser(cls):
346 """Augment argument parser for the MeasurePhotonTransferCurveTask."""
347 parser = pipeBase.ArgumentParser(name=cls._DefaultName)
348 parser.add_argument("--visit-pairs", dest="visitPairs", nargs="*",
349 help="Visit pairs to use. Each pair must be of the form INT,INT e.g. 123,456")
350 parser.add_id_argument("--id", datasetType="photonTransferCurveDataset",
351 ContainerClass=NonexistentDatasetTaskDataIdContainer,
352 help="The ccds to use, e.g. --id ccd=0..100")
353 return parser
355 @pipeBase.timeMethod
356 def runDataRef(self, dataRef, visitPairs):
357 """Run the Photon Transfer Curve (PTC) measurement task.
359 For a dataRef (which is each detector here),
360 and given a list of visit pairs (postISR) at different exposure times,
361 measure the PTC.
363 Parameters
364 ----------
365 dataRef : list of lsst.daf.persistence.ButlerDataRef
366 dataRef for the detector for the visits to be fit.
368 visitPairs : `iterable` of `tuple` of `int`
369 Pairs of visit numbers to be processed together
370 """
372 # setup necessary objects
373 detNum = dataRef.dataId[self.config.ccdKey]
374 detector = dataRef.get('camera')[dataRef.dataId[self.config.ccdKey]]
375 # expand some missing fields that we need for lsstCam. This is a work-around
376 # for Gen2 problems that I (RHL) don't feel like solving. The calibs pipelines
377 # (which inherit from CalibTask) use addMissingKeys() to do basically the same thing
378 #
379 # Basically, the butler's trying to look up the fields in `raw_visit` which won't work
380 for name in dataRef.getButler().getKeys('bias'):
381 if name not in dataRef.dataId:
382 try:
383 dataRef.dataId[name] = \
384 dataRef.getButler().queryMetadata('raw', [name], detector=detNum)[0]
385 except OperationalError:
386 pass
388 amps = detector.getAmplifiers()
389 ampNames = [amp.getName() for amp in amps]
390 datasetPtc = PhotonTransferCurveDataset(ampNames, self.config.ptcFitType)
391 self.log.info('Measuring PTC using %s visits for detector %s' % (visitPairs, detector.getId()))
393 tupleRecords = []
394 allTags = []
395 for (v1, v2) in visitPairs:
396 # Get postISR exposures.
397 dataRef.dataId['expId'] = v1
398 exp1 = dataRef.get("postISRCCD", immediate=True)
399 dataRef.dataId['expId'] = v2
400 exp2 = dataRef.get("postISRCCD", immediate=True)
401 del dataRef.dataId['expId']
403 checkExpLengthEqual(exp1, exp2, v1, v2, raiseWithMessage=True)
404 expTime = exp1.getInfo().getVisitInfo().getExposureTime()
406 tupleRows = []
407 for ampNumber, amp in enumerate(detector):
408 ampName = amp.getName()
409 # covAstier: (i, j, var (cov[0,0]), cov, npix)
410 doRealSpace = self.config.covAstierRealSpace
411 muDiff, varDiff, covAstier = self.measureMeanVarCov(exp1, exp2, region=amp.getBBox(),
412 covAstierRealSpace=doRealSpace)
414 datasetPtc.rawExpTimes[ampName].append(expTime)
415 datasetPtc.rawMeans[ampName].append(muDiff)
416 datasetPtc.rawVars[ampName].append(varDiff)
417 datasetPtc.inputVisitPairs[ampName].append((v1, v2))
419 tupleRows += [(muDiff, ) + covRow + (ampNumber, expTime, ampName) for covRow in covAstier]
420 tags = ['mu', 'i', 'j', 'var', 'cov', 'npix', 'ext', 'expTime', 'ampName']
421 allTags += tags
422 tupleRecords += tupleRows
423 covariancesWithTags = np.core.records.fromrecords(tupleRecords, names=allTags)
425 if self.config.ptcFitType in ["FULLCOVARIANCE", ]:
426 # Calculate covariances and fit them, including the PTC, to Astier+19 full model (Eq. 20)
427 datasetPtc = self.fitCovariancesAstier(datasetPtc, covariancesWithTags)
428 elif self.config.ptcFitType in ["EXPAPPROXIMATION", "POLYNOMIAL"]:
429 # Fit the PTC to a polynomial or to Astier+19 exponential approximation (Eq. 16)
430 # Fill up PhotonTransferCurveDataset object.
431 datasetPtc = self.fitPtc(datasetPtc, self.config.ptcFitType)
433 # Fit a poynomial to calculate non-linearity and persist linearizer.
434 if self.config.doCreateLinearizer:
435 numberAmps = len(amps)
436 numberAduValues = self.config.maxAduForLookupTableLinearizer
437 lookupTableArray = np.zeros((numberAmps, numberAduValues), dtype=np.float32)
439 # Fit (non)linearity of signal vs time curve.
440 # Fill up PhotonTransferCurveDataset object.
441 # Fill up array for LUT linearizer (tableArray).
442 # Produce coefficients for Polynomial ans Squared linearizers.
443 # Build linearizer objects.
444 linearizer = self.fitNonLinearityAndBuildLinearizers(datasetPtc, detector,
445 tableArray=lookupTableArray,
446 log=self.log)
448 if self.config.linearizerType == "LINEARIZEPOLYNOMIAL":
449 linDataType = 'linearizePolynomial'
450 linMsg = "polynomial (coefficients for a polynomial correction)."
451 elif self.config.linearizerType == "LINEARIZESQUARED":
452 linDataType = 'linearizePolynomial'
453 linMsg = "squared (c0, derived from k_i coefficients of a polynomial fit)."
454 elif self.config.linearizerType == "LOOKUPTABLE":
455 linDataType = 'linearizePolynomial'
456 linMsg = "lookup table (linear component of polynomial fit)."
457 else:
458 raise RuntimeError("Invalid config.linearizerType {selg.config.linearizerType}. "
459 "Supported: 'LOOKUPTABLE', 'LINEARIZESQUARED', or 'LINEARIZEPOLYNOMIAL'")
461 butler = dataRef.getButler()
462 self.log.info(f"Writing linearizer: \n {linMsg}")
464 detName = detector.getName()
465 now = datetime.datetime.utcnow()
466 calibDate = now.strftime("%Y-%m-%d")
468 butler.put(linearizer, datasetType=linDataType, dataId={'detector': detNum,
469 'detectorName': detName, 'calibDate': calibDate})
471 self.log.info(f"Writing PTC data to {dataRef.getUri(write=True)}")
472 dataRef.put(datasetPtc, datasetType="photonTransferCurveDataset")
474 return pipeBase.Struct(exitStatus=0)
476 def fitCovariancesAstier(self, dataset, covariancesWithTagsArray):
477 """Fit measured flat covariances to full model in Astier+19.
479 Parameters
480 ----------
481 dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
482 The dataset containing information such as the means, variances and exposure times.
484 covariancesWithTagsArray : `numpy.recarray`
485 Tuple with at least (mu, cov, var, i, j, npix), where:
486 mu : 0.5*(m1 + m2), where:
487 mu1: mean value of flat1
488 mu2: mean value of flat2
489 cov: covariance value at lag(i, j)
490 var: variance(covariance value at lag(0, 0))
491 i: lag dimension
492 j: lag dimension
493 npix: number of pixels used for covariance calculation.
495 Returns
496 -------
497 dataset: `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
498 This is the same dataset as the input paramter, however, it has been modified
499 to include information such as the fit vectors and the fit parameters. See
500 the class `PhotonTransferCurveDatase`.
501 """
503 covFits, covFitsNoB = fitData(covariancesWithTagsArray, maxMu=self.config.maxMeanSignal,
504 r=self.config.maximumRangeCovariancesAstier)
506 dataset.covariancesTuple = covariancesWithTagsArray
507 dataset.covariancesFits = covFits
508 dataset.covariancesFitsWithNoB = covFitsNoB
509 dataset = self.getOutputPtcDataCovAstier(dataset, covFits)
511 return dataset
513 def getOutputPtcDataCovAstier(self, dataset, covFits):
514 """Get output data for PhotonTransferCurveCovAstierDataset from CovFit objects.
516 Parameters
517 ----------
518 dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
519 The dataset containing information such as the means, variances and exposure times.
521 covFits: `dict`
522 Dictionary of CovFit objects, with amp names as keys.
524 Returns
525 -------
526 dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
527 This is the same dataset as the input paramter, however, it has been modified
528 to include extra information such as the mask 1D array, gains, reoudout noise, measured signal,
529 measured variance, modeled variance, a, and b coefficient matrices (see Astier+19) per amplifier.
530 See the class `PhotonTransferCurveDatase`.
531 """
533 for i, amp in enumerate(covFits):
534 fit = covFits[amp]
535 meanVecFinal, varVecFinal, varVecModel, wc = fit.getNormalizedFitData(0, 0, divideByMu=False)
536 gain = fit.getGain()
537 dataset.visitMask[amp] = fit.getMaskVar()
538 dataset.gain[amp] = gain
539 dataset.gainErr[amp] = fit.getGainErr()
540 dataset.noise[amp] = np.sqrt(np.fabs(fit.getRon()))
541 dataset.noiseErr[amp] = fit.getRonErr()
542 dataset.finalVars[amp].append(varVecFinal/(gain**2))
543 dataset.finalModelVars[amp].append(varVecModel/(gain**2))
544 dataset.finalMeans[amp].append(meanVecFinal/gain)
545 dataset.aMatrix[amp].append(fit.getA())
546 dataset.bMatrix[amp].append(fit.getB())
548 return dataset
550 def measureMeanVarCov(self, exposure1, exposure2, region=None, covAstierRealSpace=False):
551 """Calculate the mean of each of two exposures and the variance and covariance of their difference.
553 The variance is calculated via afwMath, and the covariance via the methods in Astier+19 (appendix A).
554 In theory, var = covariance[0,0]. This should be validated, and in the future, we may decide to just
555 keep one (covariance).
557 Parameters
558 ----------
559 exposure1 : `lsst.afw.image.exposure.exposure.ExposureF`
560 First exposure of flat field pair.
562 exposure2 : `lsst.afw.image.exposure.exposure.ExposureF`
563 Second exposure of flat field pair.
565 region : `lsst.geom.Box2I`, optional
566 Region of each exposure where to perform the calculations (e.g, an amplifier).
568 covAstierRealSpace : `bool`, optional
569 Should the covariannces in Astier+19 be calculated in real space or via FFT?
570 See Appendix A of Astier+19.
572 Returns
573 -------
574 mu : `float`
575 0.5*(mu1 + mu2), where mu1, and mu2 are the clipped means of the regions in
576 both exposures.
578 varDiff : `float`
579 Half of the clipped variance of the difference of the regions inthe two input
580 exposures.
582 covDiffAstier : `list`
583 List with tuples of the form (dx, dy, var, cov, npix), where:
584 dx : `int`
585 Lag in x
586 dy : `int`
587 Lag in y
588 var : `float`
589 Variance at (dx, dy).
590 cov : `float`
591 Covariance at (dx, dy).
592 nPix : `int`
593 Number of pixel pairs used to evaluate var and cov.
594 """
596 if region is not None:
597 im1Area = exposure1.maskedImage[region]
598 im2Area = exposure2.maskedImage[region]
599 else:
600 im1Area = exposure1.maskedImage
601 im2Area = exposure2.maskedImage
603 im1Area = afwMath.binImage(im1Area, self.config.binSize)
604 im2Area = afwMath.binImage(im2Area, self.config.binSize)
606 im1MaskVal = exposure1.getMask().getPlaneBitMask(self.config.maskNameList)
607 im1StatsCtrl = afwMath.StatisticsControl(self.config.nSigmaClipPtc,
608 self.config.nIterSigmaClipPtc,
609 im1MaskVal)
610 im1StatsCtrl.setAndMask(im1MaskVal)
612 im2MaskVal = exposure2.getMask().getPlaneBitMask(self.config.maskNameList)
613 im2StatsCtrl = afwMath.StatisticsControl(self.config.nSigmaClipPtc,
614 self.config.nIterSigmaClipPtc,
615 im2MaskVal)
616 im2StatsCtrl.setAndMask(im2MaskVal)
618 # Clipped mean of images; then average of mean.
619 mu1 = afwMath.makeStatistics(im1Area, afwMath.MEANCLIP, im1StatsCtrl).getValue()
620 mu2 = afwMath.makeStatistics(im2Area, afwMath.MEANCLIP, im2StatsCtrl).getValue()
621 mu = 0.5*(mu1 + mu2)
623 # Take difference of pairs
624 # symmetric formula: diff = (mu2*im1-mu1*im2)/(0.5*(mu1+mu2))
625 temp = im2Area.clone()
626 temp *= mu1
627 diffIm = im1Area.clone()
628 diffIm *= mu2
629 diffIm -= temp
630 diffIm /= mu
632 diffImMaskVal = diffIm.getMask().getPlaneBitMask(self.config.maskNameList)
633 diffImStatsCtrl = afwMath.StatisticsControl(self.config.nSigmaClipPtc,
634 self.config.nIterSigmaClipPtc,
635 diffImMaskVal)
636 diffImStatsCtrl.setAndMask(diffImMaskVal)
638 varDiff = 0.5*(afwMath.makeStatistics(diffIm, afwMath.VARIANCECLIP, diffImStatsCtrl).getValue())
640 # Get the mask and identify good pixels as '1', and the rest as '0'.
641 w1 = np.where(im1Area.getMask().getArray() == 0, 1, 0)
642 w2 = np.where(im2Area.getMask().getArray() == 0, 1, 0)
644 w12 = w1*w2
645 wDiff = np.where(diffIm.getMask().getArray() == 0, 1, 0)
646 w = w12*wDiff
648 maxRangeCov = self.config.maximumRangeCovariancesAstier
649 if covAstierRealSpace:
650 covDiffAstier = computeCovDirect(diffIm.getImage().getArray(), w, maxRangeCov)
651 else:
652 shapeDiff = diffIm.getImage().getArray().shape
653 fftShape = (fftSize(shapeDiff[0] + maxRangeCov), fftSize(shapeDiff[1]+maxRangeCov))
654 c = CovFft(diffIm.getImage().getArray(), w, fftShape, maxRangeCov)
655 covDiffAstier = c.reportCovFft(maxRangeCov)
657 return mu, varDiff, covDiffAstier
659 def computeCovDirect(self, diffImage, weightImage, maxRange):
660 """Compute covariances of diffImage in real space.
662 For lags larger than ~25, it is slower than the FFT way.
663 Taken from https://github.com/PierreAstier/bfptc/
665 Parameters
666 ----------
667 diffImage : `numpy.array`
668 Image to compute the covariance of.
670 weightImage : `numpy.array`
671 Weight image of diffImage (1's and 0's for good and bad pixels, respectively).
673 maxRange : `int`
674 Last index of the covariance to be computed.
676 Returns
677 -------
678 outList : `list`
679 List with tuples of the form (dx, dy, var, cov, npix), where:
680 dx : `int`
681 Lag in x
682 dy : `int`
683 Lag in y
684 var : `float`
685 Variance at (dx, dy).
686 cov : `float`
687 Covariance at (dx, dy).
688 nPix : `int`
689 Number of pixel pairs used to evaluate var and cov.
690 """
691 outList = []
692 var = 0
693 # (dy,dx) = (0,0) has to be first
694 for dy in range(maxRange + 1):
695 for dx in range(0, maxRange + 1):
696 if (dx*dy > 0):
697 cov1, nPix1 = self.covDirectValue(diffImage, weightImage, dx, dy)
698 cov2, nPix2 = self.covDirectValue(diffImage, weightImage, dx, -dy)
699 cov = 0.5*(cov1 + cov2)
700 nPix = nPix1 + nPix2
701 else:
702 cov, nPix = self.covDirectValue(diffImage, weightImage, dx, dy)
703 if (dx == 0 and dy == 0):
704 var = cov
705 outList.append((dx, dy, var, cov, nPix))
707 return outList
709 def covDirectValue(self, diffImage, weightImage, dx, dy):
710 """Compute covariances of diffImage in real space at lag (dx, dy).
712 Taken from https://github.com/PierreAstier/bfptc/ (c.f., appendix of Astier+19).
714 Parameters
715 ----------
716 diffImage : `numpy.array`
717 Image to compute the covariance of.
719 weightImage : `numpy.array`
720 Weight image of diffImage (1's and 0's for good and bad pixels, respectively).
722 dx : `int`
723 Lag in x.
725 dy : `int`
726 Lag in y.
728 Returns
729 -------
730 cov : `float`
731 Covariance at (dx, dy)
733 nPix : `int`
734 Number of pixel pairs used to evaluate var and cov.
735 """
736 (nCols, nRows) = diffImage.shape
737 # switching both signs does not change anything:
738 # it just swaps im1 and im2 below
739 if (dx < 0):
740 (dx, dy) = (-dx, -dy)
741 # now, we have dx >0. We have to distinguish two cases
742 # depending on the sign of dy
743 if dy >= 0:
744 im1 = diffImage[dy:, dx:]
745 w1 = weightImage[dy:, dx:]
746 im2 = diffImage[:nCols - dy, :nRows - dx]
747 w2 = weightImage[:nCols - dy, :nRows - dx]
748 else:
749 im1 = diffImage[:nCols + dy, dx:]
750 w1 = weightImage[:nCols + dy, dx:]
751 im2 = diffImage[-dy:, :nRows - dx]
752 w2 = weightImage[-dy:, :nRows - dx]
753 # use the same mask for all 3 calculations
754 wAll = w1*w2
755 # do not use mean() because weightImage=0 pixels would then count
756 nPix = wAll.sum()
757 im1TimesW = im1*wAll
758 s1 = im1TimesW.sum()/nPix
759 s2 = (im2*wAll).sum()/nPix
760 p = (im1TimesW*im2).sum()/nPix
761 cov = p - s1*s2
763 return cov, nPix
765 def fitNonLinearityAndBuildLinearizers(self, datasetPtc, detector, tableArray=None, log=None):
766 """Fit non-linearity function and build linearizer objects.
768 Parameters
769 ----------
770 datasePtct : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
771 The dataset containing information such as the means, variances and exposure times.
772 nLinearity
774 detector : `lsst.afw.cameraGeom.Detector`
775 Detector object.
777 tableArray : `np.array`, optional
778 Optional. Look-up table array with size rows=nAmps and columns=DN values.
779 It will be modified in-place if supplied.
781 log : `lsst.log.Log`, optional
782 Logger to handle messages.
784 Returns
785 -------
786 linearizer : `lsst.ip.isr.Linearizer`
787 Linearizer object
788 """
790 # Fit NonLinearity
791 datasetNonLinearity = self.fitNonLinearity(datasetPtc, tableArray=tableArray)
793 # Produce linearizer
794 now = datetime.datetime.utcnow()
795 calibDate = now.strftime("%Y-%m-%d")
796 linType = self.config.linearizerType
798 if linType == "LOOKUPTABLE":
799 tableArray = tableArray
800 else:
801 tableArray = None
803 linearizer = self.buildLinearizerObject(datasetNonLinearity, detector, calibDate, linType,
804 instruName=self.config.instrumentName,
805 tableArray=tableArray,
806 log=log)
808 return linearizer
810 def fitNonLinearity(self, datasetPtc, tableArray=None):
811 """Fit a polynomial to signal vs effective time curve to calculate linearity and residuals.
813 Parameters
814 ----------
815 datasetPtc : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
816 The dataset containing the means, variances and exposure times.
818 tableArray : `np.array`
819 Optional. Look-up table array with size rows=nAmps and columns=DN values.
820 It will be modified in-place if supplied.
822 Returns
823 -------
824 datasetNonLinearity : `dict`
825 Dictionary of `lsst.cp.pipe.ptc.LinearityResidualsAndLinearizersDataset`
826 dataclasses. Each one holds the output of `calculateLinearityResidualAndLinearizers` per
827 amplifier.
828 """
829 datasetNonLinearity = {ampName: [] for ampName in datasetPtc.ampNames}
830 for i, ampName in enumerate(datasetPtc.ampNames):
831 # If a mask is not found, use all points.
832 if (len(datasetPtc.visitMask[ampName]) == 0):
833 self.log.warn(f"Mask not found for {ampName} in non-linearity fit. Using all points.")
834 mask = np.repeat(True, len(datasetPtc.rawExpTimes[ampName]))
835 else:
836 mask = datasetPtc.visitMask[ampName]
838 timeVecFinal = np.array(datasetPtc.rawExpTimes[ampName])[mask]
839 meanVecFinal = np.array(datasetPtc.rawMeans[ampName])[mask]
841 # Non-linearity residuals (NL of mean vs time curve): percentage, and fit to a quadratic function
842 # In this case, len(parsIniNonLinearity) = 3 indicates that we want a quadratic fit
843 datasetLinRes = self.calculateLinearityResidualAndLinearizers(timeVecFinal, meanVecFinal)
845 # LinearizerLookupTable
846 if tableArray is not None:
847 tableArray[i, :] = datasetLinRes.linearizerTableRow
849 datasetNonLinearity[ampName] = datasetLinRes
851 return datasetNonLinearity
853 def calculateLinearityResidualAndLinearizers(self, exposureTimeVector, meanSignalVector):
854 """Calculate linearity residual and fit an n-order polynomial to the mean vs time curve
855 to produce corrections (deviation from linear part of polynomial) for a particular amplifier
856 to populate LinearizeLookupTable.
857 Use the coefficients of this fit to calculate the correction coefficients for LinearizePolynomial
858 and LinearizeSquared."
860 Parameters
861 ---------
863 exposureTimeVector: `list` of `float`
864 List of exposure times for each flat pair
866 meanSignalVector: `list` of `float`
867 List of mean signal from diference image of flat pairs
869 Returns
870 -------
871 dataset : `lsst.cp.pipe.ptc.LinearityResidualsAndLinearizersDataset`
872 The dataset containing the fit parameters, the NL correction coefficients, and the
873 LUT row for the amplifier at hand.
875 Notes
876 -----
877 datase members:
879 dataset.polynomialLinearizerCoefficients : `list` of `float`
880 Coefficients for LinearizePolynomial, where corrImage = uncorrImage + sum_i c_i uncorrImage^(2 +
881 i).
882 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
883 DN/t^j, and they are fit from meanSignalVector = k0 + k1*exposureTimeVector +
884 k2*exposureTimeVector^2 + ... + kn*exposureTimeVector^n, with
885 n = "polynomialFitDegreeNonLinearity". k_0 and k_1 and degenerate with bias level and gain,
886 and are not used by the non-linearity correction. Therefore, j = 2...n in the above expression
887 (see `LinearizePolynomial` class in `linearize.py`.)
889 dataset.quadraticPolynomialLinearizerCoefficient : `float`
890 Coefficient for LinearizeSquared, where corrImage = uncorrImage + c0*uncorrImage^2.
891 c0 = -k2/(k1^2), where k1 and k2 are fit from
892 meanSignalVector = k0 + k1*exposureTimeVector + k2*exposureTimeVector^2 +...
893 + kn*exposureTimeVector^n, with n = "polynomialFitDegreeNonLinearity".
895 dataset.linearizerTableRow : `list` of `float`
896 One dimensional array with deviation from linear part of n-order polynomial fit
897 to mean vs time curve. This array will be one row (for the particular amplifier at hand)
898 of the table array for LinearizeLookupTable.
900 dataset.meanSignalVsTimePolyFitPars : `list` of `float`
901 Parameters from n-order polynomial fit to meanSignalVector vs exposureTimeVector.
903 dataset.meanSignalVsTimePolyFitParsErr : `list` of `float`
904 Parameters from n-order polynomial fit to meanSignalVector vs exposureTimeVector.
906 dataset.meanSignalVsTimePolyFitReducedChiSq : `float`
907 Reduced unweighted chi squared from polynomial fit to meanSignalVector vs exposureTimeVector.
908 """
910 # Lookup table linearizer
911 parsIniNonLinearity = self._initialParsForPolynomial(self.config.polynomialFitDegreeNonLinearity + 1)
912 if self.config.doFitBootstrap:
913 parsFit, parsFitErr, reducedChiSquaredNonLinearityFit = fitBootstrap(parsIniNonLinearity,
914 exposureTimeVector,
915 meanSignalVector,
916 funcPolynomial)
917 else:
918 parsFit, parsFitErr, reducedChiSquaredNonLinearityFit = fitLeastSq(parsIniNonLinearity,
919 exposureTimeVector,
920 meanSignalVector,
921 funcPolynomial)
923 # LinearizeLookupTable:
924 # Use linear part to get time at wich signal is maxAduForLookupTableLinearizer DN
925 tMax = (self.config.maxAduForLookupTableLinearizer - parsFit[0])/parsFit[1]
926 timeRange = np.linspace(0, tMax, self.config.maxAduForLookupTableLinearizer)
927 signalIdeal = parsFit[0] + parsFit[1]*timeRange
928 signalUncorrected = funcPolynomial(parsFit, timeRange)
929 linearizerTableRow = signalIdeal - signalUncorrected # LinearizerLookupTable has corrections
930 # LinearizePolynomial and LinearizeSquared:
931 # Check that magnitude of higher order (>= 3) coefficents of the polyFit are small,
932 # i.e., less than threshold = 1e-10 (typical quadratic and cubic coefficents are ~1e-6
933 # and ~1e-12).
934 k1 = parsFit[1]
935 polynomialLinearizerCoefficients = []
936 for i, coefficient in enumerate(parsFit):
937 c = -coefficient/(k1**i)
938 polynomialLinearizerCoefficients.append(c)
939 if np.fabs(c) > 1e-10:
940 msg = f"Coefficient {c} in polynomial fit larger than threshold 1e-10."
941 self.log.warn(msg)
942 # Coefficient for LinearizedSquared. Called "c0" in linearize.py
943 c0 = polynomialLinearizerCoefficients[2]
945 dataset = LinearityResidualsAndLinearizersDataset([], None, [], [], [], None)
946 dataset.polynomialLinearizerCoefficients = polynomialLinearizerCoefficients
947 dataset.quadraticPolynomialLinearizerCoefficient = c0
948 dataset.linearizerTableRow = linearizerTableRow
949 dataset.meanSignalVsTimePolyFitPars = parsFit
950 dataset.meanSignalVsTimePolyFitParsErr = parsFitErr
951 dataset.meanSignalVsTimePolyFitReducedChiSq = reducedChiSquaredNonLinearityFit
953 return dataset
955 def buildLinearizerObject(self, datasetNonLinearity, detector, calibDate, linearizerType, instruName='',
956 tableArray=None, log=None):
957 """Build linearizer object to persist.
959 Parameters
960 ----------
961 datasetNonLinearity : `dict`
962 Dictionary of `lsst.cp.pipe.ptc.LinearityResidualsAndLinearizersDataset` objects.
964 detector : `lsst.afw.cameraGeom.Detector`
965 Detector object
967 calibDate : `datetime.datetime`
968 Calibration date
970 linearizerType : `str`
971 'LOOKUPTABLE', 'LINEARIZESQUARED', or 'LINEARIZEPOLYNOMIAL'
973 instruName : `str`, optional
974 Instrument name
976 tableArray : `np.array`, optional
977 Look-up table array with size rows=nAmps and columns=DN values
979 log : `lsst.log.Log`, optional
980 Logger to handle messages
982 Returns
983 -------
984 linearizer : `lsst.ip.isr.Linearizer`
985 Linearizer object
986 """
987 detName = detector.getName()
988 detNum = detector.getId()
989 if linearizerType == "LOOKUPTABLE":
990 if tableArray is not None:
991 linearizer = Linearizer(detector=detector, table=tableArray, log=log)
992 else:
993 raise RuntimeError("tableArray must be provided when creating a LookupTable linearizer")
994 elif linearizerType in ("LINEARIZESQUARED", "LINEARIZEPOLYNOMIAL"):
995 linearizer = Linearizer(log=log)
996 else:
997 raise RuntimeError("Invalid linearizerType {linearizerType} to build a Linearizer object. "
998 "Supported: 'LOOKUPTABLE', 'LINEARIZESQUARED', or 'LINEARIZEPOLYNOMIAL'")
999 for i, amp in enumerate(detector.getAmplifiers()):
1000 ampName = amp.getName()
1001 datasetNonLinAmp = datasetNonLinearity[ampName]
1002 if linearizerType == "LOOKUPTABLE":
1003 linearizer.linearityCoeffs[ampName] = [i, 0]
1004 linearizer.linearityType[ampName] = "LookupTable"
1005 elif linearizerType == "LINEARIZESQUARED":
1006 linearizer.fitParams[ampName] = datasetNonLinAmp.meanSignalVsTimePolyFitPars
1007 linearizer.fitParamsErr[ampName] = datasetNonLinAmp.meanSignalVsTimePolyFitParsErr
1008 linearizer.linearityFitReducedChiSquared[ampName] = (
1009 datasetNonLinAmp.meanSignalVsTimePolyFitReducedChiSq)
1010 linearizer.linearityCoeffs[ampName] = [
1011 datasetNonLinAmp.quadraticPolynomialLinearizerCoefficient]
1012 linearizer.linearityType[ampName] = "Squared"
1013 elif linearizerType == "LINEARIZEPOLYNOMIAL":
1014 linearizer.fitParams[ampName] = datasetNonLinAmp.meanSignalVsTimePolyFitPars
1015 linearizer.fitParamsErr[ampName] = datasetNonLinAmp.meanSignalVsTimePolyFitParsErr
1016 linearizer.linearityFitReducedChiSquared[ampName] = (
1017 datasetNonLinAmp.meanSignalVsTimePolyFitReducedChiSq)
1018 # Slice correction coefficients (starting at 2) for polynomial linearizer
1019 # (and squared linearizer above). The first and second are reduntant with
1020 # the bias and gain, respectively, and are not used by LinearizerPolynomial.
1021 polyLinCoeffs = np.array(datasetNonLinAmp.polynomialLinearizerCoefficients[2:])
1022 linearizer.linearityCoeffs[ampName] = polyLinCoeffs
1023 linearizer.linearityType[ampName] = "Polynomial"
1024 linearizer.linearityBBox[ampName] = amp.getBBox()
1025 linearizer.validate()
1026 calibId = f"detectorName={detName} detector={detNum} calibDate={calibDate} ccd={detNum} filter=NONE"
1028 try:
1029 raftName = detName.split("_")[0]
1030 calibId += f" raftName={raftName}"
1031 except Exception:
1032 raftname = "NONE"
1033 calibId += f" raftName={raftname}"
1035 serial = detector.getSerial()
1036 linearizer.updateMetadata(instrumentName=instruName, detectorId=f"{detNum}",
1037 calibId=calibId, serial=serial, detectorName=f"{detName}")
1039 return linearizer
1041 @staticmethod
1042 def _initialParsForPolynomial(order):
1043 assert(order >= 2)
1044 pars = np.zeros(order, dtype=np.float)
1045 pars[0] = 10
1046 pars[1] = 1
1047 pars[2:] = 0.0001
1048 return pars
1050 @staticmethod
1051 def _boundsForPolynomial(initialPars):
1052 lowers = [np.NINF for p in initialPars]
1053 uppers = [np.inf for p in initialPars]
1054 lowers[1] = 0 # no negative gains
1055 return (lowers, uppers)
1057 @staticmethod
1058 def _boundsForAstier(initialPars):
1059 lowers = [np.NINF for p in initialPars]
1060 uppers = [np.inf for p in initialPars]
1061 return (lowers, uppers)
1063 @staticmethod
1064 def _getInitialGoodPoints(means, variances, maxDeviationPositive, maxDeviationNegative):
1065 """Return a boolean array to mask bad points.
1067 A linear function has a constant ratio, so find the median
1068 value of the ratios, and exclude the points that deviate
1069 from that by more than a factor of maxDeviationPositive/negative.
1070 Asymmetric deviations are supported as we expect the PTC to turn
1071 down as the flux increases, but sometimes it anomalously turns
1072 upwards just before turning over, which ruins the fits, so it
1073 is wise to be stricter about restricting positive outliers than
1074 negative ones.
1076 Too high and points that are so bad that fit will fail will be included
1077 Too low and the non-linear points will be excluded, biasing the NL fit."""
1078 ratios = [b/a for (a, b) in zip(means, variances)]
1079 medianRatio = np.median(ratios)
1080 ratioDeviations = [(r/medianRatio)-1 for r in ratios]
1082 # so that it doesn't matter if the deviation is expressed as positive or negative
1083 maxDeviationPositive = abs(maxDeviationPositive)
1084 maxDeviationNegative = -1. * abs(maxDeviationNegative)
1086 goodPoints = np.array([True if (r < maxDeviationPositive and r > maxDeviationNegative)
1087 else False for r in ratioDeviations])
1088 return goodPoints
1090 def _makeZeroSafe(self, array, warn=True, substituteValue=1e-9):
1091 """"""
1092 nBad = Counter(array)[0]
1093 if nBad == 0:
1094 return array
1096 if warn:
1097 msg = f"Found {nBad} zeros in array at elements {[x for x in np.where(array==0)[0]]}"
1098 self.log.warn(msg)
1100 array[array == 0] = substituteValue
1101 return array
1103 def fitPtc(self, dataset, ptcFitType):
1104 """Fit the photon transfer curve to a polynimial or to Astier+19 approximation.
1106 Fit the photon transfer curve with either a polynomial of the order
1107 specified in the task config, or using the Astier approximation.
1109 Sigma clipping is performed iteratively for the fit, as well as an
1110 initial clipping of data points that are more than
1111 config.initialNonLinearityExclusionThreshold away from lying on a
1112 straight line. This other step is necessary because the photon transfer
1113 curve turns over catastrophically at very high flux (because saturation
1114 drops the variance to ~0) and these far outliers cause the initial fit
1115 to fail, meaning the sigma cannot be calculated to perform the
1116 sigma-clipping.
1118 Parameters
1119 ----------
1120 dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
1121 The dataset containing the means, variances and exposure times
1123 ptcFitType : `str`
1124 Fit a 'POLYNOMIAL' (degree: 'polynomialFitDegree') or
1125 'EXPAPPROXIMATION' (Eq. 16 of Astier+19) to the PTC
1127 Returns
1128 -------
1129 dataset: `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
1130 This is the same dataset as the input paramter, however, it has been modified
1131 to include information such as the fit vectors and the fit parameters. See
1132 the class `PhotonTransferCurveDatase`.
1133 """
1135 def errFunc(p, x, y):
1136 return ptcFunc(p, x) - y
1138 sigmaCutPtcOutliers = self.config.sigmaCutPtcOutliers
1139 maxIterationsPtcOutliers = self.config.maxIterationsPtcOutliers
1141 for i, ampName in enumerate(dataset.ampNames):
1142 timeVecOriginal = np.array(dataset.rawExpTimes[ampName])
1143 meanVecOriginal = np.array(dataset.rawMeans[ampName])
1144 varVecOriginal = np.array(dataset.rawVars[ampName])
1145 varVecOriginal = self._makeZeroSafe(varVecOriginal)
1147 mask = ((meanVecOriginal >= self.config.minMeanSignal) &
1148 (meanVecOriginal <= self.config.maxMeanSignal))
1150 goodPoints = self._getInitialGoodPoints(meanVecOriginal, varVecOriginal,
1151 self.config.initialNonLinearityExclusionThresholdPositive,
1152 self.config.initialNonLinearityExclusionThresholdNegative)
1153 mask = mask & goodPoints
1155 if ptcFitType == 'EXPAPPROXIMATION':
1156 ptcFunc = funcAstier
1157 parsIniPtc = [-1e-9, 1.0, 10.] # a00, gain, noise
1158 bounds = self._boundsForAstier(parsIniPtc)
1159 if ptcFitType == 'POLYNOMIAL':
1160 ptcFunc = funcPolynomial
1161 parsIniPtc = self._initialParsForPolynomial(self.config.polynomialFitDegree + 1)
1162 bounds = self._boundsForPolynomial(parsIniPtc)
1164 # Before bootstrap fit, do an iterative fit to get rid of outliers
1165 count = 1
1166 while count <= maxIterationsPtcOutliers:
1167 # Note that application of the mask actually shrinks the array
1168 # to size rather than setting elements to zero (as we want) so
1169 # always update mask itself and re-apply to the original data
1170 meanTempVec = meanVecOriginal[mask]
1171 varTempVec = varVecOriginal[mask]
1172 res = least_squares(errFunc, parsIniPtc, bounds=bounds, args=(meanTempVec, varTempVec))
1173 pars = res.x
1175 # change this to the original from the temp because the masks are ANDed
1176 # meaning once a point is masked it's always masked, and the masks must
1177 # always be the same length for broadcasting
1178 sigResids = (varVecOriginal - ptcFunc(pars, meanVecOriginal))/np.sqrt(varVecOriginal)
1179 newMask = np.array([True if np.abs(r) < sigmaCutPtcOutliers else False for r in sigResids])
1180 mask = mask & newMask
1182 nDroppedTotal = Counter(mask)[False]
1183 self.log.debug(f"Iteration {count}: discarded {nDroppedTotal} points in total for {ampName}")
1184 count += 1
1185 # objects should never shrink
1186 assert (len(mask) == len(timeVecOriginal) == len(meanVecOriginal) == len(varVecOriginal))
1188 dataset.visitMask[ampName] = mask # store the final mask
1189 parsIniPtc = pars
1190 meanVecFinal = meanVecOriginal[mask]
1191 varVecFinal = varVecOriginal[mask]
1193 if Counter(mask)[False] > 0:
1194 self.log.info((f"Number of points discarded in PTC of amplifier {ampName}:" +
1195 f" {Counter(mask)[False]} out of {len(meanVecOriginal)}"))
1197 if (len(meanVecFinal) < len(parsIniPtc)):
1198 msg = (f"\nSERIOUS: Not enough data points ({len(meanVecFinal)}) compared to the number of"
1199 f"parameters of the PTC model({len(parsIniPtc)}). Setting {ampName} to BAD.")
1200 self.log.warn(msg)
1201 # The first and second parameters of initial fit are discarded (bias and gain)
1202 # for the final NL coefficients
1203 dataset.badAmps.append(ampName)
1204 dataset.gain[ampName] = np.nan
1205 dataset.gainErr[ampName] = np.nan
1206 dataset.noise[ampName] = np.nan
1207 dataset.noiseErr[ampName] = np.nan
1208 dataset.ptcFitPars[ampName] = np.nan
1209 dataset.ptcFitParsError[ampName] = np.nan
1210 dataset.ptcFitReducedChiSquared[ampName] = np.nan
1211 continue
1213 # Fit the PTC
1214 if self.config.doFitBootstrap:
1215 parsFit, parsFitErr, reducedChiSqPtc = fitBootstrap(parsIniPtc, meanVecFinal,
1216 varVecFinal, ptcFunc)
1217 else:
1218 parsFit, parsFitErr, reducedChiSqPtc = fitLeastSq(parsIniPtc, meanVecFinal,
1219 varVecFinal, ptcFunc)
1220 dataset.ptcFitPars[ampName] = parsFit
1221 dataset.ptcFitParsError[ampName] = parsFitErr
1222 dataset.ptcFitReducedChiSquared[ampName] = reducedChiSqPtc
1224 if ptcFitType == 'EXPAPPROXIMATION':
1225 ptcGain = parsFit[1]
1226 ptcGainErr = parsFitErr[1]
1227 ptcNoise = np.sqrt(np.fabs(parsFit[2]))
1228 ptcNoiseErr = 0.5*(parsFitErr[2]/np.fabs(parsFit[2]))*np.sqrt(np.fabs(parsFit[2]))
1229 if ptcFitType == 'POLYNOMIAL':
1230 ptcGain = 1./parsFit[1]
1231 ptcGainErr = np.fabs(1./parsFit[1])*(parsFitErr[1]/parsFit[1])
1232 ptcNoise = np.sqrt(np.fabs(parsFit[0]))*ptcGain
1233 ptcNoiseErr = (0.5*(parsFitErr[0]/np.fabs(parsFit[0]))*(np.sqrt(np.fabs(parsFit[0]))))*ptcGain
1234 dataset.gain[ampName] = ptcGain
1235 dataset.gainErr[ampName] = ptcGainErr
1236 dataset.noise[ampName] = ptcNoise
1237 dataset.noiseErr[ampName] = ptcNoiseErr
1238 if not len(dataset.ptcFitType) == 0:
1239 dataset.ptcFitType = ptcFitType
1241 return dataset