23 __all__ = [
'MeasurePhotonTransferCurveTask',
24 'MeasurePhotonTransferCurveTaskConfig',
25 'PhotonTransferCurveDataset']
28 import matplotlib.pyplot
as plt
29 from sqlite3
import OperationalError
30 from collections
import Counter
31 from dataclasses
import dataclass
34 import lsst.pex.config
as pexConfig
36 from .utils
import (NonexistentDatasetTaskDataIdContainer, PairedVisitListTaskRunner,
37 checkExpLengthEqual, fitLeastSq, fitBootstrap, funcPolynomial, funcAstier)
38 from scipy.optimize
import least_squares
43 from .astierCovPtcUtils
import (fftSize, CovFft, computeCovDirect, fitData)
47 """Config class for photon transfer curve measurement task"""
48 ccdKey = pexConfig.Field(
50 doc=
"The key by which to pull a detector from a dataId, e.g. 'ccd' or 'detector'.",
53 ptcFitType = pexConfig.ChoiceField(
55 doc=
"Fit PTC to approximation in Astier+19 (Equation 16) or to a polynomial.",
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)"
63 sigmaClipFullFitCovariancesAstier = pexConfig.Field(
65 doc=
"sigma clip for full model fit for FULLCOVARIANCE ptcFitType ",
68 maxIterFullFitCovariancesAstier = pexConfig.Field(
70 doc=
"Maximum number of iterations in full model fit for FULLCOVARIANCE ptcFitType",
73 maximumRangeCovariancesAstier = pexConfig.Field(
75 doc=
"Maximum range of covariances as in Astier+19",
78 covAstierRealSpace = pexConfig.Field(
80 doc=
"Calculate covariances in real space or via FFT? (see appendix A of Astier+19).",
83 polynomialFitDegree = pexConfig.Field(
85 doc=
"Degree of polynomial to fit the PTC, when 'ptcFitType'=POLYNOMIAL.",
88 doCreateLinearizer = pexConfig.Field(
90 doc=
"Calculate non-linearity and persist linearizer?",
93 linearizerType = pexConfig.ChoiceField(
95 doc=
"Linearizer type, if doCreateLinearizer=True",
96 default=
"LINEARIZEPOLYNOMIAL",
98 "LINEARIZEPOLYNOMIAL":
"n-degree polynomial (use 'polynomialFitDegreeNonLinearity' to set 'n').",
99 "LINEARIZESQUARED":
"c0 quadratic coefficient derived from coefficients of polynomiual fit",
100 "LOOKUPTABLE":
"Loouk table formed from linear part of polynomial fit."
103 polynomialFitDegreeNonLinearity = pexConfig.Field(
105 doc=
"If doCreateLinearizer, degree of polynomial to fit the meanSignal vs exposureTime" +
106 " curve to produce the table for LinearizeLookupTable.",
109 binSize = pexConfig.Field(
111 doc=
"Bin the image by this factor in both dimensions.",
114 minMeanSignal = pexConfig.Field(
116 doc=
"Minimum value (inclusive) of mean signal (in DN) above which to consider.",
119 maxMeanSignal = pexConfig.Field(
121 doc=
"Maximum value (inclusive) of mean signal (in DN) below which to consider.",
124 initialNonLinearityExclusionThresholdPositive = pexConfig.RangeField(
126 doc=
"Initially exclude data points with a variance that are more than a factor of this from being"
127 " linear in the positive 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.",
134 initialNonLinearityExclusionThresholdNegative = pexConfig.RangeField(
136 doc=
"Initially exclude data points with a variance that are more than a factor of this from being"
137 " linear in the negative direction, from the PTC fit. Note that these points will also be"
138 " excluded from the non-linearity fit. This is done before the iterative outlier rejection,"
139 " to allow an accurate determination of the sigmas for said iterative fit.",
144 sigmaCutPtcOutliers = pexConfig.Field(
146 doc=
"Sigma cut for outlier rejection in PTC.",
149 maskNameList = pexConfig.ListField(
151 doc=
"Mask list to exclude from statistics calculations.",
152 default=[
'SUSPECT',
'BAD',
'NO_DATA'],
154 nSigmaClipPtc = pexConfig.Field(
156 doc=
"Sigma cut for afwMath.StatisticsControl()",
159 nIterSigmaClipPtc = pexConfig.Field(
161 doc=
"Number of sigma-clipping iterations for afwMath.StatisticsControl()",
164 maxIterationsPtcOutliers = pexConfig.Field(
166 doc=
"Maximum number of iterations for outlier rejection in PTC.",
169 doFitBootstrap = pexConfig.Field(
171 doc=
"Use bootstrap for the PTC fit parameters and errors?.",
174 maxAduForLookupTableLinearizer = pexConfig.Field(
176 doc=
"Maximum DN value for the LookupTable linearizer.",
179 instrumentName = pexConfig.Field(
181 doc=
"Instrument name.",
188 """A simple class to hold the output from the
189 `calculateLinearityResidualAndLinearizers` function.
192 polynomialLinearizerCoefficients: list
194 quadraticPolynomialLinearizerCoefficient: float
196 linearizerTableRow: list
197 meanSignalVsTimePolyFitPars: list
198 meanSignalVsTimePolyFitParsErr: list
199 meanSignalVsTimePolyFitReducedChiSq: float
203 """A simple class to hold the output data from the PTC task.
205 The dataset is made up of a dictionary for each item, keyed by the
206 amplifiers' names, which much be supplied at construction time.
208 New items cannot be added to the class to save accidentally saving to the
209 wrong property, and the class can be frozen if desired.
211 inputVisitPairs records the visits used to produce the data.
212 When fitPtc() or fitCovariancesAstier() is run, a mask is built up, which is by definition
213 always the same length as inputVisitPairs, rawExpTimes, rawMeans
214 and rawVars, and is a list of bools, which are incrementally set to False
215 as points are discarded from the fits.
217 PTC fit parameters for polynomials are stored in a list in ascending order
218 of polynomial term, i.e. par[0]*x^0 + par[1]*x + par[2]*x^2 etc
219 with the length of the list corresponding to the order of the polynomial
225 List with the names of the amplifiers of the detector at hand.
228 Type of model fitted to the PTC: "POLYNOMIAL", "EXPAPPROXIMATION", or "FULLCOVARIANCE".
232 `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
233 Output dataset from MeasurePhotonTransferCurveTask.
240 self.__dict__[
"ptcFitType"] = ptcFitType
241 self.__dict__[
"ampNames"] = ampNames
242 self.__dict__[
"badAmps"] = []
247 self.__dict__[
"inputVisitPairs"] = {ampName: []
for ampName
in ampNames}
248 self.__dict__[
"visitMask"] = {ampName: []
for ampName
in ampNames}
249 self.__dict__[
"rawExpTimes"] = {ampName: []
for ampName
in ampNames}
250 self.__dict__[
"rawMeans"] = {ampName: []
for ampName
in ampNames}
251 self.__dict__[
"rawVars"] = {ampName: []
for ampName
in ampNames}
254 self.__dict__[
"gain"] = {ampName: -1.
for ampName
in ampNames}
255 self.__dict__[
"gainErr"] = {ampName: -1.
for ampName
in ampNames}
256 self.__dict__[
"noise"] = {ampName: -1.
for ampName
in ampNames}
257 self.__dict__[
"noiseErr"] = {ampName: -1.
for ampName
in ampNames}
261 self.__dict__[
"ptcFitPars"] = {ampName: []
for ampName
in ampNames}
262 self.__dict__[
"ptcFitParsError"] = {ampName: []
for ampName
in ampNames}
263 self.__dict__[
"ptcFitReducedChiSquared"] = {ampName: []
for ampName
in ampNames}
270 self.__dict__[
"covariancesTuple"] = {ampName: []
for ampName
in ampNames}
271 self.__dict__[
"covariancesFitsWithNoB"] = {ampName: []
for ampName
in ampNames}
272 self.__dict__[
"covariancesFits"] = {ampName: []
for ampName
in ampNames}
273 self.__dict__[
"aMatrix"] = {ampName: []
for ampName
in ampNames}
274 self.__dict__[
"bMatrix"] = {ampName: []
for ampName
in ampNames}
277 self.__dict__[
"finalVars"] = {ampName: []
for ampName
in ampNames}
278 self.__dict__[
"finalModelVars"] = {ampName: []
for ampName
in ampNames}
279 self.__dict__[
"finalMeans"] = {ampName: []
for ampName
in ampNames}
282 """Protect class attributes"""
283 if attribute
not in self.__dict__:
284 raise AttributeError(f
"{attribute} is not already a member of PhotonTransferCurveDataset, which"
285 " does not support setting of new attributes.")
287 self.__dict__[attribute] = value
290 """Get the visits used, i.e. not discarded, for a given amp.
292 If no mask has been created yet, all visits are returned.
294 if len(self.visitMask[ampName]) == 0:
295 return self.inputVisitPairs[ampName]
298 assert len(self.visitMask[ampName]) == len(self.inputVisitPairs[ampName])
300 pairs = self.inputVisitPairs[ampName]
301 mask = self.visitMask[ampName]
303 return [(v1, v2)
for ((v1, v2), m)
in zip(pairs, mask)
if bool(m)
is True]
306 return [amp
for amp
in self.ampNames
if amp
not in self.badAmps]
310 """A class to calculate, fit, and plot a PTC from a set of flat pairs.
312 The Photon Transfer Curve (var(signal) vs mean(signal)) is a standard tool
313 used in astronomical detectors characterization (e.g., Janesick 2001,
314 Janesick 2007). If ptcFitType is "EXPAPPROXIMATION" or "POLYNOMIAL", this task calculates the
315 PTC from a series of pairs of flat-field images; each pair taken at identical exposure
316 times. The difference image of each pair is formed to eliminate fixed pattern noise,
317 and then the variance of the difference image and the mean of the average image
318 are used to produce the PTC. An n-degree polynomial or the approximation in Equation
319 16 of Astier+19 ("The Shape of the Photon Transfer Curve of CCD sensors",
320 arXiv:1905.08677) can be fitted to the PTC curve. These models include
321 parameters such as the gain (e/DN) and readout noise.
323 Linearizers to correct for signal-chain non-linearity are also calculated.
324 The `Linearizer` class, in general, can support per-amp linearizers, but in this
325 task this is not supported.
327 If ptcFitType is "FULLCOVARIANCE", the covariances of the difference images are calculated via the
328 DFT methods described in Astier+19 and the variances for the PTC are given by the cov[0,0] elements
329 at each signal level. The full model in Equation 20 of Astier+19 is fit to the PTC to get the gain
336 Positional arguments passed to the Task constructor. None used at this
339 Keyword arguments passed on to the Task constructor. None used at this
344 RunnerClass = PairedVisitListTaskRunner
345 ConfigClass = MeasurePhotonTransferCurveTaskConfig
346 _DefaultName =
"measurePhotonTransferCurve"
349 pipeBase.CmdLineTask.__init__(self, *args, **kwargs)
350 plt.interactive(
False)
351 self.config.validate()
355 def _makeArgumentParser(cls):
356 """Augment argument parser for the MeasurePhotonTransferCurveTask."""
358 parser.add_argument(
"--visit-pairs", dest=
"visitPairs", nargs=
"*",
359 help=
"Visit pairs to use. Each pair must be of the form INT,INT e.g. 123,456")
360 parser.add_id_argument(
"--id", datasetType=
"photonTransferCurveDataset",
361 ContainerClass=NonexistentDatasetTaskDataIdContainer,
362 help=
"The ccds to use, e.g. --id ccd=0..100")
367 """Run the Photon Transfer Curve (PTC) measurement task.
369 For a dataRef (which is each detector here),
370 and given a list of visit pairs (postISR) at different exposure times,
375 dataRef : list of lsst.daf.persistence.ButlerDataRef
376 dataRef for the detector for the visits to be fit.
378 visitPairs : `iterable` of `tuple` of `int`
379 Pairs of visit numbers to be processed together
383 detNum = dataRef.dataId[self.config.ccdKey]
384 detector = dataRef.get(
'camera')[dataRef.dataId[self.config.ccdKey]]
390 for name
in dataRef.getButler().getKeys(
'bias'):
391 if name
not in dataRef.dataId:
393 dataRef.dataId[name] = \
394 dataRef.getButler().queryMetadata(
'raw', [name], detector=detNum)[0]
395 except OperationalError:
398 amps = detector.getAmplifiers()
399 ampNames = [amp.getName()
for amp
in amps]
401 self.log.info(
'Measuring PTC using %s visits for detector %s' % (visitPairs, detector.getId()))
405 for (v1, v2)
in visitPairs:
407 dataRef.dataId[
'expId'] = v1
408 exp1 = dataRef.get(
"postISRCCD", immediate=
True)
409 dataRef.dataId[
'expId'] = v2
410 exp2 = dataRef.get(
"postISRCCD", immediate=
True)
411 del dataRef.dataId[
'expId']
414 expTime = exp1.getInfo().getVisitInfo().getExposureTime()
417 for ampNumber, amp
in enumerate(detector):
418 ampName = amp.getName()
420 doRealSpace = self.config.covAstierRealSpace
421 muDiff, varDiff, covAstier = self.
measureMeanVarCov(exp1, exp2, region=amp.getBBox(),
422 covAstierRealSpace=doRealSpace)
423 if np.isnan(muDiff)
or np.isnan(varDiff)
or (covAstier
is None):
424 msg = (f
"NaN mean or var, or None cov in amp {ampNumber} in visit pair {v1}, {v2} "
425 "of detector {detNum}.")
429 tags = [
'mu',
'i',
'j',
'var',
'cov',
'npix',
'ext',
'expTime',
'ampName']
430 if (muDiff <= self.config.minMeanSignal)
or (muDiff >= self.config.maxMeanSignal):
432 datasetPtc.rawExpTimes[ampName].append(expTime)
433 datasetPtc.rawMeans[ampName].append(muDiff)
434 datasetPtc.rawVars[ampName].append(varDiff)
435 datasetPtc.inputVisitPairs[ampName].append((v1, v2))
437 tupleRows += [(muDiff, ) + covRow + (ampNumber, expTime, ampName)
for covRow
in covAstier]
438 if nAmpsNan == len(ampNames):
439 msg = f
"NaN mean in all amps of visit pair {v1}, {v2} of detector {detNum}."
443 tupleRecords += tupleRows
444 covariancesWithTags = np.core.records.fromrecords(tupleRecords, names=allTags)
446 if self.config.ptcFitType
in [
"FULLCOVARIANCE", ]:
449 elif self.config.ptcFitType
in [
"EXPAPPROXIMATION",
"POLYNOMIAL"]:
452 datasetPtc = self.
fitPtc(datasetPtc, self.config.ptcFitType)
455 if self.config.doCreateLinearizer:
456 numberAmps = len(amps)
457 numberAduValues = self.config.maxAduForLookupTableLinearizer
458 lookupTableArray = np.zeros((numberAmps, numberAduValues), dtype=np.float32)
466 tableArray=lookupTableArray,
469 if self.config.linearizerType ==
"LINEARIZEPOLYNOMIAL":
470 linDataType =
'linearizePolynomial'
471 linMsg =
"polynomial (coefficients for a polynomial correction)."
472 elif self.config.linearizerType ==
"LINEARIZESQUARED":
473 linDataType =
'linearizePolynomial'
474 linMsg =
"squared (c0, derived from k_i coefficients of a polynomial fit)."
475 elif self.config.linearizerType ==
"LOOKUPTABLE":
476 linDataType =
'linearizePolynomial'
477 linMsg =
"lookup table (linear component of polynomial fit)."
479 raise RuntimeError(
"Invalid config.linearizerType {selg.config.linearizerType}. "
480 "Supported: 'LOOKUPTABLE', 'LINEARIZESQUARED', or 'LINEARIZEPOLYNOMIAL'")
482 butler = dataRef.getButler()
483 self.log.info(f
"Writing linearizer: \n {linMsg}")
485 detName = detector.getName()
486 now = datetime.datetime.utcnow()
487 calibDate = now.strftime(
"%Y-%m-%d")
489 butler.put(linearizer, datasetType=linDataType, dataId={
'detector': detNum,
490 'detectorName': detName,
'calibDate': calibDate})
492 self.log.info(f
"Writing PTC data to {dataRef.getUri(write=True)}")
493 dataRef.put(datasetPtc, datasetType=
"photonTransferCurveDataset")
495 return pipeBase.Struct(exitStatus=0)
498 """Fit measured flat covariances to full model in Astier+19.
502 dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
503 The dataset containing information such as the means, variances and exposure times.
505 covariancesWithTagsArray : `numpy.recarray`
506 Tuple with at least (mu, cov, var, i, j, npix), where:
507 mu : 0.5*(m1 + m2), where:
508 mu1: mean value of flat1
509 mu2: mean value of flat2
510 cov: covariance value at lag(i, j)
511 var: variance(covariance value at lag(0, 0))
514 npix: number of pixels used for covariance calculation.
518 dataset: `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
519 This is the same dataset as the input paramter, however, it has been modified
520 to include information such as the fit vectors and the fit parameters. See
521 the class `PhotonTransferCurveDatase`.
524 covFits, covFitsNoB =
fitData(covariancesWithTagsArray, maxMu=self.config.maxMeanSignal,
525 r=self.config.maximumRangeCovariancesAstier,
526 nSigmaFullFit=self.config.sigmaClipFullFitCovariancesAstier,
527 maxIterFullFit=self.config.maxIterFullFitCovariancesAstier)
529 dataset.covariancesTuple = covariancesWithTagsArray
530 dataset.covariancesFits = covFits
531 dataset.covariancesFitsWithNoB = covFitsNoB
537 """Get output data for PhotonTransferCurveCovAstierDataset from CovFit objects.
541 dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
542 The dataset containing information such as the means, variances and exposure times.
545 Dictionary of CovFit objects, with amp names as keys.
549 dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
550 This is the same dataset as the input paramter, however, it has been modified
551 to include extra information such as the mask 1D array, gains, reoudout noise, measured signal,
552 measured variance, modeled variance, a, and b coefficient matrices (see Astier+19) per amplifier.
553 See the class `PhotonTransferCurveDatase`.
556 for i, amp
in enumerate(covFits):
558 (meanVecFinal, varVecFinal, varVecModel,
559 wc, varMask) = fit.getFitData(0, 0, divideByMu=
False, returnMasked=
True)
561 dataset.visitMask[amp] = varMask
562 dataset.gain[amp] = gain
563 dataset.gainErr[amp] = fit.getGainErr()
564 dataset.noise[amp] = np.sqrt(np.fabs(fit.getRon()))
565 dataset.noiseErr[amp] = fit.getRonErr()
566 dataset.finalVars[amp].append(varVecFinal/(gain**2))
567 dataset.finalModelVars[amp].append(varVecModel/(gain**2))
568 dataset.finalMeans[amp].append(meanVecFinal/gain)
569 dataset.aMatrix[amp].append(fit.getA())
570 dataset.bMatrix[amp].append(fit.getB())
575 """Calculate the mean of each of two exposures and the variance and covariance of their difference.
577 The variance is calculated via afwMath, and the covariance via the methods in Astier+19 (appendix A).
578 In theory, var = covariance[0,0]. This should be validated, and in the future, we may decide to just
579 keep one (covariance).
583 exposure1 : `lsst.afw.image.exposure.exposure.ExposureF`
584 First exposure of flat field pair.
586 exposure2 : `lsst.afw.image.exposure.exposure.ExposureF`
587 Second exposure of flat field pair.
589 region : `lsst.geom.Box2I`, optional
590 Region of each exposure where to perform the calculations (e.g, an amplifier).
592 covAstierRealSpace : `bool`, optional
593 Should the covariannces in Astier+19 be calculated in real space or via FFT?
594 See Appendix A of Astier+19.
598 mu : `float` or `NaN`
599 0.5*(mu1 + mu2), where mu1, and mu2 are the clipped means of the regions in
600 both exposures. If either mu1 or m2 are NaN's, the returned value is NaN.
602 varDiff : `float` or `NaN`
603 Half of the clipped variance of the difference of the regions inthe two input
604 exposures. If either mu1 or m2 are NaN's, the returned value is NaN.
606 covDiffAstier : `list` or `NaN`
607 List with tuples of the form (dx, dy, var, cov, npix), where:
613 Variance at (dx, dy).
615 Covariance at (dx, dy).
617 Number of pixel pairs used to evaluate var and cov.
618 If either mu1 or m2 are NaN's, the returned value is NaN.
621 if region
is not None:
622 im1Area = exposure1.maskedImage[region]
623 im2Area = exposure2.maskedImage[region]
625 im1Area = exposure1.maskedImage
626 im2Area = exposure2.maskedImage
628 im1Area = afwMath.binImage(im1Area, self.config.binSize)
629 im2Area = afwMath.binImage(im2Area, self.config.binSize)
631 im1MaskVal = exposure1.getMask().getPlaneBitMask(self.config.maskNameList)
632 im1StatsCtrl = afwMath.StatisticsControl(self.config.nSigmaClipPtc,
633 self.config.nIterSigmaClipPtc,
635 im1StatsCtrl.setNanSafe(
True)
636 im1StatsCtrl.setAndMask(im1MaskVal)
638 im2MaskVal = exposure2.getMask().getPlaneBitMask(self.config.maskNameList)
639 im2StatsCtrl = afwMath.StatisticsControl(self.config.nSigmaClipPtc,
640 self.config.nIterSigmaClipPtc,
642 im2StatsCtrl.setNanSafe(
True)
643 im2StatsCtrl.setAndMask(im2MaskVal)
646 mu1 = afwMath.makeStatistics(im1Area, afwMath.MEANCLIP, im1StatsCtrl).getValue()
647 mu2 = afwMath.makeStatistics(im2Area, afwMath.MEANCLIP, im2StatsCtrl).getValue()
648 if np.isnan(mu1)
or np.isnan(mu2):
649 return np.nan, np.nan,
None
654 temp = im2Area.clone()
656 diffIm = im1Area.clone()
661 diffImMaskVal = diffIm.getMask().getPlaneBitMask(self.config.maskNameList)
662 diffImStatsCtrl = afwMath.StatisticsControl(self.config.nSigmaClipPtc,
663 self.config.nIterSigmaClipPtc,
665 diffImStatsCtrl.setNanSafe(
True)
666 diffImStatsCtrl.setAndMask(diffImMaskVal)
668 varDiff = 0.5*(afwMath.makeStatistics(diffIm, afwMath.VARIANCECLIP, diffImStatsCtrl).getValue())
671 w1 = np.where(im1Area.getMask().getArray() == 0, 1, 0)
672 w2 = np.where(im2Area.getMask().getArray() == 0, 1, 0)
675 wDiff = np.where(diffIm.getMask().getArray() == 0, 1, 0)
678 maxRangeCov = self.config.maximumRangeCovariancesAstier
679 if covAstierRealSpace:
680 covDiffAstier =
computeCovDirect(diffIm.getImage().getArray(), w, maxRangeCov)
682 shapeDiff = diffIm.getImage().getArray().shape
683 fftShape = (
fftSize(shapeDiff[0] + maxRangeCov),
fftSize(shapeDiff[1]+maxRangeCov))
684 c =
CovFft(diffIm.getImage().getArray(), w, fftShape, maxRangeCov)
685 covDiffAstier = c.reportCovFft(maxRangeCov)
687 return mu, varDiff, covDiffAstier
690 """Compute covariances of diffImage in real space.
692 For lags larger than ~25, it is slower than the FFT way.
693 Taken from https://github.com/PierreAstier/bfptc/
697 diffImage : `numpy.array`
698 Image to compute the covariance of.
700 weightImage : `numpy.array`
701 Weight image of diffImage (1's and 0's for good and bad pixels, respectively).
704 Last index of the covariance to be computed.
709 List with tuples of the form (dx, dy, var, cov, npix), where:
715 Variance at (dx, dy).
717 Covariance at (dx, dy).
719 Number of pixel pairs used to evaluate var and cov.
724 for dy
in range(maxRange + 1):
725 for dx
in range(0, maxRange + 1):
728 cov2, nPix2 = self.
covDirectValue(diffImage, weightImage, dx, -dy)
729 cov = 0.5*(cov1 + cov2)
733 if (dx == 0
and dy == 0):
735 outList.append((dx, dy, var, cov, nPix))
740 """Compute covariances of diffImage in real space at lag (dx, dy).
742 Taken from https://github.com/PierreAstier/bfptc/ (c.f., appendix of Astier+19).
746 diffImage : `numpy.array`
747 Image to compute the covariance of.
749 weightImage : `numpy.array`
750 Weight image of diffImage (1's and 0's for good and bad pixels, respectively).
761 Covariance at (dx, dy)
764 Number of pixel pairs used to evaluate var and cov.
766 (nCols, nRows) = diffImage.shape
770 (dx, dy) = (-dx, -dy)
774 im1 = diffImage[dy:, dx:]
775 w1 = weightImage[dy:, dx:]
776 im2 = diffImage[:nCols - dy, :nRows - dx]
777 w2 = weightImage[:nCols - dy, :nRows - dx]
779 im1 = diffImage[:nCols + dy, dx:]
780 w1 = weightImage[:nCols + dy, dx:]
781 im2 = diffImage[-dy:, :nRows - dx]
782 w2 = weightImage[-dy:, :nRows - dx]
788 s1 = im1TimesW.sum()/nPix
789 s2 = (im2*wAll).sum()/nPix
790 p = (im1TimesW*im2).sum()/nPix
796 """Fit non-linearity function and build linearizer objects.
800 datasePtct : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
801 The dataset containing information such as the means, variances and exposure times.
804 detector : `lsst.afw.cameraGeom.Detector`
807 tableArray : `np.array`, optional
808 Optional. Look-up table array with size rows=nAmps and columns=DN values.
809 It will be modified in-place if supplied.
811 log : `lsst.log.Log`, optional
812 Logger to handle messages.
816 linearizer : `lsst.ip.isr.Linearizer`
821 datasetNonLinearity = self.
fitNonLinearity(datasetPtc, tableArray=tableArray)
824 now = datetime.datetime.utcnow()
825 calibDate = now.strftime(
"%Y-%m-%d")
826 linType = self.config.linearizerType
828 if linType ==
"LOOKUPTABLE":
829 tableArray = tableArray
834 instruName=self.config.instrumentName,
835 tableArray=tableArray,
841 """Fit a polynomial to signal vs effective time curve to calculate linearity and residuals.
845 datasetPtc : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
846 The dataset containing the means, variances and exposure times.
848 tableArray : `np.array`
849 Optional. Look-up table array with size rows=nAmps and columns=DN values.
850 It will be modified in-place if supplied.
854 datasetNonLinearity : `dict`
855 Dictionary of `lsst.cp.pipe.ptc.LinearityResidualsAndLinearizersDataset`
856 dataclasses. Each one holds the output of `calculateLinearityResidualAndLinearizers` per
859 datasetNonLinearity = {ampName: []
for ampName
in datasetPtc.ampNames}
860 for i, ampName
in enumerate(datasetPtc.ampNames):
862 if (len(datasetPtc.visitMask[ampName]) == 0):
863 self.log.warn(f
"Mask not found for {ampName} in non-linearity fit. Using all points.")
864 mask = np.repeat(
True, len(datasetPtc.rawExpTimes[ampName]))
866 mask = datasetPtc.visitMask[ampName]
868 timeVecFinal = np.array(datasetPtc.rawExpTimes[ampName])[mask]
869 meanVecFinal = np.array(datasetPtc.rawMeans[ampName])[mask]
876 if tableArray
is not None:
877 tableArray[i, :] = datasetLinRes.linearizerTableRow
879 datasetNonLinearity[ampName] = datasetLinRes
881 return datasetNonLinearity
884 """Calculate linearity residual and fit an n-order polynomial to the mean vs time curve
885 to produce corrections (deviation from linear part of polynomial) for a particular amplifier
886 to populate LinearizeLookupTable.
887 Use the coefficients of this fit to calculate the correction coefficients for LinearizePolynomial
888 and LinearizeSquared."
893 exposureTimeVector: `list` of `float`
894 List of exposure times for each flat pair
896 meanSignalVector: `list` of `float`
897 List of mean signal from diference image of flat pairs
901 dataset : `lsst.cp.pipe.ptc.LinearityResidualsAndLinearizersDataset`
902 The dataset containing the fit parameters, the NL correction coefficients, and the
903 LUT row for the amplifier at hand.
909 dataset.polynomialLinearizerCoefficients : `list` of `float`
910 Coefficients for LinearizePolynomial, where corrImage = uncorrImage + sum_i c_i uncorrImage^(2 +
912 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
913 DN/t^j, and they are fit from meanSignalVector = k0 + k1*exposureTimeVector +
914 k2*exposureTimeVector^2 + ... + kn*exposureTimeVector^n, with
915 n = "polynomialFitDegreeNonLinearity". k_0 and k_1 and degenerate with bias level and gain,
916 and are not used by the non-linearity correction. Therefore, j = 2...n in the above expression
917 (see `LinearizePolynomial` class in `linearize.py`.)
919 dataset.quadraticPolynomialLinearizerCoefficient : `float`
920 Coefficient for LinearizeSquared, where corrImage = uncorrImage + c0*uncorrImage^2.
921 c0 = -k2/(k1^2), where k1 and k2 are fit from
922 meanSignalVector = k0 + k1*exposureTimeVector + k2*exposureTimeVector^2 +...
923 + kn*exposureTimeVector^n, with n = "polynomialFitDegreeNonLinearity".
925 dataset.linearizerTableRow : `list` of `float`
926 One dimensional array with deviation from linear part of n-order polynomial fit
927 to mean vs time curve. This array will be one row (for the particular amplifier at hand)
928 of the table array for LinearizeLookupTable.
930 dataset.meanSignalVsTimePolyFitPars : `list` of `float`
931 Parameters from n-order polynomial fit to meanSignalVector vs exposureTimeVector.
933 dataset.meanSignalVsTimePolyFitParsErr : `list` of `float`
934 Parameters from n-order polynomial fit to meanSignalVector vs exposureTimeVector.
936 dataset.meanSignalVsTimePolyFitReducedChiSq : `float`
937 Reduced unweighted chi squared from polynomial fit to meanSignalVector vs exposureTimeVector.
942 if self.config.doFitBootstrap:
943 parsFit, parsFitErr, reducedChiSquaredNonLinearityFit =
fitBootstrap(parsIniNonLinearity,
948 parsFit, parsFitErr, reducedChiSquaredNonLinearityFit =
fitLeastSq(parsIniNonLinearity,
955 tMax = (self.config.maxAduForLookupTableLinearizer - parsFit[0])/parsFit[1]
956 timeRange = np.linspace(0, tMax, self.config.maxAduForLookupTableLinearizer)
957 signalIdeal = parsFit[0] + parsFit[1]*timeRange
959 linearizerTableRow = signalIdeal - signalUncorrected
965 polynomialLinearizerCoefficients = []
966 for i, coefficient
in enumerate(parsFit):
967 c = -coefficient/(k1**i)
968 polynomialLinearizerCoefficients.append(c)
969 if np.fabs(c) > 1e-10:
970 msg = f
"Coefficient {c} in polynomial fit larger than threshold 1e-10."
973 c0 = polynomialLinearizerCoefficients[2]
976 dataset.polynomialLinearizerCoefficients = polynomialLinearizerCoefficients
977 dataset.quadraticPolynomialLinearizerCoefficient = c0
978 dataset.linearizerTableRow = linearizerTableRow
979 dataset.meanSignalVsTimePolyFitPars = parsFit
980 dataset.meanSignalVsTimePolyFitParsErr = parsFitErr
981 dataset.meanSignalVsTimePolyFitReducedChiSq = reducedChiSquaredNonLinearityFit
986 tableArray=None, log=None):
987 """Build linearizer object to persist.
991 datasetNonLinearity : `dict`
992 Dictionary of `lsst.cp.pipe.ptc.LinearityResidualsAndLinearizersDataset` objects.
994 detector : `lsst.afw.cameraGeom.Detector`
997 calibDate : `datetime.datetime`
1000 linearizerType : `str`
1001 'LOOKUPTABLE', 'LINEARIZESQUARED', or 'LINEARIZEPOLYNOMIAL'
1003 instruName : `str`, optional
1006 tableArray : `np.array`, optional
1007 Look-up table array with size rows=nAmps and columns=DN values
1009 log : `lsst.log.Log`, optional
1010 Logger to handle messages
1014 linearizer : `lsst.ip.isr.Linearizer`
1017 detName = detector.getName()
1018 detNum = detector.getId()
1019 if linearizerType ==
"LOOKUPTABLE":
1020 if tableArray
is not None:
1021 linearizer =
Linearizer(detector=detector, table=tableArray, log=log)
1023 raise RuntimeError(
"tableArray must be provided when creating a LookupTable linearizer")
1024 elif linearizerType
in (
"LINEARIZESQUARED",
"LINEARIZEPOLYNOMIAL"):
1027 raise RuntimeError(
"Invalid linearizerType {linearizerType} to build a Linearizer object. "
1028 "Supported: 'LOOKUPTABLE', 'LINEARIZESQUARED', or 'LINEARIZEPOLYNOMIAL'")
1029 for i, amp
in enumerate(detector.getAmplifiers()):
1030 ampName = amp.getName()
1031 datasetNonLinAmp = datasetNonLinearity[ampName]
1032 if linearizerType ==
"LOOKUPTABLE":
1033 linearizer.linearityCoeffs[ampName] = [i, 0]
1034 linearizer.linearityType[ampName] =
"LookupTable"
1035 elif linearizerType ==
"LINEARIZESQUARED":
1036 linearizer.fitParams[ampName] = datasetNonLinAmp.meanSignalVsTimePolyFitPars
1037 linearizer.fitParamsErr[ampName] = datasetNonLinAmp.meanSignalVsTimePolyFitParsErr
1038 linearizer.linearityFitReducedChiSquared[ampName] = (
1039 datasetNonLinAmp.meanSignalVsTimePolyFitReducedChiSq)
1040 linearizer.linearityCoeffs[ampName] = [
1041 datasetNonLinAmp.quadraticPolynomialLinearizerCoefficient]
1042 linearizer.linearityType[ampName] =
"Squared"
1043 elif linearizerType ==
"LINEARIZEPOLYNOMIAL":
1044 linearizer.fitParams[ampName] = datasetNonLinAmp.meanSignalVsTimePolyFitPars
1045 linearizer.fitParamsErr[ampName] = datasetNonLinAmp.meanSignalVsTimePolyFitParsErr
1046 linearizer.linearityFitReducedChiSquared[ampName] = (
1047 datasetNonLinAmp.meanSignalVsTimePolyFitReducedChiSq)
1051 polyLinCoeffs = np.array(datasetNonLinAmp.polynomialLinearizerCoefficients[2:])
1052 linearizer.linearityCoeffs[ampName] = polyLinCoeffs
1053 linearizer.linearityType[ampName] =
"Polynomial"
1054 linearizer.linearityBBox[ampName] = amp.getBBox()
1055 linearizer.validate()
1056 calibId = f
"detectorName={detName} detector={detNum} calibDate={calibDate} ccd={detNum} filter=NONE"
1059 raftName = detName.split(
"_")[0]
1060 calibId += f
" raftName={raftName}"
1063 calibId += f
" raftName={raftname}"
1065 serial = detector.getSerial()
1066 linearizer.updateMetadata(instrumentName=instruName, detectorId=f
"{detNum}",
1067 calibId=calibId, serial=serial, detectorName=f
"{detName}")
1072 def _initialParsForPolynomial(order):
1074 pars = np.zeros(order, dtype=np.float)
1081 def _boundsForPolynomial(initialPars):
1082 lowers = [np.NINF
for p
in initialPars]
1083 uppers = [np.inf
for p
in initialPars]
1085 return (lowers, uppers)
1088 def _boundsForAstier(initialPars):
1089 lowers = [np.NINF
for p
in initialPars]
1090 uppers = [np.inf
for p
in initialPars]
1091 return (lowers, uppers)
1094 def _getInitialGoodPoints(means, variances, maxDeviationPositive, maxDeviationNegative):
1095 """Return a boolean array to mask bad points.
1097 A linear function has a constant ratio, so find the median
1098 value of the ratios, and exclude the points that deviate
1099 from that by more than a factor of maxDeviationPositive/negative.
1100 Asymmetric deviations are supported as we expect the PTC to turn
1101 down as the flux increases, but sometimes it anomalously turns
1102 upwards just before turning over, which ruins the fits, so it
1103 is wise to be stricter about restricting positive outliers than
1106 Too high and points that are so bad that fit will fail will be included
1107 Too low and the non-linear points will be excluded, biasing the NL fit."""
1108 ratios = [b/a
for (a, b)
in zip(means, variances)]
1109 medianRatio = np.median(ratios)
1110 ratioDeviations = [(r/medianRatio)-1
for r
in ratios]
1113 maxDeviationPositive = abs(maxDeviationPositive)
1114 maxDeviationNegative = -1. * abs(maxDeviationNegative)
1116 goodPoints = np.array([
True if (r < maxDeviationPositive
and r > maxDeviationNegative)
1117 else False for r
in ratioDeviations])
1120 def _makeZeroSafe(self, array, warn=True, substituteValue=1e-9):
1122 nBad = Counter(array)[0]
1127 msg = f
"Found {nBad} zeros in array at elements {[x for x in np.where(array==0)[0]]}"
1130 array[array == 0] = substituteValue
1134 """Fit the photon transfer curve to a polynimial or to Astier+19 approximation.
1136 Fit the photon transfer curve with either a polynomial of the order
1137 specified in the task config, or using the Astier approximation.
1139 Sigma clipping is performed iteratively for the fit, as well as an
1140 initial clipping of data points that are more than
1141 config.initialNonLinearityExclusionThreshold away from lying on a
1142 straight line. This other step is necessary because the photon transfer
1143 curve turns over catastrophically at very high flux (because saturation
1144 drops the variance to ~0) and these far outliers cause the initial fit
1145 to fail, meaning the sigma cannot be calculated to perform the
1150 dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
1151 The dataset containing the means, variances and exposure times
1154 Fit a 'POLYNOMIAL' (degree: 'polynomialFitDegree') or
1155 'EXPAPPROXIMATION' (Eq. 16 of Astier+19) to the PTC
1159 dataset: `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
1160 This is the same dataset as the input paramter, however, it has been modified
1161 to include information such as the fit vectors and the fit parameters. See
1162 the class `PhotonTransferCurveDatase`.
1165 def errFunc(p, x, y):
1166 return ptcFunc(p, x) - y
1168 sigmaCutPtcOutliers = self.config.sigmaCutPtcOutliers
1169 maxIterationsPtcOutliers = self.config.maxIterationsPtcOutliers
1171 for i, ampName
in enumerate(dataset.ampNames):
1172 timeVecOriginal = np.array(dataset.rawExpTimes[ampName])
1173 meanVecOriginal = np.array(dataset.rawMeans[ampName])
1174 varVecOriginal = np.array(dataset.rawVars[ampName])
1177 mask = ((meanVecOriginal >= self.config.minMeanSignal) &
1178 (meanVecOriginal <= self.config.maxMeanSignal))
1181 self.config.initialNonLinearityExclusionThresholdPositive,
1182 self.config.initialNonLinearityExclusionThresholdNegative)
1183 mask = mask & goodPoints
1185 if ptcFitType ==
'EXPAPPROXIMATION':
1186 ptcFunc = funcAstier
1187 parsIniPtc = [-1e-9, 1.0, 10.]
1189 if ptcFitType ==
'POLYNOMIAL':
1190 ptcFunc = funcPolynomial
1196 while count <= maxIterationsPtcOutliers:
1200 meanTempVec = meanVecOriginal[mask]
1201 varTempVec = varVecOriginal[mask]
1202 res = least_squares(errFunc, parsIniPtc, bounds=bounds, args=(meanTempVec, varTempVec))
1208 sigResids = (varVecOriginal - ptcFunc(pars, meanVecOriginal))/np.sqrt(varVecOriginal)
1209 newMask = np.array([
True if np.abs(r) < sigmaCutPtcOutliers
else False for r
in sigResids])
1210 mask = mask & newMask
1212 nDroppedTotal = Counter(mask)[
False]
1213 self.log.debug(f
"Iteration {count}: discarded {nDroppedTotal} points in total for {ampName}")
1216 assert (len(mask) == len(timeVecOriginal) == len(meanVecOriginal) == len(varVecOriginal))
1218 dataset.visitMask[ampName] = mask
1220 meanVecFinal = meanVecOriginal[mask]
1221 varVecFinal = varVecOriginal[mask]
1223 if Counter(mask)[
False] > 0:
1224 self.log.info((f
"Number of points discarded in PTC of amplifier {ampName}:" +
1225 f
" {Counter(mask)[False]} out of {len(meanVecOriginal)}"))
1227 if (len(meanVecFinal) < len(parsIniPtc)):
1228 msg = (f
"\nSERIOUS: Not enough data points ({len(meanVecFinal)}) compared to the number of"
1229 f
"parameters of the PTC model({len(parsIniPtc)}). Setting {ampName} to BAD.")
1233 dataset.badAmps.append(ampName)
1234 dataset.gain[ampName] = np.nan
1235 dataset.gainErr[ampName] = np.nan
1236 dataset.noise[ampName] = np.nan
1237 dataset.noiseErr[ampName] = np.nan
1238 dataset.ptcFitPars[ampName] = np.nan
1239 dataset.ptcFitParsError[ampName] = np.nan
1240 dataset.ptcFitReducedChiSquared[ampName] = np.nan
1244 if self.config.doFitBootstrap:
1245 parsFit, parsFitErr, reducedChiSqPtc =
fitBootstrap(parsIniPtc, meanVecFinal,
1246 varVecFinal, ptcFunc)
1248 parsFit, parsFitErr, reducedChiSqPtc =
fitLeastSq(parsIniPtc, meanVecFinal,
1249 varVecFinal, ptcFunc)
1250 dataset.ptcFitPars[ampName] = parsFit
1251 dataset.ptcFitParsError[ampName] = parsFitErr
1252 dataset.ptcFitReducedChiSquared[ampName] = reducedChiSqPtc
1254 if ptcFitType ==
'EXPAPPROXIMATION':
1255 ptcGain = parsFit[1]
1256 ptcGainErr = parsFitErr[1]
1257 ptcNoise = np.sqrt(np.fabs(parsFit[2]))
1258 ptcNoiseErr = 0.5*(parsFitErr[2]/np.fabs(parsFit[2]))*np.sqrt(np.fabs(parsFit[2]))
1259 if ptcFitType ==
'POLYNOMIAL':
1260 ptcGain = 1./parsFit[1]
1261 ptcGainErr = np.fabs(1./parsFit[1])*(parsFitErr[1]/parsFit[1])
1262 ptcNoise = np.sqrt(np.fabs(parsFit[0]))*ptcGain
1263 ptcNoiseErr = (0.5*(parsFitErr[0]/np.fabs(parsFit[0]))*(np.sqrt(np.fabs(parsFit[0]))))*ptcGain
1264 dataset.gain[ampName] = ptcGain
1265 dataset.gainErr[ampName] = ptcGainErr
1266 dataset.noise[ampName] = ptcNoise
1267 dataset.noiseErr[ampName] = ptcNoiseErr
1268 if not len(dataset.ptcFitType) == 0:
1269 dataset.ptcFitType = ptcFitType