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 maximumRangeCovariancesAstier = pexConfig.Field(
65 doc=
"Maximum range of covariances as in Astier+19",
68 covAstierRealSpace = pexConfig.Field(
70 doc=
"Calculate covariances in real space or via FFT? (see appendix A of Astier+19).",
73 polynomialFitDegree = pexConfig.Field(
75 doc=
"Degree of polynomial to fit the PTC, when 'ptcFitType'=POLYNOMIAL.",
78 doCreateLinearizer = pexConfig.Field(
80 doc=
"Calculate non-linearity and persist linearizer?",
83 linearizerType = pexConfig.ChoiceField(
85 doc=
"Linearizer type, if doCreateLinearizer=True",
86 default=
"LINEARIZEPOLYNOMIAL",
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."
93 polynomialFitDegreeNonLinearity = pexConfig.Field(
95 doc=
"If doCreateLinearizer, degree of polynomial to fit the meanSignal vs exposureTime" +
96 " curve to produce the table for LinearizeLookupTable.",
99 binSize = pexConfig.Field(
101 doc=
"Bin the image by this factor in both dimensions.",
104 minMeanSignal = pexConfig.Field(
106 doc=
"Minimum value (inclusive) of mean signal (in DN) above which to consider.",
109 maxMeanSignal = pexConfig.Field(
111 doc=
"Maximum value (inclusive) of mean signal (in DN) below which to consider.",
114 initialNonLinearityExclusionThresholdPositive = pexConfig.RangeField(
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.",
124 initialNonLinearityExclusionThresholdNegative = 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 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.",
134 sigmaCutPtcOutliers = pexConfig.Field(
136 doc=
"Sigma cut for outlier rejection in PTC.",
139 maskNameList = pexConfig.ListField(
141 doc=
"Mask list to exclude from statistics calculations.",
142 default=[
'SUSPECT',
'BAD',
'NO_DATA'],
144 nSigmaClipPtc = pexConfig.Field(
146 doc=
"Sigma cut for afwMath.StatisticsControl()",
149 nIterSigmaClipPtc = pexConfig.Field(
151 doc=
"Number of sigma-clipping iterations for afwMath.StatisticsControl()",
154 maxIterationsPtcOutliers = pexConfig.Field(
156 doc=
"Maximum number of iterations for outlier rejection in PTC.",
159 doFitBootstrap = pexConfig.Field(
161 doc=
"Use bootstrap for the PTC fit parameters and errors?.",
164 maxAduForLookupTableLinearizer = pexConfig.Field(
166 doc=
"Maximum DN value for the LookupTable linearizer.",
169 instrumentName = pexConfig.Field(
171 doc=
"Instrument name.",
178 """A simple class to hold the output from the
179 `calculateLinearityResidualAndLinearizers` function.
182 polynomialLinearizerCoefficients: list
184 quadraticPolynomialLinearizerCoefficient: float
186 linearizerTableRow: list
187 meanSignalVsTimePolyFitPars: list
188 meanSignalVsTimePolyFitParsErr: list
189 meanSignalVsTimePolyFitReducedChiSq: float
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
215 List with the names of the amplifiers of the detector at hand.
218 Type of model fitted to the PTC: "POLYNOMIAL", "EXPAPPROXIMATION", or "FULLCOVARIANCE".
222 `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
223 Output dataset from MeasurePhotonTransferCurveTask.
230 self.__dict__[
"ptcFitType"] = ptcFitType
231 self.__dict__[
"ampNames"] = ampNames
232 self.__dict__[
"badAmps"] = []
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}
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}
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}
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}
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}
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.")
277 self.__dict__[attribute] = value
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.
284 if len(self.visitMask[ampName]) == 0:
285 return self.inputVisitPairs[ampName]
288 assert len(self.visitMask[ampName]) == len(self.inputVisitPairs[ampName])
290 pairs = self.inputVisitPairs[ampName]
291 mask = self.visitMask[ampName]
293 return [(v1, v2)
for ((v1, v2), m)
in zip(pairs, mask)
if bool(m)
is True]
296 return [amp
for amp
in self.ampNames
if amp
not in self.badAmps]
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
326 Positional arguments passed to the Task constructor. None used at this
329 Keyword arguments passed on to the Task constructor. None used at this
334 RunnerClass = PairedVisitListTaskRunner
335 ConfigClass = MeasurePhotonTransferCurveTaskConfig
336 _DefaultName =
"measurePhotonTransferCurve"
339 pipeBase.CmdLineTask.__init__(self, *args, **kwargs)
340 plt.interactive(
False)
341 self.config.validate()
345 def _makeArgumentParser(cls):
346 """Augment argument parser for the MeasurePhotonTransferCurveTask."""
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")
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,
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
373 detNum = dataRef.dataId[self.config.ccdKey]
374 detector = dataRef.get(
'camera')[dataRef.dataId[self.config.ccdKey]]
380 for name
in dataRef.getButler().getKeys(
'bias'):
381 if name
not in dataRef.dataId:
383 dataRef.dataId[name] = \
384 dataRef.getButler().queryMetadata(
'raw', [name], detector=detNum)[0]
385 except OperationalError:
388 amps = detector.getAmplifiers()
389 ampNames = [amp.getName()
for amp
in amps]
391 self.log.info(
'Measuring PTC using %s visits for detector %s' % (visitPairs, detector.getId()))
395 for (v1, v2)
in visitPairs:
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']
404 expTime = exp1.getInfo().getVisitInfo().getExposureTime()
408 for ampNumber, amp
in enumerate(detector):
409 ampName = amp.getName()
411 doRealSpace = self.config.covAstierRealSpace
412 muDiff, varDiff, covAstier = self.
measureMeanVarCov(exp1, exp2, region=amp.getBBox(),
413 covAstierRealSpace=doRealSpace)
414 if np.isnan(muDiff)
or np.isnan(varDiff)
or np.isnan(covAstier):
415 msg = f
"NaN mean or cov in amp {ampNumber} in visit pair {v1}, {v2} of detector {detNum}."
420 datasetPtc.rawExpTimes[ampName].append(expTime)
421 datasetPtc.rawMeans[ampName].append(muDiff)
422 datasetPtc.rawVars[ampName].append(varDiff)
423 datasetPtc.inputVisitPairs[ampName].append((v1, v2))
425 tupleRows += [(muDiff, ) + covRow + (ampNumber, expTime, ampName)
for covRow
in covAstier]
426 tags = [
'mu',
'i',
'j',
'var',
'cov',
'npix',
'ext',
'expTime',
'ampName']
427 if nAmpsNan == len(ampNames):
428 msg = f
"NaN mean in all amps of visit pair {v1}, {v2} of detector {detNum}."
432 tupleRecords += tupleRows
433 covariancesWithTags = np.core.records.fromrecords(tupleRecords, names=allTags)
435 if self.config.ptcFitType
in [
"FULLCOVARIANCE", ]:
438 elif self.config.ptcFitType
in [
"EXPAPPROXIMATION",
"POLYNOMIAL"]:
441 datasetPtc = self.
fitPtc(datasetPtc, self.config.ptcFitType)
444 if self.config.doCreateLinearizer:
445 numberAmps = len(amps)
446 numberAduValues = self.config.maxAduForLookupTableLinearizer
447 lookupTableArray = np.zeros((numberAmps, numberAduValues), dtype=np.float32)
455 tableArray=lookupTableArray,
458 if self.config.linearizerType ==
"LINEARIZEPOLYNOMIAL":
459 linDataType =
'linearizePolynomial'
460 linMsg =
"polynomial (coefficients for a polynomial correction)."
461 elif self.config.linearizerType ==
"LINEARIZESQUARED":
462 linDataType =
'linearizePolynomial'
463 linMsg =
"squared (c0, derived from k_i coefficients of a polynomial fit)."
464 elif self.config.linearizerType ==
"LOOKUPTABLE":
465 linDataType =
'linearizePolynomial'
466 linMsg =
"lookup table (linear component of polynomial fit)."
468 raise RuntimeError(
"Invalid config.linearizerType {selg.config.linearizerType}. "
469 "Supported: 'LOOKUPTABLE', 'LINEARIZESQUARED', or 'LINEARIZEPOLYNOMIAL'")
471 butler = dataRef.getButler()
472 self.log.info(f
"Writing linearizer: \n {linMsg}")
474 detName = detector.getName()
475 now = datetime.datetime.utcnow()
476 calibDate = now.strftime(
"%Y-%m-%d")
478 butler.put(linearizer, datasetType=linDataType, dataId={
'detector': detNum,
479 'detectorName': detName,
'calibDate': calibDate})
481 self.log.info(f
"Writing PTC data to {dataRef.getUri(write=True)}")
482 dataRef.put(datasetPtc, datasetType=
"photonTransferCurveDataset")
484 return pipeBase.Struct(exitStatus=0)
487 """Fit measured flat covariances to full model in Astier+19.
491 dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
492 The dataset containing information such as the means, variances and exposure times.
494 covariancesWithTagsArray : `numpy.recarray`
495 Tuple with at least (mu, cov, var, i, j, npix), where:
496 mu : 0.5*(m1 + m2), where:
497 mu1: mean value of flat1
498 mu2: mean value of flat2
499 cov: covariance value at lag(i, j)
500 var: variance(covariance value at lag(0, 0))
503 npix: number of pixels used for covariance calculation.
507 dataset: `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
508 This is the same dataset as the input paramter, however, it has been modified
509 to include information such as the fit vectors and the fit parameters. See
510 the class `PhotonTransferCurveDatase`.
513 covFits, covFitsNoB =
fitData(covariancesWithTagsArray, maxMu=self.config.maxMeanSignal,
514 r=self.config.maximumRangeCovariancesAstier)
516 dataset.covariancesTuple = covariancesWithTagsArray
517 dataset.covariancesFits = covFits
518 dataset.covariancesFitsWithNoB = covFitsNoB
524 """Get output data for PhotonTransferCurveCovAstierDataset from CovFit objects.
528 dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
529 The dataset containing information such as the means, variances and exposure times.
532 Dictionary of CovFit objects, with amp names as keys.
536 dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
537 This is the same dataset as the input paramter, however, it has been modified
538 to include extra information such as the mask 1D array, gains, reoudout noise, measured signal,
539 measured variance, modeled variance, a, and b coefficient matrices (see Astier+19) per amplifier.
540 See the class `PhotonTransferCurveDatase`.
543 for i, amp
in enumerate(covFits):
545 meanVecFinal, varVecFinal, varVecModel, wc = fit.getNormalizedFitData(0, 0, divideByMu=
False)
547 dataset.visitMask[amp] = fit.getMaskVar()
548 dataset.gain[amp] = gain
549 dataset.gainErr[amp] = fit.getGainErr()
550 dataset.noise[amp] = np.sqrt(np.fabs(fit.getRon()))
551 dataset.noiseErr[amp] = fit.getRonErr()
552 dataset.finalVars[amp].append(varVecFinal/(gain**2))
553 dataset.finalModelVars[amp].append(varVecModel/(gain**2))
554 dataset.finalMeans[amp].append(meanVecFinal/gain)
555 dataset.aMatrix[amp].append(fit.getA())
556 dataset.bMatrix[amp].append(fit.getB())
561 """Calculate the mean of each of two exposures and the variance and covariance of their difference.
563 The variance is calculated via afwMath, and the covariance via the methods in Astier+19 (appendix A).
564 In theory, var = covariance[0,0]. This should be validated, and in the future, we may decide to just
565 keep one (covariance).
569 exposure1 : `lsst.afw.image.exposure.exposure.ExposureF`
570 First exposure of flat field pair.
572 exposure2 : `lsst.afw.image.exposure.exposure.ExposureF`
573 Second exposure of flat field pair.
575 region : `lsst.geom.Box2I`, optional
576 Region of each exposure where to perform the calculations (e.g, an amplifier).
578 covAstierRealSpace : `bool`, optional
579 Should the covariannces in Astier+19 be calculated in real space or via FFT?
580 See Appendix A of Astier+19.
584 mu : `float` or `NaN`
585 0.5*(mu1 + mu2), where mu1, and mu2 are the clipped means of the regions in
586 both exposures. If either mu1 or m2 are NaN's, the returned value is NaN.
588 varDiff : `float` or `NaN`
589 Half of the clipped variance of the difference of the regions inthe two input
590 exposures. If either mu1 or m2 are NaN's, the returned value is NaN.
592 covDiffAstier : `list` or `NaN`
593 List with tuples of the form (dx, dy, var, cov, npix), where:
599 Variance at (dx, dy).
601 Covariance at (dx, dy).
603 Number of pixel pairs used to evaluate var and cov.
604 If either mu1 or m2 are NaN's, the returned value is NaN.
607 if region
is not None:
608 im1Area = exposure1.maskedImage[region]
609 im2Area = exposure2.maskedImage[region]
611 im1Area = exposure1.maskedImage
612 im2Area = exposure2.maskedImage
614 im1Area = afwMath.binImage(im1Area, self.config.binSize)
615 im2Area = afwMath.binImage(im2Area, self.config.binSize)
617 im1MaskVal = exposure1.getMask().getPlaneBitMask(self.config.maskNameList)
618 im1StatsCtrl = afwMath.StatisticsControl(self.config.nSigmaClipPtc,
619 self.config.nIterSigmaClipPtc,
621 im1StatsCtrl.setNanSafe(
True)
622 im1StatsCtrl.setAndMask(im1MaskVal)
624 im2MaskVal = exposure2.getMask().getPlaneBitMask(self.config.maskNameList)
625 im2StatsCtrl = afwMath.StatisticsControl(self.config.nSigmaClipPtc,
626 self.config.nIterSigmaClipPtc,
628 im2StatsCtrl.setNanSafe(
True)
629 im2StatsCtrl.setAndMask(im2MaskVal)
632 mu1 = afwMath.makeStatistics(im1Area, afwMath.MEANCLIP, im1StatsCtrl).getValue()
633 mu2 = afwMath.makeStatistics(im2Area, afwMath.MEANCLIP, im2StatsCtrl).getValue()
634 if np.isnan(mu1)
or np.isnan(mu2):
635 return np.nan, np.nan, np.nan
640 temp = im2Area.clone()
642 diffIm = im1Area.clone()
647 diffImMaskVal = diffIm.getMask().getPlaneBitMask(self.config.maskNameList)
648 diffImStatsCtrl = afwMath.StatisticsControl(self.config.nSigmaClipPtc,
649 self.config.nIterSigmaClipPtc,
651 diffImStatsCtrl.setNanSafe(
True)
652 diffImStatsCtrl.setAndMask(diffImMaskVal)
654 varDiff = 0.5*(afwMath.makeStatistics(diffIm, afwMath.VARIANCECLIP, diffImStatsCtrl).getValue())
657 w1 = np.where(im1Area.getMask().getArray() == 0, 1, 0)
658 w2 = np.where(im2Area.getMask().getArray() == 0, 1, 0)
661 wDiff = np.where(diffIm.getMask().getArray() == 0, 1, 0)
664 maxRangeCov = self.config.maximumRangeCovariancesAstier
665 if covAstierRealSpace:
666 covDiffAstier =
computeCovDirect(diffIm.getImage().getArray(), w, maxRangeCov)
668 shapeDiff = diffIm.getImage().getArray().shape
669 fftShape = (
fftSize(shapeDiff[0] + maxRangeCov),
fftSize(shapeDiff[1]+maxRangeCov))
670 c =
CovFft(diffIm.getImage().getArray(), w, fftShape, maxRangeCov)
671 covDiffAstier = c.reportCovFft(maxRangeCov)
673 return mu, varDiff, covDiffAstier
676 """Compute covariances of diffImage in real space.
678 For lags larger than ~25, it is slower than the FFT way.
679 Taken from https://github.com/PierreAstier/bfptc/
683 diffImage : `numpy.array`
684 Image to compute the covariance of.
686 weightImage : `numpy.array`
687 Weight image of diffImage (1's and 0's for good and bad pixels, respectively).
690 Last index of the covariance to be computed.
695 List with tuples of the form (dx, dy, var, cov, npix), where:
701 Variance at (dx, dy).
703 Covariance at (dx, dy).
705 Number of pixel pairs used to evaluate var and cov.
710 for dy
in range(maxRange + 1):
711 for dx
in range(0, maxRange + 1):
714 cov2, nPix2 = self.
covDirectValue(diffImage, weightImage, dx, -dy)
715 cov = 0.5*(cov1 + cov2)
719 if (dx == 0
and dy == 0):
721 outList.append((dx, dy, var, cov, nPix))
726 """Compute covariances of diffImage in real space at lag (dx, dy).
728 Taken from https://github.com/PierreAstier/bfptc/ (c.f., appendix of Astier+19).
732 diffImage : `numpy.array`
733 Image to compute the covariance of.
735 weightImage : `numpy.array`
736 Weight image of diffImage (1's and 0's for good and bad pixels, respectively).
747 Covariance at (dx, dy)
750 Number of pixel pairs used to evaluate var and cov.
752 (nCols, nRows) = diffImage.shape
756 (dx, dy) = (-dx, -dy)
760 im1 = diffImage[dy:, dx:]
761 w1 = weightImage[dy:, dx:]
762 im2 = diffImage[:nCols - dy, :nRows - dx]
763 w2 = weightImage[:nCols - dy, :nRows - dx]
765 im1 = diffImage[:nCols + dy, dx:]
766 w1 = weightImage[:nCols + dy, dx:]
767 im2 = diffImage[-dy:, :nRows - dx]
768 w2 = weightImage[-dy:, :nRows - dx]
774 s1 = im1TimesW.sum()/nPix
775 s2 = (im2*wAll).sum()/nPix
776 p = (im1TimesW*im2).sum()/nPix
782 """Fit non-linearity function and build linearizer objects.
786 datasePtct : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
787 The dataset containing information such as the means, variances and exposure times.
790 detector : `lsst.afw.cameraGeom.Detector`
793 tableArray : `np.array`, optional
794 Optional. Look-up table array with size rows=nAmps and columns=DN values.
795 It will be modified in-place if supplied.
797 log : `lsst.log.Log`, optional
798 Logger to handle messages.
802 linearizer : `lsst.ip.isr.Linearizer`
807 datasetNonLinearity = self.
fitNonLinearity(datasetPtc, tableArray=tableArray)
810 now = datetime.datetime.utcnow()
811 calibDate = now.strftime(
"%Y-%m-%d")
812 linType = self.config.linearizerType
814 if linType ==
"LOOKUPTABLE":
815 tableArray = tableArray
820 instruName=self.config.instrumentName,
821 tableArray=tableArray,
827 """Fit a polynomial to signal vs effective time curve to calculate linearity and residuals.
831 datasetPtc : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
832 The dataset containing the means, variances and exposure times.
834 tableArray : `np.array`
835 Optional. Look-up table array with size rows=nAmps and columns=DN values.
836 It will be modified in-place if supplied.
840 datasetNonLinearity : `dict`
841 Dictionary of `lsst.cp.pipe.ptc.LinearityResidualsAndLinearizersDataset`
842 dataclasses. Each one holds the output of `calculateLinearityResidualAndLinearizers` per
845 datasetNonLinearity = {ampName: []
for ampName
in datasetPtc.ampNames}
846 for i, ampName
in enumerate(datasetPtc.ampNames):
848 if (len(datasetPtc.visitMask[ampName]) == 0):
849 self.log.warn(f
"Mask not found for {ampName} in non-linearity fit. Using all points.")
850 mask = np.repeat(
True, len(datasetPtc.rawExpTimes[ampName]))
852 mask = datasetPtc.visitMask[ampName]
854 timeVecFinal = np.array(datasetPtc.rawExpTimes[ampName])[mask]
855 meanVecFinal = np.array(datasetPtc.rawMeans[ampName])[mask]
862 if tableArray
is not None:
863 tableArray[i, :] = datasetLinRes.linearizerTableRow
865 datasetNonLinearity[ampName] = datasetLinRes
867 return datasetNonLinearity
870 """Calculate linearity residual and fit an n-order polynomial to the mean vs time curve
871 to produce corrections (deviation from linear part of polynomial) for a particular amplifier
872 to populate LinearizeLookupTable.
873 Use the coefficients of this fit to calculate the correction coefficients for LinearizePolynomial
874 and LinearizeSquared."
879 exposureTimeVector: `list` of `float`
880 List of exposure times for each flat pair
882 meanSignalVector: `list` of `float`
883 List of mean signal from diference image of flat pairs
887 dataset : `lsst.cp.pipe.ptc.LinearityResidualsAndLinearizersDataset`
888 The dataset containing the fit parameters, the NL correction coefficients, and the
889 LUT row for the amplifier at hand.
895 dataset.polynomialLinearizerCoefficients : `list` of `float`
896 Coefficients for LinearizePolynomial, where corrImage = uncorrImage + sum_i c_i uncorrImage^(2 +
898 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
899 DN/t^j, and they are fit from meanSignalVector = k0 + k1*exposureTimeVector +
900 k2*exposureTimeVector^2 + ... + kn*exposureTimeVector^n, with
901 n = "polynomialFitDegreeNonLinearity". k_0 and k_1 and degenerate with bias level and gain,
902 and are not used by the non-linearity correction. Therefore, j = 2...n in the above expression
903 (see `LinearizePolynomial` class in `linearize.py`.)
905 dataset.quadraticPolynomialLinearizerCoefficient : `float`
906 Coefficient for LinearizeSquared, where corrImage = uncorrImage + c0*uncorrImage^2.
907 c0 = -k2/(k1^2), where k1 and k2 are fit from
908 meanSignalVector = k0 + k1*exposureTimeVector + k2*exposureTimeVector^2 +...
909 + kn*exposureTimeVector^n, with n = "polynomialFitDegreeNonLinearity".
911 dataset.linearizerTableRow : `list` of `float`
912 One dimensional array with deviation from linear part of n-order polynomial fit
913 to mean vs time curve. This array will be one row (for the particular amplifier at hand)
914 of the table array for LinearizeLookupTable.
916 dataset.meanSignalVsTimePolyFitPars : `list` of `float`
917 Parameters from n-order polynomial fit to meanSignalVector vs exposureTimeVector.
919 dataset.meanSignalVsTimePolyFitParsErr : `list` of `float`
920 Parameters from n-order polynomial fit to meanSignalVector vs exposureTimeVector.
922 dataset.meanSignalVsTimePolyFitReducedChiSq : `float`
923 Reduced unweighted chi squared from polynomial fit to meanSignalVector vs exposureTimeVector.
928 if self.config.doFitBootstrap:
929 parsFit, parsFitErr, reducedChiSquaredNonLinearityFit =
fitBootstrap(parsIniNonLinearity,
934 parsFit, parsFitErr, reducedChiSquaredNonLinearityFit =
fitLeastSq(parsIniNonLinearity,
941 tMax = (self.config.maxAduForLookupTableLinearizer - parsFit[0])/parsFit[1]
942 timeRange = np.linspace(0, tMax, self.config.maxAduForLookupTableLinearizer)
943 signalIdeal = parsFit[0] + parsFit[1]*timeRange
945 linearizerTableRow = signalIdeal - signalUncorrected
951 polynomialLinearizerCoefficients = []
952 for i, coefficient
in enumerate(parsFit):
953 c = -coefficient/(k1**i)
954 polynomialLinearizerCoefficients.append(c)
955 if np.fabs(c) > 1e-10:
956 msg = f
"Coefficient {c} in polynomial fit larger than threshold 1e-10."
959 c0 = polynomialLinearizerCoefficients[2]
962 dataset.polynomialLinearizerCoefficients = polynomialLinearizerCoefficients
963 dataset.quadraticPolynomialLinearizerCoefficient = c0
964 dataset.linearizerTableRow = linearizerTableRow
965 dataset.meanSignalVsTimePolyFitPars = parsFit
966 dataset.meanSignalVsTimePolyFitParsErr = parsFitErr
967 dataset.meanSignalVsTimePolyFitReducedChiSq = reducedChiSquaredNonLinearityFit
972 tableArray=None, log=None):
973 """Build linearizer object to persist.
977 datasetNonLinearity : `dict`
978 Dictionary of `lsst.cp.pipe.ptc.LinearityResidualsAndLinearizersDataset` objects.
980 detector : `lsst.afw.cameraGeom.Detector`
983 calibDate : `datetime.datetime`
986 linearizerType : `str`
987 'LOOKUPTABLE', 'LINEARIZESQUARED', or 'LINEARIZEPOLYNOMIAL'
989 instruName : `str`, optional
992 tableArray : `np.array`, optional
993 Look-up table array with size rows=nAmps and columns=DN values
995 log : `lsst.log.Log`, optional
996 Logger to handle messages
1000 linearizer : `lsst.ip.isr.Linearizer`
1003 detName = detector.getName()
1004 detNum = detector.getId()
1005 if linearizerType ==
"LOOKUPTABLE":
1006 if tableArray
is not None:
1007 linearizer =
Linearizer(detector=detector, table=tableArray, log=log)
1009 raise RuntimeError(
"tableArray must be provided when creating a LookupTable linearizer")
1010 elif linearizerType
in (
"LINEARIZESQUARED",
"LINEARIZEPOLYNOMIAL"):
1013 raise RuntimeError(
"Invalid linearizerType {linearizerType} to build a Linearizer object. "
1014 "Supported: 'LOOKUPTABLE', 'LINEARIZESQUARED', or 'LINEARIZEPOLYNOMIAL'")
1015 for i, amp
in enumerate(detector.getAmplifiers()):
1016 ampName = amp.getName()
1017 datasetNonLinAmp = datasetNonLinearity[ampName]
1018 if linearizerType ==
"LOOKUPTABLE":
1019 linearizer.linearityCoeffs[ampName] = [i, 0]
1020 linearizer.linearityType[ampName] =
"LookupTable"
1021 elif linearizerType ==
"LINEARIZESQUARED":
1022 linearizer.fitParams[ampName] = datasetNonLinAmp.meanSignalVsTimePolyFitPars
1023 linearizer.fitParamsErr[ampName] = datasetNonLinAmp.meanSignalVsTimePolyFitParsErr
1024 linearizer.linearityFitReducedChiSquared[ampName] = (
1025 datasetNonLinAmp.meanSignalVsTimePolyFitReducedChiSq)
1026 linearizer.linearityCoeffs[ampName] = [
1027 datasetNonLinAmp.quadraticPolynomialLinearizerCoefficient]
1028 linearizer.linearityType[ampName] =
"Squared"
1029 elif linearizerType ==
"LINEARIZEPOLYNOMIAL":
1030 linearizer.fitParams[ampName] = datasetNonLinAmp.meanSignalVsTimePolyFitPars
1031 linearizer.fitParamsErr[ampName] = datasetNonLinAmp.meanSignalVsTimePolyFitParsErr
1032 linearizer.linearityFitReducedChiSquared[ampName] = (
1033 datasetNonLinAmp.meanSignalVsTimePolyFitReducedChiSq)
1037 polyLinCoeffs = np.array(datasetNonLinAmp.polynomialLinearizerCoefficients[2:])
1038 linearizer.linearityCoeffs[ampName] = polyLinCoeffs
1039 linearizer.linearityType[ampName] =
"Polynomial"
1040 linearizer.linearityBBox[ampName] = amp.getBBox()
1041 linearizer.validate()
1042 calibId = f
"detectorName={detName} detector={detNum} calibDate={calibDate} ccd={detNum} filter=NONE"
1045 raftName = detName.split(
"_")[0]
1046 calibId += f
" raftName={raftName}"
1049 calibId += f
" raftName={raftname}"
1051 serial = detector.getSerial()
1052 linearizer.updateMetadata(instrumentName=instruName, detectorId=f
"{detNum}",
1053 calibId=calibId, serial=serial, detectorName=f
"{detName}")
1058 def _initialParsForPolynomial(order):
1060 pars = np.zeros(order, dtype=np.float)
1067 def _boundsForPolynomial(initialPars):
1068 lowers = [np.NINF
for p
in initialPars]
1069 uppers = [np.inf
for p
in initialPars]
1071 return (lowers, uppers)
1074 def _boundsForAstier(initialPars):
1075 lowers = [np.NINF
for p
in initialPars]
1076 uppers = [np.inf
for p
in initialPars]
1077 return (lowers, uppers)
1080 def _getInitialGoodPoints(means, variances, maxDeviationPositive, maxDeviationNegative):
1081 """Return a boolean array to mask bad points.
1083 A linear function has a constant ratio, so find the median
1084 value of the ratios, and exclude the points that deviate
1085 from that by more than a factor of maxDeviationPositive/negative.
1086 Asymmetric deviations are supported as we expect the PTC to turn
1087 down as the flux increases, but sometimes it anomalously turns
1088 upwards just before turning over, which ruins the fits, so it
1089 is wise to be stricter about restricting positive outliers than
1092 Too high and points that are so bad that fit will fail will be included
1093 Too low and the non-linear points will be excluded, biasing the NL fit."""
1094 ratios = [b/a
for (a, b)
in zip(means, variances)]
1095 medianRatio = np.median(ratios)
1096 ratioDeviations = [(r/medianRatio)-1
for r
in ratios]
1099 maxDeviationPositive = abs(maxDeviationPositive)
1100 maxDeviationNegative = -1. * abs(maxDeviationNegative)
1102 goodPoints = np.array([
True if (r < maxDeviationPositive
and r > maxDeviationNegative)
1103 else False for r
in ratioDeviations])
1106 def _makeZeroSafe(self, array, warn=True, substituteValue=1e-9):
1108 nBad = Counter(array)[0]
1113 msg = f
"Found {nBad} zeros in array at elements {[x for x in np.where(array==0)[0]]}"
1116 array[array == 0] = substituteValue
1120 """Fit the photon transfer curve to a polynimial or to Astier+19 approximation.
1122 Fit the photon transfer curve with either a polynomial of the order
1123 specified in the task config, or using the Astier approximation.
1125 Sigma clipping is performed iteratively for the fit, as well as an
1126 initial clipping of data points that are more than
1127 config.initialNonLinearityExclusionThreshold away from lying on a
1128 straight line. This other step is necessary because the photon transfer
1129 curve turns over catastrophically at very high flux (because saturation
1130 drops the variance to ~0) and these far outliers cause the initial fit
1131 to fail, meaning the sigma cannot be calculated to perform the
1136 dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
1137 The dataset containing the means, variances and exposure times
1140 Fit a 'POLYNOMIAL' (degree: 'polynomialFitDegree') or
1141 'EXPAPPROXIMATION' (Eq. 16 of Astier+19) to the PTC
1145 dataset: `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
1146 This is the same dataset as the input paramter, however, it has been modified
1147 to include information such as the fit vectors and the fit parameters. See
1148 the class `PhotonTransferCurveDatase`.
1151 def errFunc(p, x, y):
1152 return ptcFunc(p, x) - y
1154 sigmaCutPtcOutliers = self.config.sigmaCutPtcOutliers
1155 maxIterationsPtcOutliers = self.config.maxIterationsPtcOutliers
1157 for i, ampName
in enumerate(dataset.ampNames):
1158 timeVecOriginal = np.array(dataset.rawExpTimes[ampName])
1159 meanVecOriginal = np.array(dataset.rawMeans[ampName])
1160 varVecOriginal = np.array(dataset.rawVars[ampName])
1163 mask = ((meanVecOriginal >= self.config.minMeanSignal) &
1164 (meanVecOriginal <= self.config.maxMeanSignal))
1167 self.config.initialNonLinearityExclusionThresholdPositive,
1168 self.config.initialNonLinearityExclusionThresholdNegative)
1169 mask = mask & goodPoints
1171 if ptcFitType ==
'EXPAPPROXIMATION':
1172 ptcFunc = funcAstier
1173 parsIniPtc = [-1e-9, 1.0, 10.]
1175 if ptcFitType ==
'POLYNOMIAL':
1176 ptcFunc = funcPolynomial
1182 while count <= maxIterationsPtcOutliers:
1186 meanTempVec = meanVecOriginal[mask]
1187 varTempVec = varVecOriginal[mask]
1188 res = least_squares(errFunc, parsIniPtc, bounds=bounds, args=(meanTempVec, varTempVec))
1194 sigResids = (varVecOriginal - ptcFunc(pars, meanVecOriginal))/np.sqrt(varVecOriginal)
1195 newMask = np.array([
True if np.abs(r) < sigmaCutPtcOutliers
else False for r
in sigResids])
1196 mask = mask & newMask
1198 nDroppedTotal = Counter(mask)[
False]
1199 self.log.debug(f
"Iteration {count}: discarded {nDroppedTotal} points in total for {ampName}")
1202 assert (len(mask) == len(timeVecOriginal) == len(meanVecOriginal) == len(varVecOriginal))
1204 dataset.visitMask[ampName] = mask
1206 meanVecFinal = meanVecOriginal[mask]
1207 varVecFinal = varVecOriginal[mask]
1209 if Counter(mask)[
False] > 0:
1210 self.log.info((f
"Number of points discarded in PTC of amplifier {ampName}:" +
1211 f
" {Counter(mask)[False]} out of {len(meanVecOriginal)}"))
1213 if (len(meanVecFinal) < len(parsIniPtc)):
1214 msg = (f
"\nSERIOUS: Not enough data points ({len(meanVecFinal)}) compared to the number of"
1215 f
"parameters of the PTC model({len(parsIniPtc)}). Setting {ampName} to BAD.")
1219 dataset.badAmps.append(ampName)
1220 dataset.gain[ampName] = np.nan
1221 dataset.gainErr[ampName] = np.nan
1222 dataset.noise[ampName] = np.nan
1223 dataset.noiseErr[ampName] = np.nan
1224 dataset.ptcFitPars[ampName] = np.nan
1225 dataset.ptcFitParsError[ampName] = np.nan
1226 dataset.ptcFitReducedChiSquared[ampName] = np.nan
1230 if self.config.doFitBootstrap:
1231 parsFit, parsFitErr, reducedChiSqPtc =
fitBootstrap(parsIniPtc, meanVecFinal,
1232 varVecFinal, ptcFunc)
1234 parsFit, parsFitErr, reducedChiSqPtc =
fitLeastSq(parsIniPtc, meanVecFinal,
1235 varVecFinal, ptcFunc)
1236 dataset.ptcFitPars[ampName] = parsFit
1237 dataset.ptcFitParsError[ampName] = parsFitErr
1238 dataset.ptcFitReducedChiSquared[ampName] = reducedChiSqPtc
1240 if ptcFitType ==
'EXPAPPROXIMATION':
1241 ptcGain = parsFit[1]
1242 ptcGainErr = parsFitErr[1]
1243 ptcNoise = np.sqrt(np.fabs(parsFit[2]))
1244 ptcNoiseErr = 0.5*(parsFitErr[2]/np.fabs(parsFit[2]))*np.sqrt(np.fabs(parsFit[2]))
1245 if ptcFitType ==
'POLYNOMIAL':
1246 ptcGain = 1./parsFit[1]
1247 ptcGainErr = np.fabs(1./parsFit[1])*(parsFitErr[1]/parsFit[1])
1248 ptcNoise = np.sqrt(np.fabs(parsFit[0]))*ptcGain
1249 ptcNoiseErr = (0.5*(parsFitErr[0]/np.fabs(parsFit[0]))*(np.sqrt(np.fabs(parsFit[0]))))*ptcGain
1250 dataset.gain[ampName] = ptcGain
1251 dataset.gainErr[ampName] = ptcGainErr
1252 dataset.noise[ampName] = ptcNoise
1253 dataset.noiseErr[ampName] = ptcNoiseErr
1254 if not len(dataset.ptcFitType) == 0:
1255 dataset.ptcFitType = ptcFitType