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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
29import os
30from matplotlib.backends.backend_pdf import PdfPages
31from sqlite3 import OperationalError
32from collections import Counter
34import lsst.afw.math as afwMath
35import lsst.pex.config as pexConfig
36import lsst.pipe.base as pipeBase
37from lsst.ip.isr import IsrTask
38from .utils import (NonexistentDatasetTaskDataIdContainer, PairedVisitListTaskRunner,
39 checkExpLengthEqual, validateIsrConfig)
40from scipy.optimize import leastsq, least_squares
41import numpy.polynomial.polynomial as poly
44class MeasurePhotonTransferCurveTaskConfig(pexConfig.Config):
45 """Config class for photon transfer curve measurement task"""
46 isr = pexConfig.ConfigurableField(
47 target=IsrTask,
48 doc="""Task to perform instrumental signature removal.""",
49 )
50 isrMandatorySteps = pexConfig.ListField(
51 dtype=str,
52 doc="isr operations that must be performed for valid results. Raises if any of these are False.",
53 default=['doAssembleCcd']
54 )
55 isrForbiddenSteps = pexConfig.ListField(
56 dtype=str,
57 doc="isr operations that must NOT be performed for valid results. Raises if any of these are True",
58 default=['doFlat', 'doFringe', 'doAddDistortionModel', 'doBrighterFatter', 'doUseOpticsTransmission',
59 'doUseFilterTransmission', 'doUseSensorTransmission', 'doUseAtmosphereTransmission']
60 )
61 isrDesirableSteps = pexConfig.ListField(
62 dtype=str,
63 doc="isr operations that it is advisable to perform, but are not mission-critical." +
64 " WARNs are logged for any of these found to be False.",
65 default=['doBias', 'doDark', 'doCrosstalk', 'doDefect']
66 )
67 isrUndesirableSteps = pexConfig.ListField(
68 dtype=str,
69 doc="isr operations that it is *not* advisable to perform in the general case, but are not" +
70 " forbidden as some use-cases might warrant them." +
71 " WARNs are logged for any of these found to be True.",
72 default=['doLinearize']
73 )
74 ccdKey = pexConfig.Field(
75 dtype=str,
76 doc="The key by which to pull a detector from a dataId, e.g. 'ccd' or 'detector'.",
77 default='ccd',
78 )
79 makePlots = pexConfig.Field(
80 dtype=bool,
81 doc="Plot the PTC curves?",
82 default=False,
83 )
84 ptcFitType = pexConfig.ChoiceField(
85 dtype=str,
86 doc="Fit PTC to approximation in Astier+19 (Equation 16) or to a polynomial.",
87 default="POLYNOMIAL",
88 allowed={
89 "POLYNOMIAL": "n-degree polynomial (use 'polynomialFitDegree' to set 'n').",
90 "ASTIERAPPROXIMATION": "Approximation in Astier+19 (Eq. 16)."
91 }
92 )
93 polynomialFitDegree = pexConfig.Field(
94 dtype=int,
95 doc="Degree of polynomial to fit the PTC, when 'ptcFitType'=POLYNOMIAL.",
96 default=2,
97 )
98 polynomialFitDegreeNonLinearity = pexConfig.Field(
99 dtype=int,
100 doc="Degree of polynomial to fit the meanSignal vs exposureTime curve to produce" +
101 " the table for LinearizerLookupTable.",
102 default=3,
103 )
104 binSize = pexConfig.Field(
105 dtype=int,
106 doc="Bin the image by this factor in both dimensions.",
107 default=1,
108 )
109 minMeanSignal = pexConfig.Field(
110 dtype=float,
111 doc="Minimum value (inclusive) of mean signal (in ADU) above which to consider.",
112 default=0,
113 )
114 maxMeanSignal = pexConfig.Field(
115 dtype=float,
116 doc="Maximum value (inclusive) of mean signal (in ADU) below which to consider.",
117 default=9e6,
118 )
119 initialNonLinearityExclusionThresholdPositive = pexConfig.RangeField(
120 dtype=float,
121 doc="Initially exclude data points with a variance that are more than a factor of this from being"
122 " linear in the positive direction, from the PTC fit. Note that these points will also be"
123 " excluded from the non-linearity fit. This is done before the iterative outlier rejection,"
124 " to allow an accurate determination of the sigmas for said iterative fit.",
125 default=0.12,
126 min=0.0,
127 max=1.0,
128 )
129 initialNonLinearityExclusionThresholdNegative = pexConfig.RangeField(
130 dtype=float,
131 doc="Initially exclude data points with a variance that are more than a factor of this from being"
132 " linear in the negative direction, from the PTC fit. Note that these points will also be"
133 " excluded from the non-linearity fit. This is done before the iterative outlier rejection,"
134 " to allow an accurate determination of the sigmas for said iterative fit.",
135 default=0.25,
136 min=0.0,
137 max=1.0,
138 )
139 sigmaCutPtcOutliers = pexConfig.Field(
140 dtype=float,
141 doc="Sigma cut for outlier rejection in PTC.",
142 default=5.0,
143 )
144 maxIterationsPtcOutliers = pexConfig.Field(
145 dtype=int,
146 doc="Maximum number of iterations for outlier rejection in PTC.",
147 default=2,
148 )
149 doFitBootstrap = pexConfig.Field(
150 dtype=bool,
151 doc="Use bootstrap for the PTC fit parameters and errors?.",
152 default=False,
153 )
154 linResidualTimeIndex = pexConfig.Field(
155 dtype=int,
156 doc="Index position in time array for reference time in linearity residual calculation.",
157 default=2,
158 )
159 maxAduForLookupTableLinearizer = pexConfig.Field(
160 dtype=int,
161 doc="Maximum ADU value for the LookupTable linearizer.",
162 default=2**18,
163 )
166class PhotonTransferCurveDataset:
167 """A simple class to hold the output data from the PTC task.
169 The dataset is made up of a dictionary for each item, keyed by the
170 amplifiers' names, which much be supplied at construction time.
172 New items cannot be added to the class to save accidentally saving to the
173 wrong property, and the class can be frozen if desired.
175 inputVisitPairs records the visits used to produce the data.
176 When fitPtcAndNonLinearity() is run, a mask is built up, which is by definition
177 always the same length as inputVisitPairs, rawExpTimes, rawMeans
178 and rawVars, and is a list of bools, which are incrementally set to False
179 as points are discarded from the fits.
181 PTC fit parameters for polynomials are stored in a list in ascending order
182 of polynomial term, i.e. par[0]*x^0 + par[1]*x + par[2]*x^2 etc
183 with the length of the list corresponding to the order of the polynomial
184 plus one.
185 """
186 def __init__(self, ampNames):
187 # add items to __dict__ directly because __setattr__ is overridden
189 # instance variables
190 self.__dict__["ampNames"] = ampNames
191 self.__dict__["badAmps"] = []
193 # raw data variables
194 self.__dict__["inputVisitPairs"] = {ampName: [] for ampName in ampNames}
195 self.__dict__["visitMask"] = {ampName: [] for ampName in ampNames}
196 self.__dict__["rawExpTimes"] = {ampName: [] for ampName in ampNames}
197 self.__dict__["rawMeans"] = {ampName: [] for ampName in ampNames}
198 self.__dict__["rawVars"] = {ampName: [] for ampName in ampNames}
200 # fit information
201 self.__dict__["ptcFitType"] = {ampName: "" for ampName in ampNames}
202 self.__dict__["ptcFitPars"] = {ampName: [] for ampName in ampNames}
203 self.__dict__["ptcFitParsError"] = {ampName: [] for ampName in ampNames}
204 self.__dict__["nonLinearity"] = {ampName: [] for ampName in ampNames}
205 self.__dict__["nonLinearityError"] = {ampName: [] for ampName in ampNames}
206 self.__dict__["nonLinearityResiduals"] = {ampName: [] for ampName in ampNames}
207 self.__dict__["coefficientLinearizeSquared"] = {ampName: [] for ampName in ampNames}
209 # final results
210 self.__dict__["gain"] = {ampName: -1. for ampName in ampNames}
211 self.__dict__["gainErr"] = {ampName: -1. for ampName in ampNames}
212 self.__dict__["noise"] = {ampName: -1. for ampName in ampNames}
213 self.__dict__["noiseErr"] = {ampName: -1. for ampName in ampNames}
214 self.__dict__["coefficientLinearizeSquared"] = {ampName: [] for ampName in ampNames}
216 def __setattr__(self, attribute, value):
217 """Protect class attributes"""
218 if attribute not in self.__dict__:
219 raise AttributeError(f"{attribute} is not already a member of PhotonTransferCurveDataset, which"
220 " does not support setting of new attributes.")
221 else:
222 self.__dict__[attribute] = value
224 def getVisitsUsed(self, ampName):
225 """Get the visits used, i.e. not discarded, for a given amp.
227 If no mask has been created yet, all visits are returned.
228 """
229 if self.visitMask[ampName] == []:
230 return self.inputVisitPairs[ampName]
232 # if the mask exists it had better be the same length as the visitPairs
233 assert len(self.visitMask[ampName]) == len(self.inputVisitPairs[ampName])
235 pairs = self.inputVisitPairs[ampName]
236 mask = self.visitMask[ampName]
237 # cast to bool required because numpy
238 return [(v1, v2) for ((v1, v2), m) in zip(pairs, mask) if bool(m) is True]
240 def getGoodAmps(self):
241 return [amp for amp in self.ampNames if amp not in self.badAmps]
244class MeasurePhotonTransferCurveTask(pipeBase.CmdLineTask):
245 """A class to calculate, fit, and plot a PTC from a set of flat pairs.
247 The Photon Transfer Curve (var(signal) vs mean(signal)) is a standard tool
248 used in astronomical detectors characterization (e.g., Janesick 2001,
249 Janesick 2007). This task calculates the PTC from a series of pairs of
250 flat-field images; each pair taken at identical exposure times. The
251 difference image of each pair is formed to eliminate fixed pattern noise,
252 and then the variance of the difference image and the mean of the average image
253 are used to produce the PTC. An n-degree polynomial or the approximation in Equation
254 16 of Astier+19 ("The Shape of the Photon Transfer Curve of CCD sensors",
255 arXiv:1905.08677) can be fitted to the PTC curve. These models include
256 parameters such as the gain (e/ADU) and readout noise.
258 Parameters
259 ----------
261 *args: `list`
262 Positional arguments passed to the Task constructor. None used at this
263 time.
264 **kwargs: `dict`
265 Keyword arguments passed on to the Task constructor. None used at this
266 time.
268 """
270 RunnerClass = PairedVisitListTaskRunner
271 ConfigClass = MeasurePhotonTransferCurveTaskConfig
272 _DefaultName = "measurePhotonTransferCurve"
274 def __init__(self, *args, **kwargs):
275 pipeBase.CmdLineTask.__init__(self, *args, **kwargs)
276 self.makeSubtask("isr")
277 plt.interactive(False) # stop windows popping up when plotting. When headless, use 'agg' backend too
278 validateIsrConfig(self.isr, self.config.isrMandatorySteps,
279 self.config.isrForbiddenSteps, self.config.isrDesirableSteps, checkTrim=False)
280 self.config.validate()
281 self.config.freeze()
283 @classmethod
284 def _makeArgumentParser(cls):
285 """Augment argument parser for the MeasurePhotonTransferCurveTask."""
286 parser = pipeBase.ArgumentParser(name=cls._DefaultName)
287 parser.add_argument("--visit-pairs", dest="visitPairs", nargs="*",
288 help="Visit pairs to use. Each pair must be of the form INT,INT e.g. 123,456")
289 parser.add_id_argument("--id", datasetType="photonTransferCurveDataset",
290 ContainerClass=NonexistentDatasetTaskDataIdContainer,
291 help="The ccds to use, e.g. --id ccd=0..100")
292 return parser
294 @pipeBase.timeMethod
295 def runDataRef(self, dataRef, visitPairs):
296 """Run the Photon Transfer Curve (PTC) measurement task.
298 For a dataRef (which is each detector here),
299 and given a list of visit pairs at different exposure times,
300 measure the PTC.
302 Parameters
303 ----------
304 dataRef : list of lsst.daf.persistence.ButlerDataRef
305 dataRef for the detector for the visits to be fit.
306 visitPairs : `iterable` of `tuple` of `int`
307 Pairs of visit numbers to be processed together
308 """
310 # setup necessary objects
311 detNum = dataRef.dataId[self.config.ccdKey]
312 detector = dataRef.get('camera')[dataRef.dataId[self.config.ccdKey]]
313 # expand some missing fields that we need for lsstCam. This is a work-around
314 # for Gen2 problems that I (RHL) don't feel like solving. The calibs pipelines
315 # (which inherit from CalibTask) use addMissingKeys() to do basically the same thing
316 #
317 # Basically, the butler's trying to look up the fields in `raw_visit` which won't work
318 for name in dataRef.getButler().getKeys('bias'):
319 if name not in dataRef.dataId:
320 try:
321 dataRef.dataId[name] = \
322 dataRef.getButler().queryMetadata('raw', [name], detector=detNum)[0]
323 except OperationalError:
324 pass
326 amps = detector.getAmplifiers()
327 ampNames = [amp.getName() for amp in amps]
328 dataset = PhotonTransferCurveDataset(ampNames)
330 self.log.info('Measuring PTC using %s visits for detector %s' % (visitPairs, detNum))
332 for (v1, v2) in visitPairs:
333 # Perform ISR on each exposure
334 dataRef.dataId['visit'] = v1
335 exp1 = self.isr.runDataRef(dataRef).exposure
336 dataRef.dataId['visit'] = v2
337 exp2 = self.isr.runDataRef(dataRef).exposure
338 del dataRef.dataId['visit']
340 checkExpLengthEqual(exp1, exp2, v1, v2, raiseWithMessage=True)
341 expTime = exp1.getInfo().getVisitInfo().getExposureTime()
343 for amp in detector:
344 mu, varDiff = self.measureMeanVarPair(exp1, exp2, region=amp.getBBox())
345 ampName = amp.getName()
347 dataset.rawExpTimes[ampName].append(expTime)
348 dataset.rawMeans[ampName].append(mu)
349 dataset.rawVars[ampName].append(varDiff)
350 dataset.inputVisitPairs[ampName].append((v1, v2))
352 numberAmps = len(detector.getAmplifiers())
353 numberAduValues = self.config.maxAduForLookupTableLinearizer
354 lookupTableArray = np.zeros((numberAmps, numberAduValues), dtype=np.float32)
356 # Fit PTC and (non)linearity of signal vs time curve, produce linearizer
357 self.fitPtcAndNonLinearity(dataset, lookupTableArray, ptcFitType=self.config.ptcFitType)
359 if self.config.makePlots:
360 self.plot(dataRef, dataset, ptcFitType=self.config.ptcFitType)
362 # Save data, PTC fit, and NL fit dictionaries
363 self.log.info(f"Writing PTC and NL data to {dataRef.getUri(write=True)}")
364 dataRef.put(dataset, datasetType="photonTransferCurveDataset")
366 self.log.info('Finished measuring PTC for in detector %s' % detNum)
368 return pipeBase.Struct(exitStatus=0)
370 def measureMeanVarPair(self, exposure1, exposure2, region=None):
371 """Calculate the mean signal of two exposures and the variance of their difference.
373 Parameters
374 ----------
375 exposure1 : `lsst.afw.image.exposure.exposure.ExposureF`
376 First exposure of flat field pair.
378 exposure2 : `lsst.afw.image.exposure.exposure.ExposureF`
379 Second exposure of flat field pair.
381 region : `lsst.geom.Box2I`
382 Region of each exposure where to perform the calculations (e.g, an amplifier).
384 Return
385 ------
387 mu : `np.float`
388 0.5*(mu1 + mu2), where mu1, and mu2 are the clipped means of the regions in
389 both exposures.
391 varDiff : `np.float`
392 Half of the clipped variance of the difference of the regions inthe two input
393 exposures.
394 """
396 if region is not None:
397 im1Area = exposure1.maskedImage[region]
398 im2Area = exposure2.maskedImage[region]
399 else:
400 im1Area = exposure1.maskedImage
401 im2Area = exposure2.maskedImage
403 im1Area = afwMath.binImage(im1Area, self.config.binSize)
404 im2Area = afwMath.binImage(im2Area, self.config.binSize)
406 # Clipped mean of images; then average of mean.
407 mu1 = afwMath.makeStatistics(im1Area, afwMath.MEANCLIP).getValue()
408 mu2 = afwMath.makeStatistics(im2Area, afwMath.MEANCLIP).getValue()
409 mu = 0.5*(mu1 + mu2)
411 # Take difference of pairs
412 # symmetric formula: diff = (mu2*im1-mu1*im2)/(0.5*(mu1+mu2))
413 temp = im2Area.clone()
414 temp *= mu1
415 diffIm = im1Area.clone()
416 diffIm *= mu2
417 diffIm -= temp
418 diffIm /= mu
420 varDiff = 0.5*(afwMath.makeStatistics(diffIm, afwMath.VARIANCECLIP).getValue())
422 return mu, varDiff
424 def _fitLeastSq(self, initialParams, dataX, dataY, function):
425 """Do a fit and estimate the parameter errors using using scipy.optimize.leastq.
427 optimize.leastsq returns the fractional covariance matrix. To estimate the
428 standard deviation of the fit parameters, multiply the entries of this matrix
429 by the reduced chi squared and take the square root of the diagonal elements.
431 Parameters
432 ----------
433 initialParams : list of np.float
434 initial values for fit parameters. For ptcFitType=POLYNOMIAL, its length
435 determines the degree of the polynomial.
437 dataX : np.array of np.float
438 Data in the abscissa axis.
440 dataY : np.array of np.float
441 Data in the ordinate axis.
443 function : callable object (function)
444 Function to fit the data with.
446 Return
447 ------
448 pFitSingleLeastSquares : list of np.float
449 List with fitted parameters.
451 pErrSingleLeastSquares : list of np.float
452 List with errors for fitted parameters.
453 """
455 def errFunc(p, x, y):
456 return function(p, x) - y
458 pFit, pCov, infoDict, errMessage, success = leastsq(errFunc, initialParams,
459 args=(dataX, dataY), full_output=1, epsfcn=0.0001)
461 if (len(dataY) > len(initialParams)) and pCov is not None:
462 reducedChiSq = (errFunc(pFit, dataX, dataY)**2).sum()/(len(dataY)-len(initialParams))
463 pCov *= reducedChiSq
464 else:
465 pCov[:, :] = np.inf
467 errorVec = []
468 for i in range(len(pFit)):
469 errorVec.append(np.fabs(pCov[i][i])**0.5)
471 pFitSingleLeastSquares = pFit
472 pErrSingleLeastSquares = np.array(errorVec)
474 return pFitSingleLeastSquares, pErrSingleLeastSquares
476 def _fitBootstrap(self, initialParams, dataX, dataY, function, confidenceSigma=1.):
477 """Do a fit using least squares and bootstrap to estimate parameter errors.
479 The bootstrap error bars are calculated by fitting 100 random data sets.
481 Parameters
482 ----------
483 initialParams : list of np.float
484 initial values for fit parameters. For ptcFitType=POLYNOMIAL, its length
485 determines the degree of the polynomial.
487 dataX : np.array of np.float
488 Data in the abscissa axis.
490 dataY : np.array of np.float
491 Data in the ordinate axis.
493 function : callable object (function)
494 Function to fit the data with.
496 confidenceSigma : np.float
497 Number of sigmas that determine confidence interval for the bootstrap errors.
499 Return
500 ------
501 pFitBootstrap : list of np.float
502 List with fitted parameters.
504 pErrBootstrap : list of np.float
505 List with errors for fitted parameters.
506 """
508 def errFunc(p, x, y):
509 return function(p, x) - y
511 # Fit first time
512 pFit, _ = leastsq(errFunc, initialParams, args=(dataX, dataY), full_output=0)
514 # Get the stdev of the residuals
515 residuals = errFunc(pFit, dataX, dataY)
516 sigmaErrTotal = np.std(residuals)
518 # 100 random data sets are generated and fitted
519 pars = []
520 for i in range(100):
521 randomDelta = np.random.normal(0., sigmaErrTotal, len(dataY))
522 randomDataY = dataY + randomDelta
523 randomFit, _ = leastsq(errFunc, initialParams,
524 args=(dataX, randomDataY), full_output=0)
525 pars.append(randomFit)
526 pars = np.array(pars)
527 meanPfit = np.mean(pars, 0)
529 # confidence interval for parameter estimates
530 nSigma = confidenceSigma
531 errPfit = nSigma*np.std(pars, 0)
532 pFitBootstrap = meanPfit
533 pErrBootstrap = errPfit
534 return pFitBootstrap, pErrBootstrap
536 def funcPolynomial(self, pars, x):
537 """Polynomial function definition"""
538 return poly.polyval(x, [*pars])
540 def funcAstier(self, pars, x):
541 """Single brighter-fatter parameter model for PTC; Equation 16 of Astier+19"""
542 a00, gain, noise = pars
543 return 0.5/(a00*gain*gain)*(np.exp(2*a00*x*gain)-1) + noise/(gain*gain)
545 @staticmethod
546 def _initialParsForPolynomial(order):
547 assert(order >= 2)
548 pars = np.zeros(order, dtype=np.float)
549 pars[0] = 10
550 pars[1] = 1
551 pars[2:] = 0.0001
552 return pars
554 @staticmethod
555 def _boundsForPolynomial(initialPars):
556 lowers = [np.NINF for p in initialPars]
557 uppers = [np.inf for p in initialPars]
558 lowers[1] = 0 # no negative gains
559 return (lowers, uppers)
561 @staticmethod
562 def _boundsForAstier(initialPars):
563 lowers = [np.NINF for p in initialPars]
564 uppers = [np.inf for p in initialPars]
565 return (lowers, uppers)
567 @staticmethod
568 def _getInitialGoodPoints(means, variances, maxDeviationPositive, maxDeviationNegative):
569 """Return a boolean array to mask bad points.
571 A linear function has a constant ratio, so find the median
572 value of the ratios, and exclude the points that deviate
573 from that by more than a factor of maxDeviationPositive/negative.
574 Asymmetric deviations are supported as we expect the PTC to turn
575 down as the flux increases, but sometimes it anomalously turns
576 upwards just before turning over, which ruins the fits, so it
577 is wise to be stricter about restricting positive outliers than
578 negative ones.
580 Too high and points that are so bad that fit will fail will be included
581 Too low and the non-linear points will be excluded, biasing the NL fit."""
582 ratios = [b/a for (a, b) in zip(means, variances)]
583 medianRatio = np.median(ratios)
584 ratioDeviations = [(r/medianRatio)-1 for r in ratios]
586 # so that it doesn't matter if the deviation is expressed as positive or negative
587 maxDeviationPositive = abs(maxDeviationPositive)
588 maxDeviationNegative = -1. * abs(maxDeviationNegative)
590 goodPoints = np.array([True if (r < maxDeviationPositive and r > maxDeviationNegative)
591 else False for r in ratioDeviations])
592 return goodPoints
594 def _makeZeroSafe(self, array, warn=True, substituteValue=1e-9):
595 """"""
596 nBad = Counter(array)[0]
597 if nBad == 0:
598 return array
600 if warn:
601 msg = f"Found {nBad} zeros in array at elements {[x for x in np.where(array==0)[0]]}"
602 self.log.warn(msg)
604 array[array == 0] = substituteValue
605 return array
607 def calculateLinearityResidualAndLinearizers(self, exposureTimeVector, meanSignalVector):
608 """Calculate linearity residual and fit an n-order polynomial to the mean vs time curve
609 to produce corrections (deviation from linear part of polynomial) for a particular amplifier
610 to populate LinearizeLookupTable. Use quadratic and linear parts of this polynomial to approximate
611 c0 for LinearizeSquared."
613 Parameters
614 ---------
616 exposureTimeVector: `list` of `np.float`
617 List of exposure times for each flat pair
619 meanSignalVector: `list` of `np.float`
620 List of mean signal from diference image of flat pairs
622 Returns
623 -------
624 c0: `np.float`
625 Coefficient for LinearizeSquared, where corrImage = uncorrImage + c0*uncorrImage^2.
626 c0 ~ -k2/(k1^2), where k1 and k2 are fit from
627 meanSignalVector = k0 + k1*exposureTimeVector + k2*exposureTimeVector^2 +...
628 + kn*exposureTimeVector^n, with n = "polynomialFitDegreeNonLinearity".
630 linearizerTableRow: list of `np.float`
631 One dimensional array with deviation from linear part of n-order polynomial fit
632 to mean vs time curve. This array will be one row (for the particular amplifier at hand)
633 of the table array for LinearizeLookupTable.
635 linResidual: list of `np.float`
636 Linearity residual from the mean vs time curve, defined as
637 100*(1 - meanSignalReference/expTimeReference/(meanSignal/expTime).
639 parsFit: list of `np.float`
640 Parameters from n-order polynomial fit to mean vs time curve.
642 parsFitErr: list of `np.float`
643 Parameters from n-order polynomial fit to mean vs time curve.
645 """
647 # Lookup table linearizer
648 parsIniNonLinearity = self._initialParsForPolynomial(self.config.polynomialFitDegreeNonLinearity + 1)
649 if self.config.doFitBootstrap:
650 parsFit, parsFitErr = self._fitBootstrap(parsIniNonLinearity, exposureTimeVector,
651 meanSignalVector, self.funcPolynomial)
652 else:
653 parsFit, parsFitErr = self._fitLeastSq(parsIniNonLinearity, exposureTimeVector, meanSignalVector,
654 self.funcPolynomial)
656 # Use linear part to get time at wich signal is maxAduForLookupTableLinearizer ADU
657 tMax = (self.config.maxAduForLookupTableLinearizer - parsFit[0])/parsFit[1]
658 timeRange = np.linspace(0, tMax, self.config.maxAduForLookupTableLinearizer)
659 signalIdeal = parsFit[0] + parsFit[1]*timeRange
660 signalUncorrected = self.funcPolynomial(parsFit, timeRange)
661 linearizerTableRow = signalIdeal - signalUncorrected # LinearizerLookupTable has corrections
663 # Use quadratic and linear part of fit to produce c0 for LinearizeSquared
664 # Check that magnitude of higher order (>= 3) coefficents of the polyFit are small,
665 # i.e., less than threshold = 1e-10 (typical quadratic and cubic coefficents are ~1e-6
666 # and ~1e-12).
667 k1, k2 = parsFit[1], parsFit[2]
668 c0 = -k2/(k1**2) # c0 coefficient for LinearizeSquared
669 for coefficient in parsFit[3:]:
670 if np.fabs(coefficient) > 1e-10:
671 msg = f"Coefficient {coefficient} in polynomial fit larger than threshold 1e-10."
672 self.log.warn(msg)
674 # Linearity residual
675 linResidualTimeIndex = self.config.linResidualTimeIndex
676 if exposureTimeVector[linResidualTimeIndex] == 0.0:
677 raise RuntimeError("Reference time for linearity residual can't be 0.0")
678 linResidual = 100*(1 - ((meanSignalVector[linResidualTimeIndex] /
679 exposureTimeVector[linResidualTimeIndex]) /
680 (meanSignalVector/exposureTimeVector)))
682 return c0, linearizerTableRow, linResidual, parsFit, parsFitErr
684 def fitPtcAndNonLinearity(self, dataset, tableArray, ptcFitType):
685 """Fit the photon transfer curve and calculate linearity and residuals.
687 Fit the photon transfer curve with either a polynomial of the order
688 specified in the task config, or using the Astier approximation.
690 Sigma clipping is performed iteratively for the fit, as well as an
691 initial clipping of data points that are more than
692 config.initialNonLinearityExclusionThreshold away from lying on a
693 straight line. This other step is necessary because the photon transfer
694 curve turns over catastrophically at very high flux (because saturation
695 drops the variance to ~0) and these far outliers cause the initial fit
696 to fail, meaning the sigma cannot be calculated to perform the
697 sigma-clipping.
699 Parameters
700 ----------
701 dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
702 The dataset containing the means, variances and exposure times
703 ptcFitType : `str`
704 Fit a 'POLYNOMIAL' (degree: 'polynomialFitDegree') or
705 'ASTIERAPPROXIMATION' to the PTC
706 tableArray : `np.array`
707 Look-up table array with size rows=nAmps and columns=ADU values
708 """
710 def errFunc(p, x, y):
711 return ptcFunc(p, x) - y
713 sigmaCutPtcOutliers = self.config.sigmaCutPtcOutliers
714 maxIterationsPtcOutliers = self.config.maxIterationsPtcOutliers
716 for i, ampName in enumerate(dataset.ampNames):
717 timeVecOriginal = np.array(dataset.rawExpTimes[ampName])
718 meanVecOriginal = np.array(dataset.rawMeans[ampName])
719 varVecOriginal = np.array(dataset.rawVars[ampName])
720 varVecOriginal = self._makeZeroSafe(varVecOriginal)
722 mask = ((meanVecOriginal >= self.config.minMeanSignal) &
723 (meanVecOriginal <= self.config.maxMeanSignal))
725 goodPoints = self._getInitialGoodPoints(meanVecOriginal, varVecOriginal,
726 self.config.initialNonLinearityExclusionThresholdPositive,
727 self.config.initialNonLinearityExclusionThresholdNegative)
728 mask = mask & goodPoints
730 if ptcFitType == 'ASTIERAPPROXIMATION':
731 ptcFunc = self.funcAstier
732 parsIniPtc = [-1e-9, 1.0, 10.] # a00, gain, noise
733 bounds = self._boundsForAstier(parsIniPtc)
734 if ptcFitType == 'POLYNOMIAL':
735 ptcFunc = self.funcPolynomial
736 parsIniPtc = self._initialParsForPolynomial(self.config.polynomialFitDegree + 1)
737 bounds = self._boundsForPolynomial(parsIniPtc)
739 # Before bootstrap fit, do an iterative fit to get rid of outliers
740 count = 1
741 while count <= maxIterationsPtcOutliers:
742 # Note that application of the mask actually shrinks the array
743 # to size rather than setting elements to zero (as we want) so
744 # always update mask itself and re-apply to the original data
745 meanTempVec = meanVecOriginal[mask]
746 varTempVec = varVecOriginal[mask]
747 res = least_squares(errFunc, parsIniPtc, bounds=bounds, args=(meanTempVec, varTempVec))
748 pars = res.x
750 # change this to the original from the temp because the masks are ANDed
751 # meaning once a point is masked it's always masked, and the masks must
752 # always be the same length for broadcasting
753 sigResids = (varVecOriginal - ptcFunc(pars, meanVecOriginal))/np.sqrt(varVecOriginal)
754 newMask = np.array([True if np.abs(r) < sigmaCutPtcOutliers else False for r in sigResids])
755 mask = mask & newMask
757 nDroppedTotal = Counter(mask)[False]
758 self.log.debug(f"Iteration {count}: discarded {nDroppedTotal} points in total for {ampName}")
759 count += 1
760 # objects should never shrink
761 assert (len(mask) == len(timeVecOriginal) == len(meanVecOriginal) == len(varVecOriginal))
763 dataset.visitMask[ampName] = mask # store the final mask
765 parsIniPtc = pars
766 timeVecFinal = timeVecOriginal[mask]
767 meanVecFinal = meanVecOriginal[mask]
768 varVecFinal = varVecOriginal[mask]
770 if Counter(mask)[False] > 0:
771 self.log.info((f"Number of points discarded in PTC of amplifier {ampName}:" +
772 f" {Counter(mask)[False]} out of {len(meanVecOriginal)}"))
774 if (len(meanVecFinal) < len(parsIniPtc)):
775 msg = (f"\nSERIOUS: Not enough data points ({len(meanVecFinal)}) compared to the number of"
776 f"parameters of the PTC model({len(parsIniPtc)}). Setting {ampName} to BAD.")
777 self.log.warn(msg)
778 dataset.badAmps.append(ampName)
779 dataset.gain[ampName] = np.nan
780 dataset.gainErr[ampName] = np.nan
781 dataset.noise[ampName] = np.nan
782 dataset.noiseErr[ampName] = np.nan
783 dataset.nonLinearity[ampName] = np.nan
784 dataset.nonLinearityError[ampName] = np.nan
785 dataset.nonLinearityResiduals[ampName] = np.nan
786 continue
788 # Fit the PTC
789 if self.config.doFitBootstrap:
790 parsFit, parsFitErr = self._fitBootstrap(parsIniPtc, meanVecFinal, varVecFinal, ptcFunc)
791 else:
792 parsFit, parsFitErr = self._fitLeastSq(parsIniPtc, meanVecFinal, varVecFinal, ptcFunc)
794 dataset.ptcFitPars[ampName] = parsFit
795 dataset.ptcFitParsError[ampName] = parsFitErr
797 if ptcFitType == 'ASTIERAPPROXIMATION':
798 ptcGain = parsFit[1]
799 ptcGainErr = parsFitErr[1]
800 ptcNoise = np.sqrt(np.fabs(parsFit[2]))
801 ptcNoiseErr = 0.5*(parsFitErr[2]/np.fabs(parsFit[2]))*np.sqrt(np.fabs(parsFit[2]))
802 if ptcFitType == 'POLYNOMIAL':
803 ptcGain = 1./parsFit[1]
804 ptcGainErr = np.fabs(1./parsFit[1])*(parsFitErr[1]/parsFit[1])
805 ptcNoise = np.sqrt(np.fabs(parsFit[0]))*ptcGain
806 ptcNoiseErr = (0.5*(parsFitErr[0]/np.fabs(parsFit[0]))*(np.sqrt(np.fabs(parsFit[0]))))*ptcGain
808 dataset.gain[ampName] = ptcGain
809 dataset.gainErr[ampName] = ptcGainErr
810 dataset.noise[ampName] = ptcNoise
811 dataset.noiseErr[ampName] = ptcNoiseErr
812 dataset.ptcFitType[ampName] = ptcFitType
814 # Non-linearity residuals (NL of mean vs time curve): percentage, and fit to a quadratic function
815 # In this case, len(parsIniNonLinearity) = 3 indicates that we want a quadratic fit
817 (c0, linearizerTableRow, linResidualNonLinearity, parsFitNonLinearity,
818 parsFitErrNonLinearity) = self.calculateLinearityResidualAndLinearizers(timeVecFinal,
819 meanVecFinal)
820 # LinearizerLookupTable
821 tableArray[i, :] = linearizerTableRow
823 dataset.nonLinearity[ampName] = parsFitNonLinearity
824 dataset.nonLinearityError[ampName] = parsFitErrNonLinearity
825 dataset.nonLinearityResiduals[ampName] = linResidualNonLinearity
826 dataset.coefficientLinearizeSquared[ampName] = c0
828 return
830 def plot(self, dataRef, dataset, ptcFitType):
831 dirname = dataRef.getUri(datasetType='cpPipePlotRoot', write=True)
832 if not os.path.exists(dirname):
833 os.makedirs(dirname)
835 detNum = dataRef.dataId[self.config.ccdKey]
836 filename = f"PTC_det{detNum}.pdf"
837 filenameFull = os.path.join(dirname, filename)
838 with PdfPages(filenameFull) as pdfPages:
839 self._plotPtc(dataset, ptcFitType, pdfPages)
841 def _plotPtc(self, dataset, ptcFitType, pdfPages):
842 """Plot PTC, linearity, and linearity residual per amplifier"""
844 if ptcFitType == 'ASTIERAPPROXIMATION':
845 ptcFunc = self.funcAstier
846 stringTitle = r"Var = $\frac{1}{2g^2a_{00}}(\exp (2a_{00} \mu g) - 1) + \frac{n_{00}}{g^2}$"
848 if ptcFitType == 'POLYNOMIAL':
849 ptcFunc = self.funcPolynomial
850 stringTitle = f"Polynomial (degree: {self.config.polynomialFitDegree})"
852 legendFontSize = 7.5
853 labelFontSize = 8
854 titleFontSize = 10
855 supTitleFontSize = 18
856 markerSize = 25
858 # General determination of the size of the plot grid
859 nAmps = len(dataset.ampNames)
860 if nAmps == 2:
861 nRows, nCols = 2, 1
862 nRows = np.sqrt(nAmps)
863 mantissa, _ = np.modf(nRows)
864 if mantissa > 0:
865 nRows = int(nRows) + 1
866 nCols = nRows
867 else:
868 nRows = int(nRows)
869 nCols = nRows
871 f, ax = plt.subplots(nrows=nRows, ncols=nCols, sharex='col', sharey='row', figsize=(13, 10))
872 f2, ax2 = plt.subplots(nrows=nRows, ncols=nCols, sharex='col', sharey='row', figsize=(13, 10))
874 for i, (amp, a, a2) in enumerate(zip(dataset.ampNames, ax.flatten(), ax2.flatten())):
875 meanVecOriginal = np.array(dataset.rawMeans[amp])
876 varVecOriginal = np.array(dataset.rawVars[amp])
877 mask = dataset.visitMask[amp]
878 meanVecFinal = meanVecOriginal[mask]
879 varVecFinal = varVecOriginal[mask]
880 meanVecOutliers = meanVecOriginal[np.invert(mask)]
881 varVecOutliers = varVecOriginal[np.invert(mask)]
882 pars, parsErr = dataset.ptcFitPars[amp], dataset.ptcFitParsError[amp]
884 if ptcFitType == 'ASTIERAPPROXIMATION':
885 ptcA00, ptcA00error = pars[0], parsErr[0]
886 ptcGain, ptcGainError = pars[1], parsErr[1]
887 ptcNoise = np.sqrt(np.fabs(pars[2]))
888 ptcNoiseError = 0.5*(parsErr[2]/np.fabs(pars[2]))*np.sqrt(np.fabs(pars[2]))
889 stringLegend = (f"a00: {ptcA00:.2e}+/-{ptcA00error:.2e}"
890 f"\n Gain: {ptcGain:.4}+/-{ptcGainError:.2e}"
891 f"\n Noise: {ptcNoise:.4}+/-{ptcNoiseError:.2e}")
893 if ptcFitType == 'POLYNOMIAL':
894 ptcGain, ptcGainError = 1./pars[1], np.fabs(1./pars[1])*(parsErr[1]/pars[1])
895 ptcNoise = np.sqrt(np.fabs(pars[0]))*ptcGain
896 ptcNoiseError = (0.5*(parsErr[0]/np.fabs(pars[0]))*(np.sqrt(np.fabs(pars[0]))))*ptcGain
897 stringLegend = (f"Noise: {ptcNoise:.4}+/-{ptcNoiseError:.2e} \n"
898 f"Gain: {ptcGain:.4}+/-{ptcGainError:.2e}")
900 minMeanVecFinal = np.min(meanVecFinal)
901 maxMeanVecFinal = np.max(meanVecFinal)
902 meanVecFit = np.linspace(minMeanVecFinal, maxMeanVecFinal, 100*len(meanVecFinal))
903 minMeanVecOriginal = np.min(meanVecOriginal)
904 maxMeanVecOriginal = np.max(meanVecOriginal)
905 deltaXlim = maxMeanVecOriginal - minMeanVecOriginal
907 a.plot(meanVecFit, ptcFunc(pars, meanVecFit), color='red')
908 a.plot(meanVecFinal, pars[0] + pars[1]*meanVecFinal, color='green', linestyle='--')
909 a.scatter(meanVecFinal, varVecFinal, c='blue', marker='o', s=markerSize)
910 a.scatter(meanVecOutliers, varVecOutliers, c='magenta', marker='s', s=markerSize)
911 a.set_xlabel(r'Mean signal ($\mu$, ADU)', fontsize=labelFontSize)
912 a.set_xticks(meanVecOriginal)
913 a.set_ylabel(r'Variance (ADU$^2$)', fontsize=labelFontSize)
914 a.tick_params(labelsize=11)
915 a.text(0.03, 0.8, stringLegend, transform=a.transAxes, fontsize=legendFontSize)
916 a.set_xscale('linear', fontsize=labelFontSize)
917 a.set_yscale('linear', fontsize=labelFontSize)
918 a.set_title(amp, fontsize=titleFontSize)
919 a.set_xlim([minMeanVecOriginal - 0.2*deltaXlim, maxMeanVecOriginal + 0.2*deltaXlim])
921 # Same, but in log-scale
922 a2.plot(meanVecFit, ptcFunc(pars, meanVecFit), color='red')
923 a2.scatter(meanVecFinal, varVecFinal, c='blue', marker='o', s=markerSize)
924 a2.scatter(meanVecOutliers, varVecOutliers, c='magenta', marker='s', s=markerSize)
925 a2.set_xlabel(r'Mean Signal ($\mu$, ADU)', fontsize=labelFontSize)
926 a2.set_ylabel(r'Variance (ADU$^2$)', fontsize=labelFontSize)
927 a2.tick_params(labelsize=11)
928 a2.text(0.03, 0.8, stringLegend, transform=a2.transAxes, fontsize=legendFontSize)
929 a2.set_xscale('log')
930 a2.set_yscale('log')
931 a2.set_title(amp, fontsize=titleFontSize)
932 a2.set_xlim([minMeanVecOriginal, maxMeanVecOriginal])
934 f.suptitle(f"PTC \n Fit: " + stringTitle, fontsize=20)
935 pdfPages.savefig(f)
936 f2.suptitle(f"PTC (log-log)", fontsize=20)
937 pdfPages.savefig(f2)
939 # Plot mean vs time
940 f, ax = plt.subplots(nrows=4, ncols=4, sharex='col', sharey='row', figsize=(13, 10))
941 for i, (amp, a) in enumerate(zip(dataset.ampNames, ax.flatten())):
942 meanVecFinal = np.array(dataset.rawMeans[amp])[dataset.visitMask[amp]]
943 timeVecFinal = np.array(dataset.rawExpTimes[amp])[dataset.visitMask[amp]]
945 pars, parsErr = dataset.nonLinearity[amp], dataset.nonLinearityError[amp]
946 c0, c0Error = pars[0], parsErr[0]
947 c1, c1Error = pars[1], parsErr[1]
948 c2, c2Error = pars[2], parsErr[2]
949 stringLegend = f"c0: {c0:.4}+/-{c0Error:.2e}\n c1: {c1:.4}+/-{c1Error:.2e}" \
950 + f"\n c2(NL): {c2:.2e}+/-{c2Error:.2e}"
951 a.scatter(timeVecFinal, meanVecFinal)
952 a.plot(timeVecFinal, self.funcPolynomial(pars, timeVecFinal), color='red')
953 a.set_xlabel('Time (sec)', fontsize=labelFontSize)
954 a.set_xticks(timeVecFinal)
955 a.set_ylabel(r'Mean signal ($\mu$, ADU)', fontsize=labelFontSize)
956 a.tick_params(labelsize=labelFontSize)
957 a.text(0.03, 0.75, stringLegend, transform=a.transAxes, fontsize=legendFontSize)
958 a.set_xscale('linear', fontsize=labelFontSize)
959 a.set_yscale('linear', fontsize=labelFontSize)
960 a.set_title(amp, fontsize=titleFontSize)
962 f.suptitle("Linearity \n Fit: " + r"$\mu = c_0 + c_1 t + c_2 t^2$", fontsize=supTitleFontSize)
963 pdfPages.savefig()
965 # Plot linearity residual
966 f, ax = plt.subplots(nrows=4, ncols=4, sharex='col', sharey='row', figsize=(13, 10))
967 for i, (amp, a) in enumerate(zip(dataset.ampNames, ax.flatten())):
968 meanVecFinal = np.array(dataset.rawMeans[amp])[dataset.visitMask[amp]]
969 linRes = np.array(dataset.nonLinearityResiduals[amp])
971 a.scatter(meanVecFinal, linRes)
972 a.axhline(y=0, color='k')
973 a.axvline(x=timeVecFinal[self.config.linResidualTimeIndex], color='g', linestyle='--')
974 a.set_xlabel(r'Mean signal ($\mu$, ADU)', fontsize=labelFontSize)
975 a.set_xticks(meanVecFinal)
976 a.set_ylabel('LR (%)', fontsize=labelFontSize)
977 a.tick_params(labelsize=labelFontSize)
978 a.set_xscale('linear', fontsize=labelFontSize)
979 a.set_yscale('linear', fontsize=labelFontSize)
980 a.set_title(amp, fontsize=titleFontSize)
982 f.suptitle(r"Linearity Residual: $100(1 - \mu_{\rm{ref}}/t_{\rm{ref}})/(\mu / t))$" + "\n" +
983 r"$t_{\rm{ref}}$: " + f"{timeVecFinal[2]} s", fontsize=supTitleFontSize)
984 pdfPages.savefig()
986 return