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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__ = ['PairedVisitListTaskRunner', 'SingleVisitListTaskRunner',
24 'NonexistentDatasetTaskDataIdContainer', 'parseCmdlineNumberString',
25 'countMaskedPixels', 'checkExpLengthEqual', 'ddict2dict']
27import re
28import numpy as np
29from scipy.optimize import leastsq
30import numpy.polynomial.polynomial as poly
31from scipy.stats import norm
33import lsst.pipe.base as pipeBase
34import lsst.ip.isr as ipIsr
35from lsst.ip.isr import isrMock
36import lsst.log
37import lsst.afw.image
39import galsim
42def sigmaClipCorrection(nSigClip):
43 """Correct measured sigma to account for clipping.
45 If we clip our input data and then measure sigma, then the
46 measured sigma is smaller than the true value because real
47 points beyond the clip threshold have been removed. This is a
48 small (1.5% at nSigClip=3) effect when nSigClip >~ 3, but the
49 default parameters for measure crosstalk use nSigClip=2.0.
50 This causes the measured sigma to be about 15% smaller than
51 real. This formula corrects the issue, for the symmetric case
52 (upper clip threshold equal to lower clip threshold).
54 Parameters
55 ----------
56 nSigClip : `float`
57 Number of sigma the measurement was clipped by.
59 Returns
60 -------
61 scaleFactor : `float`
62 Scale factor to increase the measured sigma by.
64 """
65 varFactor = 1.0 - (2 * nSigClip * norm.pdf(nSigClip)) / (norm.cdf(nSigClip) - norm.cdf(-nSigClip))
66 return 1.0 / np.sqrt(varFactor)
69def calculateWeightedReducedChi2(measured, model, weightsMeasured, nData, nParsModel):
70 """Calculate weighted reduced chi2.
72 Parameters
73 ----------
75 measured : `list`
76 List with measured data.
78 model : `list`
79 List with modeled data.
81 weightsMeasured : `list`
82 List with weights for the measured data.
84 nData : `int`
85 Number of data points.
87 nParsModel : `int`
88 Number of parameters in the model.
90 Returns
91 -------
93 redWeightedChi2 : `float`
94 Reduced weighted chi2.
95 """
97 wRes = (measured - model)*weightsMeasured
98 return ((wRes*wRes).sum())/(nData-nParsModel)
101def makeMockFlats(expTime, gain=1.0, readNoiseElectrons=5, fluxElectrons=1000,
102 randomSeedFlat1=1984, randomSeedFlat2=666, powerLawBfParams=[],
103 expId1=0, expId2=1):
104 """Create a pair or mock flats with isrMock.
106 Parameters
107 ----------
108 expTime : `float`
109 Exposure time of the flats.
111 gain : `float`, optional
112 Gain, in e/ADU.
114 readNoiseElectrons : `float`, optional
115 Read noise rms, in electrons.
117 fluxElectrons : `float`, optional
118 Flux of flats, in electrons per second.
120 randomSeedFlat1 : `int`, optional
121 Random seed for the normal distrubutions for the mean signal and noise (flat1).
123 randomSeedFlat2 : `int`, optional
124 Random seed for the normal distrubutions for the mean signal and noise (flat2).
126 powerLawBfParams : `list`, optional
127 Parameters for `galsim.cdmodel.PowerLawCD` to simulate the brightter-fatter effect.
129 expId1 : `int`, optional
130 Exposure ID for first flat.
132 expId2 : `int`, optional
133 Exposure ID for second flat.
135 Returns
136 -------
138 flatExp1 : `lsst.afw.image.exposure.exposure.ExposureF`
139 First exposure of flat field pair.
141 flatExp2 : `lsst.afw.image.exposure.exposure.ExposureF`
142 Second exposure of flat field pair.
144 Notes
145 -----
146 The parameters of `galsim.cdmodel.PowerLawCD` are `n, r0, t0, rx, tx, r, t, alpha`. For more
147 information about their meaning, see the Galsim documentation
148 https://galsim-developers.github.io/GalSim/_build/html/_modules/galsim/cdmodel.html
149 and Gruen+15 (1501.02802).
151 Example: galsim.cdmodel.PowerLawCD(8, 1.1e-7, 1.1e-7, 1.0e-8, 1.0e-8, 1.0e-9, 1.0e-9, 2.0)
152 """
153 flatFlux = fluxElectrons # e/s
154 flatMean = flatFlux*expTime # e
155 readNoise = readNoiseElectrons # e
157 mockImageConfig = isrMock.IsrMock.ConfigClass()
159 mockImageConfig.flatDrop = 0.99999
160 mockImageConfig.isTrimmed = True
162 flatExp1 = isrMock.FlatMock(config=mockImageConfig).run()
163 flatExp2 = flatExp1.clone()
164 (shapeY, shapeX) = flatExp1.getDimensions()
165 flatWidth = np.sqrt(flatMean)
167 rng1 = np.random.RandomState(randomSeedFlat1)
168 flatData1 = rng1.normal(flatMean, flatWidth, (shapeX, shapeY)) + rng1.normal(0.0, readNoise,
169 (shapeX, shapeY))
170 rng2 = np.random.RandomState(randomSeedFlat2)
171 flatData2 = rng2.normal(flatMean, flatWidth, (shapeX, shapeY)) + rng2.normal(0.0, readNoise,
172 (shapeX, shapeY))
173 # Simulate BF with power law model in galsim
174 if len(powerLawBfParams):
175 if not len(powerLawBfParams) == 8:
176 raise RuntimeError("Wrong number of parameters for `galsim.cdmodel.PowerLawCD`. "
177 f"Expected 8; passed {len(powerLawBfParams)}.")
178 cd = galsim.cdmodel.PowerLawCD(*powerLawBfParams)
179 tempFlatData1 = galsim.Image(flatData1)
180 temp2FlatData1 = cd.applyForward(tempFlatData1)
182 tempFlatData2 = galsim.Image(flatData2)
183 temp2FlatData2 = cd.applyForward(tempFlatData2)
185 flatExp1.image.array[:] = temp2FlatData1.array/gain # ADU
186 flatExp2.image.array[:] = temp2FlatData2.array/gain # ADU
187 else:
188 flatExp1.image.array[:] = flatData1/gain # ADU
189 flatExp2.image.array[:] = flatData2/gain # ADU
191 visitInfoExp1 = lsst.afw.image.VisitInfo(exposureId=expId1, exposureTime=expTime)
192 visitInfoExp2 = lsst.afw.image.VisitInfo(exposureId=expId2, exposureTime=expTime)
194 flatExp1.getInfo().setVisitInfo(visitInfoExp1)
195 flatExp2.getInfo().setVisitInfo(visitInfoExp2)
197 return flatExp1, flatExp2
200def countMaskedPixels(maskedIm, maskPlane):
201 """Count the number of pixels in a given mask plane."""
202 maskBit = maskedIm.mask.getPlaneBitMask(maskPlane)
203 nPix = np.where(np.bitwise_and(maskedIm.mask.array, maskBit))[0].flatten().size
204 return nPix
207class PairedVisitListTaskRunner(pipeBase.TaskRunner):
208 """Subclass of TaskRunner for handling intrinsically paired visits.
210 This transforms the processed arguments generated by the ArgumentParser
211 into the arguments expected by tasks which take visit pairs for their
212 run() methods.
214 Such tasks' run() methods tend to take two arguments,
215 one of which is the dataRef (as usual), and the other is the list
216 of visit-pairs, in the form of a list of tuples.
217 This list is supplied on the command line as documented,
218 and this class parses that, and passes the parsed version
219 to the run() method.
221 See pipeBase.TaskRunner for more information.
222 """
224 @staticmethod
225 def getTargetList(parsedCmd, **kwargs):
226 """Parse the visit list and pass through explicitly."""
227 visitPairs = []
228 for visitStringPair in parsedCmd.visitPairs:
229 visitStrings = visitStringPair.split(",")
230 if len(visitStrings) != 2:
231 raise RuntimeError("Found {} visits in {} instead of 2".format(len(visitStrings),
232 visitStringPair))
233 try:
234 visits = [int(visit) for visit in visitStrings]
235 except Exception:
236 raise RuntimeError("Could not parse {} as two integer visit numbers".format(visitStringPair))
237 visitPairs.append(visits)
239 return pipeBase.TaskRunner.getTargetList(parsedCmd, visitPairs=visitPairs, **kwargs)
242def parseCmdlineNumberString(inputString):
243 """Parse command line numerical expression sytax and return as list of int
245 Take an input of the form "'1..5:2^123..126'" as a string, and return
246 a list of ints as [1, 3, 5, 123, 124, 125, 126]
247 """
248 outList = []
249 for subString in inputString.split("^"):
250 mat = re.search(r"^(\d+)\.\.(\d+)(?::(\d+))?$", subString)
251 if mat:
252 v1 = int(mat.group(1))
253 v2 = int(mat.group(2))
254 v3 = mat.group(3)
255 v3 = int(v3) if v3 else 1
256 for v in range(v1, v2 + 1, v3):
257 outList.append(int(v))
258 else:
259 outList.append(int(subString))
260 return outList
263class SingleVisitListTaskRunner(pipeBase.TaskRunner):
264 """Subclass of TaskRunner for tasks requiring a list of visits per dataRef.
266 This transforms the processed arguments generated by the ArgumentParser
267 into the arguments expected by tasks which require a list of visits
268 to be supplied for each dataRef, as is common in `lsst.cp.pipe` code.
270 Such tasks' run() methods tend to take two arguments,
271 one of which is the dataRef (as usual), and the other is the list
272 of visits.
273 This list is supplied on the command line as documented,
274 and this class parses that, and passes the parsed version
275 to the run() method.
277 See `lsst.pipe.base.TaskRunner` for more information.
278 """
280 @staticmethod
281 def getTargetList(parsedCmd, **kwargs):
282 """Parse the visit list and pass through explicitly."""
283 # if this has been pre-parsed and therefore doesn't have length of one
284 # then something has gone wrong, so execution should stop here.
285 assert len(parsedCmd.visitList) == 1, 'visitList parsing assumptions violated'
286 visits = parseCmdlineNumberString(parsedCmd.visitList[0])
288 return pipeBase.TaskRunner.getTargetList(parsedCmd, visitList=visits, **kwargs)
291class NonexistentDatasetTaskDataIdContainer(pipeBase.DataIdContainer):
292 """A DataIdContainer for the tasks for which the output does
293 not yet exist."""
295 def makeDataRefList(self, namespace):
296 """Compute refList based on idList.
298 This method must be defined as the dataset does not exist before this
299 task is run.
301 Parameters
302 ----------
303 namespace
304 Results of parsing the command-line.
306 Notes
307 -----
308 Not called if ``add_id_argument`` called
309 with ``doMakeDataRefList=False``.
310 Note that this is almost a copy-and-paste of the vanilla
311 implementation, but without checking if the datasets already exist,
312 as this task exists to make them.
313 """
314 if self.datasetType is None:
315 raise RuntimeError("Must call setDatasetType first")
316 butler = namespace.butler
317 for dataId in self.idList:
318 refList = list(butler.subset(datasetType=self.datasetType, level=self.level, dataId=dataId))
319 # exclude nonexistent data
320 # this is a recursive test, e.g. for the sake of "raw" data
321 if not refList:
322 namespace.log.warn("No data found for dataId=%s", dataId)
323 continue
324 self.refList += refList
327def irlsFit(initialParams, dataX, dataY, function, weightsY=None, weightType='Cauchy'):
328 """Iteratively reweighted least squares fit.
330 This uses the `lsst.cp.pipe.utils.fitLeastSq`, but applies weights
331 based on the Cauchy distribution by default. Other weight options
332 are implemented. See e.g. Holland and Welsch, 1977,
333 doi:10.1080/03610927708827533
335 Parameters
336 ----------
337 initialParams : `list` [`float`]
338 Starting parameters.
339 dataX : `numpy.array` [`float`]
340 Abscissa data.
341 dataY : `numpy.array` [`float`]
342 Ordinate data.
343 function : callable
344 Function to fit.
345 weightsY : `numpy.array` [`float`]
346 Weights to apply to the data.
347 weightType : `str`, optional
348 Type of weighting to use. One of Cauchy, Anderson, bisquare,
349 box, Welsch, Huber, logistic, or Fair.
351 Returns
352 -------
353 polyFit : `list` [`float`]
354 Final best fit parameters.
355 polyFitErr : `list` [`float`]
356 Final errors on fit parameters.
357 chiSq : `float`
358 Reduced chi squared.
359 weightsY : `list` [`float`]
360 Final weights used for each point.
362 Raises
363 ------
364 RuntimeError :
365 Raised if an unknown weightType string is passed.
367 """
368 if not weightsY:
369 weightsY = np.ones_like(dataX)
371 polyFit, polyFitErr, chiSq = fitLeastSq(initialParams, dataX, dataY, function, weightsY=weightsY)
372 for iteration in range(10):
373 resid = np.abs(dataY - function(polyFit, dataX)) / np.sqrt(dataY)
374 if weightType == 'Cauchy':
375 # Use Cauchy weighting. This is a soft weight.
376 # At [2, 3, 5, 10] sigma, weights are [.59, .39, .19, .05].
377 Z = resid / 2.385
378 weightsY = 1.0 / (1.0 + np.square(Z))
379 elif weightType == 'Anderson':
380 # Anderson+1972 weighting. This is a hard weight.
381 # At [2, 3, 5, 10] sigma, weights are [.67, .35, 0.0, 0.0].
382 Z = resid / (1.339 * np.pi)
383 weightsY = np.where(Z < 1.0, np.sinc(Z), 0.0)
384 elif weightType == 'bisquare':
385 # Beaton and Tukey (1974) biweight. This is a hard weight.
386 # At [2, 3, 5, 10] sigma, weights are [.81, .59, 0.0, 0.0].
387 Z = resid / 4.685
388 weightsY = np.where(Z < 1.0, 1.0 - np.square(Z), 0.0)
389 elif weightType == 'box':
390 # Hinich and Talwar (1975). This is a hard weight.
391 # At [2, 3, 5, 10] sigma, weights are [1.0, 0.0, 0.0, 0.0].
392 weightsY = np.where(resid < 2.795, 1.0, 0.0)
393 elif weightType == 'Welsch':
394 # Dennis and Welsch (1976). This is a hard weight.
395 # At [2, 3, 5, 10] sigma, weights are [.64, .36, .06, 1e-5].
396 Z = resid / 2.985
397 weightsY = np.exp(-1.0 * np.square(Z))
398 elif weightType == 'Huber':
399 # Huber (1964) weighting. This is a soft weight.
400 # At [2, 3, 5, 10] sigma, weights are [.67, .45, .27, .13].
401 Z = resid / 1.345
402 weightsY = np.where(Z < 1.0, 1.0, 1 / Z)
403 elif weightType == 'logistic':
404 # Logistic weighting. This is a soft weight.
405 # At [2, 3, 5, 10] sigma, weights are [.56, .40, .24, .12].
406 Z = resid / 1.205
407 weightsY = np.tanh(Z) / Z
408 elif weightType == 'Fair':
409 # Fair (1974) weighting. This is a soft weight.
410 # At [2, 3, 5, 10] sigma, weights are [.41, .32, .22, .12].
411 Z = resid / 1.4
412 weightsY = (1.0 / (1.0 + (Z)))
413 else:
414 raise RuntimeError(f"Unknown weighting type: {weightType}")
415 polyFit, polyFitErr, chiSq = fitLeastSq(initialParams, dataX, dataY, function, weightsY=weightsY)
417 return polyFit, polyFitErr, chiSq, weightsY
420def fitLeastSq(initialParams, dataX, dataY, function, weightsY=None):
421 """Do a fit and estimate the parameter errors using using scipy.optimize.leastq.
423 optimize.leastsq returns the fractional covariance matrix. To estimate the
424 standard deviation of the fit parameters, multiply the entries of this matrix
425 by the unweighted reduced chi squared and take the square root of the diagonal elements.
427 Parameters
428 ----------
429 initialParams : `list` of `float`
430 initial values for fit parameters. For ptcFitType=POLYNOMIAL, its length
431 determines the degree of the polynomial.
433 dataX : `numpy.array` of `float`
434 Data in the abscissa axis.
436 dataY : `numpy.array` of `float`
437 Data in the ordinate axis.
439 function : callable object (function)
440 Function to fit the data with.
442 weightsY : `numpy.array` of `float`
443 Weights of the data in the ordinate axis.
445 Return
446 ------
447 pFitSingleLeastSquares : `list` of `float`
448 List with fitted parameters.
450 pErrSingleLeastSquares : `list` of `float`
451 List with errors for fitted parameters.
453 reducedChiSqSingleLeastSquares : `float`
454 Reduced chi squared, unweighted if weightsY is not provided.
455 """
456 if weightsY is None:
457 weightsY = np.ones(len(dataX))
459 def errFunc(p, x, y, weightsY=None):
460 if weightsY is None:
461 weightsY = np.ones(len(x))
462 return (function(p, x) - y)*weightsY
464 pFit, pCov, infoDict, errMessage, success = leastsq(errFunc, initialParams,
465 args=(dataX, dataY, weightsY), full_output=1,
466 epsfcn=0.0001)
468 if (len(dataY) > len(initialParams)) and pCov is not None:
469 reducedChiSq = calculateWeightedReducedChi2(dataY, function(pFit, dataX), weightsY, len(dataY),
470 len(initialParams))
471 pCov *= reducedChiSq
472 else:
473 pCov = np.zeros((len(initialParams), len(initialParams)))
474 pCov[:, :] = np.nan
475 reducedChiSq = np.nan
477 errorVec = []
478 for i in range(len(pFit)):
479 errorVec.append(np.fabs(pCov[i][i])**0.5)
481 pFitSingleLeastSquares = pFit
482 pErrSingleLeastSquares = np.array(errorVec)
484 return pFitSingleLeastSquares, pErrSingleLeastSquares, reducedChiSq
487def fitBootstrap(initialParams, dataX, dataY, function, weightsY=None, confidenceSigma=1.):
488 """Do a fit using least squares and bootstrap to estimate parameter errors.
490 The bootstrap error bars are calculated by fitting 100 random data sets.
492 Parameters
493 ----------
494 initialParams : `list` of `float`
495 initial values for fit parameters. For ptcFitType=POLYNOMIAL, its length
496 determines the degree of the polynomial.
498 dataX : `numpy.array` of `float`
499 Data in the abscissa axis.
501 dataY : `numpy.array` of `float`
502 Data in the ordinate axis.
504 function : callable object (function)
505 Function to fit the data with.
507 weightsY : `numpy.array` of `float`, optional.
508 Weights of the data in the ordinate axis.
510 confidenceSigma : `float`, optional.
511 Number of sigmas that determine confidence interval for the bootstrap errors.
513 Return
514 ------
515 pFitBootstrap : `list` of `float`
516 List with fitted parameters.
518 pErrBootstrap : `list` of `float`
519 List with errors for fitted parameters.
521 reducedChiSqBootstrap : `float`
522 Reduced chi squared, unweighted if weightsY is not provided.
523 """
524 if weightsY is None:
525 weightsY = np.ones(len(dataX))
527 def errFunc(p, x, y, weightsY):
528 if weightsY is None:
529 weightsY = np.ones(len(x))
530 return (function(p, x) - y)*weightsY
532 # Fit first time
533 pFit, _ = leastsq(errFunc, initialParams, args=(dataX, dataY, weightsY), full_output=0)
535 # Get the stdev of the residuals
536 residuals = errFunc(pFit, dataX, dataY, weightsY)
537 # 100 random data sets are generated and fitted
538 pars = []
539 for i in range(100):
540 randomDelta = np.random.normal(0., np.fabs(residuals), len(dataY))
541 randomDataY = dataY + randomDelta
542 randomFit, _ = leastsq(errFunc, initialParams,
543 args=(dataX, randomDataY, weightsY), full_output=0)
544 pars.append(randomFit)
545 pars = np.array(pars)
546 meanPfit = np.mean(pars, 0)
548 # confidence interval for parameter estimates
549 errPfit = confidenceSigma*np.std(pars, 0)
550 pFitBootstrap = meanPfit
551 pErrBootstrap = errPfit
553 reducedChiSq = calculateWeightedReducedChi2(dataY, function(pFitBootstrap, dataX), weightsY, len(dataY),
554 len(initialParams))
555 return pFitBootstrap, pErrBootstrap, reducedChiSq
558def funcPolynomial(pars, x):
559 """Polynomial function definition
560 Parameters
561 ----------
562 params : `list`
563 Polynomial coefficients. Its length determines the polynomial order.
565 x : `numpy.array`
566 Abscisa array.
568 Returns
569 -------
570 Ordinate array after evaluating polynomial of order len(pars)-1 at `x`.
571 """
572 return poly.polyval(x, [*pars])
575def funcAstier(pars, x):
576 """Single brighter-fatter parameter model for PTC; Equation 16 of Astier+19.
578 Parameters
579 ----------
580 params : `list`
581 Parameters of the model: a00 (brightter-fatter), gain (e/ADU), and noise (e^2).
583 x : `numpy.array`
584 Signal mu (ADU).
586 Returns
587 -------
588 C_00 (variance) in ADU^2.
589 """
590 a00, gain, noise = pars
591 return 0.5/(a00*gain*gain)*(np.exp(2*a00*x*gain)-1) + noise/(gain*gain) # C_00
594def arrangeFlatsByExpTime(exposureList, exposureIdList):
595 """Arrange exposures by exposure time.
597 Parameters
598 ----------
599 exposureList : `list`[`lsst.afw.image.exposure.exposure.ExposureF`]
600 Input list of exposures.
602 exposureIdList : `list`[`int`]
603 List of exposure ids as obtained by dataId[`exposure`].
605 Returns
606 ------
607 flatsAtExpTime : `dict` [`float`,
608 `list`[(`lsst.afw.image.exposure.exposure.ExposureF`, `int`)]]
609 Dictionary that groups flat-field exposures (and their IDs) that have
610 the same exposure time (seconds).
611 """
612 flatsAtExpTime = {}
613 assert len(exposureList) == len(exposureIdList), "Different lengths for exp. list and exp. ID lists"
614 for exp, expId in zip(exposureList, exposureIdList):
615 expTime = exp.getInfo().getVisitInfo().getExposureTime()
616 listAtExpTime = flatsAtExpTime.setdefault(expTime, [])
617 listAtExpTime.append((exp, expId))
619 return flatsAtExpTime
622def arrangeFlatsByExpId(exposureList, exposureIdList):
623 """Arrange exposures by exposure ID.
625 There is no guarantee that this will properly group exposures, but
626 allows a sequence of flats that have different illumination
627 (despite having the same exposure time) to be processed.
629 Parameters
630 ----------
631 exposureList : `list`[`lsst.afw.image.exposure.exposure.ExposureF`]
632 Input list of exposures.
634 exposureIdList : `list`[`int`]
635 List of exposure ids as obtained by dataId[`exposure`].
637 Returns
638 ------
639 flatsAtExpId : `dict` [`float`,
640 `list`[(`lsst.afw.image.exposure.exposure.ExposureF`, `int`)]]
641 Dictionary that groups flat-field exposures (and their IDs)
642 sequentially by their exposure id.
644 Notes
645 -----
647 This algorithm sorts the input exposures by their exposure id, and
648 then assigns each pair of exposures (exp_j, exp_{j+1}) to pair k,
649 such that 2*k = j, where j is the python index of one of the
650 exposures (starting from zero). By checking for the IndexError
651 while appending, we can ensure that there will only ever be fully
652 populated pairs.
653 """
654 flatsAtExpId = {}
655 # sortedExposures = sorted(exposureList, key=lambda exp: exp.getInfo().getVisitInfo().getExposureId())
656 assert len(exposureList) == len(exposureIdList), "Different lengths for exp. list and exp. ID lists"
657 # Sort exposures by expIds, which are in the second list `exposureIdList`.
658 sortedExposures = sorted(zip(exposureList, exposureIdList), key=lambda pair: pair[1])
660 for jPair, expTuple in enumerate(sortedExposures):
661 if (jPair + 1) % 2:
662 kPair = jPair // 2
663 listAtExpId = flatsAtExpId.setdefault(kPair, [])
664 try:
665 listAtExpId.append(expTuple)
666 listAtExpId.append(sortedExposures[jPair + 1])
667 except IndexError:
668 pass
670 return flatsAtExpId
673def checkExpLengthEqual(exp1, exp2, v1=None, v2=None, raiseWithMessage=False):
674 """Check the exposure lengths of two exposures are equal.
676 Parameters:
677 -----------
678 exp1 : `lsst.afw.image.exposure.ExposureF`
679 First exposure to check
680 exp2 : `lsst.afw.image.exposure.ExposureF`
681 Second exposure to check
682 v1 : `int` or `str`, optional
683 First visit of the visit pair
684 v2 : `int` or `str`, optional
685 Second visit of the visit pair
686 raiseWithMessage : `bool`
687 If True, instead of returning a bool, raise a RuntimeError if exposure
688 times are not equal, with a message about which visits mismatch if the
689 information is available.
691 Raises:
692 -------
693 RuntimeError
694 Raised if the exposure lengths of the two exposures are not equal
695 """
696 expTime1 = exp1.getInfo().getVisitInfo().getExposureTime()
697 expTime2 = exp2.getInfo().getVisitInfo().getExposureTime()
698 if expTime1 != expTime2:
699 if raiseWithMessage:
700 msg = "Exposure lengths for visit pairs must be equal. " + \
701 "Found %s and %s" % (expTime1, expTime2)
702 if v1 and v2:
703 msg += " for visit pair %s, %s" % (v1, v2)
704 raise RuntimeError(msg)
705 else:
706 return False
707 return True
710def validateIsrConfig(isrTask, mandatory=None, forbidden=None, desirable=None, undesirable=None,
711 checkTrim=True, logName=None):
712 """Check that appropriate ISR settings have been selected for the task.
714 Note that this checks that the task itself is configured correctly rather
715 than checking a config.
717 Parameters
718 ----------
719 isrTask : `lsst.ip.isr.IsrTask`
720 The task whose config is to be validated
722 mandatory : `iterable` of `str`
723 isr steps that must be set to True. Raises if False or missing
725 forbidden : `iterable` of `str`
726 isr steps that must be set to False. Raises if True, warns if missing
728 desirable : `iterable` of `str`
729 isr steps that should probably be set to True. Warns is False, info if
730 missing
732 undesirable : `iterable` of `str`
733 isr steps that should probably be set to False. Warns is True, info if
734 missing
736 checkTrim : `bool`
737 Check to ensure the isrTask's assembly subtask is trimming the images.
738 This is a separate config as it is very ugly to do this within the
739 normal configuration lists as it is an option of a sub task.
741 Raises
742 ------
743 RuntimeError
744 Raised if ``mandatory`` config parameters are False,
745 or if ``forbidden`` parameters are True.
747 TypeError
748 Raised if parameter ``isrTask`` is an invalid type.
750 Notes
751 -----
752 Logs warnings using an isrValidation logger for desirable/undesirable
753 options that are of the wrong polarity or if keys are missing.
754 """
755 if not isinstance(isrTask, ipIsr.IsrTask):
756 raise TypeError(f'Must supply an instance of lsst.ip.isr.IsrTask not {type(isrTask)}')
758 configDict = isrTask.config.toDict()
760 if logName and isinstance(logName, str):
761 log = lsst.log.getLogger(logName)
762 else:
763 log = lsst.log.getLogger("isrValidation")
765 if mandatory:
766 for configParam in mandatory:
767 if configParam not in configDict:
768 raise RuntimeError(f"Mandatory parameter {configParam} not found in the isr configuration.")
769 if configDict[configParam] is False:
770 raise RuntimeError(f"Must set config.isr.{configParam} to True for this task.")
772 if forbidden:
773 for configParam in forbidden:
774 if configParam not in configDict:
775 log.warn(f"Failed to find forbidden key {configParam} in the isr config. The keys in the"
776 " forbidden list should each have an associated Field in IsrConfig:"
777 " check that there is not a typo in this case.")
778 continue
779 if configDict[configParam] is True:
780 raise RuntimeError(f"Must set config.isr.{configParam} to False for this task.")
782 if desirable:
783 for configParam in desirable:
784 if configParam not in configDict:
785 log.info(f"Failed to find key {configParam} in the isr config. You probably want"
786 " to set the equivalent for your obs_package to True.")
787 continue
788 if configDict[configParam] is False:
789 log.warn(f"Found config.isr.{configParam} set to False for this task."
790 " The cp_pipe Config recommends setting this to True.")
791 if undesirable:
792 for configParam in undesirable:
793 if configParam not in configDict:
794 log.info(f"Failed to find key {configParam} in the isr config. You probably want"
795 " to set the equivalent for your obs_package to False.")
796 continue
797 if configDict[configParam] is True:
798 log.warn(f"Found config.isr.{configParam} set to True for this task."
799 " The cp_pipe Config recommends setting this to False.")
801 if checkTrim: # subtask setting, seems non-trivial to combine with above lists
802 if not isrTask.assembleCcd.config.doTrim:
803 raise RuntimeError("Must trim when assembling CCDs. Set config.isr.assembleCcd.doTrim to True")
806def ddict2dict(d):
807 """Convert nested default dictionaries to regular dictionaries.
809 This is needed to prevent yaml persistence issues.
811 Parameters
812 ----------
813 d : `defaultdict`
814 A possibly nested set of `defaultdict`.
816 Returns
817 -------
818 dict : `dict`
819 A possibly nested set of `dict`.
820 """
821 for k, v in d.items():
822 if isinstance(v, dict):
823 d[k] = ddict2dict(v)
824 return dict(d)