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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):
328 """Iteratively reweighted least squares fit.
330 This uses the `lsst.cp.pipe.utils.fitLeastSq`, but applies
331 weights based on the Cauchy distribution to the fitter. See
332 e.g. Holland and Welsch, 1977, doi:10.1080/03610927708827533
334 Parameters
335 ----------
336 initialParams : `list` [`float`]
337 Starting parameters.
338 dataX : `numpy.array` [`float`]
339 Abscissa data.
340 dataY : `numpy.array` [`float`]
341 Ordinate data.
342 function : callable
343 Function to fit.
344 weightsY : `numpy.array` [`float`]
345 Weights to apply to the data.
347 Returns
348 -------
349 polyFit : `list` [`float`]
350 Final best fit parameters.
351 polyFitErr : `list` [`float`]
352 Final errors on fit parameters.
353 chiSq : `float`
354 Reduced chi squared.
355 weightsY : `list` [`float`]
356 Final weights used for each point.
358 """
359 if not weightsY:
360 weightsY = np.ones_like(dataX)
362 polyFit, polyFitErr, chiSq = fitLeastSq(initialParams, dataX, dataY, function, weightsY=weightsY)
363 for iteration in range(10):
364 # Use Cauchy weights
365 resid = np.abs(dataY - function(polyFit, dataX)) / np.sqrt(dataY)
366 weightsY = 1.0 / (1.0 + np.sqrt(resid / 2.385))
367 polyFit, polyFitErr, chiSq = fitLeastSq(initialParams, dataX, dataY, function, weightsY=weightsY)
369 return polyFit, polyFitErr, chiSq, weightsY
372def fitLeastSq(initialParams, dataX, dataY, function, weightsY=None):
373 """Do a fit and estimate the parameter errors using using scipy.optimize.leastq.
375 optimize.leastsq returns the fractional covariance matrix. To estimate the
376 standard deviation of the fit parameters, multiply the entries of this matrix
377 by the unweighted reduced chi squared and take the square root of the diagonal elements.
379 Parameters
380 ----------
381 initialParams : `list` of `float`
382 initial values for fit parameters. For ptcFitType=POLYNOMIAL, its length
383 determines the degree of the polynomial.
385 dataX : `numpy.array` of `float`
386 Data in the abscissa axis.
388 dataY : `numpy.array` of `float`
389 Data in the ordinate axis.
391 function : callable object (function)
392 Function to fit the data with.
394 weightsY : `numpy.array` of `float`
395 Weights of the data in the ordinate axis.
397 Return
398 ------
399 pFitSingleLeastSquares : `list` of `float`
400 List with fitted parameters.
402 pErrSingleLeastSquares : `list` of `float`
403 List with errors for fitted parameters.
405 reducedChiSqSingleLeastSquares : `float`
406 Reduced chi squared, unweighted if weightsY is not provided.
407 """
408 if weightsY is None:
409 weightsY = np.ones(len(dataX))
411 def errFunc(p, x, y, weightsY=None):
412 if weightsY is None:
413 weightsY = np.ones(len(x))
414 return (function(p, x) - y)*weightsY
416 pFit, pCov, infoDict, errMessage, success = leastsq(errFunc, initialParams,
417 args=(dataX, dataY, weightsY), full_output=1,
418 epsfcn=0.0001)
420 if (len(dataY) > len(initialParams)) and pCov is not None:
421 reducedChiSq = calculateWeightedReducedChi2(dataY, function(pFit, dataX), weightsY, len(dataY),
422 len(initialParams))
423 pCov *= reducedChiSq
424 else:
425 pCov = np.zeros((len(initialParams), len(initialParams)))
426 pCov[:, :] = np.nan
427 reducedChiSq = np.nan
429 errorVec = []
430 for i in range(len(pFit)):
431 errorVec.append(np.fabs(pCov[i][i])**0.5)
433 pFitSingleLeastSquares = pFit
434 pErrSingleLeastSquares = np.array(errorVec)
436 return pFitSingleLeastSquares, pErrSingleLeastSquares, reducedChiSq
439def fitBootstrap(initialParams, dataX, dataY, function, weightsY=None, confidenceSigma=1.):
440 """Do a fit using least squares and bootstrap to estimate parameter errors.
442 The bootstrap error bars are calculated by fitting 100 random data sets.
444 Parameters
445 ----------
446 initialParams : `list` of `float`
447 initial values for fit parameters. For ptcFitType=POLYNOMIAL, its length
448 determines the degree of the polynomial.
450 dataX : `numpy.array` of `float`
451 Data in the abscissa axis.
453 dataY : `numpy.array` of `float`
454 Data in the ordinate axis.
456 function : callable object (function)
457 Function to fit the data with.
459 weightsY : `numpy.array` of `float`, optional.
460 Weights of the data in the ordinate axis.
462 confidenceSigma : `float`, optional.
463 Number of sigmas that determine confidence interval for the bootstrap errors.
465 Return
466 ------
467 pFitBootstrap : `list` of `float`
468 List with fitted parameters.
470 pErrBootstrap : `list` of `float`
471 List with errors for fitted parameters.
473 reducedChiSqBootstrap : `float`
474 Reduced chi squared, unweighted if weightsY is not provided.
475 """
476 if weightsY is None:
477 weightsY = np.ones(len(dataX))
479 def errFunc(p, x, y, weightsY):
480 if weightsY is None:
481 weightsY = np.ones(len(x))
482 return (function(p, x) - y)*weightsY
484 # Fit first time
485 pFit, _ = leastsq(errFunc, initialParams, args=(dataX, dataY, weightsY), full_output=0)
487 # Get the stdev of the residuals
488 residuals = errFunc(pFit, dataX, dataY, weightsY)
489 # 100 random data sets are generated and fitted
490 pars = []
491 for i in range(100):
492 randomDelta = np.random.normal(0., np.fabs(residuals), len(dataY))
493 randomDataY = dataY + randomDelta
494 randomFit, _ = leastsq(errFunc, initialParams,
495 args=(dataX, randomDataY, weightsY), full_output=0)
496 pars.append(randomFit)
497 pars = np.array(pars)
498 meanPfit = np.mean(pars, 0)
500 # confidence interval for parameter estimates
501 errPfit = confidenceSigma*np.std(pars, 0)
502 pFitBootstrap = meanPfit
503 pErrBootstrap = errPfit
505 reducedChiSq = calculateWeightedReducedChi2(dataY, function(pFitBootstrap, dataX), weightsY, len(dataY),
506 len(initialParams))
507 return pFitBootstrap, pErrBootstrap, reducedChiSq
510def funcPolynomial(pars, x):
511 """Polynomial function definition
512 Parameters
513 ----------
514 params : `list`
515 Polynomial coefficients. Its length determines the polynomial order.
517 x : `numpy.array`
518 Abscisa array.
520 Returns
521 -------
522 Ordinate array after evaluating polynomial of order len(pars)-1 at `x`.
523 """
524 return poly.polyval(x, [*pars])
527def funcAstier(pars, x):
528 """Single brighter-fatter parameter model for PTC; Equation 16 of Astier+19.
530 Parameters
531 ----------
532 params : `list`
533 Parameters of the model: a00 (brightter-fatter), gain (e/ADU), and noise (e^2).
535 x : `numpy.array`
536 Signal mu (ADU).
538 Returns
539 -------
540 C_00 (variance) in ADU^2.
541 """
542 a00, gain, noise = pars
543 return 0.5/(a00*gain*gain)*(np.exp(2*a00*x*gain)-1) + noise/(gain*gain) # C_00
546def arrangeFlatsByExpTime(exposureList):
547 """Arrange exposures by exposure time.
549 Parameters
550 ----------
551 exposureList : `list`[`lsst.afw.image.exposure.exposure.ExposureF`]
552 Input list of exposures.
554 Returns
555 ------
556 flatsAtExpTime : `dict` [`float`,
557 `list`[`lsst.afw.image.exposure.exposure.ExposureF`]]
558 Dictionary that groups flat-field exposures that have the same
559 exposure time (seconds).
560 """
561 flatsAtExpTime = {}
562 for exp in exposureList:
563 tempFlat = exp
564 expTime = tempFlat.getInfo().getVisitInfo().getExposureTime()
565 listAtExpTime = flatsAtExpTime.setdefault(expTime, [])
566 listAtExpTime.append(tempFlat)
568 return flatsAtExpTime
571def arrangeFlatsByExpId(exposureList):
572 """Arrange exposures by exposure ID.
574 There is no guarantee that this will properly group exposures, but
575 allows a sequence of flats that have different illumination
576 (despite having the same exposure time) to be processed.
578 Parameters
579 ----------
580 exposureList : `list`[`lsst.afw.image.exposure.exposure.ExposureF`]
581 Input list of exposures.
583 Returns
584 ------
585 flatsAtExpId : `dict` [`float`,
586 `list`[`lsst.afw.image.exposure.exposure.ExposureF`]]
587 Dictionary that groups flat-field exposures sequentially by
588 their exposure id.
590 Notes
591 -----
593 This algorithm sorts the input exposures by their exposure id, and
594 then assigns each pair of exposures (exp_j, exp_{j+1}) to pair k,
595 such that 2*k = j, where j is the python index of one of the
596 exposures (starting from zero). By checking for the IndexError
597 while appending, we can ensure that there will only ever be fully
598 populated pairs.
599 """
600 flatsAtExpId = {}
601 sortedExposures = sorted(exposureList, key=lambda exp: exp.getInfo().getVisitInfo().getExposureId())
603 for jPair, exp in enumerate(sortedExposures):
604 if (jPair + 1) % 2:
605 kPair = jPair // 2
606 listAtExpId = flatsAtExpId.setdefault(kPair, [])
607 try:
608 listAtExpId.append(exp)
609 listAtExpId.append(sortedExposures[jPair + 1])
610 except IndexError:
611 pass
613 return flatsAtExpId
616def checkExpLengthEqual(exp1, exp2, v1=None, v2=None, raiseWithMessage=False):
617 """Check the exposure lengths of two exposures are equal.
619 Parameters:
620 -----------
621 exp1 : `lsst.afw.image.exposure.ExposureF`
622 First exposure to check
623 exp2 : `lsst.afw.image.exposure.ExposureF`
624 Second exposure to check
625 v1 : `int` or `str`, optional
626 First visit of the visit pair
627 v2 : `int` or `str`, optional
628 Second visit of the visit pair
629 raiseWithMessage : `bool`
630 If True, instead of returning a bool, raise a RuntimeError if exposure
631 times are not equal, with a message about which visits mismatch if the
632 information is available.
634 Raises:
635 -------
636 RuntimeError
637 Raised if the exposure lengths of the two exposures are not equal
638 """
639 expTime1 = exp1.getInfo().getVisitInfo().getExposureTime()
640 expTime2 = exp2.getInfo().getVisitInfo().getExposureTime()
641 if expTime1 != expTime2:
642 if raiseWithMessage:
643 msg = "Exposure lengths for visit pairs must be equal. " + \
644 "Found %s and %s" % (expTime1, expTime2)
645 if v1 and v2:
646 msg += " for visit pair %s, %s" % (v1, v2)
647 raise RuntimeError(msg)
648 else:
649 return False
650 return True
653def validateIsrConfig(isrTask, mandatory=None, forbidden=None, desirable=None, undesirable=None,
654 checkTrim=True, logName=None):
655 """Check that appropriate ISR settings have been selected for the task.
657 Note that this checks that the task itself is configured correctly rather
658 than checking a config.
660 Parameters
661 ----------
662 isrTask : `lsst.ip.isr.IsrTask`
663 The task whose config is to be validated
665 mandatory : `iterable` of `str`
666 isr steps that must be set to True. Raises if False or missing
668 forbidden : `iterable` of `str`
669 isr steps that must be set to False. Raises if True, warns if missing
671 desirable : `iterable` of `str`
672 isr steps that should probably be set to True. Warns is False, info if
673 missing
675 undesirable : `iterable` of `str`
676 isr steps that should probably be set to False. Warns is True, info if
677 missing
679 checkTrim : `bool`
680 Check to ensure the isrTask's assembly subtask is trimming the images.
681 This is a separate config as it is very ugly to do this within the
682 normal configuration lists as it is an option of a sub task.
684 Raises
685 ------
686 RuntimeError
687 Raised if ``mandatory`` config parameters are False,
688 or if ``forbidden`` parameters are True.
690 TypeError
691 Raised if parameter ``isrTask`` is an invalid type.
693 Notes
694 -----
695 Logs warnings using an isrValidation logger for desirable/undesirable
696 options that are of the wrong polarity or if keys are missing.
697 """
698 if not isinstance(isrTask, ipIsr.IsrTask):
699 raise TypeError(f'Must supply an instance of lsst.ip.isr.IsrTask not {type(isrTask)}')
701 configDict = isrTask.config.toDict()
703 if logName and isinstance(logName, str):
704 log = lsst.log.getLogger(logName)
705 else:
706 log = lsst.log.getLogger("isrValidation")
708 if mandatory:
709 for configParam in mandatory:
710 if configParam not in configDict:
711 raise RuntimeError(f"Mandatory parameter {configParam} not found in the isr configuration.")
712 if configDict[configParam] is False:
713 raise RuntimeError(f"Must set config.isr.{configParam} to True for this task.")
715 if forbidden:
716 for configParam in forbidden:
717 if configParam not in configDict:
718 log.warn(f"Failed to find forbidden key {configParam} in the isr config. The keys in the"
719 " forbidden list should each have an associated Field in IsrConfig:"
720 " check that there is not a typo in this case.")
721 continue
722 if configDict[configParam] is True:
723 raise RuntimeError(f"Must set config.isr.{configParam} to False for this task.")
725 if desirable:
726 for configParam in desirable:
727 if configParam not in configDict:
728 log.info(f"Failed to find key {configParam} in the isr config. You probably want"
729 " to set the equivalent for your obs_package to True.")
730 continue
731 if configDict[configParam] is False:
732 log.warn(f"Found config.isr.{configParam} set to False for this task."
733 " The cp_pipe Config recommends setting this to True.")
734 if undesirable:
735 for configParam in undesirable:
736 if configParam not in configDict:
737 log.info(f"Failed to find key {configParam} in the isr config. You probably want"
738 " to set the equivalent for your obs_package to False.")
739 continue
740 if configDict[configParam] is True:
741 log.warn(f"Found config.isr.{configParam} set to True for this task."
742 " The cp_pipe Config recommends setting this to False.")
744 if checkTrim: # subtask setting, seems non-trivial to combine with above lists
745 if not isrTask.assembleCcd.config.doTrim:
746 raise RuntimeError("Must trim when assembling CCDs. Set config.isr.assembleCcd.doTrim to True")
749def ddict2dict(d):
750 """Convert nested default dictionaries to regular dictionaries.
752 This is needed to prevent yaml persistence issues.
754 Parameters
755 ----------
756 d : `defaultdict`
757 A possibly nested set of `defaultdict`.
759 Returns
760 -------
761 dict : `dict`
762 A possibly nested set of `dict`.
763 """
764 for k, v in d.items():
765 if isinstance(v, dict):
766 d[k] = ddict2dict(v)
767 return dict(d)