23 __all__ = [
'PairedVisitListTaskRunner',
'SingleVisitListTaskRunner',
24 'NonexistentDatasetTaskDataIdContainer',
'parseCmdlineNumberString',
25 'countMaskedPixels',
'checkExpLengthEqual']
29 from scipy.optimize
import leastsq
30 import numpy.polynomial.polynomial
as poly
41 """Calculate weighted reduced chi2.
47 List with measured data.
50 List with modeled data.
52 weightsMeasured : `list`
53 List with weights for the measured data.
56 Number of data points.
59 Number of parameters in the model.
64 redWeightedChi2 : `float`
65 Reduced weighted chi2.
68 wRes = (measured - model)*weightsMeasured
69 return ((wRes*wRes).sum())/(nData-nParsModel)
72 def makeMockFlats(expTime, gain=1.0, readNoiseElectrons=5, fluxElectrons=1000,
73 randomSeedFlat1=1984, randomSeedFlat2=666, powerLawBfParams=[]):
74 """Create a pair or mock flats with isrMock.
79 Exposure time of the flats.
81 gain : `float`, optional
84 readNoiseElectrons : `float`, optional
85 Read noise rms, in electrons.
87 fluxElectrons : `float`, optional
88 Flux of flats, in electrons per second.
90 randomSeedFlat1 : `int`, optional
91 Random seed for the normal distrubutions for the mean signal and noise (flat1).
93 randomSeedFlat2 : `int`, optional
94 Random seed for the normal distrubutions for the mean signal and noise (flat2).
96 powerLawBfParams : `list`, optional
97 Parameters for `galsim.cdmodel.PowerLawCD` to simulate the brightter-fatter effect.
102 flatExp1 : `lsst.afw.image.exposure.exposure.ExposureF`
103 First exposure of flat field pair.
105 flatExp2 : `lsst.afw.image.exposure.exposure.ExposureF`
106 Second exposure of flat field pair.
110 The parameters of `galsim.cdmodel.PowerLawCD` are `n, r0, t0, rx, tx, r, t, alpha`. For more
111 information about their meaning, see the Galsim documentation
112 https://galsim-developers.github.io/GalSim/_build/html/_modules/galsim/cdmodel.html
113 and Gruen+15 (1501.02802).
115 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)
117 flatFlux = fluxElectrons
118 flatMean = flatFlux*expTime
119 readNoise = readNoiseElectrons
121 mockImageConfig = isrMock.IsrMock.ConfigClass()
123 mockImageConfig.flatDrop = 0.99999
124 mockImageConfig.isTrimmed =
True
126 flatExp1 = isrMock.FlatMock(config=mockImageConfig).run()
127 flatExp2 = flatExp1.clone()
128 (shapeY, shapeX) = flatExp1.getDimensions()
129 flatWidth = np.sqrt(flatMean)
131 rng1 = np.random.RandomState(randomSeedFlat1)
132 flatData1 = rng1.normal(flatMean, flatWidth, (shapeX, shapeY)) + rng1.normal(0.0, readNoise,
134 rng2 = np.random.RandomState(randomSeedFlat2)
135 flatData2 = rng2.normal(flatMean, flatWidth, (shapeX, shapeY)) + rng2.normal(0.0, readNoise,
138 if len(powerLawBfParams):
139 if not len(powerLawBfParams) == 8:
140 raise RuntimeError(
"Wrong number of parameters for `galsim.cdmodel.PowerLawCD`. " +
141 f
"Expected 8; passed {len(powerLawBfParams)}.")
142 cd = galsim.cdmodel.PowerLawCD(*powerLawBfParams)
143 tempFlatData1 = galsim.Image(flatData1)
144 temp2FlatData1 = cd.applyForward(tempFlatData1)
146 tempFlatData2 = galsim.Image(flatData2)
147 temp2FlatData2 = cd.applyForward(tempFlatData2)
149 flatExp1.image.array[:] = temp2FlatData1.array/gain
150 flatExp2.image.array[:] = temp2FlatData2.array/gain
152 flatExp1.image.array[:] = flatData1/gain
153 flatExp2.image.array[:] = flatData2/gain
155 return flatExp1, flatExp2
159 """Count the number of pixels in a given mask plane."""
160 maskBit = maskedIm.mask.getPlaneBitMask(maskPlane)
161 nPix = np.where(np.bitwise_and(maskedIm.mask.array, maskBit))[0].flatten().size
166 """Subclass of TaskRunner for handling intrinsically paired visits.
168 This transforms the processed arguments generated by the ArgumentParser
169 into the arguments expected by tasks which take visit pairs for their
172 Such tasks' run() methods tend to take two arguments,
173 one of which is the dataRef (as usual), and the other is the list
174 of visit-pairs, in the form of a list of tuples.
175 This list is supplied on the command line as documented,
176 and this class parses that, and passes the parsed version
179 See pipeBase.TaskRunner for more information.
184 """Parse the visit list and pass through explicitly."""
186 for visitStringPair
in parsedCmd.visitPairs:
187 visitStrings = visitStringPair.split(
",")
188 if len(visitStrings) != 2:
189 raise RuntimeError(
"Found {} visits in {} instead of 2".format(len(visitStrings),
192 visits = [int(visit)
for visit
in visitStrings]
194 raise RuntimeError(
"Could not parse {} as two integer visit numbers".format(visitStringPair))
195 visitPairs.append(visits)
197 return pipeBase.TaskRunner.getTargetList(parsedCmd, visitPairs=visitPairs, **kwargs)
201 """Parse command line numerical expression sytax and return as list of int
203 Take an input of the form "'1..5:2^123..126'" as a string, and return
204 a list of ints as [1, 3, 5, 123, 124, 125, 126]
207 for subString
in inputString.split(
"^"):
208 mat = re.search(
r"^(\d+)\.\.(\d+)(?::(\d+))?$", subString)
210 v1 = int(mat.group(1))
211 v2 = int(mat.group(2))
213 v3 = int(v3)
if v3
else 1
214 for v
in range(v1, v2 + 1, v3):
215 outList.append(int(v))
217 outList.append(int(subString))
222 """Subclass of TaskRunner for tasks requiring a list of visits per dataRef.
224 This transforms the processed arguments generated by the ArgumentParser
225 into the arguments expected by tasks which require a list of visits
226 to be supplied for each dataRef, as is common in `lsst.cp.pipe` code.
228 Such tasks' run() methods tend to take two arguments,
229 one of which is the dataRef (as usual), and the other is the list
231 This list is supplied on the command line as documented,
232 and this class parses that, and passes the parsed version
235 See `lsst.pipe.base.TaskRunner` for more information.
240 """Parse the visit list and pass through explicitly."""
243 assert len(parsedCmd.visitList) == 1,
'visitList parsing assumptions violated'
246 return pipeBase.TaskRunner.getTargetList(parsedCmd, visitList=visits, **kwargs)
250 """A DataIdContainer for the tasks for which the output does
254 """Compute refList based on idList.
256 This method must be defined as the dataset does not exist before this
262 Results of parsing the command-line.
266 Not called if ``add_id_argument`` called
267 with ``doMakeDataRefList=False``.
268 Note that this is almost a copy-and-paste of the vanilla
269 implementation, but without checking if the datasets already exist,
270 as this task exists to make them.
272 if self.datasetType
is None:
273 raise RuntimeError(
"Must call setDatasetType first")
274 butler = namespace.butler
275 for dataId
in self.idList:
276 refList = list(butler.subset(datasetType=self.datasetType, level=self.level, dataId=dataId))
280 namespace.log.warn(
"No data found for dataId=%s", dataId)
282 self.refList += refList
286 """Do a fit and estimate the parameter errors using using scipy.optimize.leastq.
288 optimize.leastsq returns the fractional covariance matrix. To estimate the
289 standard deviation of the fit parameters, multiply the entries of this matrix
290 by the unweighted reduced chi squared and take the square root of the diagonal elements.
294 initialParams : `list` of `float`
295 initial values for fit parameters. For ptcFitType=POLYNOMIAL, its length
296 determines the degree of the polynomial.
298 dataX : `numpy.array` of `float`
299 Data in the abscissa axis.
301 dataY : `numpy.array` of `float`
302 Data in the ordinate axis.
304 function : callable object (function)
305 Function to fit the data with.
309 pFitSingleLeastSquares : `list` of `float`
310 List with fitted parameters.
312 pErrSingleLeastSquares : `list` of `float`
313 List with errors for fitted parameters.
315 reducedChiSqSingleLeastSquares : `float`
316 Unweighted reduced chi squared
319 def errFunc(p, x, y):
320 return function(p, x) - y
322 pFit, pCov, infoDict, errMessage, success = leastsq(errFunc, initialParams,
323 args=(dataX, dataY), full_output=1, epsfcn=0.0001)
325 if (len(dataY) > len(initialParams))
and pCov
is not None:
326 reducedChiSq = (errFunc(pFit, dataX, dataY)**2).sum()/(len(dataY)-len(initialParams))
329 pCov = np.zeros((len(initialParams), len(initialParams)))
331 reducedChiSq = np.inf
334 for i
in range(len(pFit)):
335 errorVec.append(np.fabs(pCov[i][i])**0.5)
337 pFitSingleLeastSquares = pFit
338 pErrSingleLeastSquares = np.array(errorVec)
340 return pFitSingleLeastSquares, pErrSingleLeastSquares, reducedChiSq
343 def fitBootstrap(initialParams, dataX, dataY, function, confidenceSigma=1.):
344 """Do a fit using least squares and bootstrap to estimate parameter errors.
346 The bootstrap error bars are calculated by fitting 100 random data sets.
350 initialParams : `list` of `float`
351 initial values for fit parameters. For ptcFitType=POLYNOMIAL, its length
352 determines the degree of the polynomial.
354 dataX : `numpy.array` of `float`
355 Data in the abscissa axis.
357 dataY : `numpy.array` of `float`
358 Data in the ordinate axis.
360 function : callable object (function)
361 Function to fit the data with.
363 confidenceSigma : `float`
364 Number of sigmas that determine confidence interval for the bootstrap errors.
368 pFitBootstrap : `list` of `float`
369 List with fitted parameters.
371 pErrBootstrap : `list` of `float`
372 List with errors for fitted parameters.
374 reducedChiSqBootstrap : `float`
378 def errFunc(p, x, y):
379 return function(p, x) - y
382 pFit, _ = leastsq(errFunc, initialParams, args=(dataX, dataY), full_output=0)
385 residuals = errFunc(pFit, dataX, dataY)
386 sigmaErrTotal = np.std(residuals)
391 randomDelta = np.random.normal(0., sigmaErrTotal, len(dataY))
392 randomDataY = dataY + randomDelta
393 randomFit, _ = leastsq(errFunc, initialParams,
394 args=(dataX, randomDataY), full_output=0)
395 pars.append(randomFit)
396 pars = np.array(pars)
397 meanPfit = np.mean(pars, 0)
400 nSigma = confidenceSigma
401 errPfit = nSigma*np.std(pars, 0)
402 pFitBootstrap = meanPfit
403 pErrBootstrap = errPfit
405 reducedChiSq = (errFunc(pFitBootstrap, dataX, dataY)**2).sum()/(len(dataY)-len(initialParams))
406 return pFitBootstrap, pErrBootstrap, reducedChiSq
410 """Polynomial function definition
414 Polynomial coefficients. Its length determines the polynomial order.
421 C_00 (variance) in ADU^2.
423 return poly.polyval(x, [*pars])
427 """Single brighter-fatter parameter model for PTC; Equation 16 of Astier+19.
432 Parameters of the model: a00 (brightter-fatter), gain (e/ADU), and noise (e^2).
439 C_00 (variance) in ADU^2.
441 a00, gain, noise = pars
442 return 0.5/(a00*gain*gain)*(np.exp(2*a00*x*gain)-1) + noise/(gain*gain)
446 """Check the exposure lengths of two exposures are equal.
450 exp1 : `lsst.afw.image.exposure.ExposureF`
451 First exposure to check
452 exp2 : `lsst.afw.image.exposure.ExposureF`
453 Second exposure to check
454 v1 : `int` or `str`, optional
455 First visit of the visit pair
456 v2 : `int` or `str`, optional
457 Second visit of the visit pair
458 raiseWithMessage : `bool`
459 If True, instead of returning a bool, raise a RuntimeError if exposure
460 times are not equal, with a message about which visits mismatch if the
461 information is available.
466 Raised if the exposure lengths of the two exposures are not equal
468 expTime1 = exp1.getInfo().getVisitInfo().getExposureTime()
469 expTime2 = exp2.getInfo().getVisitInfo().getExposureTime()
470 if expTime1 != expTime2:
472 msg =
"Exposure lengths for visit pairs must be equal. " + \
473 "Found %s and %s" % (expTime1, expTime2)
475 msg +=
" for visit pair %s, %s" % (v1, v2)
476 raise RuntimeError(msg)
482 def validateIsrConfig(isrTask, mandatory=None, forbidden=None, desirable=None, undesirable=None,
483 checkTrim=True, logName=None):
484 """Check that appropriate ISR settings have been selected for the task.
486 Note that this checks that the task itself is configured correctly rather
487 than checking a config.
491 isrTask : `lsst.ip.isr.IsrTask`
492 The task whose config is to be validated
494 mandatory : `iterable` of `str`
495 isr steps that must be set to True. Raises if False or missing
497 forbidden : `iterable` of `str`
498 isr steps that must be set to False. Raises if True, warns if missing
500 desirable : `iterable` of `str`
501 isr steps that should probably be set to True. Warns is False, info if
504 undesirable : `iterable` of `str`
505 isr steps that should probably be set to False. Warns is True, info if
509 Check to ensure the isrTask's assembly subtask is trimming the images.
510 This is a separate config as it is very ugly to do this within the
511 normal configuration lists as it is an option of a sub task.
516 Raised if ``mandatory`` config parameters are False,
517 or if ``forbidden`` parameters are True.
520 Raised if parameter ``isrTask`` is an invalid type.
524 Logs warnings using an isrValidation logger for desirable/undesirable
525 options that are of the wrong polarity or if keys are missing.
527 if not isinstance(isrTask, ipIsr.IsrTask):
528 raise TypeError(f
'Must supply an instance of lsst.ip.isr.IsrTask not {type(isrTask)}')
530 configDict = isrTask.config.toDict()
532 if logName
and isinstance(logName, str):
533 log = lsst.log.getLogger(logName)
535 log = lsst.log.getLogger(
"isrValidation")
538 for configParam
in mandatory:
539 if configParam
not in configDict:
540 raise RuntimeError(f
"Mandatory parameter {configParam} not found in the isr configuration.")
541 if configDict[configParam]
is False:
542 raise RuntimeError(f
"Must set config.isr.{configParam} to True for this task.")
545 for configParam
in forbidden:
546 if configParam
not in configDict:
547 log.warn(f
"Failed to find forbidden key {configParam} in the isr config. The keys in the"
548 " forbidden list should each have an associated Field in IsrConfig:"
549 " check that there is not a typo in this case.")
551 if configDict[configParam]
is True:
552 raise RuntimeError(f
"Must set config.isr.{configParam} to False for this task.")
555 for configParam
in desirable:
556 if configParam
not in configDict:
557 log.info(f
"Failed to find key {configParam} in the isr config. You probably want" +
558 " to set the equivalent for your obs_package to True.")
560 if configDict[configParam]
is False:
561 log.warn(f
"Found config.isr.{configParam} set to False for this task." +
562 " The cp_pipe Config recommends setting this to True.")
564 for configParam
in undesirable:
565 if configParam
not in configDict:
566 log.info(f
"Failed to find key {configParam} in the isr config. You probably want" +
567 " to set the equivalent for your obs_package to False.")
569 if configDict[configParam]
is True:
570 log.warn(f
"Found config.isr.{configParam} set to True for this task." +
571 " The cp_pipe Config recommends setting this to False.")
574 if not isrTask.assembleCcd.config.doTrim:
575 raise RuntimeError(
"Must trim when assembling CCDs. Set config.isr.assembleCcd.doTrim to True")