Coverage for python/lsst/cp/pipe/utils.py : 10%

Hot-keys on this page
r m x p toggle line displays
j k next/prev highlighted chunk
0 (zero) top of page
1 (one) first highlighted chunk
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']
27import re
28import numpy as np
29from scipy.optimize import leastsq
30import numpy.polynomial.polynomial as poly
32import lsst.pipe.base as pipeBase
33import lsst.ip.isr as ipIsr
34from lsst.ip.isr import isrMock
35import lsst.log
37import galsim
40def calculateWeightedReducedChi2(measured, model, weightsMeasured, nData, nParsModel):
41 """Calculate weighted reduced chi2.
43 Parameters
44 ----------
46 measured : `list`
47 List with measured data.
49 model : `list`
50 List with modeled data.
52 weightsMeasured : `list`
53 List with weights for the measured data.
55 nData : `int`
56 Number of data points.
58 nParsModel : `int`
59 Number of parameters in the model.
61 Returns
62 -------
64 redWeightedChi2 : `float`
65 Reduced weighted chi2.
66 """
68 wRes = (measured - model)*weightsMeasured
69 return ((wRes*wRes).sum())/(nData-nParsModel)
72def makeMockFlats(expTime, gain=1.0, readNoiseElectrons=5, fluxElectrons=1000,
73 randomSeedFlat1=1984, randomSeedFlat2=666, powerLawBfParams=[]):
74 """Create a pair or mock flats with isrMock.
76 Parameters
77 ----------
78 expTime : `float`
79 Exposure time of the flats.
81 gain : `float`, optional
82 Gain, in e/ADU.
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.
99 Returns
100 -------
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.
108 Notes
109 -----
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)
116 """
117 flatFlux = fluxElectrons # e/s
118 flatMean = flatFlux*expTime # e
119 readNoise = readNoiseElectrons # e
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,
133 (shapeX, shapeY))
134 rng2 = np.random.RandomState(randomSeedFlat2)
135 flatData2 = rng2.normal(flatMean, flatWidth, (shapeX, shapeY)) + rng2.normal(0.0, readNoise,
136 (shapeX, shapeY))
137 # Simulate BF with power law model in galsim
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 # ADU
150 flatExp2.image.array[:] = temp2FlatData2.array/gain # ADU
151 else:
152 flatExp1.image.array[:] = flatData1/gain # ADU
153 flatExp2.image.array[:] = flatData2/gain # ADU
155 return flatExp1, flatExp2
158def countMaskedPixels(maskedIm, maskPlane):
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
162 return nPix
165class PairedVisitListTaskRunner(pipeBase.TaskRunner):
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
170 run() methods.
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
177 to the run() method.
179 See pipeBase.TaskRunner for more information.
180 """
182 @staticmethod
183 def getTargetList(parsedCmd, **kwargs):
184 """Parse the visit list and pass through explicitly."""
185 visitPairs = []
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),
190 visitStringPair))
191 try:
192 visits = [int(visit) for visit in visitStrings]
193 except Exception:
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)
200def parseCmdlineNumberString(inputString):
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]
205 """
206 outList = []
207 for subString in inputString.split("^"):
208 mat = re.search(r"^(\d+)\.\.(\d+)(?::(\d+))?$", subString)
209 if mat:
210 v1 = int(mat.group(1))
211 v2 = int(mat.group(2))
212 v3 = mat.group(3)
213 v3 = int(v3) if v3 else 1
214 for v in range(v1, v2 + 1, v3):
215 outList.append(int(v))
216 else:
217 outList.append(int(subString))
218 return outList
221class SingleVisitListTaskRunner(pipeBase.TaskRunner):
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
230 of visits.
231 This list is supplied on the command line as documented,
232 and this class parses that, and passes the parsed version
233 to the run() method.
235 See `lsst.pipe.base.TaskRunner` for more information.
236 """
238 @staticmethod
239 def getTargetList(parsedCmd, **kwargs):
240 """Parse the visit list and pass through explicitly."""
241 # if this has been pre-parsed and therefore doesn't have length of one
242 # then something has gone wrong, so execution should stop here.
243 assert len(parsedCmd.visitList) == 1, 'visitList parsing assumptions violated'
244 visits = parseCmdlineNumberString(parsedCmd.visitList[0])
246 return pipeBase.TaskRunner.getTargetList(parsedCmd, visitList=visits, **kwargs)
249class NonexistentDatasetTaskDataIdContainer(pipeBase.DataIdContainer):
250 """A DataIdContainer for the tasks for which the output does
251 not yet exist."""
253 def makeDataRefList(self, namespace):
254 """Compute refList based on idList.
256 This method must be defined as the dataset does not exist before this
257 task is run.
259 Parameters
260 ----------
261 namespace
262 Results of parsing the command-line.
264 Notes
265 -----
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.
271 """
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))
277 # exclude nonexistent data
278 # this is a recursive test, e.g. for the sake of "raw" data
279 if not refList:
280 namespace.log.warn("No data found for dataId=%s", dataId)
281 continue
282 self.refList += refList
285def fitLeastSq(initialParams, dataX, dataY, function):
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.
292 Parameters
293 ----------
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.
307 Return
308 ------
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
317 """
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))
327 pCov *= reducedChiSq
328 else:
329 pCov = np.zeros((len(initialParams), len(initialParams)))
330 pCov[:, :] = np.inf
331 reducedChiSq = np.inf
333 errorVec = []
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
343def 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.
348 Parameters
349 ----------
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.
366 Return
367 ------
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`
375 Reduced chi squared.
376 """
378 def errFunc(p, x, y):
379 return function(p, x) - y
381 # Fit first time
382 pFit, _ = leastsq(errFunc, initialParams, args=(dataX, dataY), full_output=0)
384 # Get the stdev of the residuals
385 residuals = errFunc(pFit, dataX, dataY)
386 sigmaErrTotal = np.std(residuals)
388 # 100 random data sets are generated and fitted
389 pars = []
390 for i in range(100):
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)
399 # confidence interval for parameter estimates
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
409def funcPolynomial(pars, x):
410 """Polynomial function definition
411 Parameters
412 ----------
413 params : `list`
414 Polynomial coefficients. Its length determines the polynomial order.
416 x : `numpy.array`
417 Signal mu (ADU).
419 Returns
420 -------
421 C_00 (variance) in ADU^2.
422 """
423 return poly.polyval(x, [*pars]) # C_00
426def funcAstier(pars, x):
427 """Single brighter-fatter parameter model for PTC; Equation 16 of Astier+19.
429 Parameters
430 ----------
431 params : `list`
432 Parameters of the model: a00 (brightter-fatter), gain (e/ADU), and noise (e^2).
434 x : `numpy.array`
435 Signal mu (ADU).
437 Returns
438 -------
439 C_00 (variance) in ADU^2.
440 """
441 a00, gain, noise = pars
442 return 0.5/(a00*gain*gain)*(np.exp(2*a00*x*gain)-1) + noise/(gain*gain) # C_00
445def checkExpLengthEqual(exp1, exp2, v1=None, v2=None, raiseWithMessage=False):
446 """Check the exposure lengths of two exposures are equal.
448 Parameters:
449 -----------
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.
463 Raises:
464 -------
465 RuntimeError
466 Raised if the exposure lengths of the two exposures are not equal
467 """
468 expTime1 = exp1.getInfo().getVisitInfo().getExposureTime()
469 expTime2 = exp2.getInfo().getVisitInfo().getExposureTime()
470 if expTime1 != expTime2:
471 if raiseWithMessage:
472 msg = "Exposure lengths for visit pairs must be equal. " + \
473 "Found %s and %s" % (expTime1, expTime2)
474 if v1 and v2:
475 msg += " for visit pair %s, %s" % (v1, v2)
476 raise RuntimeError(msg)
477 else:
478 return False
479 return True
482def 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.
489 Parameters
490 ----------
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
502 missing
504 undesirable : `iterable` of `str`
505 isr steps that should probably be set to False. Warns is True, info if
506 missing
508 checkTrim : `bool`
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.
513 Raises
514 ------
515 RuntimeError
516 Raised if ``mandatory`` config parameters are False,
517 or if ``forbidden`` parameters are True.
519 TypeError
520 Raised if parameter ``isrTask`` is an invalid type.
522 Notes
523 -----
524 Logs warnings using an isrValidation logger for desirable/undesirable
525 options that are of the wrong polarity or if keys are missing.
526 """
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)
534 else:
535 log = lsst.log.getLogger("isrValidation")
537 if mandatory:
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.")
544 if forbidden:
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.")
550 continue
551 if configDict[configParam] is True:
552 raise RuntimeError(f"Must set config.isr.{configParam} to False for this task.")
554 if desirable:
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.")
559 continue
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.")
563 if undesirable:
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.")
568 continue
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.")
573 if checkTrim: # subtask setting, seems non-trivial to combine with above lists
574 if not isrTask.assembleCcd.config.doTrim:
575 raise RuntimeError("Must trim when assembling CCDs. Set config.isr.assembleCcd.doTrim to True")