Coverage for python/lsst/cp/pipe/ptc/cpExtractPtcTask.py: 11%
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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#
22import numpy as np
23from lmfit.models import GaussianModel
24import scipy.stats
25import warnings
27import lsst.afw.math as afwMath
28import lsst.pex.config as pexConfig
29import lsst.pipe.base as pipeBase
30from lsst.cp.pipe.utils import (arrangeFlatsByExpTime, arrangeFlatsByExpId,
31 arrangeFlatsByExpFlux, sigmaClipCorrection,
32 CovFastFourierTransform)
34import lsst.pipe.base.connectionTypes as cT
36from lsst.ip.isr import PhotonTransferCurveDataset
37from lsst.ip.isr import IsrTask
39__all__ = ['PhotonTransferCurveExtractConfig', 'PhotonTransferCurveExtractTask']
42class PhotonTransferCurveExtractConnections(pipeBase.PipelineTaskConnections,
43 dimensions=("instrument", "detector")):
45 inputExp = cT.Input(
46 name="ptcInputExposurePairs",
47 doc="Input post-ISR processed exposure pairs (flats) to"
48 "measure covariances from.",
49 storageClass="Exposure",
50 dimensions=("instrument", "exposure", "detector"),
51 multiple=True,
52 deferLoad=True,
53 )
54 inputPhotodiodeData = cT.Input(
55 name="photodiode",
56 doc="Photodiode readings data.",
57 storageClass="IsrCalib",
58 dimensions=("instrument", "exposure"),
59 multiple=True,
60 deferLoad=True,
61 )
62 taskMetadata = cT.Input(
63 name="isr_metadata",
64 doc="Input task metadata to extract statistics from.",
65 storageClass="TaskMetadata",
66 dimensions=("instrument", "exposure", "detector"),
67 multiple=True,
68 )
69 outputCovariances = cT.Output(
70 name="ptcCovariances",
71 doc="Extracted flat (co)variances.",
72 storageClass="PhotonTransferCurveDataset",
73 dimensions=("instrument", "exposure", "detector"),
74 isCalibration=True,
75 multiple=True,
76 )
78 def __init__(self, *, config=None):
79 if not config.doExtractPhotodiodeData:
80 del self.inputPhotodiodeData
83class PhotonTransferCurveExtractConfig(pipeBase.PipelineTaskConfig,
84 pipelineConnections=PhotonTransferCurveExtractConnections):
85 """Configuration for the measurement of covariances from flats.
86 """
87 matchExposuresType = pexConfig.ChoiceField(
88 dtype=str,
89 doc="Match input exposures by time, flux, or expId",
90 default='TIME',
91 allowed={
92 "TIME": "Match exposures by exposure time.",
93 "FLUX": "Match exposures by target flux. Use header keyword"
94 " in matchExposuresByFluxKeyword to find the flux.",
95 "EXPID": "Match exposures by exposure ID."
96 }
97 )
98 matchExposuresByFluxKeyword = pexConfig.Field(
99 dtype=str,
100 doc="Header keyword for flux if matchExposuresType is FLUX.",
101 default='CCOBFLUX',
102 )
103 maximumRangeCovariancesAstier = pexConfig.Field(
104 dtype=int,
105 doc="Maximum range of covariances as in Astier+19",
106 default=8,
107 )
108 binSize = pexConfig.Field(
109 dtype=int,
110 doc="Bin the image by this factor in both dimensions.",
111 default=1,
112 )
113 minMeanSignal = pexConfig.DictField(
114 keytype=str,
115 itemtype=float,
116 doc="Minimum values (inclusive) of mean signal (in ADU) per amp to use."
117 " The same cut is applied to all amps if this parameter [`dict`] is passed as "
118 " {'ALL_AMPS': value}",
119 default={'ALL_AMPS': 0.0},
120 deprecated="This config has been moved to cpSolvePtcTask, and will be removed after v26.",
121 )
122 maxMeanSignal = pexConfig.DictField(
123 keytype=str,
124 itemtype=float,
125 doc="Maximum values (inclusive) of mean signal (in ADU) below which to consider, per amp."
126 " The same cut is applied to all amps if this dictionary is of the form"
127 " {'ALL_AMPS': value}",
128 default={'ALL_AMPS': 1e6},
129 deprecated="This config has been moved to cpSolvePtcTask, and will be removed after v26.",
130 )
131 maskNameList = pexConfig.ListField(
132 dtype=str,
133 doc="Mask list to exclude from statistics calculations.",
134 default=['SUSPECT', 'BAD', 'NO_DATA', 'SAT'],
135 )
136 nSigmaClipPtc = pexConfig.Field(
137 dtype=float,
138 doc="Sigma cut for afwMath.StatisticsControl()",
139 default=5.5,
140 )
141 nIterSigmaClipPtc = pexConfig.Field(
142 dtype=int,
143 doc="Number of sigma-clipping iterations for afwMath.StatisticsControl()",
144 default=3,
145 )
146 minNumberGoodPixelsForCovariance = pexConfig.Field(
147 dtype=int,
148 doc="Minimum number of acceptable good pixels per amp to calculate the covariances (via FFT or"
149 " direclty).",
150 default=10000,
151 )
152 thresholdDiffAfwVarVsCov00 = pexConfig.Field(
153 dtype=float,
154 doc="If the absolute fractional differece between afwMath.VARIANCECLIP and Cov00 "
155 "for a region of a difference image is greater than this threshold (percentage), "
156 "a warning will be issued.",
157 default=1.,
158 )
159 detectorMeasurementRegion = pexConfig.ChoiceField(
160 dtype=str,
161 doc="Region of each exposure where to perform the calculations (amplifier or full image).",
162 default='AMP',
163 allowed={
164 "AMP": "Amplifier of the detector.",
165 "FULL": "Full image."
166 }
167 )
168 numEdgeSuspect = pexConfig.Field(
169 dtype=int,
170 doc="Number of edge pixels to be flagged as untrustworthy.",
171 default=0,
172 )
173 edgeMaskLevel = pexConfig.ChoiceField(
174 dtype=str,
175 doc="Mask edge pixels in which coordinate frame: DETECTOR or AMP?",
176 default="DETECTOR",
177 allowed={
178 'DETECTOR': 'Mask only the edges of the full detector.',
179 'AMP': 'Mask edges of each amplifier.',
180 },
181 )
182 doGain = pexConfig.Field(
183 dtype=bool,
184 doc="Calculate a gain per input flat pair.",
185 default=True,
186 )
187 gainCorrectionType = pexConfig.ChoiceField(
188 dtype=str,
189 doc="Correction type for the gain.",
190 default='FULL',
191 allowed={
192 'NONE': 'No correction.',
193 'SIMPLE': 'First order correction.',
194 'FULL': 'Second order correction.'
195 }
196 )
197 ksHistNBins = pexConfig.Field(
198 dtype=int,
199 doc="Number of bins for the KS test histogram.",
200 default=100,
201 )
202 ksHistLimitMultiplier = pexConfig.Field(
203 dtype=float,
204 doc="Number of sigma (as predicted from the mean value) to compute KS test histogram.",
205 default=8.0,
206 )
207 ksHistMinDataValues = pexConfig.Field(
208 dtype=int,
209 doc="Minimum number of good data values to compute KS test histogram.",
210 default=100,
211 )
212 auxiliaryHeaderKeys = pexConfig.ListField(
213 dtype=str,
214 doc="Auxiliary header keys to store with the PTC dataset.",
215 default=[],
216 )
217 doExtractPhotodiodeData = pexConfig.Field(
218 dtype=bool,
219 doc="Extract photodiode data?",
220 default=False,
221 )
222 photodiodeIntegrationMethod = pexConfig.ChoiceField(
223 dtype=str,
224 doc="Integration method for photodiode monitoring data.",
225 default="CHARGE_SUM",
226 allowed={
227 "DIRECT_SUM": ("Use numpy's trapz integrator on all photodiode "
228 "readout entries"),
229 "TRIMMED_SUM": ("Use numpy's trapz integrator, clipping the "
230 "leading and trailing entries, which are "
231 "nominally at zero baseline level."),
232 "CHARGE_SUM": ("Treat the current values as integrated charge "
233 "over the sampling interval and simply sum "
234 "the values, after subtracting a baseline level."),
235 },
236 )
237 photodiodeCurrentScale = pexConfig.Field(
238 dtype=float,
239 doc="Scale factor to apply to photodiode current values for the "
240 "``CHARGE_SUM`` integration method.",
241 default=-1.0,
242 )
245class PhotonTransferCurveExtractTask(pipeBase.PipelineTask):
246 """Task to measure covariances from flat fields.
248 This task receives as input a list of flat-field images
249 (flats), and sorts these flats in pairs taken at the
250 same time (the task will raise if there is one one flat
251 at a given exposure time, and it will discard extra flats if
252 there are more than two per exposure time). This task measures
253 the mean, variance, and covariances from a region (e.g.,
254 an amplifier) of the difference image of the two flats with
255 the same exposure time (alternatively, all input images could have
256 the same exposure time but their flux changed).
258 The variance is calculated via afwMath, and the covariance
259 via the methods in Astier+19 (appendix A). In theory,
260 var = covariance[0,0]. This should be validated, and in the
261 future, we may decide to just keep one (covariance).
262 At this moment, if the two values differ by more than the value
263 of `thresholdDiffAfwVarVsCov00` (default: 1%), a warning will
264 be issued.
266 The measured covariances at a given exposure time (along with
267 other quantities such as the mean) are stored in a PTC dataset
268 object (`~lsst.ip.isr.PhotonTransferCurveDataset`), which gets
269 partially filled at this stage (the remainder of the attributes
270 of the dataset will be filled after running the second task of
271 the PTC-measurement pipeline, `~PhotonTransferCurveSolveTask`).
273 The number of partially-filled
274 `~lsst.ip.isr.PhotonTransferCurveDataset` objects will be less
275 than the number of input exposures because the task combines
276 input flats in pairs. However, it is required at this moment
277 that the number of input dimensions matches
278 bijectively the number of output dimensions. Therefore, a number
279 of "dummy" PTC datasets are inserted in the output list. This
280 output list will then be used as input of the next task in the
281 PTC-measurement pipeline, `PhotonTransferCurveSolveTask`,
282 which will assemble the multiple `PhotonTransferCurveDataset`
283 objects into a single one in order to fit the measured covariances
284 as a function of flux to one of three models
285 (see `PhotonTransferCurveSolveTask` for details).
287 Reference: Astier+19: "The Shape of the Photon Transfer Curve of CCD
288 sensors", arXiv:1905.08677.
289 """
291 ConfigClass = PhotonTransferCurveExtractConfig
292 _DefaultName = 'cpPtcExtract'
294 def runQuantum(self, butlerQC, inputRefs, outputRefs):
295 """Ensure that the input and output dimensions are passed along.
297 Parameters
298 ----------
299 butlerQC : `~lsst.daf.butler.butlerQuantumContext.ButlerQuantumContext`
300 Butler to operate on.
301 inputRefs : `~lsst.pipe.base.connections.InputQuantizedConnection`
302 Input data refs to load.
303 ouptutRefs : `~lsst.pipe.base.connections.OutputQuantizedConnection`
304 Output data refs to persist.
305 """
306 inputs = butlerQC.get(inputRefs)
307 # Ids of input list of exposure references
308 # (deferLoad=True in the input connections)
309 inputs['inputDims'] = [expRef.datasetRef.dataId['exposure'] for expRef in inputRefs.inputExp]
311 # Dictionary, keyed by expTime (or expFlux or expId), with tuples
312 # containing flat exposures and their IDs.
313 matchType = self.config.matchExposuresType
314 if matchType == 'TIME':
315 inputs['inputExp'] = arrangeFlatsByExpTime(inputs['inputExp'], inputs['inputDims'])
316 elif matchType == 'FLUX':
317 inputs['inputExp'] = arrangeFlatsByExpFlux(inputs['inputExp'], inputs['inputDims'],
318 self.config.matchExposuresByFluxKeyword)
319 else:
320 inputs['inputExp'] = arrangeFlatsByExpId(inputs['inputExp'], inputs['inputDims'])
322 outputs = self.run(**inputs)
323 outputs = self._guaranteeOutputs(inputs['inputDims'], outputs, outputRefs)
324 butlerQC.put(outputs, outputRefs)
326 def _guaranteeOutputs(self, inputDims, outputs, outputRefs):
327 """Ensure that all outputRefs have a matching output, and if they do
328 not, fill the output with dummy PTC datasets.
330 Parameters
331 ----------
332 inputDims : `dict` [`str`, `int`]
333 Input exposure dimensions.
334 outputs : `lsst.pipe.base.Struct`
335 Outputs from the ``run`` method. Contains the entry:
337 ``outputCovariances``
338 Output PTC datasets (`list` [`lsst.ip.isr.IsrCalib`])
339 outputRefs : `~lsst.pipe.base.connections.OutputQuantizedConnection`
340 Container with all of the outputs expected to be generated.
342 Returns
343 -------
344 outputs : `lsst.pipe.base.Struct`
345 Dummy dataset padded version of the input ``outputs`` with
346 the same entries.
347 """
348 newCovariances = []
349 for ref in outputRefs.outputCovariances:
350 outputExpId = ref.dataId['exposure']
351 if outputExpId in inputDims:
352 entry = inputDims.index(outputExpId)
353 newCovariances.append(outputs.outputCovariances[entry])
354 else:
355 newPtc = PhotonTransferCurveDataset(['no amp'], 'DUMMY', 1)
356 newPtc.setAmpValuesPartialDataset('no amp')
357 newCovariances.append(newPtc)
358 return pipeBase.Struct(outputCovariances=newCovariances)
360 def run(self, inputExp, inputDims, taskMetadata, inputPhotodiodeData=None):
362 """Measure covariances from difference of flat pairs
364 Parameters
365 ----------
366 inputExp : `dict` [`float`, `list`
367 [`~lsst.pipe.base.connections.DeferredDatasetRef`]]
368 Dictionary that groups references to flat-field exposures that
369 have the same exposure time (seconds), or that groups them
370 sequentially by their exposure id.
371 inputDims : `list`
372 List of exposure IDs.
373 taskMetadata : `list` [`lsst.pipe.base.TaskMetadata`]
374 List of exposures metadata from ISR.
375 inputPhotodiodeData : `dict` [`str`, `lsst.ip.isr.PhotodiodeCalib`]
376 Photodiode readings data (optional).
378 Returns
379 -------
380 results : `lsst.pipe.base.Struct`
381 The resulting Struct contains:
383 ``outputCovariances``
384 A list containing the per-pair PTC measurements (`list`
385 [`lsst.ip.isr.PhotonTransferCurveDataset`])
386 """
387 # inputExp.values() returns a view, which we turn into a list. We then
388 # access the first exposure-ID tuple to get the detector.
389 # The first "get()" retrieves the exposure from the exposure reference.
390 detector = list(inputExp.values())[0][0][0].get(component='detector')
391 detNum = detector.getId()
392 amps = detector.getAmplifiers()
393 ampNames = [amp.getName() for amp in amps]
395 # Each amp may have a different min and max ADU signal
396 # specified in the config.
397 maxMeanSignalDict = {ampName: 1e6 for ampName in ampNames}
398 minMeanSignalDict = {ampName: 0.0 for ampName in ampNames}
399 for ampName in ampNames:
400 if 'ALL_AMPS' in self.config.maxMeanSignal:
401 maxMeanSignalDict[ampName] = self.config.maxMeanSignal['ALL_AMPS']
402 elif ampName in self.config.maxMeanSignal:
403 maxMeanSignalDict[ampName] = self.config.maxMeanSignal[ampName]
405 if 'ALL_AMPS' in self.config.minMeanSignal:
406 minMeanSignalDict[ampName] = self.config.minMeanSignal['ALL_AMPS']
407 elif ampName in self.config.minMeanSignal:
408 minMeanSignalDict[ampName] = self.config.minMeanSignal[ampName]
409 # These are the column names for `tupleRows` below.
410 tags = [('mu', '<f8'), ('afwVar', '<f8'), ('i', '<i8'), ('j', '<i8'), ('var', '<f8'),
411 ('cov', '<f8'), ('npix', '<i8'), ('ext', '<i8'), ('expTime', '<f8'), ('ampName', '<U3')]
412 # Create a dummy ptcDataset. Dummy datasets will be
413 # used to ensure that the number of output and input
414 # dimensions match.
415 dummyPtcDataset = PhotonTransferCurveDataset(ampNames, 'DUMMY',
416 self.config.maximumRangeCovariancesAstier)
417 for ampName in ampNames:
418 dummyPtcDataset.setAmpValuesPartialDataset(ampName)
420 # Extract the photodiode data if requested.
421 if self.config.doExtractPhotodiodeData:
422 # Compute the photodiode integrals once, at the start.
423 monitorDiodeCharge = {}
424 for handle in inputPhotodiodeData:
425 expId = handle.dataId['exposure']
426 pdCalib = handle.get()
427 pdCalib.integrationMethod = self.config.photodiodeIntegrationMethod
428 pdCalib.currentScale = self.config.photodiodeCurrentScale
429 monitorDiodeCharge[expId] = pdCalib.integrate()
431 # Get read noise. Try from the exposure, then try
432 # taskMetadata. This adds a get() for the exposures.
433 readNoiseLists = {}
434 for pairIndex, expRefs in inputExp.items():
435 # This yields an index (exposure_time, seq_num, or flux)
436 # and a pair of references at that index.
437 for expRef, expId in expRefs:
438 # This yields an exposure ref and an exposureId.
439 exposureMetadata = expRef.get(component="metadata")
440 metadataIndex = inputDims.index(expId)
441 thisTaskMetadata = taskMetadata[metadataIndex]
443 for ampName in ampNames:
444 if ampName not in readNoiseLists:
445 readNoiseLists[ampName] = [self.getReadNoise(exposureMetadata,
446 thisTaskMetadata, ampName)]
447 else:
448 readNoiseLists[ampName].append(self.getReadNoise(exposureMetadata,
449 thisTaskMetadata, ampName))
451 readNoiseDict = {ampName: 0.0 for ampName in ampNames}
452 for ampName in ampNames:
453 # Take median read noise value
454 readNoiseDict[ampName] = np.nanmedian(readNoiseLists[ampName])
456 # Output list with PTC datasets.
457 partialPtcDatasetList = []
458 # The number of output references needs to match that of input
459 # references: initialize outputlist with dummy PTC datasets.
460 for i in range(len(inputDims)):
461 partialPtcDatasetList.append(dummyPtcDataset)
463 if self.config.numEdgeSuspect > 0:
464 isrTask = IsrTask()
465 self.log.info("Masking %d pixels from the edges of all %ss as SUSPECT.",
466 self.config.numEdgeSuspect, self.config.edgeMaskLevel)
468 # Depending on the value of config.matchExposuresType
469 # 'expTime' can stand for exposure time, flux, or ID.
470 for expTime in inputExp:
471 exposures = inputExp[expTime]
472 if len(exposures) == 1:
473 self.log.warning("Only one exposure found at %s %f. Dropping exposure %d.",
474 self.config.matchExposuresType, expTime, exposures[0][1])
475 continue
476 else:
477 # Only use the first two exposures at expTime. Each
478 # element is a tuple (exposure, expId)
479 expRef1, expId1 = exposures[0]
480 expRef2, expId2 = exposures[1]
481 # use get() to obtain `lsst.afw.image.Exposure`
482 exp1, exp2 = expRef1.get(), expRef2.get()
484 if len(exposures) > 2:
485 self.log.warning("Already found 2 exposures at %s %f. Ignoring exposures: %s",
486 self.config.matchExposuresType, expTime,
487 ", ".join(str(i[1]) for i in exposures[2:]))
488 # Mask pixels at the edge of the detector or of each amp
489 if self.config.numEdgeSuspect > 0:
490 isrTask.maskEdges(exp1, numEdgePixels=self.config.numEdgeSuspect,
491 maskPlane="SUSPECT", level=self.config.edgeMaskLevel)
492 isrTask.maskEdges(exp2, numEdgePixels=self.config.numEdgeSuspect,
493 maskPlane="SUSPECT", level=self.config.edgeMaskLevel)
495 # Extract any metadata keys from the headers.
496 auxDict = {}
497 metadata = exp1.getMetadata()
498 for key in self.config.auxiliaryHeaderKeys:
499 if key not in metadata:
500 self.log.warning(
501 "Requested auxiliary keyword %s not found in exposure metadata for %d",
502 key,
503 expId1,
504 )
505 value = np.nan
506 else:
507 value = metadata[key]
509 auxDict[key] = value
511 nAmpsNan = 0
512 partialPtcDataset = PhotonTransferCurveDataset(ampNames, 'PARTIAL',
513 self.config.maximumRangeCovariancesAstier)
514 for ampNumber, amp in enumerate(detector):
515 ampName = amp.getName()
516 if self.config.detectorMeasurementRegion == 'AMP':
517 region = amp.getBBox()
518 elif self.config.detectorMeasurementRegion == 'FULL':
519 region = None
521 # Get masked image regions, masking planes, statistic control
522 # objects, and clipped means. Calculate once to reuse in
523 # `measureMeanVarCov` and `getGainFromFlatPair`.
524 im1Area, im2Area, imStatsCtrl, mu1, mu2 = self.getImageAreasMasksStats(exp1, exp2,
525 region=region)
527 # We demand that both mu1 and mu2 be finite and greater than 0.
528 if not np.isfinite(mu1) or not np.isfinite(mu2) \
529 or ((np.nan_to_num(mu1) + np.nan_to_num(mu2)/2.) <= 0.0):
530 self.log.warning(
531 "Illegal mean value(s) detected for amp %s on exposure pair %d/%d",
532 ampName,
533 expId1,
534 expId2,
535 )
536 partialPtcDataset.setAmpValuesPartialDataset(
537 ampName,
538 inputExpIdPair=(expId1, expId2),
539 rawExpTime=expTime,
540 expIdMask=False,
541 )
542 continue
544 # `measureMeanVarCov` is the function that measures
545 # the variance and covariances from a region of
546 # the difference image of two flats at the same
547 # exposure time. The variable `covAstier` that is
548 # returned is of the form:
549 # [(i, j, var (cov[0,0]), cov, npix) for (i,j) in
550 # {maxLag, maxLag}^2].
551 muDiff, varDiff, covAstier = self.measureMeanVarCov(im1Area, im2Area, imStatsCtrl, mu1, mu2)
552 # Estimate the gain from the flat pair
553 if self.config.doGain:
554 gain = self.getGainFromFlatPair(im1Area, im2Area, imStatsCtrl, mu1, mu2,
555 correctionType=self.config.gainCorrectionType,
556 readNoise=readNoiseDict[ampName])
557 else:
558 gain = np.nan
560 # Correction factor for bias introduced by sigma
561 # clipping.
562 # Function returns 1/sqrt(varFactor), so it needs
563 # to be squared. varDiff is calculated via
564 # afwMath.VARIANCECLIP.
565 varFactor = sigmaClipCorrection(self.config.nSigmaClipPtc)**2
566 varDiff *= varFactor
568 expIdMask = True
569 # Mask data point at this mean signal level if
570 # the signal, variance, or covariance calculations
571 # from `measureMeanVarCov` resulted in NaNs.
572 if np.isnan(muDiff) or np.isnan(varDiff) or (covAstier is None):
573 self.log.warning("NaN mean or var, or None cov in amp %s in exposure pair %d, %d of "
574 "detector %d.", ampName, expId1, expId2, detNum)
575 nAmpsNan += 1
576 expIdMask = False
577 covArray = np.full((1, self.config.maximumRangeCovariancesAstier,
578 self.config.maximumRangeCovariancesAstier), np.nan)
579 covSqrtWeights = np.full_like(covArray, np.nan)
581 # Mask data point if it is outside of the
582 # specified mean signal range.
583 if (muDiff <= minMeanSignalDict[ampName]) or (muDiff >= maxMeanSignalDict[ampName]):
584 expIdMask = False
586 if covAstier is not None:
587 # Turn the tuples with the measured information
588 # into covariance arrays.
589 # covrow: (i, j, var (cov[0,0]), cov, npix)
590 tupleRows = [(muDiff, varDiff) + covRow + (ampNumber, expTime,
591 ampName) for covRow in covAstier]
592 tempStructArray = np.array(tupleRows, dtype=tags)
594 covArray, vcov, _ = self.makeCovArray(tempStructArray,
595 self.config.maximumRangeCovariancesAstier)
597 # The returned covArray should only have 1 entry;
598 # raise if this is not the case.
599 if covArray.shape[0] != 1:
600 raise RuntimeError("Serious programming error in covArray shape.")
602 covSqrtWeights = np.nan_to_num(1./np.sqrt(vcov))
604 # Correct covArray for sigma clipping:
605 # 1) Apply varFactor twice for the whole covariance matrix
606 covArray *= varFactor**2
607 # 2) But, only once for the variance element of the
608 # matrix, covArray[0, 0, 0] (so divide one factor out).
609 # (the first 0 is because this is a 3D array for insertion into
610 # the combined dataset).
611 covArray[0, 0, 0] /= varFactor
613 if expIdMask:
614 # Run the Gaussian histogram only if this is a legal
615 # amplifier.
616 histVar, histChi2Dof, kspValue = self.computeGaussianHistogramParameters(
617 im1Area,
618 im2Area,
619 imStatsCtrl,
620 mu1,
621 mu2,
622 )
623 else:
624 histVar = np.nan
625 histChi2Dof = np.nan
626 kspValue = 0.0
628 if self.config.doExtractPhotodiodeData:
629 nExps = 0
630 photoCharge = 0.0
631 for expId in [expId1, expId2]:
632 if expId in monitorDiodeCharge:
633 photoCharge += monitorDiodeCharge[expId]
634 nExps += 1
635 if nExps > 0:
636 photoCharge /= nExps
637 else:
638 photoCharge = np.nan
639 else:
640 photoCharge = np.nan
642 partialPtcDataset.setAmpValuesPartialDataset(
643 ampName,
644 inputExpIdPair=(expId1, expId2),
645 rawExpTime=expTime,
646 rawMean=muDiff,
647 rawVar=varDiff,
648 photoCharge=photoCharge,
649 expIdMask=expIdMask,
650 covariance=covArray[0, :, :],
651 covSqrtWeights=covSqrtWeights[0, :, :],
652 gain=gain,
653 noise=readNoiseDict[ampName],
654 histVar=histVar,
655 histChi2Dof=histChi2Dof,
656 kspValue=kspValue,
657 )
659 partialPtcDataset.setAuxValuesPartialDataset(auxDict)
661 # Use location of exp1 to save PTC dataset from (exp1, exp2) pair.
662 # Below, np.where(expId1 == np.array(inputDims)) returns a tuple
663 # with a single-element array, so [0][0]
664 # is necessary to extract the required index.
665 datasetIndex = np.where(expId1 == np.array(inputDims))[0][0]
666 # `partialPtcDatasetList` is a list of
667 # `PhotonTransferCurveDataset` objects. Some of them
668 # will be dummy datasets (to match length of input
669 # and output references), and the rest will have
670 # datasets with the mean signal, variance, and
671 # covariance measurements at a given exposure
672 # time. The next ppart of the PTC-measurement
673 # pipeline, `solve`, will take this list as input,
674 # and assemble the measurements in the datasets
675 # in an addecuate manner for fitting a PTC
676 # model.
677 partialPtcDataset.updateMetadataFromExposures([exp1, exp2])
678 partialPtcDataset.updateMetadata(setDate=True, detector=detector)
679 partialPtcDatasetList[datasetIndex] = partialPtcDataset
681 if nAmpsNan == len(ampNames):
682 msg = f"NaN mean in all amps of exposure pair {expId1}, {expId2} of detector {detNum}."
683 self.log.warning(msg)
685 return pipeBase.Struct(
686 outputCovariances=partialPtcDatasetList,
687 )
689 def makeCovArray(self, inputTuple, maxRangeFromTuple):
690 """Make covariances array from tuple.
692 Parameters
693 ----------
694 inputTuple : `numpy.ndarray`
695 Structured array with rows with at least
696 (mu, afwVar, cov, var, i, j, npix), where:
697 mu : `float`
698 0.5*(m1 + m2), where mu1 is the mean value of flat1
699 and mu2 is the mean value of flat2.
700 afwVar : `float`
701 Variance of difference flat, calculated with afw.
702 cov : `float`
703 Covariance value at lag(i, j)
704 var : `float`
705 Variance(covariance value at lag(0, 0))
706 i : `int`
707 Lag in dimension "x".
708 j : `int`
709 Lag in dimension "y".
710 npix : `int`
711 Number of pixels used for covariance calculation.
712 maxRangeFromTuple : `int`
713 Maximum range to select from tuple.
715 Returns
716 -------
717 cov : `numpy.array`
718 Covariance arrays, indexed by mean signal mu.
719 vCov : `numpy.array`
720 Variance of the [co]variance arrays, indexed by mean signal mu.
721 muVals : `numpy.array`
722 List of mean signal values.
723 """
724 if maxRangeFromTuple is not None:
725 cut = (inputTuple['i'] < maxRangeFromTuple) & (inputTuple['j'] < maxRangeFromTuple)
726 cutTuple = inputTuple[cut]
727 else:
728 cutTuple = inputTuple
729 # increasing mu order, so that we can group measurements with the
730 # same mu
731 muTemp = cutTuple['mu']
732 ind = np.argsort(muTemp)
734 cutTuple = cutTuple[ind]
735 # should group measurements on the same image pairs(same average)
736 mu = cutTuple['mu']
737 xx = np.hstack(([mu[0]], mu))
738 delta = xx[1:] - xx[:-1]
739 steps, = np.where(delta > 0)
740 ind = np.zeros_like(mu, dtype=int)
741 ind[steps] = 1
742 ind = np.cumsum(ind) # this acts as an image pair index.
743 # now fill the 3-d cov array(and variance)
744 muVals = np.array(np.unique(mu))
745 i = cutTuple['i'].astype(int)
746 j = cutTuple['j'].astype(int)
747 c = 0.5*cutTuple['cov']
748 n = cutTuple['npix']
749 v = 0.5*cutTuple['var']
750 # book and fill
751 cov = np.ndarray((len(muVals), np.max(i)+1, np.max(j)+1))
752 var = np.zeros_like(cov)
753 cov[ind, i, j] = c
754 var[ind, i, j] = v**2/n
755 var[:, 0, 0] *= 2 # var(v) = 2*v**2/N
757 return cov, var, muVals
759 def measureMeanVarCov(self, im1Area, im2Area, imStatsCtrl, mu1, mu2):
760 """Calculate the mean of each of two exposures and the variance
761 and covariance of their difference. The variance is calculated
762 via afwMath, and the covariance via the methods in Astier+19
763 (appendix A). In theory, var = covariance[0,0]. This should
764 be validated, and in the future, we may decide to just keep
765 one (covariance).
767 Parameters
768 ----------
769 im1Area : `lsst.afw.image.maskedImage.MaskedImageF`
770 Masked image from exposure 1.
771 im2Area : `lsst.afw.image.maskedImage.MaskedImageF`
772 Masked image from exposure 2.
773 imStatsCtrl : `lsst.afw.math.StatisticsControl`
774 Statistics control object.
775 mu1: `float`
776 Clipped mean of im1Area (ADU).
777 mu2: `float`
778 Clipped mean of im2Area (ADU).
780 Returns
781 -------
782 mu : `float` or `NaN`
783 0.5*(mu1 + mu2), where mu1, and mu2 are the clipped means
784 of the regions in both exposures. If either mu1 or m2 are
785 NaN's, the returned value is NaN.
786 varDiff : `float` or `NaN`
787 Half of the clipped variance of the difference of the
788 regions inthe two input exposures. If either mu1 or m2 are
789 NaN's, the returned value is NaN.
790 covDiffAstier : `list` or `NaN`
791 List with tuples of the form (dx, dy, var, cov, npix), where:
792 dx : `int`
793 Lag in x
794 dy : `int`
795 Lag in y
796 var : `float`
797 Variance at (dx, dy).
798 cov : `float`
799 Covariance at (dx, dy).
800 nPix : `int`
801 Number of pixel pairs used to evaluate var and cov.
803 If either mu1 or m2 are NaN's, the returned value is NaN.
804 """
805 if np.isnan(mu1) or np.isnan(mu2):
806 self.log.warning("Mean of amp in image 1 or 2 is NaN: %f, %f.", mu1, mu2)
807 return np.nan, np.nan, None
808 mu = 0.5*(mu1 + mu2)
810 # Take difference of pairs
811 # symmetric formula: diff = (mu2*im1-mu1*im2)/(0.5*(mu1+mu2))
812 temp = im2Area.clone()
813 temp *= mu1
814 diffIm = im1Area.clone()
815 diffIm *= mu2
816 diffIm -= temp
817 diffIm /= mu
819 if self.config.binSize > 1:
820 diffIm = afwMath.binImage(diffIm, self.config.binSize)
822 # Variance calculation via afwMath
823 varDiff = 0.5*(afwMath.makeStatistics(diffIm, afwMath.VARIANCECLIP, imStatsCtrl).getValue())
825 # Covariances calculations
826 # Get the pixels that were not clipped
827 varClip = afwMath.makeStatistics(diffIm, afwMath.VARIANCECLIP, imStatsCtrl).getValue()
828 meanClip = afwMath.makeStatistics(diffIm, afwMath.MEANCLIP, imStatsCtrl).getValue()
829 cut = meanClip + self.config.nSigmaClipPtc*np.sqrt(varClip)
830 unmasked = np.where(np.fabs(diffIm.image.array) <= cut, 1, 0)
832 # Get the pixels in the mask planes of the difference image
833 # that were ignored by the clipping algorithm
834 wDiff = np.where(diffIm.getMask().getArray() == 0, 1, 0)
835 # Combine the two sets of pixels ('1': use; '0': don't use)
836 # into a final weight matrix to be used in the covariance
837 # calculations below.
838 w = unmasked*wDiff
840 if np.sum(w) < self.config.minNumberGoodPixelsForCovariance/(self.config.binSize**2):
841 self.log.warning("Number of good points for covariance calculation (%s) is less "
842 "(than threshold %s)", np.sum(w),
843 self.config.minNumberGoodPixelsForCovariance/(self.config.binSize**2))
844 return np.nan, np.nan, None
846 maxRangeCov = self.config.maximumRangeCovariancesAstier
848 # Calculate covariances via FFT.
849 shapeDiff = np.array(diffIm.image.array.shape)
850 # Calculate the sizes of FFT dimensions.
851 s = shapeDiff + maxRangeCov
852 tempSize = np.array(np.log(s)/np.log(2.)).astype(int)
853 fftSize = np.array(2**(tempSize+1)).astype(int)
854 fftShape = (fftSize[0], fftSize[1])
855 c = CovFastFourierTransform(diffIm.image.array, w, fftShape, maxRangeCov)
856 # np.sum(w) is the same as npix[0][0] returned in covDiffAstier
857 try:
858 covDiffAstier = c.reportCovFastFourierTransform(maxRangeCov)
859 except ValueError:
860 # This is raised if there are not enough pixels.
861 self.log.warning("Not enough pixels covering the requested covariance range in x/y (%d)",
862 self.config.maximumRangeCovariancesAstier)
863 return np.nan, np.nan, None
865 # Compare Cov[0,0] and afwMath.VARIANCECLIP covDiffAstier[0]
866 # is the Cov[0,0] element, [3] is the variance, and there's a
867 # factor of 0.5 difference with afwMath.VARIANCECLIP.
868 thresholdPercentage = self.config.thresholdDiffAfwVarVsCov00
869 fractionalDiff = 100*np.fabs(1 - varDiff/(covDiffAstier[0][3]*0.5))
870 if fractionalDiff >= thresholdPercentage:
871 self.log.warning("Absolute fractional difference between afwMatch.VARIANCECLIP and Cov[0,0] "
872 "is more than %f%%: %f", thresholdPercentage, fractionalDiff)
874 return mu, varDiff, covDiffAstier
876 def getImageAreasMasksStats(self, exposure1, exposure2, region=None):
877 """Get image areas in a region as well as masks and statistic objects.
879 Parameters
880 ----------
881 exposure1 : `lsst.afw.image.ExposureF`
882 First exposure of flat field pair.
883 exposure2 : `lsst.afw.image.ExposureF`
884 Second exposure of flat field pair.
885 region : `lsst.geom.Box2I`, optional
886 Region of each exposure where to perform the calculations
887 (e.g, an amplifier).
889 Returns
890 -------
891 im1Area : `lsst.afw.image.MaskedImageF`
892 Masked image from exposure 1.
893 im2Area : `lsst.afw.image.MaskedImageF`
894 Masked image from exposure 2.
895 imStatsCtrl : `lsst.afw.math.StatisticsControl`
896 Statistics control object.
897 mu1 : `float`
898 Clipped mean of im1Area (ADU).
899 mu2 : `float`
900 Clipped mean of im2Area (ADU).
901 """
902 if region is not None:
903 im1Area = exposure1.maskedImage[region]
904 im2Area = exposure2.maskedImage[region]
905 else:
906 im1Area = exposure1.maskedImage
907 im2Area = exposure2.maskedImage
909 # Get mask planes and construct statistics control object from one
910 # of the exposures
911 imMaskVal = exposure1.getMask().getPlaneBitMask(self.config.maskNameList)
912 imStatsCtrl = afwMath.StatisticsControl(self.config.nSigmaClipPtc,
913 self.config.nIterSigmaClipPtc,
914 imMaskVal)
915 imStatsCtrl.setNanSafe(True)
916 imStatsCtrl.setAndMask(imMaskVal)
918 mu1 = afwMath.makeStatistics(im1Area, afwMath.MEANCLIP, imStatsCtrl).getValue()
919 mu2 = afwMath.makeStatistics(im2Area, afwMath.MEANCLIP, imStatsCtrl).getValue()
921 return (im1Area, im2Area, imStatsCtrl, mu1, mu2)
923 def getGainFromFlatPair(self, im1Area, im2Area, imStatsCtrl, mu1, mu2,
924 correctionType='NONE', readNoise=None):
925 """Estimate the gain from a single pair of flats.
927 The basic premise is 1/g = <(I1 - I2)^2/(I1 + I2)> = 1/const,
928 where I1 and I2 correspond to flats 1 and 2, respectively.
929 Corrections for the variable QE and the read-noise are then
930 made following the derivation in Robert Lupton's forthcoming
931 book, which gets
933 1/g = <(I1 - I2)^2/(I1 + I2)> - 1/mu(sigma^2 - 1/2g^2).
935 This is a quadratic equation, whose solutions are given by:
937 g = mu +/- sqrt(2*sigma^2 - 2*const*mu + mu^2)/(2*const*mu*2
938 - 2*sigma^2)
940 where 'mu' is the average signal level and 'sigma' is the
941 amplifier's readnoise. The positive solution will be used.
942 The way the correction is applied depends on the value
943 supplied for correctionType.
945 correctionType is one of ['NONE', 'SIMPLE' or 'FULL']
946 'NONE' : uses the 1/g = <(I1 - I2)^2/(I1 + I2)> formula.
947 'SIMPLE' : uses the gain from the 'NONE' method for the
948 1/2g^2 term.
949 'FULL' : solves the full equation for g, discarding the
950 non-physical solution to the resulting quadratic.
952 Parameters
953 ----------
954 im1Area : `lsst.afw.image.maskedImage.MaskedImageF`
955 Masked image from exposure 1.
956 im2Area : `lsst.afw.image.maskedImage.MaskedImageF`
957 Masked image from exposure 2.
958 imStatsCtrl : `lsst.afw.math.StatisticsControl`
959 Statistics control object.
960 mu1: `float`
961 Clipped mean of im1Area (ADU).
962 mu2: `float`
963 Clipped mean of im2Area (ADU).
964 correctionType : `str`, optional
965 The correction applied, one of ['NONE', 'SIMPLE', 'FULL']
966 readNoise : `float`, optional
967 Amplifier readout noise (ADU).
969 Returns
970 -------
971 gain : `float`
972 Gain, in e/ADU.
974 Raises
975 ------
976 RuntimeError
977 Raise if `correctionType` is not one of 'NONE',
978 'SIMPLE', or 'FULL'.
979 """
980 if correctionType not in ['NONE', 'SIMPLE', 'FULL']:
981 raise RuntimeError("Unknown correction type: %s" % correctionType)
983 if correctionType != 'NONE' and not np.isfinite(readNoise):
984 self.log.warning("'correctionType' in 'getGainFromFlatPair' is %s, "
985 "but 'readNoise' is NaN. Setting 'correctionType' "
986 "to 'NONE', so a gain value will be estimated without "
987 "corrections." % correctionType)
988 correctionType = 'NONE'
990 mu = 0.5*(mu1 + mu2)
992 # ratioIm = (I1 - I2)^2 / (I1 + I2)
993 temp = im2Area.clone()
994 ratioIm = im1Area.clone()
995 ratioIm -= temp
996 ratioIm *= ratioIm
998 # Sum of pairs
999 sumIm = im1Area.clone()
1000 sumIm += temp
1002 ratioIm /= sumIm
1004 const = afwMath.makeStatistics(ratioIm, afwMath.MEAN, imStatsCtrl).getValue()
1005 gain = 1. / const
1007 if correctionType == 'SIMPLE':
1008 gain = 1/(const - (1/mu)*(readNoise**2 - (1/2*gain**2)))
1009 elif correctionType == 'FULL':
1010 root = np.sqrt(mu**2 - 2*mu*const + 2*readNoise**2)
1011 denom = (2*const*mu - 2*readNoise**2)
1012 positiveSolution = (root + mu)/denom
1013 gain = positiveSolution
1015 return gain
1017 def getReadNoise(self, exposureMetadata, taskMetadata, ampName):
1018 """Gets readout noise for an amp from ISR metadata.
1020 If possible, this attempts to get the now-standard headers
1021 added to the exposure itself. If not found there, the ISR
1022 TaskMetadata is searched. If neither of these has the value,
1023 warn and set the read noise to NaN.
1025 Parameters
1026 ----------
1027 exposureMetadata : `lsst.daf.base.PropertySet`
1028 Metadata to check for read noise first.
1029 taskMetadata : `lsst.pipe.base.TaskMetadata`
1030 List of exposures metadata from ISR for this exposure.
1031 ampName : `str`
1032 Amplifier name.
1034 Returns
1035 -------
1036 readNoise : `float`
1037 The read noise for this set of exposure/amplifier.
1038 """
1039 # Try from the exposure first.
1040 expectedKey = f"LSST ISR OVERSCAN RESIDUAL SERIAL STDEV {ampName}"
1041 if expectedKey in exposureMetadata:
1042 return exposureMetadata[expectedKey]
1044 # If not, try getting it from the task metadata.
1045 expectedKey = f"RESIDUAL STDEV {ampName}"
1046 if "isr" in taskMetadata:
1047 if expectedKey in taskMetadata["isr"]:
1048 return taskMetadata["isr"][expectedKey]
1050 self.log.warning("Median readout noise from ISR metadata for amp %s "
1051 "could not be calculated." % ampName)
1052 return np.nan
1054 def computeGaussianHistogramParameters(self, im1Area, im2Area, imStatsCtrl, mu1, mu2):
1055 """Compute KS test for a Gaussian model fit to a histogram of the
1056 difference image.
1058 Parameters
1059 ----------
1060 im1Area : `lsst.afw.image.MaskedImageF`
1061 Masked image from exposure 1.
1062 im2Area : `lsst.afw.image.MaskedImageF`
1063 Masked image from exposure 2.
1064 imStatsCtrl : `lsst.afw.math.StatisticsControl`
1065 Statistics control object.
1066 mu1 : `float`
1067 Clipped mean of im1Area (ADU).
1068 mu2 : `float`
1069 Clipped mean of im2Area (ADU).
1071 Returns
1072 -------
1073 varFit : `float`
1074 Variance from the Gaussian fit.
1075 chi2Dof : `float`
1076 Chi-squared per degree of freedom of Gaussian fit.
1077 kspValue : `float`
1078 The KS test p-value for the Gaussian fit.
1080 Notes
1081 -----
1082 The algorithm here was originally developed by Aaron Roodman.
1083 Tests on the full focal plane of LSSTCam during testing has shown
1084 that a KS test p-value cut of 0.01 is a good discriminant for
1085 well-behaved flat pairs (p>0.01) and poorly behaved non-Gaussian
1086 flat pairs (p<0.01).
1087 """
1088 diffExp = im1Area.clone()
1089 diffExp -= im2Area
1091 sel = (((diffExp.mask.array & imStatsCtrl.getAndMask()) == 0)
1092 & np.isfinite(diffExp.mask.array))
1093 diffArr = diffExp.image.array[sel]
1095 numOk = len(diffArr)
1097 if numOk >= self.config.ksHistMinDataValues and np.isfinite(mu1) and np.isfinite(mu2):
1098 # Create a histogram symmetric around zero, with a bin size
1099 # determined from the expected variance given by the average of
1100 # the input signal levels.
1101 lim = self.config.ksHistLimitMultiplier * np.sqrt((mu1 + mu2)/2.)
1102 yVals, binEdges = np.histogram(diffArr, bins=self.config.ksHistNBins, range=[-lim, lim])
1104 # Fit the histogram with a Gaussian model.
1105 model = GaussianModel()
1106 yVals = yVals.astype(np.float64)
1107 xVals = ((binEdges[0: -1] + binEdges[1:])/2.).astype(np.float64)
1108 errVals = np.sqrt(yVals)
1109 errVals[(errVals == 0.0)] = 1.0
1110 pars = model.guess(yVals, x=xVals)
1111 with warnings.catch_warnings():
1112 warnings.simplefilter("ignore")
1113 # The least-squares fitter sometimes spouts (spurious) warnings
1114 # when the model is very bad. Swallow these warnings now and
1115 # let the KS test check the model below.
1116 out = model.fit(
1117 yVals,
1118 pars,
1119 x=xVals,
1120 weights=1./errVals,
1121 calc_covar=True,
1122 method="least_squares",
1123 )
1125 # Calculate chi2.
1126 chiArr = out.residual
1127 nDof = len(yVals) - 3
1128 chi2Dof = np.sum(chiArr**2.)/nDof
1129 sigmaFit = out.params["sigma"].value
1131 # Calculate KS test p-value for the fit.
1132 ksResult = scipy.stats.ks_1samp(
1133 diffArr,
1134 scipy.stats.norm.cdf,
1135 (out.params["center"].value, sigmaFit),
1136 )
1138 kspValue = ksResult.pvalue
1139 if kspValue < 1e-15:
1140 kspValue = 0.0
1142 varFit = sigmaFit**2.
1144 else:
1145 varFit = np.nan
1146 chi2Dof = np.nan
1147 kspValue = 0.0
1149 return varFit, chi2Dof, kspValue