32 from contextlib
import contextmanager
33 from lsstDebug
import getDebugFrame
43 from .
import isrFunctions
45 from .
import linearize
47 from .assembleCcdTask
import AssembleCcdTask
48 from .crosstalk
import CrosstalkTask
49 from .fringe
import FringeTask
50 from .isr
import maskNans
51 from .masking
import MaskingTask
52 from .straylight
import StrayLightTask
53 from .vignette
import VignetteTask
55 __all__ = [
"IsrTask",
"IsrTaskConfig",
"RunIsrTask",
"RunIsrConfig"]
59 """Configuration parameters for IsrTask. 61 Items are grouped in the order in which they are executed by the task. 66 isrName = pexConfig.Field(
73 ccdExposure = pipeBase.InputDatasetField(
74 doc=
"Input exposure to process",
77 storageClass=
"Exposure",
78 dimensions=[
"instrument",
"exposure",
"detector"],
80 camera = pipeBase.InputDatasetField(
81 doc=
"Input camera to construct complete exposures.",
84 storageClass=
"TablePersistableCamera",
85 dimensions=[
"instrument",
"calibration_label"],
87 bias = pipeBase.InputDatasetField(
88 doc=
"Input bias calibration.",
91 storageClass=
"ImageF",
92 dimensions=[
"instrument",
"calibration_label",
"detector"],
94 dark = pipeBase.InputDatasetField(
95 doc=
"Input dark calibration.",
98 storageClass=
"ImageF",
99 dimensions=[
"instrument",
"calibration_label",
"detector"],
101 flat = pipeBase.InputDatasetField(
102 doc=
"Input flat calibration.",
105 storageClass=
"MaskedImageF",
106 dimensions=[
"instrument",
"physical_filter",
"calibration_label",
"detector"],
108 bfKernel = pipeBase.InputDatasetField(
109 doc=
"Input brighter-fatter kernel.",
112 storageClass=
"NumpyArray",
113 dimensions=[
"instrument",
"calibration_label"],
115 defects = pipeBase.InputDatasetField(
116 doc=
"Input defect tables.",
119 storageClass=
"DefectsList",
120 dimensions=[
"instrument",
"calibration_label",
"detector"],
122 opticsTransmission = pipeBase.InputDatasetField(
123 doc=
"Transmission curve due to the optics.",
124 name=
"transmission_optics",
126 storageClass=
"TablePersistableTransmissionCurve",
127 dimensions=[
"instrument",
"calibration_label"],
129 filterTransmission = pipeBase.InputDatasetField(
130 doc=
"Transmission curve due to the filter.",
131 name=
"transmission_filter",
133 storageClass=
"TablePersistableTransmissionCurve",
134 dimensions=[
"instrument",
"physical_filter",
"calibration_label"],
136 sensorTransmission = pipeBase.InputDatasetField(
137 doc=
"Transmission curve due to the sensor.",
138 name=
"transmission_sensor",
140 storageClass=
"TablePersistableTransmissionCurve",
141 dimensions=[
"instrument",
"calibration_label",
"detector"],
143 atmosphereTransmission = pipeBase.InputDatasetField(
144 doc=
"Transmission curve due to the atmosphere.",
145 name=
"transmission_atmosphere",
147 storageClass=
"TablePersistableTransmissionCurve",
148 dimensions=[
"instrument"],
150 illumMaskedImage = pipeBase.InputDatasetField(
151 doc=
"Input illumination correction.",
154 storageClass=
"MaskedImageF",
155 dimensions=[
"instrument",
"physical_filter",
"calibration_label",
"detector"],
159 outputExposure = pipeBase.OutputDatasetField(
160 doc=
"Output ISR processed exposure.",
163 storageClass=
"ExposureF",
164 dimensions=[
"instrument",
"visit",
"detector"],
166 outputOssThumbnail = pipeBase.OutputDatasetField(
167 doc=
"Output Overscan-subtracted thumbnail image.",
170 storageClass=
"Thumbnail",
171 dimensions=[
"instrument",
"visit",
"detector"],
173 outputFlattenedThumbnail = pipeBase.OutputDatasetField(
174 doc=
"Output flat-corrected thumbnail image.",
175 name=
"FlattenedThumb",
177 storageClass=
"TextStorage",
178 dimensions=[
"instrument",
"visit",
"detector"],
181 quantum = pipeBase.QuantumConfig(
182 dimensions=[
"visit",
"detector",
"instrument"],
186 datasetType = pexConfig.Field(
188 doc=
"Dataset type for input data; users will typically leave this alone, " 189 "but camera-specific ISR tasks will override it",
193 fallbackFilterName = pexConfig.Field(
195 doc=
"Fallback default filter name for calibrations.",
198 expectWcs = pexConfig.Field(
201 doc=
"Expect input science images to have a WCS (set False for e.g. spectrographs)." 203 fwhm = pexConfig.Field(
205 doc=
"FWHM of PSF in arcseconds.",
208 qa = pexConfig.ConfigField(
210 doc=
"QA related configuration options.",
214 doConvertIntToFloat = pexConfig.Field(
216 doc=
"Convert integer raw images to floating point values?",
221 doSaturation = pexConfig.Field(
223 doc=
"Mask saturated pixels? NB: this is totally independent of the" 224 " interpolation option - this is ONLY setting the bits in the mask." 225 " To have them interpolated make sure doSaturationInterpolation=True",
228 saturatedMaskName = pexConfig.Field(
230 doc=
"Name of mask plane to use in saturation detection and interpolation",
233 saturation = pexConfig.Field(
235 doc=
"The saturation level to use if no Detector is present in the Exposure (ignored if NaN)",
236 default=float(
"NaN"),
238 growSaturationFootprintSize = pexConfig.Field(
240 doc=
"Number of pixels by which to grow the saturation footprints",
245 doSuspect = pexConfig.Field(
247 doc=
"Mask suspect pixels?",
250 suspectMaskName = pexConfig.Field(
252 doc=
"Name of mask plane to use for suspect pixels",
255 numEdgeSuspect = pexConfig.Field(
257 doc=
"Number of edge pixels to be flagged as untrustworthy.",
262 doSetBadRegions = pexConfig.Field(
264 doc=
"Should we set the level of all BAD patches of the chip to the chip's average value?",
267 badStatistic = pexConfig.ChoiceField(
269 doc=
"How to estimate the average value for BAD regions.",
272 "MEANCLIP":
"Correct using the (clipped) mean of good data",
273 "MEDIAN":
"Correct using the median of the good data",
278 doOverscan = pexConfig.Field(
280 doc=
"Do overscan subtraction?",
283 overscanFitType = pexConfig.ChoiceField(
285 doc=
"The method for fitting the overscan bias level.",
288 "POLY":
"Fit ordinary polynomial to the longest axis of the overscan region",
289 "CHEB":
"Fit Chebyshev polynomial to the longest axis of the overscan region",
290 "LEG":
"Fit Legendre polynomial to the longest axis of the overscan region",
291 "NATURAL_SPLINE":
"Fit natural spline to the longest axis of the overscan region",
292 "CUBIC_SPLINE":
"Fit cubic spline to the longest axis of the overscan region",
293 "AKIMA_SPLINE":
"Fit Akima spline to the longest axis of the overscan region",
294 "MEAN":
"Correct using the mean of the overscan region",
295 "MEANCLIP":
"Correct using a clipped mean of the overscan region",
296 "MEDIAN":
"Correct using the median of the overscan region",
299 overscanOrder = pexConfig.Field(
301 doc=(
"Order of polynomial or to fit if overscan fit type is a polynomial, " +
302 "or number of spline knots if overscan fit type is a spline."),
305 overscanNumSigmaClip = pexConfig.Field(
307 doc=
"Rejection threshold (sigma) for collapsing overscan before fit",
310 overscanIsInt = pexConfig.Field(
312 doc=
"Treat overscan as an integer image for purposes of overscan.FitType=MEDIAN",
315 overscanNumLeadingColumnsToSkip = pexConfig.Field(
317 doc=
"Number of columns to skip in overscan, i.e. those closest to amplifier",
320 overscanNumTrailingColumnsToSkip = pexConfig.Field(
322 doc=
"Number of columns to skip in overscan, i.e. those farthest from amplifier",
325 overscanMaxDev = pexConfig.Field(
327 doc=
"Maximum deviation from the median for overscan",
328 default=1000.0, check=
lambda x: x > 0
330 overscanBiasJump = pexConfig.Field(
332 doc=
"Fit the overscan in a piecewise-fashion to correct for bias jumps?",
335 overscanBiasJumpKeyword = pexConfig.Field(
337 doc=
"Header keyword containing information about devices.",
338 default=
"NO_SUCH_KEY",
340 overscanBiasJumpDevices = pexConfig.ListField(
342 doc=
"List of devices that need piecewise overscan correction.",
345 overscanBiasJumpLocation = pexConfig.Field(
347 doc=
"Location of bias jump along y-axis.",
352 doAssembleCcd = pexConfig.Field(
355 doc=
"Assemble amp-level exposures into a ccd-level exposure?" 357 assembleCcd = pexConfig.ConfigurableField(
358 target=AssembleCcdTask,
359 doc=
"CCD assembly task",
363 doAssembleIsrExposures = pexConfig.Field(
366 doc=
"Assemble amp-level calibration exposures into ccd-level exposure?" 368 doTrimToMatchCalib = pexConfig.Field(
371 doc=
"Trim raw data to match calibration bounding boxes?" 375 doBias = pexConfig.Field(
377 doc=
"Apply bias frame correction?",
380 biasDataProductName = pexConfig.Field(
382 doc=
"Name of the bias data product",
387 doVariance = pexConfig.Field(
389 doc=
"Calculate variance?",
392 gain = pexConfig.Field(
394 doc=
"The gain to use if no Detector is present in the Exposure (ignored if NaN)",
395 default=float(
"NaN"),
397 readNoise = pexConfig.Field(
399 doc=
"The read noise to use if no Detector is present in the Exposure",
402 doEmpiricalReadNoise = pexConfig.Field(
405 doc=
"Calculate empirical read noise instead of value from AmpInfo data?" 409 doLinearize = pexConfig.Field(
411 doc=
"Correct for nonlinearity of the detector's response?",
416 doCrosstalk = pexConfig.Field(
418 doc=
"Apply intra-CCD crosstalk correction?",
421 doCrosstalkBeforeAssemble = pexConfig.Field(
423 doc=
"Apply crosstalk correction before CCD assembly, and before trimming?",
426 crosstalk = pexConfig.ConfigurableField(
427 target=CrosstalkTask,
428 doc=
"Intra-CCD crosstalk correction",
432 doDefect = pexConfig.Field(
434 doc=
"Apply correction for CCD defects, e.g. hot pixels?",
437 numEdgeSuspect = pexConfig.Field(
439 doc=
"Number of edge pixels to be flagged as untrustworthy.",
442 doNanMasking = pexConfig.Field(
444 doc=
"Mask NAN pixels?",
447 doWidenSaturationTrails = pexConfig.Field(
449 doc=
"Widen bleed trails based on their width?",
454 doBrighterFatter = pexConfig.Field(
457 doc=
"Apply the brighter fatter correction" 459 brighterFatterLevel = pexConfig.ChoiceField(
462 doc=
"The level at which to correct for brighter-fatter.",
464 "AMP":
"Every amplifier treated separately.",
465 "DETECTOR":
"One kernel per detector",
468 brighterFatterKernelFile = pexConfig.Field(
471 doc=
"Kernel file used for the brighter fatter correction" 473 brighterFatterMaxIter = pexConfig.Field(
476 doc=
"Maximum number of iterations for the brighter fatter correction" 478 brighterFatterThreshold = pexConfig.Field(
481 doc=
"Threshold used to stop iterating the brighter fatter correction. It is the " 482 " absolute value of the difference between the current corrected image and the one" 483 " from the previous iteration summed over all the pixels." 485 brighterFatterApplyGain = pexConfig.Field(
488 doc=
"Should the gain be applied when applying the brighter fatter correction?" 492 doDark = pexConfig.Field(
494 doc=
"Apply dark frame correction?",
497 darkDataProductName = pexConfig.Field(
499 doc=
"Name of the dark data product",
504 doStrayLight = pexConfig.Field(
506 doc=
"Subtract stray light in the y-band (due to encoder LEDs)?",
509 strayLight = pexConfig.ConfigurableField(
510 target=StrayLightTask,
511 doc=
"y-band stray light correction" 515 doFlat = pexConfig.Field(
517 doc=
"Apply flat field correction?",
520 flatDataProductName = pexConfig.Field(
522 doc=
"Name of the flat data product",
525 flatScalingType = pexConfig.ChoiceField(
527 doc=
"The method for scaling the flat on the fly.",
530 "USER":
"Scale by flatUserScale",
531 "MEAN":
"Scale by the inverse of the mean",
532 "MEDIAN":
"Scale by the inverse of the median",
535 flatUserScale = pexConfig.Field(
537 doc=
"If flatScalingType is 'USER' then scale flat by this amount; ignored otherwise",
540 doTweakFlat = pexConfig.Field(
542 doc=
"Tweak flats to match observed amplifier ratios?",
547 doApplyGains = pexConfig.Field(
549 doc=
"Correct the amplifiers for their gains instead of applying flat correction",
552 normalizeGains = pexConfig.Field(
554 doc=
"Normalize all the amplifiers in each CCD to have the same median value.",
559 doFringe = pexConfig.Field(
561 doc=
"Apply fringe correction?",
564 fringe = pexConfig.ConfigurableField(
566 doc=
"Fringe subtraction task",
568 fringeAfterFlat = pexConfig.Field(
570 doc=
"Do fringe subtraction after flat-fielding?",
575 doAddDistortionModel = pexConfig.Field(
577 doc=
"Apply a distortion model based on camera geometry to the WCS?",
582 doMeasureBackground = pexConfig.Field(
584 doc=
"Measure the background level on the reduced image?",
589 doCameraSpecificMasking = pexConfig.Field(
591 doc=
"Mask camera-specific bad regions?",
594 masking = pexConfig.ConfigurableField(
601 doInterpolate = pexConfig.Field(
603 doc=
"Interpolate masked pixels?",
606 doSaturationInterpolation = pexConfig.Field(
608 doc=
"Perform interpolation over pixels masked as saturated?" 609 " NB: This is independent of doSaturation; if that is False this plane" 610 " will likely be blank, resulting in a no-op here.",
613 doNanInterpolation = pexConfig.Field(
615 doc=
"Perform interpolation over pixels masked as NaN?" 616 " NB: This is independent of doNanMasking; if that is False this plane" 617 " will likely be blank, resulting in a no-op here.",
620 doNanInterpAfterFlat = pexConfig.Field(
622 doc=(
"If True, ensure we interpolate NaNs after flat-fielding, even if we " 623 "also have to interpolate them before flat-fielding."),
626 maskListToInterpolate = pexConfig.ListField(
628 doc=
"List of mask planes that should be interpolated.",
629 default=[
'SAT',
'BAD',
'UNMASKEDNAN'],
631 doSaveInterpPixels = pexConfig.Field(
633 doc=
"Save a copy of the pre-interpolated pixel values?",
638 fluxMag0T1 = pexConfig.DictField(
641 doc=
"The approximate flux of a zero-magnitude object in a one-second exposure, per filter.",
642 default=dict((f, pow(10.0, 0.4*m))
for f, m
in ((
"Unknown", 28.0),
645 defaultFluxMag0T1 = pexConfig.Field(
647 doc=
"Default value for fluxMag0T1 (for an unrecognized filter).",
648 default=pow(10.0, 0.4*28.0)
652 doVignette = pexConfig.Field(
654 doc=
"Apply vignetting parameters?",
657 vignette = pexConfig.ConfigurableField(
659 doc=
"Vignetting task.",
663 doAttachTransmissionCurve = pexConfig.Field(
666 doc=
"Construct and attach a wavelength-dependent throughput curve for this CCD image?" 668 doUseOpticsTransmission = pexConfig.Field(
671 doc=
"Load and use transmission_optics (if doAttachTransmissionCurve is True)?" 673 doUseFilterTransmission = pexConfig.Field(
676 doc=
"Load and use transmission_filter (if doAttachTransmissionCurve is True)?" 678 doUseSensorTransmission = pexConfig.Field(
681 doc=
"Load and use transmission_sensor (if doAttachTransmissionCurve is True)?" 683 doUseAtmosphereTransmission = pexConfig.Field(
686 doc=
"Load and use transmission_atmosphere (if doAttachTransmissionCurve is True)?" 690 doIlluminationCorrection = pexConfig.Field(
693 doc=
"Perform illumination correction?" 695 illuminationCorrectionDataProductName = pexConfig.Field(
697 doc=
"Name of the illumination correction data product.",
700 illumScale = pexConfig.Field(
702 doc=
"Scale factor for the illumination correction.",
705 illumFilters = pexConfig.ListField(
708 doc=
"Only perform illumination correction for these filters." 712 doWrite = pexConfig.Field(
714 doc=
"Persist postISRCCD?",
721 raise ValueError(
"You may not specify both doFlat and doApplyGains")
723 self.config.maskListToInterpolate.append(
"SAT")
725 self.config.maskListToInterpolate.append(
"UNMASKEDNAN")
728 class IsrTask(pipeBase.PipelineTask, pipeBase.CmdLineTask):
729 """Apply common instrument signature correction algorithms to a raw frame. 731 The process for correcting imaging data is very similar from 732 camera to camera. This task provides a vanilla implementation of 733 doing these corrections, including the ability to turn certain 734 corrections off if they are not needed. The inputs to the primary 735 method, `run()`, are a raw exposure to be corrected and the 736 calibration data products. The raw input is a single chip sized 737 mosaic of all amps including overscans and other non-science 738 pixels. The method `runDataRef()` identifies and defines the 739 calibration data products, and is intended for use by a 740 `lsst.pipe.base.cmdLineTask.CmdLineTask` and takes as input only a 741 `daf.persistence.butlerSubset.ButlerDataRef`. This task may be 742 subclassed for different camera, although the most camera specific 743 methods have been split into subtasks that can be redirected 746 The __init__ method sets up the subtasks for ISR processing, using 747 the defaults from `lsst.ip.isr`. 752 Positional arguments passed to the Task constructor. None used at this time. 753 kwargs : `dict`, optional 754 Keyword arguments passed on to the Task constructor. None used at this time. 756 ConfigClass = IsrTaskConfig
761 self.makeSubtask(
"assembleCcd")
762 self.makeSubtask(
"crosstalk")
763 self.makeSubtask(
"strayLight")
764 self.makeSubtask(
"fringe")
765 self.makeSubtask(
"masking")
766 self.makeSubtask(
"vignette")
775 if config.doBias
is not True:
776 inputTypeDict.pop(
"bias",
None)
777 if config.doLinearize
is not True:
778 inputTypeDict.pop(
"linearizer",
None)
779 if config.doCrosstalk
is not True:
780 inputTypeDict.pop(
"crosstalkSources",
None)
781 if config.doBrighterFatter
is not True:
782 inputTypeDict.pop(
"bfKernel",
None)
783 if config.doDefect
is not True:
784 inputTypeDict.pop(
"defects",
None)
785 if config.doDark
is not True:
786 inputTypeDict.pop(
"dark",
None)
787 if config.doFlat
is not True:
788 inputTypeDict.pop(
"flat",
None)
789 if config.doAttachTransmissionCurve
is not True:
790 inputTypeDict.pop(
"opticsTransmission",
None)
791 inputTypeDict.pop(
"filterTransmission",
None)
792 inputTypeDict.pop(
"sensorTransmission",
None)
793 inputTypeDict.pop(
"atmosphereTransmission",
None)
794 if config.doUseOpticsTransmission
is not True:
795 inputTypeDict.pop(
"opticsTransmission",
None)
796 if config.doUseFilterTransmission
is not True:
797 inputTypeDict.pop(
"filterTransmission",
None)
798 if config.doUseSensorTransmission
is not True:
799 inputTypeDict.pop(
"sensorTransmission",
None)
800 if config.doUseAtmosphereTransmission
is not True:
801 inputTypeDict.pop(
"atmosphereTransmission",
None)
802 if config.doIlluminationCorrection
is not True:
803 inputTypeDict.pop(
"illumMaskedImage",
None)
811 if config.qa.doThumbnailOss
is not True:
812 outputTypeDict.pop(
"outputOssThumbnail",
None)
813 if config.qa.doThumbnailFlattened
is not True:
814 outputTypeDict.pop(
"outputFlattenedThumbnail",
None)
815 if config.doWrite
is not True:
816 outputTypeDict.pop(
"outputExposure",
None)
818 return outputTypeDict
827 names.remove(
"ccdExposure")
836 return frozenset([
"calibration_label"])
840 inputData[
'detectorNum'] = int(inputDataIds[
'ccdExposure'][
'detector'])
841 except Exception
as e:
842 raise ValueError(f
"Failure to find valid detectorNum value for Dataset {inputDataIds}: {e}")
844 inputData[
'isGen3'] =
True 846 if self.config.doLinearize
is True:
847 if 'linearizer' not in inputData.keys():
848 detector = inputData[
'camera'][inputData[
'detectorNum']]
849 linearityName = detector.getAmpInfoCatalog()[0].getLinearityType()
850 inputData[
'linearizer'] = linearize.getLinearityTypeByName(linearityName)()
852 if inputData[
'defects']
is not None:
855 if not isinstance(inputData[
"defects"], Defects):
856 inputData[
"defects"] = Defects.fromTable(inputData[
"defects"])
873 return super().
adaptArgsAndRun(inputData, inputDataIds, outputDataIds, butler)
879 """!Retrieve necessary frames for instrument signature removal. 881 Pre-fetching all required ISR data products limits the IO 882 required by the ISR. Any conflict between the calibration data 883 available and that needed for ISR is also detected prior to 884 doing processing, allowing it to fail quickly. 888 dataRef : `daf.persistence.butlerSubset.ButlerDataRef` 889 Butler reference of the detector data to be processed 890 rawExposure : `afw.image.Exposure` 891 The raw exposure that will later be corrected with the 892 retrieved calibration data; should not be modified in this 897 result : `lsst.pipe.base.Struct` 898 Result struct with components (which may be `None`): 899 - ``bias``: bias calibration frame (`afw.image.Exposure`) 900 - ``linearizer``: functor for linearization (`ip.isr.linearize.LinearizeBase`) 901 - ``crosstalkSources``: list of possible crosstalk sources (`list`) 902 - ``dark``: dark calibration frame (`afw.image.Exposure`) 903 - ``flat``: flat calibration frame (`afw.image.Exposure`) 904 - ``bfKernel``: Brighter-Fatter kernel (`numpy.ndarray`) 905 - ``defects``: list of defects (`lsst.meas.algorithms.Defects`) 906 - ``fringes``: `lsst.pipe.base.Struct` with components: 907 - ``fringes``: fringe calibration frame (`afw.image.Exposure`) 908 - ``seed``: random seed derived from the ccdExposureId for random 909 number generator (`uint32`) 910 - ``opticsTransmission``: `lsst.afw.image.TransmissionCurve` 911 A ``TransmissionCurve`` that represents the throughput of the optics, 912 to be evaluated in focal-plane coordinates. 913 - ``filterTransmission`` : `lsst.afw.image.TransmissionCurve` 914 A ``TransmissionCurve`` that represents the throughput of the filter 915 itself, to be evaluated in focal-plane coordinates. 916 - ``sensorTransmission`` : `lsst.afw.image.TransmissionCurve` 917 A ``TransmissionCurve`` that represents the throughput of the sensor 918 itself, to be evaluated in post-assembly trimmed detector coordinates. 919 - ``atmosphereTransmission`` : `lsst.afw.image.TransmissionCurve` 920 A ``TransmissionCurve`` that represents the throughput of the 921 atmosphere, assumed to be spatially constant. 922 - ``strayLightData`` : `object` 923 An opaque object containing calibration information for 924 stray-light correction. If `None`, no correction will be 926 - ``illumMaskedImage`` : illumination correction image (`lsst.afw.image.MaskedImage`) 930 NotImplementedError : 931 Raised if a per-amplifier brighter-fatter kernel is requested by the configuration. 933 ccd = rawExposure.getDetector()
934 filterName = afwImage.Filter(rawExposure.getFilter().getId()).getName()
935 rawExposure.mask.addMaskPlane(
"UNMASKEDNAN")
936 biasExposure = (self.
getIsrExposure(dataRef, self.config.biasDataProductName)
937 if self.config.doBias
else None)
939 linearizer = (dataRef.get(
"linearizer", immediate=
True)
941 crosstalkSources = (self.crosstalk.prepCrosstalk(dataRef)
942 if self.config.doCrosstalk
else None)
943 darkExposure = (self.
getIsrExposure(dataRef, self.config.darkDataProductName)
944 if self.config.doDark
else None)
945 flatExposure = (self.
getIsrExposure(dataRef, self.config.flatDataProductName)
946 if self.config.doFlat
else None)
948 brighterFatterKernel =
None 949 if self.config.doBrighterFatter
is True:
953 brighterFatterKernel = dataRef.get(
"brighterFatterKernel")
957 brighterFatterKernel = dataRef.get(
"bfKernel")
959 brighterFatterKernel =
None 960 if brighterFatterKernel
is not None and not isinstance(brighterFatterKernel, numpy.ndarray):
963 if self.config.brighterFatterLevel ==
'DETECTOR':
964 brighterFatterKernel = brighterFatterKernel.kernel[ccd.getId()]
967 raise NotImplementedError(
"Per-amplifier brighter-fatter correction not implemented")
969 defectList = (dataRef.get(
"defects")
970 if self.config.doDefect
else None)
971 fringeStruct = (self.fringe.readFringes(dataRef, assembler=self.assembleCcd
972 if self.config.doAssembleIsrExposures
else None)
973 if self.config.doFringe
and self.fringe.checkFilter(rawExposure)
974 else pipeBase.Struct(fringes=
None))
976 if self.config.doAttachTransmissionCurve:
977 opticsTransmission = (dataRef.get(
"transmission_optics")
978 if self.config.doUseOpticsTransmission
else None)
979 filterTransmission = (dataRef.get(
"transmission_filter")
980 if self.config.doUseFilterTransmission
else None)
981 sensorTransmission = (dataRef.get(
"transmission_sensor")
982 if self.config.doUseSensorTransmission
else None)
983 atmosphereTransmission = (dataRef.get(
"transmission_atmosphere")
984 if self.config.doUseAtmosphereTransmission
else None)
986 opticsTransmission =
None 987 filterTransmission =
None 988 sensorTransmission =
None 989 atmosphereTransmission =
None 991 if self.config.doStrayLight:
992 strayLightData = self.strayLight.
readIsrData(dataRef, rawExposure)
994 strayLightData =
None 997 self.config.illuminationCorrectionDataProductName).getMaskedImage()
998 if (self.config.doIlluminationCorrection
and 999 filterName
in self.config.illumFilters)
1003 return pipeBase.Struct(bias=biasExposure,
1004 linearizer=linearizer,
1005 crosstalkSources=crosstalkSources,
1008 bfKernel=brighterFatterKernel,
1010 fringes=fringeStruct,
1011 opticsTransmission=opticsTransmission,
1012 filterTransmission=filterTransmission,
1013 sensorTransmission=sensorTransmission,
1014 atmosphereTransmission=atmosphereTransmission,
1015 strayLightData=strayLightData,
1016 illumMaskedImage=illumMaskedImage
1019 @pipeBase.timeMethod
1020 def run(self, ccdExposure, camera=None, bias=None, linearizer=None, crosstalkSources=None,
1021 dark=None, flat=None, bfKernel=None, defects=None, fringes=None,
1022 opticsTransmission=None, filterTransmission=None,
1023 sensorTransmission=None, atmosphereTransmission=None,
1024 detectorNum=None, strayLightData=None, illumMaskedImage=None,
1027 """!Perform instrument signature removal on an exposure. 1029 Steps included in the ISR processing, in order performed, are: 1030 - saturation and suspect pixel masking 1031 - overscan subtraction 1032 - CCD assembly of individual amplifiers 1034 - variance image construction 1035 - linearization of non-linear response 1037 - brighter-fatter correction 1040 - stray light subtraction 1042 - masking of known defects and camera specific features 1043 - vignette calculation 1044 - appending transmission curve and distortion model 1048 ccdExposure : `lsst.afw.image.Exposure` 1049 The raw exposure that is to be run through ISR. The 1050 exposure is modified by this method. 1051 camera : `lsst.afw.cameraGeom.Camera`, optional 1052 The camera geometry for this exposure. Used to select the 1053 distortion model appropriate for this data. 1054 bias : `lsst.afw.image.Exposure`, optional 1055 Bias calibration frame. 1056 linearizer : `lsst.ip.isr.linearize.LinearizeBase`, optional 1057 Functor for linearization. 1058 crosstalkSources : `list`, optional 1059 List of possible crosstalk sources. 1060 dark : `lsst.afw.image.Exposure`, optional 1061 Dark calibration frame. 1062 flat : `lsst.afw.image.Exposure`, optional 1063 Flat calibration frame. 1064 bfKernel : `numpy.ndarray`, optional 1065 Brighter-fatter kernel. 1066 defects : `lsst.meas.algorithms.Defects`, optional 1068 fringes : `lsst.pipe.base.Struct`, optional 1069 Struct containing the fringe correction data, with 1071 - ``fringes``: fringe calibration frame (`afw.image.Exposure`) 1072 - ``seed``: random seed derived from the ccdExposureId for random 1073 number generator (`uint32`) 1074 opticsTransmission: `lsst.afw.image.TransmissionCurve`, optional 1075 A ``TransmissionCurve`` that represents the throughput of the optics, 1076 to be evaluated in focal-plane coordinates. 1077 filterTransmission : `lsst.afw.image.TransmissionCurve` 1078 A ``TransmissionCurve`` that represents the throughput of the filter 1079 itself, to be evaluated in focal-plane coordinates. 1080 sensorTransmission : `lsst.afw.image.TransmissionCurve` 1081 A ``TransmissionCurve`` that represents the throughput of the sensor 1082 itself, to be evaluated in post-assembly trimmed detector coordinates. 1083 atmosphereTransmission : `lsst.afw.image.TransmissionCurve` 1084 A ``TransmissionCurve`` that represents the throughput of the 1085 atmosphere, assumed to be spatially constant. 1086 detectorNum : `int`, optional 1087 The integer number for the detector to process. 1088 isGen3 : bool, optional 1089 Flag this call to run() as using the Gen3 butler environment. 1090 strayLightData : `object`, optional 1091 Opaque object containing calibration information for stray-light 1092 correction. If `None`, no correction will be performed. 1093 illumMaskedImage : `lsst.afw.image.MaskedImage`, optional 1094 Illumination correction image. 1098 result : `lsst.pipe.base.Struct` 1099 Result struct with component: 1100 - ``exposure`` : `afw.image.Exposure` 1101 The fully ISR corrected exposure. 1102 - ``outputExposure`` : `afw.image.Exposure` 1103 An alias for `exposure` 1104 - ``ossThumb`` : `numpy.ndarray` 1105 Thumbnail image of the exposure after overscan subtraction. 1106 - ``flattenedThumb`` : `numpy.ndarray` 1107 Thumbnail image of the exposure after flat-field correction. 1112 Raised if a configuration option is set to True, but the 1113 required calibration data has not been specified. 1117 The current processed exposure can be viewed by setting the 1118 appropriate lsstDebug entries in the `debug.display` 1119 dictionary. The names of these entries correspond to some of 1120 the IsrTaskConfig Boolean options, with the value denoting the 1121 frame to use. The exposure is shown inside the matching 1122 option check and after the processing of that step has 1123 finished. The steps with debug points are: 1134 In addition, setting the "postISRCCD" entry displays the 1135 exposure after all ISR processing has finished. 1143 self.config.doFringe =
False 1146 if detectorNum
is None:
1147 raise RuntimeError(
"Must supply the detectorNum if running as Gen3")
1149 ccdExposure = self.
ensureExposure(ccdExposure, camera, detectorNum)
1154 if isinstance(ccdExposure, ButlerDataRef):
1157 ccd = ccdExposure.getDetector()
1158 filterName = afwImage.Filter(ccdExposure.getFilter().getId()).getName()
1161 assert not self.config.doAssembleCcd,
"You need a Detector to run assembleCcd" 1162 ccd = [
FakeAmp(ccdExposure, self.config)]
1165 if self.config.doBias
and bias
is None:
1166 raise RuntimeError(
"Must supply a bias exposure if config.doBias=True.")
1168 raise RuntimeError(
"Must supply a linearizer if config.doLinearize=True for this detector.")
1169 if self.config.doBrighterFatter
and bfKernel
is None:
1170 raise RuntimeError(
"Must supply a kernel if config.doBrighterFatter=True.")
1171 if self.config.doDark
and dark
is None:
1172 raise RuntimeError(
"Must supply a dark exposure if config.doDark=True.")
1174 fringes = pipeBase.Struct(fringes=
None)
1175 if self.config.doFringe
and not isinstance(fringes, pipeBase.Struct):
1176 raise RuntimeError(
"Must supply fringe exposure as a pipeBase.Struct.")
1177 if self.config.doFlat
and flat
is None:
1178 raise RuntimeError(
"Must supply a flat exposure if config.doFlat=True.")
1179 if self.config.doDefect
and defects
is None:
1180 raise RuntimeError(
"Must supply defects if config.doDefect=True.")
1181 if self.config.doAddDistortionModel
and camera
is None:
1182 raise RuntimeError(
"Must supply camera if config.doAddDistortionModel=True.")
1183 if (self.config.doIlluminationCorrection
and filterName
in self.config.illumFilters
and 1184 illumMaskedImage
is None):
1185 raise RuntimeError(
"Must supply an illumcor if config.doIlluminationCorrection=True.")
1188 if self.config.doConvertIntToFloat:
1189 self.log.info(
"Converting exposure to floating point values")
1196 if ccdExposure.getBBox().contains(amp.getBBox()):
1200 if self.config.doOverscan
and not badAmp:
1203 self.log.debug(
"Corrected overscan for amplifier %s" % (amp.getName()))
1204 if self.config.qa
is not None and self.config.qa.saveStats
is True:
1205 if isinstance(overscanResults.overscanFit, float):
1206 qaMedian = overscanResults.overscanFit
1207 qaStdev = float(
"NaN")
1209 qaStats = afwMath.makeStatistics(overscanResults.overscanFit,
1210 afwMath.MEDIAN | afwMath.STDEVCLIP)
1211 qaMedian = qaStats.getValue(afwMath.MEDIAN)
1212 qaStdev = qaStats.getValue(afwMath.STDEVCLIP)
1214 self.metadata.set(
"ISR OSCAN {} MEDIAN".format(amp.getName()), qaMedian)
1215 self.metadata.set(
"ISR OSCAN {} STDEV".format(amp.getName()), qaStdev)
1216 self.log.debug(
" Overscan stats for amplifer %s: %f +/- %f" %
1217 (amp.getName(), qaMedian, qaStdev))
1218 ccdExposure.getMetadata().set(
'OVERSCAN',
"Overscan corrected")
1221 self.log.warn(
"Amplifier %s is bad." % (amp.getName()))
1222 overscanResults =
None 1224 overscans.append(overscanResults
if overscanResults
is not None else None)
1226 self.log.info(
"Skipped OSCAN")
1228 if self.config.doCrosstalk
and self.config.doCrosstalkBeforeAssemble:
1229 self.log.info(
"Applying crosstalk correction.")
1230 self.crosstalk.
run(ccdExposure, crosstalkSources=crosstalkSources)
1231 self.
debugView(ccdExposure,
"doCrosstalk")
1233 if self.config.doAssembleCcd:
1234 self.log.info(
"Assembling CCD from amplifiers")
1235 ccdExposure = self.assembleCcd.assembleCcd(ccdExposure)
1237 if self.config.expectWcs
and not ccdExposure.getWcs():
1238 self.log.warn(
"No WCS found in input exposure")
1239 self.
debugView(ccdExposure,
"doAssembleCcd")
1242 if self.config.qa.doThumbnailOss:
1243 ossThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa)
1245 if self.config.doBias:
1246 self.log.info(
"Applying bias correction.")
1247 isrFunctions.biasCorrection(ccdExposure.getMaskedImage(), bias.getMaskedImage(),
1248 trimToFit=self.config.doTrimToMatchCalib)
1251 if self.config.doVariance:
1252 for amp, overscanResults
in zip(ccd, overscans):
1253 if ccdExposure.getBBox().contains(amp.getBBox()):
1254 self.log.debug(
"Constructing variance map for amplifer %s" % (amp.getName()))
1255 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
1256 if overscanResults
is not None:
1258 overscanImage=overscanResults.overscanImage)
1262 if self.config.qa
is not None and self.config.qa.saveStats
is True:
1263 qaStats = afwMath.makeStatistics(ampExposure.getVariance(),
1264 afwMath.MEDIAN | afwMath.STDEVCLIP)
1265 self.metadata.set(
"ISR VARIANCE {} MEDIAN".format(amp.getName()),
1266 qaStats.getValue(afwMath.MEDIAN))
1267 self.metadata.set(
"ISR VARIANCE {} STDEV".format(amp.getName()),
1268 qaStats.getValue(afwMath.STDEVCLIP))
1269 self.log.debug(
" Variance stats for amplifer %s: %f +/- %f" %
1270 (amp.getName(), qaStats.getValue(afwMath.MEDIAN),
1271 qaStats.getValue(afwMath.STDEVCLIP)))
1274 self.log.info(
"Applying linearizer.")
1275 linearizer(image=ccdExposure.getMaskedImage().getImage(), detector=ccd, log=self.log)
1277 if self.config.doCrosstalk
and not self.config.doCrosstalkBeforeAssemble:
1278 self.log.info(
"Applying crosstalk correction.")
1279 self.crosstalk.
run(ccdExposure, crosstalkSources=crosstalkSources, isTrimmed=
True)
1280 self.
debugView(ccdExposure,
"doCrosstalk")
1284 if self.config.doDefect:
1285 self.log.info(
"Masking defects.")
1288 if self.config.doNanMasking:
1289 self.log.info(
"Masking NAN value pixels.")
1292 if self.config.doWidenSaturationTrails:
1293 self.log.info(
"Widening saturation trails.")
1294 isrFunctions.widenSaturationTrails(ccdExposure.getMaskedImage().getMask())
1296 if self.config.doCameraSpecificMasking:
1297 self.log.info(
"Masking regions for camera specific reasons.")
1298 self.masking.
run(ccdExposure)
1300 if self.config.doBrighterFatter:
1309 interpExp = ccdExposure.clone()
1311 isrFunctions.interpolateFromMask(
1312 maskedImage=ccdExposure.getMaskedImage(),
1313 fwhm=self.config.fwhm,
1314 growSaturatedFootprints=self.config.growSaturationFootprintSize,
1315 maskNameList=self.config.maskListToInterpolate
1317 bfExp = interpExp.clone()
1319 self.log.info(
"Applying brighter fatter correction.")
1320 isrFunctions.brighterFatterCorrection(bfExp, bfKernel,
1321 self.config.brighterFatterMaxIter,
1322 self.config.brighterFatterThreshold,
1323 self.config.brighterFatterApplyGain,
1325 image = ccdExposure.getMaskedImage().getImage()
1326 bfCorr = bfExp.getMaskedImage().getImage()
1327 bfCorr -= interpExp.getMaskedImage().getImage()
1330 self.
debugView(ccdExposure,
"doBrighterFatter")
1332 if self.config.doDark:
1333 self.log.info(
"Applying dark correction.")
1337 if self.config.doFringe
and not self.config.fringeAfterFlat:
1338 self.log.info(
"Applying fringe correction before flat.")
1339 self.fringe.
run(ccdExposure, **fringes.getDict())
1342 if self.config.doStrayLight:
1343 if strayLightData
is not None:
1344 self.log.info(
"Applying stray light correction.")
1345 self.strayLight.
run(ccdExposure, strayLightData)
1346 self.
debugView(ccdExposure,
"doStrayLight")
1348 self.log.debug(
"Skipping stray light correction: no data found for this image.")
1350 if self.config.doFlat:
1351 self.log.info(
"Applying flat correction.")
1355 if self.config.doApplyGains:
1356 self.log.info(
"Applying gain correction instead of flat.")
1357 isrFunctions.applyGains(ccdExposure, self.config.normalizeGains)
1359 if self.config.doFringe
and self.config.fringeAfterFlat:
1360 self.log.info(
"Applying fringe correction after flat.")
1361 self.fringe.
run(ccdExposure, **fringes.getDict())
1363 if self.config.doVignette:
1364 self.log.info(
"Constructing Vignette polygon.")
1367 if self.config.vignette.doWriteVignettePolygon:
1370 if self.config.doAttachTransmissionCurve:
1371 self.log.info(
"Adding transmission curves.")
1372 isrFunctions.attachTransmissionCurve(ccdExposure, opticsTransmission=opticsTransmission,
1373 filterTransmission=filterTransmission,
1374 sensorTransmission=sensorTransmission,
1375 atmosphereTransmission=atmosphereTransmission)
1377 if self.config.doAddDistortionModel:
1378 self.log.info(
"Adding a distortion model to the WCS.")
1379 isrFunctions.addDistortionModel(exposure=ccdExposure, camera=camera)
1381 flattenedThumb =
None 1382 if self.config.qa.doThumbnailFlattened:
1383 flattenedThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa)
1385 if self.config.doIlluminationCorrection
and filterName
in self.config.illumFilters:
1386 self.log.info(
"Performing illumination correction.")
1387 isrFunctions.illuminationCorrection(ccdExposure.getMaskedImage(),
1388 illumMaskedImage, illumScale=self.config.illumScale,
1389 trimToFit=self.config.doTrimToMatchCalib)
1392 if self.config.doSaveInterpPixels:
1393 preInterpExp = ccdExposure.clone()
1395 if self.config.doInterpolate:
1396 self.log.info(
"Interpolating masked pixels.")
1397 isrFunctions.interpolateFromMask(
1398 maskedImage=ccdExposure.getMaskedImage(),
1399 fwhm=self.config.fwhm,
1400 growSaturatedFootprints=self.config.growSaturationFootprintSize,
1401 maskNameList=list(self.config.maskListToInterpolate)
1404 if self.config.doSetBadRegions:
1406 badPixelCount, badPixelValue = isrFunctions.setBadRegions(ccdExposure)
1407 if badPixelCount > 0:
1408 self.log.info(
"Set %d BAD pixels to %f." % (badPixelCount, badPixelValue))
1412 if self.config.doMeasureBackground:
1413 self.log.info(
"Measuring background level:")
1416 if self.config.qa
is not None and self.config.qa.saveStats
is True:
1418 ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
1419 qaStats = afwMath.makeStatistics(ampExposure.getImage(),
1420 afwMath.MEDIAN | afwMath.STDEVCLIP)
1421 self.metadata.set(
"ISR BACKGROUND {} MEDIAN".format(amp.getName()),
1422 qaStats.getValue(afwMath.MEDIAN))
1423 self.metadata.set(
"ISR BACKGROUND {} STDEV".format(amp.getName()),
1424 qaStats.getValue(afwMath.STDEVCLIP))
1425 self.log.debug(
" Background stats for amplifer %s: %f +/- %f" %
1426 (amp.getName(), qaStats.getValue(afwMath.MEDIAN),
1427 qaStats.getValue(afwMath.STDEVCLIP)))
1429 self.
debugView(ccdExposure,
"postISRCCD")
1431 return pipeBase.Struct(
1432 exposure=ccdExposure,
1434 flattenedThumb=flattenedThumb,
1436 preInterpolatedExposure=preInterpExp,
1437 outputExposure=ccdExposure,
1438 outputOssThumbnail=ossThumb,
1439 outputFlattenedThumbnail=flattenedThumb,
1442 @pipeBase.timeMethod
1444 """Perform instrument signature removal on a ButlerDataRef of a Sensor. 1446 This method contains the `CmdLineTask` interface to the ISR 1447 processing. All IO is handled here, freeing the `run()` method 1448 to manage only pixel-level calculations. The steps performed 1450 - Read in necessary detrending/isr/calibration data. 1451 - Process raw exposure in `run()`. 1452 - Persist the ISR-corrected exposure as "postISRCCD" if 1453 config.doWrite=True. 1457 sensorRef : `daf.persistence.butlerSubset.ButlerDataRef` 1458 DataRef of the detector data to be processed 1462 result : `lsst.pipe.base.Struct` 1463 Result struct with component: 1464 - ``exposure`` : `afw.image.Exposure` 1465 The fully ISR corrected exposure. 1470 Raised if a configuration option is set to True, but the 1471 required calibration data does not exist. 1474 self.log.info(
"Performing ISR on sensor %s" % (sensorRef.dataId))
1476 ccdExposure = sensorRef.get(self.config.datasetType)
1478 camera = sensorRef.get(
"camera")
1479 if camera
is None and self.config.doAddDistortionModel:
1480 raise RuntimeError(
"config.doAddDistortionModel is True " 1481 "but could not get a camera from the butler")
1482 isrData = self.
readIsrData(sensorRef, ccdExposure)
1484 result = self.
run(ccdExposure, camera=camera, **isrData.getDict())
1486 if self.config.doWrite:
1487 sensorRef.put(result.exposure,
"postISRCCD")
1488 if result.preInterpolatedExposure
is not None:
1489 sensorRef.put(result.preInterpolatedExposure,
"postISRCCD_uninterpolated")
1490 if result.ossThumb
is not None:
1491 isrQa.writeThumbnail(sensorRef, result.ossThumb,
"ossThumb")
1492 if result.flattenedThumb
is not None:
1493 isrQa.writeThumbnail(sensorRef, result.flattenedThumb,
"flattenedThumb")
1498 """!Retrieve a calibration dataset for removing instrument signature. 1503 dataRef : `daf.persistence.butlerSubset.ButlerDataRef` 1504 DataRef of the detector data to find calibration datasets 1507 Type of dataset to retrieve (e.g. 'bias', 'flat', etc). 1509 If True, disable butler proxies to enable error handling 1510 within this routine. 1514 exposure : `lsst.afw.image.Exposure` 1515 Requested calibration frame. 1520 Raised if no matching calibration frame can be found. 1523 exp = dataRef.get(datasetType, immediate=immediate)
1524 except Exception
as exc1:
1525 if not self.config.fallbackFilterName:
1526 raise RuntimeError(
"Unable to retrieve %s for %s: %s" % (datasetType, dataRef.dataId, exc1))
1528 exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName, immediate=immediate)
1529 except Exception
as exc2:
1530 raise RuntimeError(
"Unable to retrieve %s for %s, even with fallback filter %s: %s AND %s" %
1531 (datasetType, dataRef.dataId, self.config.fallbackFilterName, exc1, exc2))
1532 self.log.warn(
"Using fallback calibration from filter %s" % self.config.fallbackFilterName)
1534 if self.config.doAssembleIsrExposures:
1535 exp = self.assembleCcd.assembleCcd(exp)
1539 """Ensure that the data returned by Butler is a fully constructed exposure. 1541 ISR requires exposure-level image data for historical reasons, so if we did 1542 not recieve that from Butler, construct it from what we have, modifying the 1547 inputExp : `lsst.afw.image.Exposure`, `lsst.afw.image.DecoratedImageU`, or 1548 `lsst.afw.image.ImageF` 1549 The input data structure obtained from Butler. 1550 camera : `lsst.afw.cameraGeom.camera` 1551 The camera associated with the image. Used to find the appropriate 1554 The detector this exposure should match. 1558 inputExp : `lsst.afw.image.Exposure` 1559 The re-constructed exposure, with appropriate detector parameters. 1564 Raised if the input data cannot be used to construct an exposure. 1566 if isinstance(inputExp, afwImage.DecoratedImageU):
1567 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp))
1568 elif isinstance(inputExp, afwImage.ImageF):
1569 inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp))
1570 elif isinstance(inputExp, afwImage.MaskedImageF):
1571 inputExp = afwImage.makeExposure(inputExp)
1572 elif isinstance(inputExp, afwImage.Exposure):
1574 elif inputExp
is None:
1578 raise TypeError(f
"Input Exposure is not known type in isrTask.ensureExposure: {type(inputExp)}")
1580 if inputExp.getDetector()
is None:
1581 inputExp.setDetector(camera[detectorNum])
1586 """Convert exposure image from uint16 to float. 1588 If the exposure does not need to be converted, the input is 1589 immediately returned. For exposures that are converted to use 1590 floating point pixels, the variance is set to unity and the 1595 exposure : `lsst.afw.image.Exposure` 1596 The raw exposure to be converted. 1600 newexposure : `lsst.afw.image.Exposure` 1601 The input ``exposure``, converted to floating point pixels. 1606 Raised if the exposure type cannot be converted to float. 1609 if isinstance(exposure, afwImage.ExposureF):
1612 if not hasattr(exposure,
"convertF"):
1613 raise RuntimeError(
"Unable to convert exposure (%s) to float" % type(exposure))
1615 newexposure = exposure.convertF()
1616 newexposure.variance[:] = 1
1617 newexposure.mask[:] = 0x0
1622 """Identify bad amplifiers, saturated and suspect pixels. 1626 ccdExposure : `lsst.afw.image.Exposure` 1627 Input exposure to be masked. 1628 amp : `lsst.afw.table.AmpInfoCatalog` 1629 Catalog of parameters defining the amplifier on this 1631 defects : `lsst.meas.algorithms.Defects` 1632 List of defects. Used to determine if the entire 1638 If this is true, the entire amplifier area is covered by 1639 defects and unusable. 1642 maskedImage = ccdExposure.getMaskedImage()
1648 if defects
is not None:
1649 badAmp = bool(sum([v.getBBox().contains(amp.getBBox())
for v
in defects]))
1654 dataView = afwImage.MaskedImageF(maskedImage, amp.getRawBBox(),
1656 maskView = dataView.getMask()
1657 maskView |= maskView.getPlaneBitMask(
"BAD")
1664 if self.config.doSaturation
and not badAmp:
1665 limits.update({self.config.saturatedMaskName: amp.getSaturation()})
1666 if self.config.doSuspect
and not badAmp:
1667 limits.update({self.config.suspectMaskName: amp.getSuspectLevel()})
1668 if math.isfinite(self.config.saturation):
1669 limits.update({self.config.saturatedMaskName: self.config.saturation})
1671 for maskName, maskThreshold
in limits.items():
1672 if not math.isnan(maskThreshold):
1673 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
1674 isrFunctions.makeThresholdMask(
1675 maskedImage=dataView,
1676 threshold=maskThreshold,
1682 maskView = afwImage.Mask(maskedImage.getMask(), amp.getRawDataBBox(),
1684 maskVal = maskView.getPlaneBitMask([self.config.saturatedMaskName,
1685 self.config.suspectMaskName])
1686 if numpy.all(maskView.getArray() & maskVal > 0):
1692 """Apply overscan correction in place. 1694 This method does initial pixel rejection of the overscan 1695 region. The overscan can also be optionally segmented to 1696 allow for discontinuous overscan responses to be fit 1697 separately. The actual overscan subtraction is performed by 1698 the `lsst.ip.isr.isrFunctions.overscanCorrection` function, 1699 which is called here after the amplifier is preprocessed. 1703 ccdExposure : `lsst.afw.image.Exposure` 1704 Exposure to have overscan correction performed. 1705 amp : `lsst.afw.table.AmpInfoCatalog` 1706 The amplifier to consider while correcting the overscan. 1710 overscanResults : `lsst.pipe.base.Struct` 1711 Result struct with components: 1712 - ``imageFit`` : scalar or `lsst.afw.image.Image` 1713 Value or fit subtracted from the amplifier image data. 1714 - ``overscanFit`` : scalar or `lsst.afw.image.Image` 1715 Value or fit subtracted from the overscan image data. 1716 - ``overscanImage`` : `lsst.afw.image.Image` 1717 Image of the overscan region with the overscan 1718 correction applied. This quantity is used to estimate 1719 the amplifier read noise empirically. 1724 Raised if the ``amp`` does not contain raw pixel information. 1728 lsst.ip.isr.isrFunctions.overscanCorrection 1730 if not amp.getHasRawInfo():
1731 raise RuntimeError(
"This method must be executed on an amp with raw information.")
1733 if amp.getRawHorizontalOverscanBBox().isEmpty():
1734 self.log.info(
"ISR_OSCAN: No overscan region. Not performing overscan correction.")
1737 statControl = afwMath.StatisticsControl()
1738 statControl.setAndMask(ccdExposure.mask.getPlaneBitMask(
"SAT"))
1741 dataBBox = amp.getRawDataBBox()
1742 oscanBBox = amp.getRawHorizontalOverscanBBox()
1746 prescanBBox = amp.getRawPrescanBBox()
1747 if (oscanBBox.getBeginX() > prescanBBox.getBeginX()):
1748 dx0 += self.config.overscanNumLeadingColumnsToSkip
1749 dx1 -= self.config.overscanNumTrailingColumnsToSkip
1751 dx0 += self.config.overscanNumTrailingColumnsToSkip
1752 dx1 -= self.config.overscanNumLeadingColumnsToSkip
1758 if ((self.config.overscanBiasJump
and 1759 self.config.overscanBiasJumpLocation)
and 1760 (ccdExposure.getMetadata().exists(self.config.overscanBiasJumpKeyword)
and 1761 ccdExposure.getMetadata().getScalar(self.config.overscanBiasJumpKeyword)
in 1762 self.config.overscanBiasJumpDevices)):
1763 if amp.getReadoutCorner()
in (afwTable.LL, afwTable.LR):
1764 yLower = self.config.overscanBiasJumpLocation
1765 yUpper = dataBBox.getHeight() - yLower
1767 yUpper = self.config.overscanBiasJumpLocation
1768 yLower = dataBBox.getHeight() - yUpper
1787 oscanBBox.getHeight())))
1790 for imageBBox, overscanBBox
in zip(imageBBoxes, overscanBBoxes):
1791 ampImage = ccdExposure.maskedImage[imageBBox]
1792 overscanImage = ccdExposure.maskedImage[overscanBBox]
1794 overscanArray = overscanImage.image.array
1795 median = numpy.ma.median(numpy.ma.masked_where(overscanImage.mask.array, overscanArray))
1796 bad = numpy.where(numpy.abs(overscanArray - median) > self.config.overscanMaxDev)
1797 overscanImage.mask.array[bad] = overscanImage.mask.getPlaneBitMask(
"SAT")
1799 statControl = afwMath.StatisticsControl()
1800 statControl.setAndMask(ccdExposure.mask.getPlaneBitMask(
"SAT"))
1802 overscanResults = isrFunctions.overscanCorrection(ampMaskedImage=ampImage,
1803 overscanImage=overscanImage,
1804 fitType=self.config.overscanFitType,
1805 order=self.config.overscanOrder,
1806 collapseRej=self.config.overscanNumSigmaClip,
1807 statControl=statControl,
1808 overscanIsInt=self.config.overscanIsInt
1812 levelStat = afwMath.MEDIAN
1813 sigmaStat = afwMath.STDEVCLIP
1815 sctrl = afwMath.StatisticsControl(self.config.qa.flatness.clipSigma,
1816 self.config.qa.flatness.nIter)
1817 metadata = ccdExposure.getMetadata()
1818 ampNum = amp.getName()
1819 if self.config.overscanFitType
in (
"MEDIAN",
"MEAN",
"MEANCLIP"):
1820 metadata.set(
"ISR_OSCAN_LEVEL%s" % ampNum, overscanResults.overscanFit)
1821 metadata.set(
"ISR_OSCAN_SIGMA%s" % ampNum, 0.0)
1823 stats = afwMath.makeStatistics(overscanResults.overscanFit, levelStat | sigmaStat, sctrl)
1824 metadata.set(
"ISR_OSCAN_LEVEL%s" % ampNum, stats.getValue(levelStat))
1825 metadata.set(
"ISR_OSCAN_SIGMA%s" % ampNum, stats.getValue(sigmaStat))
1827 return overscanResults
1830 """Set the variance plane using the amplifier gain and read noise 1832 The read noise is calculated from the ``overscanImage`` if the 1833 ``doEmpiricalReadNoise`` option is set in the configuration; otherwise 1834 the value from the amplifier data is used. 1838 ampExposure : `lsst.afw.image.Exposure` 1839 Exposure to process. 1840 amp : `lsst.afw.table.AmpInfoRecord` or `FakeAmp` 1841 Amplifier detector data. 1842 overscanImage : `lsst.afw.image.MaskedImage`, optional. 1843 Image of overscan, required only for empirical read noise. 1847 lsst.ip.isr.isrFunctions.updateVariance 1849 maskPlanes = [self.config.saturatedMaskName, self.config.suspectMaskName]
1850 gain = amp.getGain()
1852 if math.isnan(gain):
1854 self.log.warn(
"Gain set to NAN! Updating to 1.0 to generate Poisson variance.")
1857 self.log.warn(
"Gain for amp %s == %g <= 0; setting to %f" %
1858 (amp.getName(), gain, patchedGain))
1861 if self.config.doEmpiricalReadNoise
and overscanImage
is None:
1862 self.log.info(
"Overscan is none for EmpiricalReadNoise")
1864 if self.config.doEmpiricalReadNoise
and overscanImage
is not None:
1865 stats = afwMath.StatisticsControl()
1866 stats.setAndMask(overscanImage.mask.getPlaneBitMask(maskPlanes))
1867 readNoise = afwMath.makeStatistics(overscanImage, afwMath.STDEVCLIP, stats).getValue()
1868 self.log.info(
"Calculated empirical read noise for amp %s: %f", amp.getName(), readNoise)
1870 readNoise = amp.getReadNoise()
1872 isrFunctions.updateVariance(
1873 maskedImage=ampExposure.getMaskedImage(),
1875 readNoise=readNoise,
1879 """!Apply dark correction in place. 1883 exposure : `lsst.afw.image.Exposure` 1884 Exposure to process. 1885 darkExposure : `lsst.afw.image.Exposure` 1886 Dark exposure of the same size as ``exposure``. 1887 invert : `Bool`, optional 1888 If True, re-add the dark to an already corrected image. 1893 Raised if either ``exposure`` or ``darkExposure`` do not 1894 have their dark time defined. 1898 lsst.ip.isr.isrFunctions.darkCorrection 1900 expScale = exposure.getInfo().getVisitInfo().getDarkTime()
1901 if math.isnan(expScale):
1902 raise RuntimeError(
"Exposure darktime is NAN")
1903 if darkExposure.getInfo().getVisitInfo()
is not None:
1904 darkScale = darkExposure.getInfo().getVisitInfo().getDarkTime()
1910 if math.isnan(darkScale):
1911 raise RuntimeError(
"Dark calib darktime is NAN")
1912 isrFunctions.darkCorrection(
1913 maskedImage=exposure.getMaskedImage(),
1914 darkMaskedImage=darkExposure.getMaskedImage(),
1916 darkScale=darkScale,
1918 trimToFit=self.config.doTrimToMatchCalib
1922 """!Check if linearization is needed for the detector cameraGeom. 1924 Checks config.doLinearize and the linearity type of the first 1929 detector : `lsst.afw.cameraGeom.Detector` 1930 Detector to get linearity type from. 1934 doLinearize : `Bool` 1935 If True, linearization should be performed. 1937 return self.config.doLinearize
and \
1938 detector.getAmpInfoCatalog()[0].getLinearityType() != NullLinearityType
1941 """!Apply flat correction in place. 1945 exposure : `lsst.afw.image.Exposure` 1946 Exposure to process. 1947 flatExposure : `lsst.afw.image.Exposure` 1948 Flat exposure of the same size as ``exposure``. 1949 invert : `Bool`, optional 1950 If True, unflatten an already flattened image. 1954 lsst.ip.isr.isrFunctions.flatCorrection 1956 isrFunctions.flatCorrection(
1957 maskedImage=exposure.getMaskedImage(),
1958 flatMaskedImage=flatExposure.getMaskedImage(),
1959 scalingType=self.config.flatScalingType,
1960 userScale=self.config.flatUserScale,
1962 trimToFit=self.config.doTrimToMatchCalib
1966 """!Detect saturated pixels and mask them using mask plane config.saturatedMaskName, in place. 1970 exposure : `lsst.afw.image.Exposure` 1971 Exposure to process. Only the amplifier DataSec is processed. 1972 amp : `lsst.afw.table.AmpInfoCatalog` 1973 Amplifier detector data. 1977 lsst.ip.isr.isrFunctions.makeThresholdMask 1979 if not math.isnan(amp.getSaturation()):
1980 maskedImage = exposure.getMaskedImage()
1981 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
1982 isrFunctions.makeThresholdMask(
1983 maskedImage=dataView,
1984 threshold=amp.getSaturation(),
1986 maskName=self.config.saturatedMaskName,
1990 """!Interpolate over saturated pixels, in place. 1992 This method should be called after `saturationDetection`, to 1993 ensure that the saturated pixels have been identified in the 1994 SAT mask. It should also be called after `assembleCcd`, since 1995 saturated regions may cross amplifier boundaries. 1999 exposure : `lsst.afw.image.Exposure` 2000 Exposure to process. 2004 lsst.ip.isr.isrTask.saturationDetection 2005 lsst.ip.isr.isrFunctions.interpolateFromMask 2007 isrFunctions.interpolateFromMask(
2008 maskedImage=exposure.getMaskedImage(),
2009 fwhm=self.config.fwhm,
2010 growSaturatedFootprints=self.config.growSaturationFootprintSize,
2011 maskNameList=list(self.config.saturatedMaskName),
2015 """!Detect suspect pixels and mask them using mask plane config.suspectMaskName, in place. 2019 exposure : `lsst.afw.image.Exposure` 2020 Exposure to process. Only the amplifier DataSec is processed. 2021 amp : `lsst.afw.table.AmpInfoCatalog` 2022 Amplifier detector data. 2026 lsst.ip.isr.isrFunctions.makeThresholdMask 2030 Suspect pixels are pixels whose value is greater than amp.getSuspectLevel(). 2031 This is intended to indicate pixels that may be affected by unknown systematics; 2032 for example if non-linearity corrections above a certain level are unstable 2033 then that would be a useful value for suspectLevel. A value of `nan` indicates 2034 that no such level exists and no pixels are to be masked as suspicious. 2036 suspectLevel = amp.getSuspectLevel()
2037 if math.isnan(suspectLevel):
2040 maskedImage = exposure.getMaskedImage()
2041 dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
2042 isrFunctions.makeThresholdMask(
2043 maskedImage=dataView,
2044 threshold=suspectLevel,
2046 maskName=self.config.suspectMaskName,
2050 """!Mask defects using mask plane "BAD", in place. 2054 exposure : `lsst.afw.image.Exposure` 2055 Exposure to process. 2056 defectBaseList : `lsst.meas.algorithms.Defects` or `list` of 2057 `lsst.afw.image.DefectBase`. 2058 List of defects to mask and interpolate. 2062 Call this after CCD assembly, since defects may cross amplifier boundaries. 2064 maskedImage = exposure.getMaskedImage()
2065 if not isinstance(defectBaseList, Defects):
2067 defectList = Defects(defectBaseList)
2069 defectList = defectBaseList
2070 defectList.maskPixels(maskedImage, maskName=
"BAD")
2072 if self.config.numEdgeSuspect > 0:
2073 goodBBox = maskedImage.getBBox()
2075 goodBBox.grow(-self.config.numEdgeSuspect)
2077 SourceDetectionTask.setEdgeBits(
2080 maskedImage.getMask().getPlaneBitMask(
"SUSPECT")
2084 """Mask and interpolate defects using mask plane "BAD", in place. 2088 exposure : `lsst.afw.image.Exposure` 2089 Exposure to process. 2090 defectBaseList : `List` of `Defects` 2093 self.maskDefects(exposure, defectBaseList)
2094 isrFunctions.interpolateFromMask(
2095 maskedImage=exposure.getMaskedImage(),
2096 fwhm=self.config.fwhm,
2097 growSaturatedFootprints=0,
2098 maskNameList=[
"BAD"],
2102 """Mask NaNs using mask plane "UNMASKEDNAN", in place. 2106 exposure : `lsst.afw.image.Exposure` 2107 Exposure to process. 2111 We mask over all NaNs, including those that are masked with 2112 other bits (because those may or may not be interpolated over 2113 later, and we want to remove all NaNs). Despite this 2114 behaviour, the "UNMASKEDNAN" mask plane is used to preserve 2115 the historical name. 2117 maskedImage = exposure.getMaskedImage()
2120 maskedImage.getMask().addMaskPlane(
"UNMASKEDNAN")
2121 maskVal = maskedImage.getMask().getPlaneBitMask(
"UNMASKEDNAN")
2122 numNans =
maskNans(maskedImage, maskVal)
2123 self.metadata.set(
"NUMNANS", numNans)
2125 self.log.warn(f
"There were {numNans} unmasked NaNs")
2128 """"Mask and interpolate NaNs using mask plane "UNMASKEDNAN", in place. 2132 exposure : `lsst.afw.image.Exposure` 2133 Exposure to process. 2137 lsst.ip.isr.isrTask.maskNan() 2140 isrFunctions.interpolateFromMask(
2141 maskedImage=exposure.getMaskedImage(),
2142 fwhm=self.config.fwhm,
2143 growSaturatedFootprints=0,
2144 maskNameList=[
"UNMASKEDNAN"],
2148 """Measure the image background in subgrids, for quality control purposes. 2152 exposure : `lsst.afw.image.Exposure` 2153 Exposure to process. 2154 IsrQaConfig : `lsst.ip.isr.isrQa.IsrQaConfig` 2155 Configuration object containing parameters on which background 2156 statistics and subgrids to use. 2158 if IsrQaConfig
is not None:
2159 statsControl = afwMath.StatisticsControl(IsrQaConfig.flatness.clipSigma,
2160 IsrQaConfig.flatness.nIter)
2161 maskVal = exposure.getMaskedImage().getMask().getPlaneBitMask([
"BAD",
"SAT",
"DETECTED"])
2162 statsControl.setAndMask(maskVal)
2163 maskedImage = exposure.getMaskedImage()
2164 stats = afwMath.makeStatistics(maskedImage, afwMath.MEDIAN | afwMath.STDEVCLIP, statsControl)
2165 skyLevel = stats.getValue(afwMath.MEDIAN)
2166 skySigma = stats.getValue(afwMath.STDEVCLIP)
2167 self.log.info(
"Flattened sky level: %f +/- %f" % (skyLevel, skySigma))
2168 metadata = exposure.getMetadata()
2169 metadata.set(
'SKYLEVEL', skyLevel)
2170 metadata.set(
'SKYSIGMA', skySigma)
2173 stat = afwMath.MEANCLIP
if IsrQaConfig.flatness.doClip
else afwMath.MEAN
2174 meshXHalf = int(IsrQaConfig.flatness.meshX/2.)
2175 meshYHalf = int(IsrQaConfig.flatness.meshY/2.)
2176 nX = int((exposure.getWidth() + meshXHalf) / IsrQaConfig.flatness.meshX)
2177 nY = int((exposure.getHeight() + meshYHalf) / IsrQaConfig.flatness.meshY)
2178 skyLevels = numpy.zeros((nX, nY))
2181 yc = meshYHalf + j * IsrQaConfig.flatness.meshY
2183 xc = meshXHalf + i * IsrQaConfig.flatness.meshX
2185 xLLC = xc - meshXHalf
2186 yLLC = yc - meshYHalf
2187 xURC = xc + meshXHalf - 1
2188 yURC = yc + meshYHalf - 1
2191 miMesh = maskedImage.Factory(exposure.getMaskedImage(), bbox, afwImage.LOCAL)
2193 skyLevels[i, j] = afwMath.makeStatistics(miMesh, stat, statsControl).getValue()
2195 good = numpy.where(numpy.isfinite(skyLevels))
2196 skyMedian = numpy.median(skyLevels[good])
2197 flatness = (skyLevels[good] - skyMedian) / skyMedian
2198 flatness_rms = numpy.std(flatness)
2199 flatness_pp = flatness.max() - flatness.min()
if len(flatness) > 0
else numpy.nan
2201 self.log.info(
"Measuring sky levels in %dx%d grids: %f" % (nX, nY, skyMedian))
2202 self.log.info(
"Sky flatness in %dx%d grids - pp: %f rms: %f" %
2203 (nX, nY, flatness_pp, flatness_rms))
2205 metadata.set(
'FLATNESS_PP', float(flatness_pp))
2206 metadata.set(
'FLATNESS_RMS', float(flatness_rms))
2207 metadata.set(
'FLATNESS_NGRIDS',
'%dx%d' % (nX, nY))
2208 metadata.set(
'FLATNESS_MESHX', IsrQaConfig.flatness.meshX)
2209 metadata.set(
'FLATNESS_MESHY', IsrQaConfig.flatness.meshY)
2212 """Set an approximate magnitude zero point for the exposure. 2216 exposure : `lsst.afw.image.Exposure` 2217 Exposure to process. 2219 filterName = afwImage.Filter(exposure.getFilter().getId()).getName()
2220 if filterName
in self.config.fluxMag0T1:
2221 fluxMag0 = self.config.fluxMag0T1[filterName]
2223 self.log.warn(
"No rough magnitude zero point set for filter %s" % filterName)
2224 fluxMag0 = self.config.defaultFluxMag0T1
2226 expTime = exposure.getInfo().getVisitInfo().getExposureTime()
2228 self.log.warn(
"Non-positive exposure time; skipping rough zero point")
2231 self.log.info(
"Setting rough magnitude zero point: %f" % (2.5*math.log10(fluxMag0*expTime),))
2232 exposure.setPhotoCalib(afwImage.makePhotoCalibFromCalibZeroPoint(fluxMag0*expTime, 0.0))
2235 """!Set the valid polygon as the intersection of fpPolygon and the ccd corners. 2239 ccdExposure : `lsst.afw.image.Exposure` 2240 Exposure to process. 2241 fpPolygon : `lsst.afw.geom.Polygon` 2242 Polygon in focal plane coordinates. 2245 ccd = ccdExposure.getDetector()
2246 fpCorners = ccd.getCorners(FOCAL_PLANE)
2247 ccdPolygon = Polygon(fpCorners)
2250 intersect = ccdPolygon.intersectionSingle(fpPolygon)
2253 ccdPoints = ccd.transform(intersect, FOCAL_PLANE, PIXELS)
2254 validPolygon = Polygon(ccdPoints)
2255 ccdExposure.getInfo().setValidPolygon(validPolygon)
2259 """Context manager that applies and removes flats and darks, 2260 if the task is configured to apply them. 2264 exp : `lsst.afw.image.Exposure` 2265 Exposure to process. 2266 flat : `lsst.afw.image.Exposure` 2267 Flat exposure the same size as ``exp``. 2268 dark : `lsst.afw.image.Exposure`, optional 2269 Dark exposure the same size as ``exp``. 2273 exp : `lsst.afw.image.Exposure` 2274 The flat and dark corrected exposure. 2276 if self.config.doDark
and dark
is not None:
2278 if self.config.doFlat:
2283 if self.config.doFlat:
2285 if self.config.doDark
and dark
is not None:
2289 """Utility function to examine ISR exposure at different stages. 2293 exposure : `lsst.afw.image.Exposure` 2296 State of processing to view. 2298 frame = getDebugFrame(self._display, stepname)
2300 display = getDisplay(frame)
2301 display.scale(
'asinh',
'zscale')
2302 display.mtv(exposure)
2306 """A Detector-like object that supports returning gain and saturation level 2308 This is used when the input exposure does not have a detector. 2312 exposure : `lsst.afw.image.Exposure` 2313 Exposure to generate a fake amplifier for. 2314 config : `lsst.ip.isr.isrTaskConfig` 2315 Configuration to apply to the fake amplifier. 2319 self.
_bbox = exposure.getBBox(afwImage.LOCAL)
2321 self.
_gain = config.gain
2351 isr = pexConfig.ConfigurableField(target=IsrTask, doc=
"Instrument signature removal")
2355 """Task to wrap the default IsrTask to allow it to be retargeted. 2357 The standard IsrTask can be called directly from a command line 2358 program, but doing so removes the ability of the task to be 2359 retargeted. As most cameras override some set of the IsrTask 2360 methods, this would remove those data-specific methods in the 2361 output post-ISR images. This wrapping class fixes the issue, 2362 allowing identical post-ISR images to be generated by both the 2363 processCcd and isrTask code. 2365 ConfigClass = RunIsrConfig
2366 _DefaultName =
"runIsr" 2370 self.makeSubtask(
"isr")
2376 dataRef : `lsst.daf.persistence.ButlerDataRef` 2377 data reference of the detector data to be processed 2381 result : `pipeBase.Struct` 2382 Result struct with component: 2384 - exposure : `lsst.afw.image.Exposure` 2385 Post-ISR processed exposure. def getInputDatasetTypes(cls, config)
def runDataRef(self, sensorRef)
def measureBackground(self, exposure, IsrQaConfig=None)
def debugView(self, exposure, stepname)
def __init__(self, kwargs)
def ensureExposure(self, inputExp, camera, detectorNum)
def readIsrData(self, dataRef, rawExposure)
Retrieve necessary frames for instrument signature removal.
def adaptArgsAndRun(self, inputData, inputDataIds, outputDataIds, butler)
def runDataRef(self, dataRef)
def __init__(self, args, kwargs)
def getPrerequisiteDatasetTypes(cls, config)
def roughZeroPoint(self, exposure)
def maskAndInterpolateDefects(self, exposure, defectBaseList)
def getRawHorizontalOverscanBBox(self)
def maskNan(self, exposure)
def run(self, ccdExposure, camera=None, bias=None, linearizer=None, crosstalkSources=None, dark=None, flat=None, bfKernel=None, defects=None, fringes=None, opticsTransmission=None, filterTransmission=None, sensorTransmission=None, atmosphereTransmission=None, detectorNum=None, strayLightData=None, illumMaskedImage=None, isGen3=False)
Perform instrument signature removal on an exposure.
def getSuspectLevel(self)
def getOutputDatasetTypes(cls, config)
def maskDefect(self, exposure, defectBaseList)
Mask defects using mask plane "BAD", in place.
def overscanCorrection(self, ccdExposure, amp)
def convertIntToFloat(self, exposure)
def flatCorrection(self, exposure, flatExposure, invert=False)
Apply flat correction in place.
def makeDatasetType(self, dsConfig)
def getIsrExposure(self, dataRef, datasetType, immediate=True)
Retrieve a calibration dataset for removing instrument signature.
_RawHorizontalOverscanBBox
def darkCorrection(self, exposure, darkExposure, invert=False)
Apply dark correction in place.
def doLinearize(self, detector)
Check if linearization is needed for the detector cameraGeom.
def setValidPolygonIntersect(self, ccdExposure, fpPolygon)
Set the valid polygon as the intersection of fpPolygon and the ccd corners.
def maskAmplifier(self, ccdExposure, amp, defects)
def getPerDatasetTypeDimensions(cls, config)
def flatContext(self, exp, flat, dark=None)
size_t maskNans(afw::image::MaskedImage< PixelT > const &mi, afw::image::MaskPixel maskVal, afw::image::MaskPixel allow=0)
Mask NANs in an image.
def updateVariance(self, ampExposure, amp, overscanImage=None)
def maskAndInterpolateNan(self, exposure)
def suspectDetection(self, exposure, amp)
Detect suspect pixels and mask them using mask plane config.suspectMaskName, in place.
def saturationInterpolation(self, exposure)
Interpolate over saturated pixels, in place.
def saturationDetection(self, exposure, amp)
Detect saturated pixels and mask them using mask plane config.saturatedMaskName, in place...
doSaturationInterpolation
def __init__(self, exposure, config)