lsst.ip.isr.isrTask.IsrTask Class Reference

Inheritance diagram for lsst.ip.isr.isrTask.IsrTask:

Public Member Functions
def	__init__ (self, kwargs)
def	getInputDatasetTypes (cls, config)
def	getOutputDatasetTypes (cls, config)
def	getPrerequisiteDatasetTypes (cls, config)
def	getPerDatasetTypeDimensions (cls, config)
def	adaptArgsAndRun (self, inputData, inputDataIds, outputDataIds, butler)
def	makeDatasetType (self, dsConfig)
def	readIsrData (self, dataRef, rawExposure)
	Retrieve necessary frames for instrument signature removal. More...
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, isGen3=False)
	Perform instrument signature removal on an exposure. More...
def	runDataRef (self, sensorRef)
def	getIsrExposure (self, dataRef, datasetType, immediate=True)
	Retrieve a calibration dataset for removing instrument signature. More...
def	ensureExposure (self, inputExp, camera, detectorNum)
def	convertIntToFloat (self, exposure)
def	maskAmplifier (self, ccdExposure, amp, defects)
def	overscanCorrection (self, ccdExposure, amp)
def	updateVariance (self, ampExposure, amp, overscanImage=None)
def	darkCorrection (self, exposure, darkExposure, invert=False)
	Apply dark correction in place. More...
def	doLinearize (self, detector)
	Check if linearization is needed for the detector cameraGeom. More...
def	flatCorrection (self, exposure, flatExposure, invert=False)
	Apply flat correction in place. More...
def	saturationDetection (self, exposure, amp)
	Detect saturated pixels and mask them using mask plane config.saturatedMaskName, in place. More...
def	saturationInterpolation (self, exposure)
	Interpolate over saturated pixels, in place. More...
def	suspectDetection (self, exposure, amp)
	Detect suspect pixels and mask them using mask plane config.suspectMaskName, in place. More...
def	maskDefect (self, exposure, defectBaseList)
	Mask defects using mask plane "BAD", in place. More...
def	maskAndInterpolateDefects (self, exposure, defectBaseList)
def	maskNan (self, exposure)
def	maskAndInterpolateNan (self, exposure)
def	measureBackground (self, exposure, IsrQaConfig=None)
def	roughZeroPoint (self, exposure)
def	setValidPolygonIntersect (self, ccdExposure, fpPolygon)
	Set the valid polygon as the intersection of fpPolygon and the ccd corners. More...
def	flatContext (self, exp, flat, dark=None)
def	debugView (self, exposure, stepname)

Public Attributes
	vignettePolygon

Static Public Attributes
	ConfigClass = IsrTaskConfig

Detailed Description

Apply common instrument signature correction algorithms to a raw frame.

The process for correcting imaging data is very similar from
camera to camera.  This task provides a vanilla implementation of
doing these corrections, including the ability to turn certain
corrections off if they are not needed.  The inputs to the primary
method, `run()`, are a raw exposure to be corrected and the
calibration data products. The raw input is a single chip sized
mosaic of all amps including overscans and other non-science
pixels.  The method `runDataRef()` identifies and defines the
calibration data products, and is intended for use by a
`lsst.pipe.base.cmdLineTask.CmdLineTask` and takes as input only a
`daf.persistence.butlerSubset.ButlerDataRef`.  This task may be
subclassed for different camera, although the most camera specific
methods have been split into subtasks that can be redirected
appropriately.

The __init__ method sets up the subtasks for ISR processing, using
the defaults from `lsst.ip.isr`.

Parameters
----------
args : `list`
    Positional arguments passed to the Task constructor. None used at this time.
kwargs : `dict`, optional
    Keyword arguments passed on to the Task constructor. None used at this time.

Definition at line 699 of file isrTask.py.

Constructor & Destructor Documentation

__init__()

def lsst.ip.isr.isrTask.IsrTask.__init__	(		self,
			kwargs
	)

Definition at line 730 of file isrTask.py.

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.makeSubtask("assembleCcd")
        self.makeSubtask("crosstalk")
        self.makeSubtask("strayLight")
        self.makeSubtask("fringe")
        self.makeSubtask("masking")
        self.makeSubtask("vignette")

Member Function Documentation

adaptArgsAndRun()

def lsst.ip.isr.isrTask.IsrTask.adaptArgsAndRun	(		self,
			inputData,
			inputDataIds,
			outputDataIds,
			butler
	)

Definition at line 807 of file isrTask.py.

    def adaptArgsAndRun(self, inputData, inputDataIds, outputDataIds, butler):
        try:
            inputData['detectorNum'] = int(inputDataIds['ccdExposure']['detector'])
        except Exception as e:
            raise ValueError(f"Failure to find valid detectorNum value for Dataset {inputDataIds}: {e}")

        inputData['isGen3'] = True

        if self.config.doLinearize is True:
            if 'linearizer' not in inputData.keys():
                detector = inputData['camera'][inputData['detectorNum']]
                linearityName = detector.getAmpInfoCatalog()[0].getLinearityType()
                inputData['linearizer'] = linearize.getLinearityTypeByName(linearityName)()

        if inputData['defects'] is not None:
            # defects is loaded as a BaseCatalog with columns x0, y0, width, height.
            # masking expects a list of defects defined by their bounding box
            if not isinstance(inputData["defects"], Defects):
                inputData["defects"] = Defects.fromTable(inputData["defects"])

        # Broken: DM-17169
        # ci_hsc does not use crosstalkSources, as it's intra-CCD CT only.  This needs to be
        # fixed for non-HSC cameras in the future.
        # inputData['crosstalkSources'] = (self.crosstalk.prepCrosstalk(inputDataIds['ccdExposure'])
        #                        if self.config.doCrosstalk else None)

        # Broken: DM-17152
        # Fringes are not tested to be handled correctly by Gen3 butler.
        # inputData['fringes'] = (self.fringe.readFringes(inputDataIds['ccdExposure'],
        #                                                 assembler=self.assembleCcd
        #                                                 if self.config.doAssembleIsrExposures else None)
        #                         if self.config.doFringe and
        #                            self.fringe.checkFilter(inputData['ccdExposure'])
        #                         else pipeBase.Struct(fringes=None))

        return super().adaptArgsAndRun(inputData, inputDataIds, outputDataIds, butler)

convertIntToFloat()

def lsst.ip.isr.isrTask.IsrTask.convertIntToFloat	(		self,
			exposure
	)

Convert exposure image from uint16 to float.

If the exposure does not need to be converted, the input is
immediately returned.  For exposures that are converted to use
floating point pixels, the variance is set to unity and the
mask to zero.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
   The raw exposure to be converted.

Returns
-------
newexposure : `lsst.afw.image.Exposure`
   The input ``exposure``, converted to floating point pixels.

Raises
------
RuntimeError
    Raised if the exposure type cannot be converted to float.

Definition at line 1532 of file isrTask.py.

    def convertIntToFloat(self, exposure):
        """Convert exposure image from uint16 to float.

        If the exposure does not need to be converted, the input is
        immediately returned.  For exposures that are converted to use
        floating point pixels, the variance is set to unity and the
        mask to zero.

        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
           The raw exposure to be converted.

        Returns
        -------
        newexposure : `lsst.afw.image.Exposure`
           The input ``exposure``, converted to floating point pixels.

        Raises
        ------
        RuntimeError
            Raised if the exposure type cannot be converted to float.

        """
        if isinstance(exposure, afwImage.ExposureF):
            # Nothing to be done
            return exposure
        if not hasattr(exposure, "convertF"):
            raise RuntimeError("Unable to convert exposure (%s) to float" % type(exposure))

        newexposure = exposure.convertF()
        newexposure.variance[:] = 1
        newexposure.mask[:] = 0x0

        return newexposure

darkCorrection()

def lsst.ip.isr.isrTask.IsrTask.darkCorrection	(		self,
			exposure,
			darkExposure,
			invert = False
	)

Apply dark correction in place.

Parameters

exposure : lsst.afw.image.Exposure Exposure to process. darkExposure : lsst.afw.image.Exposure Dark exposure of the same size as exposure. invert : Bool, optional If True, re-add the dark to an already corrected image.

Raises

RuntimeError Raised if either exposure or darkExposure do not have their dark time defined.

See Also

lsst.ip.isr.isrFunctions.darkCorrection

Definition at line 1825 of file isrTask.py.

    def darkCorrection(self, exposure, darkExposure, invert=False):
        """!Apply dark correction in place.

        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to process.
        darkExposure : `lsst.afw.image.Exposure`
            Dark exposure of the same size as ``exposure``.
        invert : `Bool`, optional
            If True, re-add the dark to an already corrected image.

        Raises
        ------
        RuntimeError
            Raised if either ``exposure`` or ``darkExposure`` do not
            have their dark time defined.

        See Also
        --------
        lsst.ip.isr.isrFunctions.darkCorrection
        """
        expScale = exposure.getInfo().getVisitInfo().getDarkTime()
        if math.isnan(expScale):
            raise RuntimeError("Exposure darktime is NAN")
        if darkExposure.getInfo().getVisitInfo() is not None:
            darkScale = darkExposure.getInfo().getVisitInfo().getDarkTime()
        else:
            # DM-17444: darkExposure.getInfo.getVisitInfo() is None
            #           so getDarkTime() does not exist.
            darkScale = 1.0

        if math.isnan(darkScale):
            raise RuntimeError("Dark calib darktime is NAN")
        isrFunctions.darkCorrection(
            maskedImage=exposure.getMaskedImage(),
            darkMaskedImage=darkExposure.getMaskedImage(),
            expScale=expScale,
            darkScale=darkScale,
            invert=invert,
            trimToFit=self.config.doTrimToMatchCalib
        )

debugView()

def lsst.ip.isr.isrTask.IsrTask.debugView	(		self,
			exposure,
			stepname
	)

Utility function to examine ISR exposure at different stages.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure to view.
stepname : `str`
    State of processing to view.

Definition at line 2235 of file isrTask.py.

    def debugView(self, exposure, stepname):
        """Utility function to examine ISR exposure at different stages.

        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to view.
        stepname : `str`
            State of processing to view.
        """
        frame = getDebugFrame(self._display, stepname)
        if frame:
            display = getDisplay(frame)
            display.scale('asinh', 'zscale')
            display.mtv(exposure)

doLinearize()

def lsst.ip.isr.isrTask.IsrTask.doLinearize	(		self,
			detector
	)

Check if linearization is needed for the detector cameraGeom.

Checks config.doLinearize and the linearity type of the first amplifier.

Parameters

detector : lsst.afw.cameraGeom.Detector Detector to get linearity type from.

Returns

doLinearize : Bool If True, linearization should be performed.

Definition at line 1868 of file isrTask.py.

    def doLinearize(self, detector):
        """!Check if linearization is needed for the detector cameraGeom.

        Checks config.doLinearize and the linearity type of the first
        amplifier.

        Parameters
        ----------
        detector : `lsst.afw.cameraGeom.Detector`
            Detector to get linearity type from.

        Returns
        -------
        doLinearize : `Bool`
            If True, linearization should be performed.
        """
        return self.config.doLinearize and \
            detector.getAmpInfoCatalog()[0].getLinearityType() != NullLinearityType

ensureExposure()

def lsst.ip.isr.isrTask.IsrTask.ensureExposure	(		self,
			inputExp,
			camera,
			detectorNum
	)

Ensure that the data returned by Butler is a fully constructed exposure.

ISR requires exposure-level image data for historical reasons, so if we did
not recieve that from Butler, construct it from what we have, modifying the
input in place.

Parameters
----------
inputExp : `lsst.afw.image.Exposure`, `lsst.afw.image.DecoratedImageU`, or
   `lsst.afw.image.ImageF`
    The input data structure obtained from Butler.
camera : `lsst.afw.cameraGeom.camera`
    The camera associated with the image.  Used to find the appropriate
    detector.
detectorNum : `int`
    The detector this exposure should match.

Returns
-------
inputExp : `lsst.afw.image.Exposure`
    The re-constructed exposure, with appropriate detector parameters.

Raises
------
TypeError
    Raised if the input data cannot be used to construct an exposure.

Definition at line 1485 of file isrTask.py.

    def ensureExposure(self, inputExp, camera, detectorNum):
        """Ensure that the data returned by Butler is a fully constructed exposure.

        ISR requires exposure-level image data for historical reasons, so if we did
        not recieve that from Butler, construct it from what we have, modifying the
        input in place.

        Parameters
        ----------
        inputExp : `lsst.afw.image.Exposure`, `lsst.afw.image.DecoratedImageU`, or
                   `lsst.afw.image.ImageF`
            The input data structure obtained from Butler.
        camera : `lsst.afw.cameraGeom.camera`
            The camera associated with the image.  Used to find the appropriate
            detector.
        detectorNum : `int`
            The detector this exposure should match.

        Returns
        -------
        inputExp : `lsst.afw.image.Exposure`
            The re-constructed exposure, with appropriate detector parameters.

        Raises
        ------
        TypeError
            Raised if the input data cannot be used to construct an exposure.
        """
        if isinstance(inputExp, afwImage.DecoratedImageU):
            inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp))
        elif isinstance(inputExp, afwImage.ImageF):
            inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp))
        elif isinstance(inputExp, afwImage.MaskedImageF):
            inputExp = afwImage.makeExposure(inputExp)
        elif isinstance(inputExp, afwImage.Exposure):
            pass
        elif inputExp is None:
            # Assume this will be caught by the setup if it is a problem.
            return inputExp
        else:
            raise TypeError(f"Input Exposure is not known type in isrTask.ensureExposure: {type(inputExp)}")

        if inputExp.getDetector() is None:
            inputExp.setDetector(camera[detectorNum])

        return inputExp

flatContext()

def lsst.ip.isr.isrTask.IsrTask.flatContext	(		self,
			exp,
			flat,
			dark = None
	)

Context manager that applies and removes flats and darks,
if the task is configured to apply them.

Parameters
----------
exp : `lsst.afw.image.Exposure`
    Exposure to process.
flat : `lsst.afw.image.Exposure`
    Flat exposure the same size as ``exp``.
dark : `lsst.afw.image.Exposure`, optional
    Dark exposure the same size as ``exp``.

Yields
------
exp : `lsst.afw.image.Exposure`
    The flat and dark corrected exposure.

Definition at line 2205 of file isrTask.py.

    def flatContext(self, exp, flat, dark=None):
        """Context manager that applies and removes flats and darks,
        if the task is configured to apply them.

        Parameters
        ----------
        exp : `lsst.afw.image.Exposure`
            Exposure to process.
        flat : `lsst.afw.image.Exposure`
            Flat exposure the same size as ``exp``.
        dark : `lsst.afw.image.Exposure`, optional
            Dark exposure the same size as ``exp``.

        Yields
        ------
        exp : `lsst.afw.image.Exposure`
            The flat and dark corrected exposure.
        """
        if self.config.doDark and dark is not None:
            self.darkCorrection(exp, dark)
        if self.config.doFlat:
            self.flatCorrection(exp, flat)
        try:
            yield exp
        finally:
            if self.config.doFlat:
                self.flatCorrection(exp, flat, invert=True)
            if self.config.doDark and dark is not None:
                self.darkCorrection(exp, dark, invert=True)

flatCorrection()

def lsst.ip.isr.isrTask.IsrTask.flatCorrection	(		self,
			exposure,
			flatExposure,
			invert = False
	)

Apply flat correction in place.

Parameters

exposure : lsst.afw.image.Exposure Exposure to process. flatExposure : lsst.afw.image.Exposure Flat exposure of the same size as exposure. invert : Bool, optional If True, unflatten an already flattened image.

See Also

lsst.ip.isr.isrFunctions.flatCorrection

Definition at line 1887 of file isrTask.py.

    def flatCorrection(self, exposure, flatExposure, invert=False):
        """!Apply flat correction in place.

        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to process.
        flatExposure : `lsst.afw.image.Exposure`
            Flat exposure of the same size as ``exposure``.
        invert : `Bool`, optional
            If True, unflatten an already flattened image.

        See Also
        --------
        lsst.ip.isr.isrFunctions.flatCorrection
        """
        isrFunctions.flatCorrection(
            maskedImage=exposure.getMaskedImage(),
            flatMaskedImage=flatExposure.getMaskedImage(),
            scalingType=self.config.flatScalingType,
            userScale=self.config.flatUserScale,
            invert=invert,
            trimToFit=self.config.doTrimToMatchCalib
        )

getInputDatasetTypes()

def lsst.ip.isr.isrTask.IsrTask.getInputDatasetTypes	(		cls,
			config
	)

Definition at line 740 of file isrTask.py.

    def getInputDatasetTypes(cls, config):
        inputTypeDict = super().getInputDatasetTypes(config)

        # Delete entries from the dictionary of InputDatasetTypes that we know we don't
        # need because the configuration tells us we will not be bothering with the
        # correction that uses that IDT.
        if config.doBias is not True:
            inputTypeDict.pop("bias", None)
        if config.doLinearize is not True:
            inputTypeDict.pop("linearizer", None)
        if config.doCrosstalk is not True:
            inputTypeDict.pop("crosstalkSources", None)
        if config.doBrighterFatter is not True:
            inputTypeDict.pop("bfKernel", None)
        if config.doDefect is not True:
            inputTypeDict.pop("defects", None)
        if config.doDark is not True:
            inputTypeDict.pop("dark", None)
        if config.doFlat is not True:
            inputTypeDict.pop("flat", None)
        if config.doAttachTransmissionCurve is not True:
            inputTypeDict.pop("opticsTransmission", None)
            inputTypeDict.pop("filterTransmission", None)
            inputTypeDict.pop("sensorTransmission", None)
            inputTypeDict.pop("atmosphereTransmission", None)
        if config.doUseOpticsTransmission is not True:
            inputTypeDict.pop("opticsTransmission", None)
        if config.doUseFilterTransmission is not True:
            inputTypeDict.pop("filterTransmission", None)
        if config.doUseSensorTransmission is not True:
            inputTypeDict.pop("sensorTransmission", None)
        if config.doUseAtmosphereTransmission is not True:
            inputTypeDict.pop("atmosphereTransmission", None)

        return inputTypeDict

getIsrExposure()

def lsst.ip.isr.isrTask.IsrTask.getIsrExposure	(		self,
			dataRef,
			datasetType,
			immediate = True
	)

Retrieve a calibration dataset for removing instrument signature.

Parameters

dataRef : daf.persistence.butlerSubset.ButlerDataRef DataRef of the detector data to find calibration datasets for. datasetType : str Type of dataset to retrieve (e.g. 'bias', 'flat', etc). immediate : Bool If True, disable butler proxies to enable error handling within this routine.

Returns

exposure : lsst.afw.image.Exposure Requested calibration frame.

Raises

RuntimeError Raised if no matching calibration frame can be found.

Definition at line 1444 of file isrTask.py.

    def getIsrExposure(self, dataRef, datasetType, immediate=True):
        """!Retrieve a calibration dataset for removing instrument signature.

        Parameters
        ----------

        dataRef : `daf.persistence.butlerSubset.ButlerDataRef`
            DataRef of the detector data to find calibration datasets
            for.
        datasetType : `str`
            Type of dataset to retrieve (e.g. 'bias', 'flat', etc).
        immediate : `Bool`
            If True, disable butler proxies to enable error handling
            within this routine.

        Returns
        -------
        exposure : `lsst.afw.image.Exposure`
            Requested calibration frame.

        Raises
        ------
        RuntimeError
            Raised if no matching calibration frame can be found.
        """
        try:
            exp = dataRef.get(datasetType, immediate=immediate)
        except Exception as exc1:
            if not self.config.fallbackFilterName:
                raise RuntimeError("Unable to retrieve %s for %s: %s" % (datasetType, dataRef.dataId, exc1))
            try:
                exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName, immediate=immediate)
            except Exception as exc2:
                raise RuntimeError("Unable to retrieve %s for %s, even with fallback filter %s: %s AND %s" %
                                   (datasetType, dataRef.dataId, self.config.fallbackFilterName, exc1, exc2))
            self.log.warn("Using fallback calibration from filter %s" % self.config.fallbackFilterName)

        if self.config.doAssembleIsrExposures:
            exp = self.assembleCcd.assembleCcd(exp)
        return exp

getOutputDatasetTypes()

def lsst.ip.isr.isrTask.IsrTask.getOutputDatasetTypes	(		cls,
			config
	)

Definition at line 777 of file isrTask.py.

    def getOutputDatasetTypes(cls, config):
        outputTypeDict = super().getOutputDatasetTypes(config)

        if config.qa.doThumbnailOss is not True:
            outputTypeDict.pop("outputOssThumbnail", None)
        if config.qa.doThumbnailFlattened is not True:
            outputTypeDict.pop("outputFlattenedThumbnail", None)
        if config.doWrite is not True:
            outputTypeDict.pop("outputExposure", None)

        return outputTypeDict

getPerDatasetTypeDimensions()

def lsst.ip.isr.isrTask.IsrTask.getPerDatasetTypeDimensions	(		cls,
			config
	)

Definition at line 800 of file isrTask.py.

    def getPerDatasetTypeDimensions(cls, config):
        # Input calibration datasets of different types (i.e. flat, bias) need
        # not have the same validity range.  That makes calibration_label
        # (which maps directly to a validity range) a "per-DatasetType
        # dimension".
        return frozenset(["calibration_label"])

getPrerequisiteDatasetTypes()

def lsst.ip.isr.isrTask.IsrTask.getPrerequisiteDatasetTypes	(		cls,
			config
	)

Definition at line 790 of file isrTask.py.

    def getPrerequisiteDatasetTypes(cls, config):
        # Input calibration datasets should not constrain the QuantumGraph
        # (it'd be confusing if not having flats just silently resulted in no
        # data being processed).  Our nomenclature for that is that these are
        # "prerequisite" datasets (only "ccdExposure" == "raw" isn't).
        names = set(cls.getInputDatasetTypes(config))
        names.remove("ccdExposure")
        return names

makeDatasetType()

def lsst.ip.isr.isrTask.IsrTask.makeDatasetType	(		self,
			dsConfig
	)

Definition at line 844 of file isrTask.py.

    def makeDatasetType(self, dsConfig):
        return super().makeDatasetType(dsConfig)

maskAmplifier()

def lsst.ip.isr.isrTask.IsrTask.maskAmplifier	(		self,
			ccdExposure,
			amp,
			defects
	)

Identify bad amplifiers, saturated and suspect pixels.

Parameters
----------
ccdExposure : `lsst.afw.image.Exposure`
    Input exposure to be masked.
amp : `lsst.afw.table.AmpInfoCatalog`
    Catalog of parameters defining the amplifier on this
    exposure to mask.
defects : `lsst.meas.algorithms.Defects`
    List of defects.  Used to determine if the entire
    amplifier is bad.

Returns
-------
badAmp : `Bool`
    If this is true, the entire amplifier area is covered by
    defects and unusable.

Definition at line 1568 of file isrTask.py.

    def maskAmplifier(self, ccdExposure, amp, defects):
        """Identify bad amplifiers, saturated and suspect pixels.

        Parameters
        ----------
        ccdExposure : `lsst.afw.image.Exposure`
            Input exposure to be masked.
        amp : `lsst.afw.table.AmpInfoCatalog`
            Catalog of parameters defining the amplifier on this
            exposure to mask.
        defects : `lsst.meas.algorithms.Defects`
            List of defects.  Used to determine if the entire
            amplifier is bad.

        Returns
        -------
        badAmp : `Bool`
            If this is true, the entire amplifier area is covered by
            defects and unusable.

        """
        maskedImage = ccdExposure.getMaskedImage()

        badAmp = False

        # Check if entire amp region is defined as a defect (need to use amp.getBBox() for correct
        # comparison with current defects definition.
        if defects is not None:
            badAmp = bool(sum([v.getBBox().contains(amp.getBBox()) for v in defects]))

        # In the case of a bad amp, we will set mask to "BAD" (here use amp.getRawBBox() for correct
        # association with pixels in current ccdExposure).
        if badAmp:
            dataView = afwImage.MaskedImageF(maskedImage, amp.getRawBBox(),
                                             afwImage.PARENT)
            maskView = dataView.getMask()
            maskView |= maskView.getPlaneBitMask("BAD")
            del maskView
            return badAmp

        # Mask remaining defects after assembleCcd() to allow for defects that cross amplifier boundaries.
        # Saturation and suspect pixels can be masked now, though.
        limits = dict()
        if self.config.doSaturation and not badAmp:
            limits.update({self.config.saturatedMaskName: amp.getSaturation()})
        if self.config.doSuspect and not badAmp:
            limits.update({self.config.suspectMaskName: amp.getSuspectLevel()})
        if math.isfinite(self.config.saturation):
            limits.update({self.config.saturatedMaskName: self.config.saturation})

        for maskName, maskThreshold in limits.items():
            if not math.isnan(maskThreshold):
                dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
                isrFunctions.makeThresholdMask(
                    maskedImage=dataView,
                    threshold=maskThreshold,
                    growFootprints=0,
                    maskName=maskName
                )

        # Determine if we've fully masked this amplifier with SUSPECT and SAT pixels.
        maskView = afwImage.Mask(maskedImage.getMask(), amp.getRawDataBBox(),
                                 afwImage.PARENT)
        maskVal = maskView.getPlaneBitMask([self.config.saturatedMaskName,
                                            self.config.suspectMaskName])
        if numpy.all(maskView.getArray() & maskVal > 0):
            badAmp = True

        return badAmp

maskAndInterpolateDefects()

def lsst.ip.isr.isrTask.IsrTask.maskAndInterpolateDefects	(		self,
			exposure,
			defectBaseList
	)

Mask and interpolate defects using mask plane "BAD", in place.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure to process.
defectBaseList : `List` of `Defects`

Definition at line 2030 of file isrTask.py.

    def maskAndInterpolateDefects(self, exposure, defectBaseList):
        """Mask and interpolate defects using mask plane "BAD", in place.

        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to process.
        defectBaseList : `List` of `Defects`

        """
        self.maskDefects(exposure, defectBaseList)
        isrFunctions.interpolateFromMask(
            maskedImage=exposure.getMaskedImage(),
            fwhm=self.config.fwhm,
            growSaturatedFootprints=0,
            maskNameList=["BAD"],
        )

maskAndInterpolateNan()

def lsst.ip.isr.isrTask.IsrTask.maskAndInterpolateNan	(		self,
			exposure
	)

"Mask and interpolate NaNs using mask plane "UNMASKEDNAN", in place.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure to process.

See Also
--------
lsst.ip.isr.isrTask.maskNan()

Definition at line 2074 of file isrTask.py.

    def maskAndInterpolateNan(self, exposure):
        """"Mask and interpolate NaNs using mask plane "UNMASKEDNAN", in place.

        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to process.

        See Also
        --------
        lsst.ip.isr.isrTask.maskNan()
        """
        self.maskNan(exposure)
        isrFunctions.interpolateFromMask(
            maskedImage=exposure.getMaskedImage(),
            fwhm=self.config.fwhm,
            growSaturatedFootprints=0,
            maskNameList=["UNMASKEDNAN"],
        )

maskDefect()

def lsst.ip.isr.isrTask.IsrTask.maskDefect	(		self,
			exposure,
			defectBaseList
	)

Mask defects using mask plane "BAD", in place.

Parameters

exposure : lsst.afw.image.Exposure Exposure to process. defectBaseList : lsst.meas.algorithms.Defects or list of lsst.afw.image.DefectBase. List of defects to mask and interpolate.

Notes

Call this after CCD assembly, since defects may cross amplifier boundaries.

Definition at line 1996 of file isrTask.py.

    def maskDefect(self, exposure, defectBaseList):
        """!Mask defects using mask plane "BAD", in place.

        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to process.
        defectBaseList : `lsst.meas.algorithms.Defects` or `list` of
                         `lsst.afw.image.DefectBase`.
            List of defects to mask and interpolate.

        Notes
        -----
        Call this after CCD assembly, since defects may cross amplifier boundaries.
        """
        maskedImage = exposure.getMaskedImage()
        if not isinstance(defectBaseList, Defects):
            # Promotes DefectBase to Defect
            defectList = Defects(defectBaseList)
        else:
            defectList = defectBaseList
        defectList.maskPixels(maskedImage, maskName="BAD")

        if self.config.numEdgeSuspect > 0:
            goodBBox = maskedImage.getBBox()
            # This makes a bbox numEdgeSuspect pixels smaller than the image on each side
            goodBBox.grow(-self.config.numEdgeSuspect)
            # Mask pixels outside goodBBox as SUSPECT
            SourceDetectionTask.setEdgeBits(
                maskedImage,
                goodBBox,
                maskedImage.getMask().getPlaneBitMask("SUSPECT")
            )

maskNan()

def lsst.ip.isr.isrTask.IsrTask.maskNan	(		self,
			exposure
	)

Mask NaNs using mask plane "UNMASKEDNAN", in place.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure to process.

Notes
-----
We mask over all NaNs, including those that are masked with
other bits (because those may or may not be interpolated over
later, and we want to remove all NaNs).  Despite this
behaviour, the "UNMASKEDNAN" mask plane is used to preserve
the historical name.

Definition at line 2048 of file isrTask.py.

    def maskNan(self, exposure):
        """Mask NaNs using mask plane "UNMASKEDNAN", in place.

        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to process.

        Notes
        -----
        We mask over all NaNs, including those that are masked with
        other bits (because those may or may not be interpolated over
        later, and we want to remove all NaNs).  Despite this
        behaviour, the "UNMASKEDNAN" mask plane is used to preserve
        the historical name.
        """
        maskedImage = exposure.getMaskedImage()

        # Find and mask NaNs
        maskedImage.getMask().addMaskPlane("UNMASKEDNAN")
        maskVal = maskedImage.getMask().getPlaneBitMask("UNMASKEDNAN")
        numNans = maskNans(maskedImage, maskVal)
        self.metadata.set("NUMNANS", numNans)
        if numNans > 0:
            self.log.warn(f"There were {numNans} unmasked NaNs")

measureBackground()

def lsst.ip.isr.isrTask.IsrTask.measureBackground	(		self,
			exposure,
			IsrQaConfig = None
	)

Measure the image background in subgrids, for quality control purposes.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure to process.
IsrQaConfig : `lsst.ip.isr.isrQa.IsrQaConfig`
    Configuration object containing parameters on which background
    statistics and subgrids to use.

Definition at line 2094 of file isrTask.py.

    def measureBackground(self, exposure, IsrQaConfig=None):
        """Measure the image background in subgrids, for quality control purposes.

        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to process.
        IsrQaConfig : `lsst.ip.isr.isrQa.IsrQaConfig`
            Configuration object containing parameters on which background
            statistics and subgrids to use.
        """
        if IsrQaConfig is not None:
            statsControl = afwMath.StatisticsControl(IsrQaConfig.flatness.clipSigma,
                                                     IsrQaConfig.flatness.nIter)
            maskVal = exposure.getMaskedImage().getMask().getPlaneBitMask(["BAD", "SAT", "DETECTED"])
            statsControl.setAndMask(maskVal)
            maskedImage = exposure.getMaskedImage()
            stats = afwMath.makeStatistics(maskedImage, afwMath.MEDIAN | afwMath.STDEVCLIP, statsControl)
            skyLevel = stats.getValue(afwMath.MEDIAN)
            skySigma = stats.getValue(afwMath.STDEVCLIP)
            self.log.info("Flattened sky level: %f +/- %f" % (skyLevel, skySigma))
            metadata = exposure.getMetadata()
            metadata.set('SKYLEVEL', skyLevel)
            metadata.set('SKYSIGMA', skySigma)

            # calcluating flatlevel over the subgrids
            stat = afwMath.MEANCLIP if IsrQaConfig.flatness.doClip else afwMath.MEAN
            meshXHalf = int(IsrQaConfig.flatness.meshX/2.)
            meshYHalf = int(IsrQaConfig.flatness.meshY/2.)
            nX = int((exposure.getWidth() + meshXHalf) / IsrQaConfig.flatness.meshX)
            nY = int((exposure.getHeight() + meshYHalf) / IsrQaConfig.flatness.meshY)
            skyLevels = numpy.zeros((nX, nY))

            for j in range(nY):
                yc = meshYHalf + j * IsrQaConfig.flatness.meshY
                for i in range(nX):
                    xc = meshXHalf + i * IsrQaConfig.flatness.meshX

                    xLLC = xc - meshXHalf
                    yLLC = yc - meshYHalf
                    xURC = xc + meshXHalf - 1
                    yURC = yc + meshYHalf - 1

                    bbox = lsst.geom.Box2I(lsst.geom.Point2I(xLLC, yLLC), lsst.geom.Point2I(xURC, yURC))
                    miMesh = maskedImage.Factory(exposure.getMaskedImage(), bbox, afwImage.LOCAL)

                    skyLevels[i, j] = afwMath.makeStatistics(miMesh, stat, statsControl).getValue()

            good = numpy.where(numpy.isfinite(skyLevels))
            skyMedian = numpy.median(skyLevels[good])
            flatness = (skyLevels[good] - skyMedian) / skyMedian
            flatness_rms = numpy.std(flatness)
            flatness_pp = flatness.max() - flatness.min() if len(flatness) > 0 else numpy.nan

            self.log.info("Measuring sky levels in %dx%d grids: %f" % (nX, nY, skyMedian))
            self.log.info("Sky flatness in %dx%d grids - pp: %f rms: %f" %
                          (nX, nY, flatness_pp, flatness_rms))

            metadata.set('FLATNESS_PP', float(flatness_pp))
            metadata.set('FLATNESS_RMS', float(flatness_rms))
            metadata.set('FLATNESS_NGRIDS', '%dx%d' % (nX, nY))
            metadata.set('FLATNESS_MESHX', IsrQaConfig.flatness.meshX)
            metadata.set('FLATNESS_MESHY', IsrQaConfig.flatness.meshY)

overscanCorrection()

def lsst.ip.isr.isrTask.IsrTask.overscanCorrection	(		self,
			ccdExposure,
			amp
	)

Apply overscan correction in place.

This method does initial pixel rejection of the overscan
region.  The overscan can also be optionally segmented to
allow for discontinuous overscan responses to be fit
separately.  The actual overscan subtraction is performed by
the `lsst.ip.isr.isrFunctions.overscanCorrection` function,
which is called here after the amplifier is preprocessed.

Parameters
----------
ccdExposure : `lsst.afw.image.Exposure`
    Exposure to have overscan correction performed.
amp : `lsst.afw.table.AmpInfoCatalog`
    The amplifier to consider while correcting the overscan.

Returns
-------
overscanResults : `lsst.pipe.base.Struct`
    Result struct with components:
    - ``imageFit`` : scalar or `lsst.afw.image.Image`
Value or fit subtracted from the amplifier image data.
    - ``overscanFit`` : scalar or `lsst.afw.image.Image`
Value or fit subtracted from the overscan image data.
    - ``overscanImage`` : `lsst.afw.image.Image`
Image of the overscan region with the overscan
correction applied. This quantity is used to estimate
the amplifier read noise empirically.

Raises
------
RuntimeError
    Raised if the ``amp`` does not contain raw pixel information.

See Also
--------
lsst.ip.isr.isrFunctions.overscanCorrection

Definition at line 1638 of file isrTask.py.

    def overscanCorrection(self, ccdExposure, amp):
        """Apply overscan correction in place.

        This method does initial pixel rejection of the overscan
        region.  The overscan can also be optionally segmented to
        allow for discontinuous overscan responses to be fit
        separately.  The actual overscan subtraction is performed by
        the `lsst.ip.isr.isrFunctions.overscanCorrection` function,
        which is called here after the amplifier is preprocessed.

        Parameters
        ----------
        ccdExposure : `lsst.afw.image.Exposure`
            Exposure to have overscan correction performed.
        amp : `lsst.afw.table.AmpInfoCatalog`
            The amplifier to consider while correcting the overscan.

        Returns
        -------
        overscanResults : `lsst.pipe.base.Struct`
            Result struct with components:
            - ``imageFit`` : scalar or `lsst.afw.image.Image`
                Value or fit subtracted from the amplifier image data.
            - ``overscanFit`` : scalar or `lsst.afw.image.Image`
                Value or fit subtracted from the overscan image data.
            - ``overscanImage`` : `lsst.afw.image.Image`
                Image of the overscan region with the overscan
                correction applied. This quantity is used to estimate
                the amplifier read noise empirically.

        Raises
        ------
        RuntimeError
            Raised if the ``amp`` does not contain raw pixel information.

        See Also
        --------
        lsst.ip.isr.isrFunctions.overscanCorrection
        """
        if not amp.getHasRawInfo():
            raise RuntimeError("This method must be executed on an amp with raw information.")

        if amp.getRawHorizontalOverscanBBox().isEmpty():
            self.log.info("ISR_OSCAN: No overscan region.  Not performing overscan correction.")
            return None

        statControl = afwMath.StatisticsControl()
        statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT"))

        # Determine the bounding boxes
        dataBBox = amp.getRawDataBBox()
        oscanBBox = amp.getRawHorizontalOverscanBBox()
        dx0 = 0
        dx1 = 0

        prescanBBox = amp.getRawPrescanBBox()
        if (oscanBBox.getBeginX() > prescanBBox.getBeginX()):  # amp is at the right
            dx0 += self.config.overscanNumLeadingColumnsToSkip
            dx1 -= self.config.overscanNumTrailingColumnsToSkip
        else:
            dx0 += self.config.overscanNumTrailingColumnsToSkip
            dx1 -= self.config.overscanNumLeadingColumnsToSkip

        # Determine if we need to work on subregions of the amplifier and overscan.
        imageBBoxes = []
        overscanBBoxes = []

        if ((self.config.overscanBiasJump and
             self.config.overscanBiasJumpLocation) and
            (ccdExposure.getMetadata().exists(self.config.overscanBiasJumpKeyword) and
             ccdExposure.getMetadata().getScalar(self.config.overscanBiasJumpKeyword) in
             self.config.overscanBiasJumpDevices)):
            if amp.getReadoutCorner() in (afwTable.LL, afwTable.LR):
                yLower = self.config.overscanBiasJumpLocation
                yUpper = dataBBox.getHeight() - yLower
            else:
                yUpper = self.config.overscanBiasJumpLocation
                yLower = dataBBox.getHeight() - yUpper

            imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(),
                                               lsst.geom.Extent2I(dataBBox.getWidth(), yLower)))
            overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() +
                                                  lsst.geom.Extent2I(dx0, 0),
                                                  lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
                                                                     yLower)))

            imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin() + lsst.geom.Extent2I(0, yLower),
                                               lsst.geom.Extent2I(dataBBox.getWidth(), yUpper)))
            overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, yLower),
                                                  lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
                                                                     yUpper)))
        else:
            imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(),
                                               lsst.geom.Extent2I(dataBBox.getWidth(), dataBBox.getHeight())))
            overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, 0),
                                                  lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
                                                                     oscanBBox.getHeight())))

        # Perform overscan correction on subregions, ensuring saturated pixels are masked.
        for imageBBox, overscanBBox in zip(imageBBoxes, overscanBBoxes):
            ampImage = ccdExposure.maskedImage[imageBBox]
            overscanImage = ccdExposure.maskedImage[overscanBBox]

            overscanArray = overscanImage.image.array
            median = numpy.ma.median(numpy.ma.masked_where(overscanImage.mask.array, overscanArray))
            bad = numpy.where(numpy.abs(overscanArray - median) > self.config.overscanMaxDev)
            overscanImage.mask.array[bad] = overscanImage.mask.getPlaneBitMask("SAT")

            statControl = afwMath.StatisticsControl()
            statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT"))

            overscanResults = isrFunctions.overscanCorrection(ampMaskedImage=ampImage,
                                                              overscanImage=overscanImage,
                                                              fitType=self.config.overscanFitType,
                                                              order=self.config.overscanOrder,
                                                              collapseRej=self.config.overscanNumSigmaClip,
                                                              statControl=statControl,
                                                              overscanIsInt=self.config.overscanIsInt
                                                              )

            # Measure average overscan levels and record them in the metadata
            levelStat = afwMath.MEDIAN
            sigmaStat = afwMath.STDEVCLIP

            sctrl = afwMath.StatisticsControl(self.config.qa.flatness.clipSigma,
                                              self.config.qa.flatness.nIter)
            metadata = ccdExposure.getMetadata()
            ampNum = amp.getName()
            if self.config.overscanFitType in ("MEDIAN", "MEAN", "MEANCLIP"):
                metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, overscanResults.overscanFit)
                metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, 0.0)
            else:
                stats = afwMath.makeStatistics(overscanResults.overscanFit, levelStat | sigmaStat, sctrl)
                metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, stats.getValue(levelStat))
                metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, stats.getValue(sigmaStat))

        return overscanResults

readIsrData()

def lsst.ip.isr.isrTask.IsrTask.readIsrData	(		self,
			dataRef,
			rawExposure
	)

Retrieve necessary frames for instrument signature removal.

Pre-fetching all required ISR data products limits the IO required by the ISR. Any conflict between the calibration data available and that needed for ISR is also detected prior to doing processing, allowing it to fail quickly.

Parameters

dataRef : daf.persistence.butlerSubset.ButlerDataRef Butler reference of the detector data to be processed rawExposure : afw.image.Exposure The raw exposure that will later be corrected with the retrieved calibration data; should not be modified in this method.

Returns

result : lsst.pipe.base.Struct Result struct with components (which may be None):

• bias: bias calibration frame (afw.image.Exposure)
• linearizer: functor for linearization (ip.isr.linearize.LinearizeBase)
• crosstalkSources: list of possible crosstalk sources (list)
• dark: dark calibration frame (afw.image.Exposure)
• flat: flat calibration frame (afw.image.Exposure)
• bfKernel: Brighter-Fatter kernel (numpy.ndarray)
• defects: list of defects (lsst.meas.algorithms.Defects)
• fringes: lsst.pipe.base.Struct with components:

fringes: fringe calibration frame (afw.image.Exposure)

• seed: random seed derived from the ccdExposureId for random number generator (uint32)
  • opticsTransmission: lsst.afw.image.TransmissionCurve A TransmissionCurve that represents the throughput of the optics, to be evaluated in focal-plane coordinates.
  • filterTransmission : lsst.afw.image.TransmissionCurve A TransmissionCurve that represents the throughput of the filter itself, to be evaluated in focal-plane coordinates.
  • sensorTransmission : lsst.afw.image.TransmissionCurve A TransmissionCurve that represents the throughput of the sensor itself, to be evaluated in post-assembly trimmed detector coordinates.
  • atmosphereTransmission : lsst.afw.image.TransmissionCurve A TransmissionCurve that represents the throughput of the atmosphere, assumed to be spatially constant.
  • strayLightData : object An opaque object containing calibration information for stray-light correction. If None, no correction will be performed.

Raises

NotImplementedError : Raised if a per-amplifier brighter-fatter kernel is requested by the configuration.

Definition at line 847 of file isrTask.py.

    def readIsrData(self, dataRef, rawExposure):
        """!Retrieve necessary frames for instrument signature removal.

        Pre-fetching all required ISR data products limits the IO
        required by the ISR. Any conflict between the calibration data
        available and that needed for ISR is also detected prior to
        doing processing, allowing it to fail quickly.

        Parameters
        ----------
        dataRef : `daf.persistence.butlerSubset.ButlerDataRef`
            Butler reference of the detector data to be processed
        rawExposure : `afw.image.Exposure`
            The raw exposure that will later be corrected with the
            retrieved calibration data; should not be modified in this
            method.

        Returns
        -------
        result : `lsst.pipe.base.Struct`
            Result struct with components (which may be `None`):
            - ``bias``: bias calibration frame (`afw.image.Exposure`)
            - ``linearizer``: functor for linearization (`ip.isr.linearize.LinearizeBase`)
            - ``crosstalkSources``: list of possible crosstalk sources (`list`)
            - ``dark``: dark calibration frame (`afw.image.Exposure`)
            - ``flat``: flat calibration frame (`afw.image.Exposure`)
            - ``bfKernel``: Brighter-Fatter kernel (`numpy.ndarray`)
            - ``defects``: list of defects (`lsst.meas.algorithms.Defects`)
            - ``fringes``: `lsst.pipe.base.Struct` with components:
                - ``fringes``: fringe calibration frame (`afw.image.Exposure`)
                - ``seed``: random seed derived from the ccdExposureId for random
                    number generator (`uint32`)
            - ``opticsTransmission``: `lsst.afw.image.TransmissionCurve`
                A ``TransmissionCurve`` that represents the throughput of the optics,
                to be evaluated in focal-plane coordinates.
            - ``filterTransmission`` : `lsst.afw.image.TransmissionCurve`
                A ``TransmissionCurve`` that represents the throughput of the filter
                itself, to be evaluated in focal-plane coordinates.
            - ``sensorTransmission`` : `lsst.afw.image.TransmissionCurve`
                A ``TransmissionCurve`` that represents the throughput of the sensor
                itself, to be evaluated in post-assembly trimmed detector coordinates.
            - ``atmosphereTransmission`` : `lsst.afw.image.TransmissionCurve`
                A ``TransmissionCurve`` that represents the throughput of the
                atmosphere, assumed to be spatially constant.
            - ``strayLightData`` : `object`
                An opaque object containing calibration information for
                stray-light correction.  If `None`, no correction will be
                performed.

        Raises
        ------
        NotImplementedError :
            Raised if a per-amplifier brighter-fatter kernel is requested by the configuration.
        """
        ccd = rawExposure.getDetector()
        rawExposure.mask.addMaskPlane("UNMASKEDNAN")  # needed to match pre DM-15862 processing.
        biasExposure = (self.getIsrExposure(dataRef, self.config.biasDataProductName)
                        if self.config.doBias else None)
        # immediate=True required for functors and linearizers are functors; see ticket DM-6515
        linearizer = (dataRef.get("linearizer", immediate=True)
                      if self.doLinearize(ccd) else None)
        crosstalkSources = (self.crosstalk.prepCrosstalk(dataRef)
                            if self.config.doCrosstalk else None)
        darkExposure = (self.getIsrExposure(dataRef, self.config.darkDataProductName)
                        if self.config.doDark else None)
        flatExposure = (self.getIsrExposure(dataRef, self.config.flatDataProductName)
                        if self.config.doFlat else None)

        brighterFatterKernel = None
        if self.config.doBrighterFatter is True:

            # Use the new-style cp_pipe version of the kernel is it exists.
            try:
                brighterFatterKernel = dataRef.get("brighterFatterKernel")
            except NoResults:
                # Fall back to the old-style numpy-ndarray style kernel if necessary.
                try:
                    brighterFatterKernel = dataRef.get("bfKernel")
                except NoResults:
                    brighterFatterKernel = None
            if brighterFatterKernel is not None and not isinstance(brighterFatterKernel, numpy.ndarray):
                # If the kernel is not an ndarray, it's the cp_pipe version, so extract the kernel for
                # this detector, or raise an error.
                if self.config.brighterFatterLevel == 'DETECTOR':
                    brighterFatterKernel = brighterFatterKernel.kernel[ccd.getId()]
                else:
                    # TODO DM-15631 for implementing this
                    raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented")

        defectList = (dataRef.get("defects")
                      if self.config.doDefect else None)
        fringeStruct = (self.fringe.readFringes(dataRef, assembler=self.assembleCcd
                                                if self.config.doAssembleIsrExposures else None)
                        if self.config.doFringe and self.fringe.checkFilter(rawExposure)
                        else pipeBase.Struct(fringes=None))

        if self.config.doAttachTransmissionCurve:
            opticsTransmission = (dataRef.get("transmission_optics")
                                  if self.config.doUseOpticsTransmission else None)
            filterTransmission = (dataRef.get("transmission_filter")
                                  if self.config.doUseFilterTransmission else None)
            sensorTransmission = (dataRef.get("transmission_sensor")
                                  if self.config.doUseSensorTransmission else None)
            atmosphereTransmission = (dataRef.get("transmission_atmosphere")
                                      if self.config.doUseAtmosphereTransmission else None)
        else:
            opticsTransmission = None
            filterTransmission = None
            sensorTransmission = None
            atmosphereTransmission = None

        if self.config.doStrayLight:
            strayLightData = self.strayLight.readIsrData(dataRef, rawExposure)
        else:
            strayLightData = None

        # Struct should include only kwargs to run()
        return pipeBase.Struct(bias=biasExposure,
                               linearizer=linearizer,
                               crosstalkSources=crosstalkSources,
                               dark=darkExposure,
                               flat=flatExposure,
                               bfKernel=brighterFatterKernel,
                               defects=defectList,
                               fringes=fringeStruct,
                               opticsTransmission=opticsTransmission,
                               filterTransmission=filterTransmission,
                               sensorTransmission=sensorTransmission,
                               atmosphereTransmission=atmosphereTransmission,
                               strayLightData=strayLightData
                               )

roughZeroPoint()

def lsst.ip.isr.isrTask.IsrTask.roughZeroPoint	(		self,
			exposure
	)

Set an approximate magnitude zero point for the exposure.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure to process.

Definition at line 2158 of file isrTask.py.

    def roughZeroPoint(self, exposure):
        """Set an approximate magnitude zero point for the exposure.

        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to process.
        """
        filterName = afwImage.Filter(exposure.getFilter().getId()).getName()  # Canonical name for filter
        if filterName in self.config.fluxMag0T1:
            fluxMag0 = self.config.fluxMag0T1[filterName]
        else:
            self.log.warn("No rough magnitude zero point set for filter %s" % filterName)
            fluxMag0 = self.config.defaultFluxMag0T1

        expTime = exposure.getInfo().getVisitInfo().getExposureTime()
        if not expTime > 0:  # handle NaN as well as <= 0
            self.log.warn("Non-positive exposure time; skipping rough zero point")
            return

        self.log.info("Setting rough magnitude zero point: %f" % (2.5*math.log10(fluxMag0*expTime),))
        exposure.setPhotoCalib(afwImage.makePhotoCalibFromCalibZeroPoint(fluxMag0*expTime, 0.0))

run()

def lsst.ip.isr.isrTask.IsrTask.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,
			isGen3 = False
	)

Perform instrument signature removal on an exposure.

Steps included in the ISR processing, in order performed, are:

• saturation and suspect pixel masking
• overscan subtraction
• CCD assembly of individual amplifiers
• bias subtraction
• variance image construction
• linearization of non-linear response
• crosstalk masking
• brighter-fatter correction
• dark subtraction
• fringe correction
• stray light subtraction
• flat correction
• masking of known defects and camera specific features
• vignette calculation
• appending transmission curve and distortion model

Parameters

ccdExposure : lsst.afw.image.Exposure The raw exposure that is to be run through ISR. The exposure is modified by this method. camera : lsst.afw.cameraGeom.Camera, optional The camera geometry for this exposure. Used to select the distortion model appropriate for this data. bias : lsst.afw.image.Exposure, optional Bias calibration frame. linearizer : lsst.ip.isr.linearize.LinearizeBase, optional Functor for linearization. crosstalkSources : list, optional List of possible crosstalk sources. dark : lsst.afw.image.Exposure, optional Dark calibration frame. flat : lsst.afw.image.Exposure, optional Flat calibration frame. bfKernel : numpy.ndarray, optional Brighter-fatter kernel. defects : lsst.meas.algorithms.Defects, optional List of defects. fringes : lsst.pipe.base.Struct, optional Struct containing the fringe correction data, with elements:

• fringes: fringe calibration frame (afw.image.Exposure)
• seed: random seed derived from the ccdExposureId for random number generator (uint32) opticsTransmission: lsst.afw.image.TransmissionCurve, optional A TransmissionCurve that represents the throughput of the optics, to be evaluated in focal-plane coordinates. filterTransmission : lsst.afw.image.TransmissionCurve A TransmissionCurve that represents the throughput of the filter itself, to be evaluated in focal-plane coordinates. sensorTransmission : lsst.afw.image.TransmissionCurve A TransmissionCurve that represents the throughput of the sensor itself, to be evaluated in post-assembly trimmed detector coordinates. atmosphereTransmission : lsst.afw.image.TransmissionCurve A TransmissionCurve that represents the throughput of the atmosphere, assumed to be spatially constant. detectorNum : int, optional The integer number for the detector to process. isGen3 : bool, optional Flag this call to run() as using the Gen3 butler environment. strayLightData : object, optional Opaque object containing calibration information for stray-light correction. If None, no correction will be performed.

Returns

result : lsst.pipe.base.Struct Result struct with component:

• exposure : afw.image.Exposure The fully ISR corrected exposure.
• outputExposure : afw.image.Exposure An alias for exposure
• ossThumb : numpy.ndarray Thumbnail image of the exposure after overscan subtraction.
• flattenedThumb : numpy.ndarray Thumbnail image of the exposure after flat-field correction.

Raises

RuntimeError Raised if a configuration option is set to True, but the required calibration data has not been specified.

Notes

The current processed exposure can be viewed by setting the appropriate lsstDebug entries in the debug.display dictionary. The names of these entries correspond to some of the IsrTaskConfig Boolean options, with the value denoting the frame to use. The exposure is shown inside the matching option check and after the processing of that step has finished. The steps with debug points are:

doAssembleCcd doBias doCrosstalk doBrighterFatter doDark doFringe doStrayLight doFlat

In addition, setting the "postISRCCD" entry displays the exposure after all ISR processing has finished.

Definition at line 985 of file isrTask.py.

            ):
        """!Perform instrument signature removal on an exposure.

        Steps included in the ISR processing, in order performed, are:
        - saturation and suspect pixel masking
        - overscan subtraction
        - CCD assembly of individual amplifiers
        - bias subtraction
        - variance image construction
        - linearization of non-linear response
        - crosstalk masking
        - brighter-fatter correction
        - dark subtraction
        - fringe correction
        - stray light subtraction
        - flat correction
        - masking of known defects and camera specific features
        - vignette calculation
        - appending transmission curve and distortion model

        Parameters
        ----------
        ccdExposure : `lsst.afw.image.Exposure`
            The raw exposure that is to be run through ISR.  The
            exposure is modified by this method.
        camera : `lsst.afw.cameraGeom.Camera`, optional
            The camera geometry for this exposure.  Used to select the
            distortion model appropriate for this data.
        bias : `lsst.afw.image.Exposure`, optional
            Bias calibration frame.
        linearizer : `lsst.ip.isr.linearize.LinearizeBase`, optional
            Functor for linearization.
        crosstalkSources : `list`, optional
            List of possible crosstalk sources.
        dark : `lsst.afw.image.Exposure`, optional
            Dark calibration frame.
        flat : `lsst.afw.image.Exposure`, optional
            Flat calibration frame.
        bfKernel : `numpy.ndarray`, optional
            Brighter-fatter kernel.
        defects : `lsst.meas.algorithms.Defects`, optional
            List of defects.
        fringes : `lsst.pipe.base.Struct`, optional
            Struct containing the fringe correction data, with
            elements:
            - ``fringes``: fringe calibration frame (`afw.image.Exposure`)
            - ``seed``: random seed derived from the ccdExposureId for random
                number generator (`uint32`)
        opticsTransmission: `lsst.afw.image.TransmissionCurve`, optional
            A ``TransmissionCurve`` that represents the throughput of the optics,
            to be evaluated in focal-plane coordinates.
        filterTransmission : `lsst.afw.image.TransmissionCurve`
            A ``TransmissionCurve`` that represents the throughput of the filter
            itself, to be evaluated in focal-plane coordinates.
        sensorTransmission : `lsst.afw.image.TransmissionCurve`
            A ``TransmissionCurve`` that represents the throughput of the sensor
            itself, to be evaluated in post-assembly trimmed detector coordinates.
        atmosphereTransmission : `lsst.afw.image.TransmissionCurve`
            A ``TransmissionCurve`` that represents the throughput of the
            atmosphere, assumed to be spatially constant.
        detectorNum : `int`, optional
            The integer number for the detector to process.
        isGen3 : bool, optional
            Flag this call to run() as using the Gen3 butler environment.
        strayLightData : `object`, optional
            Opaque object containing calibration information for stray-light
            correction.  If `None`, no correction will be performed.

        Returns
        -------
        result : `lsst.pipe.base.Struct`
            Result struct with component:
            - ``exposure`` : `afw.image.Exposure`
                The fully ISR corrected exposure.
            - ``outputExposure`` : `afw.image.Exposure`
                An alias for `exposure`
            - ``ossThumb`` : `numpy.ndarray`
                Thumbnail image of the exposure after overscan subtraction.
            - ``flattenedThumb`` : `numpy.ndarray`
                Thumbnail image of the exposure after flat-field correction.

        Raises
        ------
        RuntimeError
            Raised if a configuration option is set to True, but the
            required calibration data has not been specified.

        Notes
        -----
        The current processed exposure can be viewed by setting the
        appropriate lsstDebug entries in the `debug.display`
        dictionary.  The names of these entries correspond to some of
        the IsrTaskConfig Boolean options, with the value denoting the
        frame to use.  The exposure is shown inside the matching
        option check and after the processing of that step has
        finished.  The steps with debug points are:

        doAssembleCcd
        doBias
        doCrosstalk
        doBrighterFatter
        doDark
        doFringe
        doStrayLight
        doFlat

        In addition, setting the "postISRCCD" entry displays the
        exposure after all ISR processing has finished.

        """

        if isGen3 is True:
            # Gen3 currently cannot automatically do configuration overrides.
            # DM-15257 looks to discuss this issue.

            self.config.doFringe = False

            # Configure input exposures;
            if detectorNum is None:
                raise RuntimeError("Must supply the detectorNum if running as Gen3")

            ccdExposure = self.ensureExposure(ccdExposure, camera, detectorNum)
            bias = self.ensureExposure(bias, camera, detectorNum)
            dark = self.ensureExposure(dark, camera, detectorNum)
            flat = self.ensureExposure(flat, camera, detectorNum)
        else:
            if isinstance(ccdExposure, ButlerDataRef):
                return self.runDataRef(ccdExposure)

        ccd = ccdExposure.getDetector()

        if not ccd:
            assert not self.config.doAssembleCcd, "You need a Detector to run assembleCcd"
            ccd = [FakeAmp(ccdExposure, self.config)]

        # Validate Input
        if self.config.doBias and bias is None:
            raise RuntimeError("Must supply a bias exposure if config.doBias=True.")
        if self.doLinearize(ccd) and linearizer is None:
            raise RuntimeError("Must supply a linearizer if config.doLinearize=True for this detector.")
        if self.config.doBrighterFatter and bfKernel is None:
            raise RuntimeError("Must supply a kernel if config.doBrighterFatter=True.")
        if self.config.doDark and dark is None:
            raise RuntimeError("Must supply a dark exposure if config.doDark=True.")
        if fringes is None:
            fringes = pipeBase.Struct(fringes=None)
        if self.config.doFringe and not isinstance(fringes, pipeBase.Struct):
            raise RuntimeError("Must supply fringe exposure as a pipeBase.Struct.")
        if self.config.doFlat and flat is None:
            raise RuntimeError("Must supply a flat exposure if config.doFlat=True.")
        if self.config.doDefect and defects is None:
            raise RuntimeError("Must supply defects if config.doDefect=True.")
        if self.config.doAddDistortionModel and camera is None:
            raise RuntimeError("Must supply camera if config.doAddDistortionModel=True.")

        # Begin ISR processing.
        if self.config.doConvertIntToFloat:
            self.log.info("Converting exposure to floating point values")
            ccdExposure = self.convertIntToFloat(ccdExposure)

        # Amplifier level processing.
        overscans = []
        for amp in ccd:
            # if ccdExposure is one amp, check for coverage to prevent performing ops multiple times
            if ccdExposure.getBBox().contains(amp.getBBox()):
                # Check for fully masked bad amplifiers, and generate masks for SUSPECT and SATURATED values.
                badAmp = self.maskAmplifier(ccdExposure, amp, defects)

                if self.config.doOverscan and not badAmp:
                    # Overscan correction on amp-by-amp basis.
                    overscanResults = self.overscanCorrection(ccdExposure, amp)
                    self.log.debug("Corrected overscan for amplifier %s" % (amp.getName()))
                    if self.config.qa is not None and self.config.qa.saveStats is True:
                        if isinstance(overscanResults.overscanFit, float):
                            qaMedian = overscanResults.overscanFit
                            qaStdev = float("NaN")
                        else:
                            qaStats = afwMath.makeStatistics(overscanResults.overscanFit,
                                                             afwMath.MEDIAN | afwMath.STDEVCLIP)
                            qaMedian = qaStats.getValue(afwMath.MEDIAN)
                            qaStdev = qaStats.getValue(afwMath.STDEVCLIP)

                        self.metadata.set("ISR OSCAN {} MEDIAN".format(amp.getName()), qaMedian)
                        self.metadata.set("ISR OSCAN {} STDEV".format(amp.getName()), qaStdev)
                        self.log.debug("  Overscan stats for amplifer %s: %f +/- %f" %
                                       (amp.getName(), qaMedian, qaStdev))
                        ccdExposure.getMetadata().set('OVERSCAN', "Overscan corrected")
                else:
                    if badAmp:
                        self.log.warn("Amplifier %s is bad." % (amp.getName()))
                    overscanResults = None

                overscans.append(overscanResults if overscanResults is not None else None)
            else:
                self.log.info("Skipped OSCAN")

        if self.config.doCrosstalk and self.config.doCrosstalkBeforeAssemble:
            self.log.info("Applying crosstalk correction.")
            self.crosstalk.run(ccdExposure, crosstalkSources=crosstalkSources)
            self.debugView(ccdExposure, "doCrosstalk")

        if self.config.doAssembleCcd:
            self.log.info("Assembling CCD from amplifiers")
            ccdExposure = self.assembleCcd.assembleCcd(ccdExposure)

            if self.config.expectWcs and not ccdExposure.getWcs():
                self.log.warn("No WCS found in input exposure")
            self.debugView(ccdExposure, "doAssembleCcd")

        ossThumb = None
        if self.config.qa.doThumbnailOss:
            ossThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa)

        if self.config.doBias:
            self.log.info("Applying bias correction.")
            isrFunctions.biasCorrection(ccdExposure.getMaskedImage(), bias.getMaskedImage(),
                                        trimToFit=self.config.doTrimToMatchCalib)
            self.debugView(ccdExposure, "doBias")

        if self.config.doVariance:
            for amp, overscanResults in zip(ccd, overscans):
                if ccdExposure.getBBox().contains(amp.getBBox()):
                    self.log.debug("Constructing variance map for amplifer %s" % (amp.getName()))
                    ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
                    if overscanResults is not None:
                        self.updateVariance(ampExposure, amp,
                                            overscanImage=overscanResults.overscanImage)
                    else:
                        self.updateVariance(ampExposure, amp,
                                            overscanImage=None)
                    if self.config.qa is not None and self.config.qa.saveStats is True:
                        qaStats = afwMath.makeStatistics(ampExposure.getVariance(),
                                                         afwMath.MEDIAN | afwMath.STDEVCLIP)
                        self.metadata.set("ISR VARIANCE {} MEDIAN".format(amp.getName()),
                                          qaStats.getValue(afwMath.MEDIAN))
                        self.metadata.set("ISR VARIANCE {} STDEV".format(amp.getName()),
                                          qaStats.getValue(afwMath.STDEVCLIP))
                        self.log.debug("  Variance stats for amplifer %s: %f +/- %f" %
                                       (amp.getName(), qaStats.getValue(afwMath.MEDIAN),
                                        qaStats.getValue(afwMath.STDEVCLIP)))

        if self.doLinearize(ccd):
            self.log.info("Applying linearizer.")
            linearizer(image=ccdExposure.getMaskedImage().getImage(), detector=ccd, log=self.log)

        if self.config.doCrosstalk and not self.config.doCrosstalkBeforeAssemble:
            self.log.info("Applying crosstalk correction.")
            self.crosstalk.run(ccdExposure, crosstalkSources=crosstalkSources, isTrimmed=True)
            self.debugView(ccdExposure, "doCrosstalk")

        # Masking block. Optionally mask known defects, NAN pixels, widen trails, and do
        # anything else the camera needs. Saturated and suspect pixels have already been masked.
        if self.config.doDefect:
            self.log.info("Masking defects.")
            self.maskDefect(ccdExposure, defects)

        if self.config.doNanMasking:
            self.log.info("Masking NAN value pixels.")
            self.maskNan(ccdExposure)

        if self.config.doWidenSaturationTrails:
            self.log.info("Widening saturation trails.")
            isrFunctions.widenSaturationTrails(ccdExposure.getMaskedImage().getMask())

        if self.config.doCameraSpecificMasking:
            self.log.info("Masking regions for camera specific reasons.")
            self.masking.run(ccdExposure)

        if self.config.doBrighterFatter:
            # We need to apply flats and darks before we can interpolate, and we
            # need to interpolate before we do B-F, but we do B-F without the
            # flats and darks applied so we can work in units of electrons or holes.
            # This context manager applies and then removes the darks and flats.
            #
            # We also do not want to interpolate values here, so operate on temporary
            # images so we can apply only the BF-correction and roll back the
            # interpolation.
            interpExp = ccdExposure.clone()
            with self.flatContext(interpExp, flat, dark):
                isrFunctions.interpolateFromMask(
                    maskedImage=ccdExposure.getMaskedImage(),
                    fwhm=self.config.fwhm,
                    growSaturatedFootprints=self.config.growSaturationFootprintSize,
                    maskNameList=self.config.maskListToInterpolate
                )
            bfExp = interpExp.clone()

            self.log.info("Applying brighter fatter correction.")
            isrFunctions.brighterFatterCorrection(bfExp, bfKernel,
                                                  self.config.brighterFatterMaxIter,
                                                  self.config.brighterFatterThreshold,
                                                  self.config.brighterFatterApplyGain,
                                                  )
            image = ccdExposure.getMaskedImage().getImage()
            bfCorr = bfExp.getMaskedImage().getImage()
            bfCorr -= interpExp.getMaskedImage().getImage()
            image += bfCorr

            self.debugView(ccdExposure, "doBrighterFatter")

        if self.config.doDark:
            self.log.info("Applying dark correction.")
            self.darkCorrection(ccdExposure, dark)
            self.debugView(ccdExposure, "doDark")

        if self.config.doFringe and not self.config.fringeAfterFlat:
            self.log.info("Applying fringe correction before flat.")
            self.fringe.run(ccdExposure, **fringes.getDict())
            self.debugView(ccdExposure, "doFringe")

        if self.config.doStrayLight:
            if strayLightData is not None:
                self.log.info("Applying stray light correction.")
                self.strayLight.run(ccdExposure, strayLightData)
                self.debugView(ccdExposure, "doStrayLight")
            else:
                self.log.debug("Skipping stray light correction: no data found for this image.")

        if self.config.doFlat:
            self.log.info("Applying flat correction.")
            self.flatCorrection(ccdExposure, flat)
            self.debugView(ccdExposure, "doFlat")

        if self.config.doApplyGains:
            self.log.info("Applying gain correction instead of flat.")
            isrFunctions.applyGains(ccdExposure, self.config.normalizeGains)

        if self.config.doFringe and self.config.fringeAfterFlat:
            self.log.info("Applying fringe correction after flat.")
            self.fringe.run(ccdExposure, **fringes.getDict())

        if self.config.doVignette:
            self.log.info("Constructing Vignette polygon.")
            self.vignettePolygon = self.vignette.run(ccdExposure)

            if self.config.vignette.doWriteVignettePolygon:
                self.setValidPolygonIntersect(ccdExposure, self.vignettePolygon)

        if self.config.doAttachTransmissionCurve:
            self.log.info("Adding transmission curves.")
            isrFunctions.attachTransmissionCurve(ccdExposure, opticsTransmission=opticsTransmission,
                                                 filterTransmission=filterTransmission,
                                                 sensorTransmission=sensorTransmission,
                                                 atmosphereTransmission=atmosphereTransmission)

        if self.config.doAddDistortionModel:
            self.log.info("Adding a distortion model to the WCS.")
            isrFunctions.addDistortionModel(exposure=ccdExposure, camera=camera)

        flattenedThumb = None
        if self.config.qa.doThumbnailFlattened:
            flattenedThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa)

        preInterpExp = None
        if self.config.doSaveInterpPixels:
            preInterpExp = ccdExposure.clone()

        if self.config.doInterpolate:
            self.log.info("Interpolating masked pixels.")
            isrFunctions.interpolateFromMask(
                maskedImage=ccdExposure.getMaskedImage(),
                fwhm=self.config.fwhm,
                growSaturatedFootprints=self.config.growSaturationFootprintSize,
                maskNameList=list(self.config.maskListToInterpolate)
            )

        if self.config.doSetBadRegions:
            # This is like an interpolation.
            badPixelCount, badPixelValue = isrFunctions.setBadRegions(ccdExposure)
            if badPixelCount > 0:
                self.log.info("Set %d BAD pixels to %f." % (badPixelCount, badPixelValue))

        self.roughZeroPoint(ccdExposure)

        if self.config.doMeasureBackground:
            self.log.info("Measuring background level:")
            self.measureBackground(ccdExposure, self.config.qa)

            if self.config.qa is not None and self.config.qa.saveStats is True:
                for amp in ccd:
                    ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
                    qaStats = afwMath.makeStatistics(ampExposure.getImage(),
                                                     afwMath.MEDIAN | afwMath.STDEVCLIP)
                    self.metadata.set("ISR BACKGROUND {} MEDIAN".format(amp.getName()),
                                      qaStats.getValue(afwMath.MEDIAN))
                    self.metadata.set("ISR BACKGROUND {} STDEV".format(amp.getName()),
                                      qaStats.getValue(afwMath.STDEVCLIP))
                    self.log.debug("  Background stats for amplifer %s: %f +/- %f" %
                                   (amp.getName(), qaStats.getValue(afwMath.MEDIAN),
                                    qaStats.getValue(afwMath.STDEVCLIP)))

        self.debugView(ccdExposure, "postISRCCD")

        return pipeBase.Struct(
            exposure=ccdExposure,
            ossThumb=ossThumb,
            flattenedThumb=flattenedThumb,

            preInterpolatedExposure=preInterpExp,
            outputExposure=ccdExposure,
            outputOssThumbnail=ossThumb,
            outputFlattenedThumbnail=flattenedThumb,
        )

runDataRef()

def lsst.ip.isr.isrTask.IsrTask.runDataRef	(		self,
			sensorRef
	)

Perform instrument signature removal on a ButlerDataRef of a Sensor.

This method contains the `CmdLineTask` interface to the ISR
processing.  All IO is handled here, freeing the `run()` method
to manage only pixel-level calculations.  The steps performed
are:
- Read in necessary detrending/isr/calibration data.
- Process raw exposure in `run()`.
- Persist the ISR-corrected exposure as "postISRCCD" if
  config.doWrite=True.

Parameters
----------
sensorRef : `daf.persistence.butlerSubset.ButlerDataRef`
    DataRef of the detector data to be processed

Returns
-------
result : `lsst.pipe.base.Struct`
    Result struct with component:
    - ``exposure`` : `afw.image.Exposure`
The fully ISR corrected exposure.

Raises
------
RuntimeError
    Raised if a configuration option is set to True, but the
    required calibration data does not exist.

Definition at line 1390 of file isrTask.py.

    def runDataRef(self, sensorRef):
        """Perform instrument signature removal on a ButlerDataRef of a Sensor.

        This method contains the `CmdLineTask` interface to the ISR
        processing.  All IO is handled here, freeing the `run()` method
        to manage only pixel-level calculations.  The steps performed
        are:
        - Read in necessary detrending/isr/calibration data.
        - Process raw exposure in `run()`.
        - Persist the ISR-corrected exposure as "postISRCCD" if
          config.doWrite=True.

        Parameters
        ----------
        sensorRef : `daf.persistence.butlerSubset.ButlerDataRef`
            DataRef of the detector data to be processed

        Returns
        -------
        result : `lsst.pipe.base.Struct`
            Result struct with component:
            - ``exposure`` : `afw.image.Exposure`
                The fully ISR corrected exposure.

        Raises
        ------
        RuntimeError
            Raised if a configuration option is set to True, but the
            required calibration data does not exist.

        """
        self.log.info("Performing ISR on sensor %s" % (sensorRef.dataId))

        ccdExposure = sensorRef.get(self.config.datasetType)

        camera = sensorRef.get("camera")
        if camera is None and self.config.doAddDistortionModel:
            raise RuntimeError("config.doAddDistortionModel is True "
                               "but could not get a camera from the butler")
        isrData = self.readIsrData(sensorRef, ccdExposure)

        result = self.run(ccdExposure, camera=camera, **isrData.getDict())

        if self.config.doWrite:
            sensorRef.put(result.exposure, "postISRCCD")
            if result.preInterpolatedExposure is not None:
                sensorRef.put(result.preInterpolatedExposure, "postISRCCD_uninterpolated")
        if result.ossThumb is not None:
            isrQa.writeThumbnail(sensorRef, result.ossThumb, "ossThumb")
        if result.flattenedThumb is not None:
            isrQa.writeThumbnail(sensorRef, result.flattenedThumb, "flattenedThumb")

        return result

saturationDetection()

def lsst.ip.isr.isrTask.IsrTask.saturationDetection	(		self,
			exposure,
			amp
	)

Detect saturated pixels and mask them using mask plane config.saturatedMaskName, in place.

Parameters

exposure : lsst.afw.image.Exposure Exposure to process. Only the amplifier DataSec is processed. amp : lsst.afw.table.AmpInfoCatalog Amplifier detector data.

See Also

lsst.ip.isr.isrFunctions.makeThresholdMask

Definition at line 1912 of file isrTask.py.

    def saturationDetection(self, exposure, amp):
        """!Detect saturated pixels and mask them using mask plane config.saturatedMaskName, in place.

        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to process.  Only the amplifier DataSec is processed.
        amp : `lsst.afw.table.AmpInfoCatalog`
            Amplifier detector data.

        See Also
        --------
        lsst.ip.isr.isrFunctions.makeThresholdMask
        """
        if not math.isnan(amp.getSaturation()):
            maskedImage = exposure.getMaskedImage()
            dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
            isrFunctions.makeThresholdMask(
                maskedImage=dataView,
                threshold=amp.getSaturation(),
                growFootprints=0,
                maskName=self.config.saturatedMaskName,
            )

saturationInterpolation()

def lsst.ip.isr.isrTask.IsrTask.saturationInterpolation	(		self,
			exposure
	)

Interpolate over saturated pixels, in place.

This method should be called after saturationDetection, to ensure that the saturated pixels have been identified in the SAT mask. It should also be called after assembleCcd, since saturated regions may cross amplifier boundaries.

Parameters

exposure : lsst.afw.image.Exposure Exposure to process.

See Also

lsst.ip.isr.isrTask.saturationDetection lsst.ip.isr.isrFunctions.interpolateFromMask

Definition at line 1936 of file isrTask.py.

    def saturationInterpolation(self, exposure):
        """!Interpolate over saturated pixels, in place.

        This method should be called after `saturationDetection`, to
        ensure that the saturated pixels have been identified in the
        SAT mask.  It should also be called after `assembleCcd`, since
        saturated regions may cross amplifier boundaries.

        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to process.

        See Also
        --------
        lsst.ip.isr.isrTask.saturationDetection
        lsst.ip.isr.isrFunctions.interpolateFromMask
        """
        isrFunctions.interpolateFromMask(
            maskedImage=exposure.getMaskedImage(),
            fwhm=self.config.fwhm,
            growSaturatedFootprints=self.config.growSaturationFootprintSize,
            maskNameList=list(self.config.saturatedMaskName),
        )

setValidPolygonIntersect()

def lsst.ip.isr.isrTask.IsrTask.setValidPolygonIntersect	(		self,
			ccdExposure,
			fpPolygon
	)

Set the valid polygon as the intersection of fpPolygon and the ccd corners.

Parameters

ccdExposure : lsst.afw.image.Exposure Exposure to process. fpPolygon : lsst.afw.geom.Polygon Polygon in focal plane coordinates.

Definition at line 2181 of file isrTask.py.

    def setValidPolygonIntersect(self, ccdExposure, fpPolygon):
        """!Set the valid polygon as the intersection of fpPolygon and the ccd corners.

        Parameters
        ----------
        ccdExposure : `lsst.afw.image.Exposure`
            Exposure to process.
        fpPolygon : `lsst.afw.geom.Polygon`
            Polygon in focal plane coordinates.
        """
        # Get ccd corners in focal plane coordinates
        ccd = ccdExposure.getDetector()
        fpCorners = ccd.getCorners(FOCAL_PLANE)
        ccdPolygon = Polygon(fpCorners)

        # Get intersection of ccd corners with fpPolygon
        intersect = ccdPolygon.intersectionSingle(fpPolygon)

        # Transform back to pixel positions and build new polygon
        ccdPoints = ccd.transform(intersect, FOCAL_PLANE, PIXELS)
        validPolygon = Polygon(ccdPoints)
        ccdExposure.getInfo().setValidPolygon(validPolygon)

suspectDetection()

def lsst.ip.isr.isrTask.IsrTask.suspectDetection	(		self,
			exposure,
			amp
	)

Detect suspect pixels and mask them using mask plane config.suspectMaskName, in place.

Parameters

exposure : lsst.afw.image.Exposure Exposure to process. Only the amplifier DataSec is processed. amp : lsst.afw.table.AmpInfoCatalog Amplifier detector data.

See Also

lsst.ip.isr.isrFunctions.makeThresholdMask

Notes

Suspect pixels are pixels whose value is greater than amp.getSuspectLevel(). This is intended to indicate pixels that may be affected by unknown systematics; for example if non-linearity corrections above a certain level are unstable then that would be a useful value for suspectLevel. A value of nan indicates that no such level exists and no pixels are to be masked as suspicious.

Definition at line 1961 of file isrTask.py.

    def suspectDetection(self, exposure, amp):
        """!Detect suspect pixels and mask them using mask plane config.suspectMaskName, in place.

        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to process.  Only the amplifier DataSec is processed.
        amp : `lsst.afw.table.AmpInfoCatalog`
            Amplifier detector data.

        See Also
        --------
        lsst.ip.isr.isrFunctions.makeThresholdMask

        Notes
        -----
        Suspect pixels are pixels whose value is greater than amp.getSuspectLevel().
        This is intended to indicate pixels that may be affected by unknown systematics;
        for example if non-linearity corrections above a certain level are unstable
        then that would be a useful value for suspectLevel. A value of `nan` indicates
        that no such level exists and no pixels are to be masked as suspicious.
        """
        suspectLevel = amp.getSuspectLevel()
        if math.isnan(suspectLevel):
            return

        maskedImage = exposure.getMaskedImage()
        dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
        isrFunctions.makeThresholdMask(
            maskedImage=dataView,
            threshold=suspectLevel,
            growFootprints=0,
            maskName=self.config.suspectMaskName,
        )

updateVariance()

def lsst.ip.isr.isrTask.IsrTask.updateVariance	(		self,
			ampExposure,
			amp,
			overscanImage = None
	)

Set the variance plane using the amplifier gain and read noise

The read noise is calculated from the ``overscanImage`` if the
``doEmpiricalReadNoise`` option is set in the configuration; otherwise
the value from the amplifier data is used.

Parameters
----------
ampExposure : `lsst.afw.image.Exposure`
    Exposure to process.
amp : `lsst.afw.table.AmpInfoRecord` or `FakeAmp`
    Amplifier detector data.
overscanImage : `lsst.afw.image.MaskedImage`, optional.
    Image of overscan, required only for empirical read noise.

See also
--------
lsst.ip.isr.isrFunctions.updateVariance

Definition at line 1776 of file isrTask.py.

    def updateVariance(self, ampExposure, amp, overscanImage=None):
        """Set the variance plane using the amplifier gain and read noise

        The read noise is calculated from the ``overscanImage`` if the
        ``doEmpiricalReadNoise`` option is set in the configuration; otherwise
        the value from the amplifier data is used.

        Parameters
        ----------
        ampExposure : `lsst.afw.image.Exposure`
            Exposure to process.
        amp : `lsst.afw.table.AmpInfoRecord` or `FakeAmp`
            Amplifier detector data.
        overscanImage : `lsst.afw.image.MaskedImage`, optional.
            Image of overscan, required only for empirical read noise.

        See also
        --------
        lsst.ip.isr.isrFunctions.updateVariance
        """
        maskPlanes = [self.config.saturatedMaskName, self.config.suspectMaskName]
        gain = amp.getGain()

        if math.isnan(gain):
            gain = 1.0
            self.log.warn("Gain set to NAN!  Updating to 1.0 to generate Poisson variance.")
        elif gain <= 0:
            patchedGain = 1.0
            self.log.warn("Gain for amp %s == %g <= 0; setting to %f" %
                          (amp.getName(), gain, patchedGain))
            gain = patchedGain

        if self.config.doEmpiricalReadNoise and overscanImage is None:
            self.log.info("Overscan is none for EmpiricalReadNoise")

        if self.config.doEmpiricalReadNoise and overscanImage is not None:
            stats = afwMath.StatisticsControl()
            stats.setAndMask(overscanImage.mask.getPlaneBitMask(maskPlanes))
            readNoise = afwMath.makeStatistics(overscanImage, afwMath.STDEVCLIP, stats).getValue()
            self.log.info("Calculated empirical read noise for amp %s: %f", amp.getName(), readNoise)
        else:
            readNoise = amp.getReadNoise()

        isrFunctions.updateVariance(
            maskedImage=ampExposure.getMaskedImage(),
            gain=gain,
            readNoise=readNoise,
        )

Member Data Documentation

ConfigClass

lsst.ip.isr.isrTask.IsrTask.ConfigClass = IsrTaskConfig

static

Definition at line 727 of file isrTask.py.

vignettePolygon

lsst.ip.isr.isrTask.IsrTask.vignettePolygon

Definition at line 1318 of file isrTask.py.

---

The documentation for this class was generated from the following file:

• /j/snowflake/release/lsstsw/stack/Linux64/ip_isr/18.1.0/python/lsst/ip/isr/isrTask.py