lsst.ip.isr.isrTask.IsrTask Class Reference

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

Public Member Functions
	__init__ (self, **kwargs)
	runQuantum (self, butlerQC, inputRefs, outputRefs)
	run (self, ccdExposure, *camera=None, bias=None, linearizer=None, crosstalk=None, crosstalkSources=None, dark=None, flat=None, ptc=None, bfKernel=None, bfGains=None, defects=None, fringes=pipeBase.Struct(fringes=None), opticsTransmission=None, filterTransmission=None, sensorTransmission=None, atmosphereTransmission=None, detectorNum=None, strayLightData=None, illumMaskedImage=None, deferredChargeCalib=None)
	defineEffectivePtc (self, ptcDataset, detector, bfGains, overScans, metadata)
	ensureExposure (self, inputExp, camera=None, detectorNum=None)
	compareCameraKeywords (self, exposureMetadata, calib, calibName)
	convertIntToFloat (self, exposure)
	maskAmplifier (self, ccdExposure, amp, defects)
	overscanCorrection (self, ccdExposure, amp)
	updateVariance (self, ampExposure, amp, ptcDataset)
	maskNegativeVariance (self, exposure)
	darkCorrection (self, exposure, darkExposure, invert=False)
	doLinearize (self, detector)
	flatCorrection (self, exposure, flatExposure, invert=False)
	saturationDetection (self, exposure, amp)
	saturationInterpolation (self, exposure)
	suspectDetection (self, exposure, amp)
	maskDefect (self, exposure, defectBaseList)
	maskEdges (self, exposure, numEdgePixels=0, maskPlane="SUSPECT", level='DETECTOR')
	maskAndInterpolateDefects (self, exposure, defectBaseList)
	maskNan (self, exposure)
	maskAndInterpolateNan (self, exposure)
	measureBackground (self, exposure, IsrQaConfig=None)
	roughZeroPoint (self, exposure)
	flatContext (self, exp, flat, dark=None)
	makeBinnedImages (self, exposure)
	debugView (self, exposure, stepname)

Static Public Member Functions
	extractCalibDate (calib)

Public Attributes
	vignettePolygon

Static Public Attributes
	ConfigClass = IsrTaskConfig

Static Protected Attributes
str	_DefaultName = "isr"

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 __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 984 of file isrTask.py.

Constructor & Destructor Documentation

__init__()

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

Definition at line 1011 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("overscan")
        self.makeSubtask("vignette")
        self.makeSubtask("ampOffset")
        self.makeSubtask("deferredChargeCorrection")
        self.makeSubtask("isrStats")
 

Member Function Documentation

compareCameraKeywords()

lsst.ip.isr.isrTask.IsrTask.compareCameraKeywords	(		self,
			exposureMetadata,
			calib,
			calibName )

Compare header keywords to confirm camera states match.

Parameters
----------
exposureMetadata : `lsst.daf.base.PropertySet`
    Header for the exposure being processed.
calib : `lsst.afw.image.Exposure` or `lsst.ip.isr.IsrCalib`
    Calibration to be applied.
calibName : `str`
    Calib type for log message.

Definition at line 1995 of file isrTask.py.

    def compareCameraKeywords(self, exposureMetadata, calib, calibName):
        """Compare header keywords to confirm camera states match.
 
        Parameters
        ----------
        exposureMetadata : `lsst.daf.base.PropertySet`
            Header for the exposure being processed.
        calib : `lsst.afw.image.Exposure` or `lsst.ip.isr.IsrCalib`
            Calibration to be applied.
        calibName : `str`
            Calib type for log message.
        """
        try:
            calibMetadata = calib.getMetadata()
        except AttributeError:
            return
        for keyword in self.config.cameraKeywordsToCompare:
            if keyword in exposureMetadata and keyword in calibMetadata:
                if exposureMetadata[keyword] != calibMetadata[keyword]:
                    if self.config.doRaiseOnCalibMismatch:
                        raise RuntimeError("Sequencer mismatch for %s [%s]: exposure: %s calib: %s",
                                           calibName, keyword,
                                           exposureMetadata[keyword], calibMetadata[keyword])
                    else:
                        self.log.warning("Sequencer mismatch for %s [%s]: exposure: %s calib: %s",
                                         calibName, keyword,
                                         exposureMetadata[keyword], calibMetadata[keyword])
            else:
                self.log.debug("Sequencer keyword %s not found.", keyword)
 

convertIntToFloat()

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 2025 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
            self.log.debug("Exposure already of type float.")
            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()

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 2280 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 \
                and not math.isnan(darkExposure.getInfo().getVisitInfo().getDarkTime()):
            darkScale = darkExposure.getInfo().getVisitInfo().getDarkTime()
        else:
            # DM-17444: darkExposure.getInfo.getVisitInfo() is None
            #           so getDarkTime() does not exist.
            self.log.warning("darkExposure.getInfo().getVisitInfo() does not exist. Using darkScale = 1.0.")
            darkScale = 1.0
 
        isrFunctions.darkCorrection(
            maskedImage=exposure.getMaskedImage(),
            darkMaskedImage=darkExposure.getMaskedImage(),
            expScale=expScale,
            darkScale=darkScale,
            invert=invert,
            trimToFit=self.config.doTrimToMatchCalib
        )
 

debugView()

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 2725 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)
            prompt = "Press Enter to continue [c]... "
            while True:
                ans = input(prompt).lower()
                if ans in ("", "c",):
                    break
 
 

defineEffectivePtc()

lsst.ip.isr.isrTask.IsrTask.defineEffectivePtc	(		self,
			ptcDataset,
			detector,
			bfGains,
			overScans,
			metadata )

Define an effective Photon Transfer Curve dataset
with nominal gains and noise.

Parameters
----------
ptcDataset : `lsst.ip.isr.PhotonTransferCurveDataset`
    Input Photon Transfer Curve dataset.
detector : `lsst.afw.cameraGeom.Detector`
    Detector object.
bfGains : `dict`
    Gains from running the brighter-fatter code.
    A dict keyed by amplifier name for the detector
    in question.
ovserScans : `list` [`lsst.pipe.base.Struct`]
    List of overscanResults structures
metadata : `lsst.daf.base.PropertyList`
    Exposure metadata to update gain and noise provenance.

Returns
-------
effectivePtc : `lsst.ip.isr.PhotonTransferCurveDataset`
    PTC dataset containing gains and readout noise
    values to be used throughout
    Instrument Signature Removal.

Definition at line 1783 of file isrTask.py.

    def defineEffectivePtc(self, ptcDataset, detector, bfGains, overScans, metadata):
        """Define an effective Photon Transfer Curve dataset
        with nominal gains and noise.
 
        Parameters
        ----------
        ptcDataset : `lsst.ip.isr.PhotonTransferCurveDataset`
            Input Photon Transfer Curve dataset.
        detector : `lsst.afw.cameraGeom.Detector`
            Detector object.
        bfGains : `dict`
            Gains from running the brighter-fatter code.
            A dict keyed by amplifier name for the detector
            in question.
        ovserScans : `list` [`lsst.pipe.base.Struct`]
            List of overscanResults structures
        metadata : `lsst.daf.base.PropertyList`
            Exposure metadata to update gain and noise provenance.
 
        Returns
        -------
        effectivePtc : `lsst.ip.isr.PhotonTransferCurveDataset`
            PTC dataset containing gains and readout noise
            values to be used throughout
            Instrument Signature Removal.
        """
        amps = detector.getAmplifiers()
        ampNames = [amp.getName() for amp in amps]
        detName = detector.getName()
        effectivePtc = PhotonTransferCurveDataset(ampNames, 'EFFECTIVE_PTC', 1)
        boolGainMismatch = False
        doWarningPtcValidation = True
 
        for amp, overscanResults in zip(amps, overScans):
            ampName = amp.getName()
            # Gain:
            # Try first with the PTC gains.
            gainProvenanceString = "amp"
            if self.config.usePtcGains:
                gain = ptcDataset.gain[ampName]
                gainProvenanceString = "ptc"
                self.log.debug("Using gain from Photon Transfer Curve.")
            else:
                # Try then with the amplifier gain.
                # We already have a detector at this point. If there was no
                # detector to begin with, one would have been created with
                # self.config.gain and self.config.noise. Same comment
                # applies for the noise block below.
                gain = amp.getGain()
 
            # Check if the gain up to this point differs from the
            # gain in bfGains. If so, raise or warn, accordingly.
            if not boolGainMismatch and bfGains is not None and ampName in bfGains:
                bfGain = bfGains[ampName]
                if not math.isclose(gain, bfGain, rel_tol=1e-4):
                    if self.config.doRaiseOnCalibMismatch:
                        raise RuntimeError("Gain mismatch for det %s amp %s: "
                                           "(gain (%s): %s, bfGain: %s)",
                                           detName, ampName, gainProvenanceString,
                                           gain, bfGain)
                    else:
                        self.log.warning("Gain mismatch for det %s amp %s: "
                                         "(gain (%s): %s, bfGain: %s)",
                                         detName, ampName, gainProvenanceString,
                                         gain, bfGain)
                    boolGainMismatch = True
 
            # Noise:
            # Try first with the empirical noise from the overscan.
            noiseProvenanceString = "amp"
            if self.config.doEmpiricalReadNoise and overscanResults is not None:
                noiseProvenanceString = "serial overscan"
                if isinstance(overscanResults.residualSigma, float):
                    # Only serial overscan was run
                    noise = overscanResults.residualSigma
                else:
                    # Both serial and parallel overscan were
                    # run.  Only report noise from serial here.
                    noise = overscanResults.residualSigma[0]
            elif self.config.usePtcReadNoise:
                # Try then with the PTC noise.
                noise = ptcDataset.noise[ampName]
                noiseProvenanceString = "ptc"
                self.log.debug("Using noise from Photon Transfer Curve.")
            else:
                # Finally, try with the amplifier noise.
                # We already have a detector at this point. If there
                # was no detector to begin with, one would have
                # been created with self.config.gain and
                # self.config.noise.
                noise = amp.getReadNoise()
 
            if math.isnan(gain):
                gain = 1.0
                self.log.warning("Gain for amp %s set to NAN! Updating to"
                                 " 1.0 to generate Poisson variance.", ampName)
            elif gain <= 0:
                patchedGain = 1.0
                self.log.warning("Gain for amp %s == %g <= 0; setting to %f.",
                                 ampName, gain, patchedGain)
                gain = patchedGain
 
            # PTC Turnoff:
            # Copy it over from the input PTC if it's positive. If it's a nan
            # set it to a high value.
            if ptcDataset is not None:
                ptcTurnoff = ptcDataset.ptcTurnoff[ampName]
            else:
                ptcTurnoff = 2e19
 
            if (isinstance(ptcTurnoff, numbers.Real) and ptcTurnoff > 0):
                effectivePtc.ptcTurnoff[ampName] = ptcTurnoff
            elif math.isnan(ptcTurnoff):
                effectivePtc.ptcTurnoff[ampName] = 2e19
 
            effectivePtc.gain[ampName] = gain
            effectivePtc.noise[ampName] = noise
            # Make sure noise,turnoff, and gain make sense
            effectivePtc.validateGainNoiseTurnoffValues(ampName, doWarn=doWarningPtcValidation)
            doWarningPtcValidation = False
 
            metadata[f"LSST GAIN {amp.getName()}"] = effectivePtc.gain[ampName]
            metadata[f"LSST READNOISE {amp.getName()}"] = effectivePtc.noise[ampName]
 
        self.log.info("Det: %s - Noise provenance: %s, Gain provenance: %s",
                      detName,
                      noiseProvenanceString,
                      gainProvenanceString)
        metadata["LSST ISR GAIN SOURCE"] = gainProvenanceString
        metadata["LSST ISR NOISE SOURCE"] = noiseProvenanceString
 
        return effectivePtc
 

doLinearize()

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 2323 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.getAmplifiers()[0].getLinearityType() != NullLinearityType
 

ensureExposure()

lsst.ip.isr.isrTask.IsrTask.ensureExposure	(		self,
			inputExp,
			camera = None,
			detectorNum = None )

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

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` image-type.
    The input data structure obtained from Butler.
    Can be  `lsst.afw.image.Exposure`,
    `lsst.afw.image.DecoratedImageU`,
    or `lsst.afw.image.ImageF`
camera : `lsst.afw.cameraGeom.camera`, optional
    The camera associated with the image.  Used to find the appropriate
    detector if detector is not already set.
detectorNum : `int`, optional
    The detector in the camera to attach, if the detector is not
    already set.

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 1916 of file isrTask.py.

    def ensureExposure(self, inputExp, camera=None, detectorNum=None):
        """Ensure that the data returned by Butler is a fully constructed exp.
 
        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` image-type.
            The input data structure obtained from Butler.
            Can be  `lsst.afw.image.Exposure`,
            `lsst.afw.image.DecoratedImageU`,
            or `lsst.afw.image.ImageF`
        camera : `lsst.afw.cameraGeom.camera`, optional
            The camera associated with the image.  Used to find the appropriate
            detector if detector is not already set.
        detectorNum : `int`, optional
            The detector in the camera to attach, if the detector is not
            already set.
 
        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("Input Exposure is not known type in isrTask.ensureExposure: %s." %
                            (type(inputExp), ))
 
        if inputExp.getDetector() is None:
            if camera is None or detectorNum is None:
                raise RuntimeError('Must supply both a camera and detector number when using exposures '
                                   'without a detector set.')
            inputExp.setDetector(camera[detectorNum])
 
        return inputExp
 

extractCalibDate()

lsst.ip.isr.isrTask.IsrTask.extractCalibDate ( calib )

static

Extract common calibration metadata values that will be written to
output header.

Parameters
----------
calib : `lsst.afw.image.Exposure` or `lsst.ip.isr.IsrCalib`
    Calibration to pull date information from.

Returns
-------
dateString : `str`
    Calibration creation date string to add to header.

Definition at line 1971 of file isrTask.py.

    def extractCalibDate(calib):
        """Extract common calibration metadata values that will be written to
        output header.
 
        Parameters
        ----------
        calib : `lsst.afw.image.Exposure` or `lsst.ip.isr.IsrCalib`
            Calibration to pull date information from.
 
        Returns
        -------
        dateString : `str`
            Calibration creation date string to add to header.
        """
        if hasattr(calib, "getMetadata"):
            if 'CALIB_CREATION_DATE' in calib.getMetadata():
                return " ".join((calib.getMetadata().get("CALIB_CREATION_DATE", "Unknown"),
                                 calib.getMetadata().get("CALIB_CREATION_TIME", "Unknown")))
            else:
                return " ".join((calib.getMetadata().get("CALIB_CREATE_DATE", "Unknown"),
                                 calib.getMetadata().get("CALIB_CREATE_TIME", "Unknown")))
        else:
            return "Unknown Unknown"
 

flatContext()

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 2673 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()

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 2342 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
        )
 

makeBinnedImages()

lsst.ip.isr.isrTask.IsrTask.makeBinnedImages	(		self,
			exposure )

Make visualizeVisit style binned exposures.

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

Returns
-------
bin1 : `lsst.afw.image.Exposure`
    Binned exposure using binFactor1.
bin2 : `lsst.afw.image.Exposure`
    Binned exposure using binFactor2.

Definition at line 2703 of file isrTask.py.

    def makeBinnedImages(self, exposure):
        """Make visualizeVisit style binned exposures.
 
        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to bin.
 
        Returns
        -------
        bin1 : `lsst.afw.image.Exposure`
            Binned exposure using binFactor1.
        bin2 : `lsst.afw.image.Exposure`
            Binned exposure using binFactor2.
        """
        mi = exposure.getMaskedImage()
 
        bin1 = afwMath.binImage(mi, self.config.binFactor1)
        bin2 = afwMath.binImage(mi, self.config.binFactor2)
 
        return bin1, bin2
 

maskAmplifier()

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.cameraGeom.Amplifier`
    Catalog of parameters defining the amplifier on this
    exposure to mask.
defects : `lsst.ip.isr.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 2062 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.cameraGeom.Amplifier`
            Catalog of parameters defining the amplifier on this
            exposure to mask.
        defects : `lsst.ip.isr.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
        # NB: 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:
            # Set to the default from the camera model.
            limits.update({self.config.saturatedMaskName: amp.getSaturation()})
            # And update if it is set in the config.
            if math.isfinite(self.config.saturation):
                limits.update({self.config.saturatedMaskName: self.config.saturation})
        if self.config.doSuspect and not badAmp:
            limits.update({self.config.suspectMaskName: amp.getSuspectLevel()})
 
        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
            maskView |= maskView.getPlaneBitMask("BAD")
 
        return badAmp
 

maskAndInterpolateDefects()

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 : defects-like
    List of defects to mask and interpolate. Can be
    `lsst.ip.isr.Defects` or `list` of `lsst.afw.image.DefectBase`.

See Also
--------
lsst.ip.isr.isrTask.maskDefect

Definition at line 2510 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 : defects-like
            List of defects to mask and interpolate. Can be
            `lsst.ip.isr.Defects` or `list` of `lsst.afw.image.DefectBase`.
 
        See Also
        --------
        lsst.ip.isr.isrTask.maskDefect
        """
        self.maskDefect(exposure, defectBaseList)
        self.maskEdges(exposure, numEdgePixels=self.config.numEdgeSuspect,
                       maskPlane="SUSPECT", level=self.config.edgeMaskLevel)
        isrFunctions.interpolateFromMask(
            maskedImage=exposure.getMaskedImage(),
            fwhm=self.config.fwhm,
            growSaturatedFootprints=0,
            maskNameList=["BAD"],
        )
 

maskAndInterpolateNan()

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

"Mask and interpolate NaN/infs 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 2561 of file isrTask.py.

    def maskAndInterpolateNan(self, exposure):
        """"Mask and interpolate NaN/infs 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()

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 : defect-type
    List of defects to mask. Can be of type  `lsst.ip.isr.Defects`
    or `list` of `lsst.afw.image.DefectBase`.

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

Definition at line 2452 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 : defect-type
            List of defects to mask. Can be of type  `lsst.ip.isr.Defects`
            or `list` of `lsst.afw.image.DefectBase`.
 
        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")
 

maskEdges()

lsst.ip.isr.isrTask.IsrTask.maskEdges	(		self,
			exposure,
			numEdgePixels = 0,
			maskPlane = "SUSPECT",
			level = 'DETECTOR' )

Mask edge pixels with applicable mask plane.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure to process.
numEdgePixels : `int`, optional
    Number of edge pixels to mask.
maskPlane : `str`, optional
    Mask plane name to use.
level : `str`, optional
    Level at which to mask edges.

Definition at line 2476 of file isrTask.py.

    def maskEdges(self, exposure, numEdgePixels=0, maskPlane="SUSPECT", level='DETECTOR'):
        """Mask edge pixels with applicable mask plane.
 
        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to process.
        numEdgePixels : `int`, optional
            Number of edge pixels to mask.
        maskPlane : `str`, optional
            Mask plane name to use.
        level : `str`, optional
            Level at which to mask edges.
        """
        maskedImage = exposure.getMaskedImage()
        maskBitMask = maskedImage.getMask().getPlaneBitMask(maskPlane)
 
        if numEdgePixels > 0:
            if level == 'DETECTOR':
                boxes = [maskedImage.getBBox()]
            elif level == 'AMP':
                boxes = [amp.getBBox() for amp in exposure.getDetector()]
 
            for box in boxes:
                # This makes a bbox numEdgeSuspect pixels smaller than the
                # image on each side
                subImage = maskedImage[box]
                box.grow(-numEdgePixels)
                # Mask pixels outside box
                SourceDetectionTask.setEdgeBits(
                    subImage,
                    box,
                    maskBitMask)
 

maskNan()

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

Definition at line 2535 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 non-finite values (NaN, inf), including those
        that are masked with other bits (because those may or may not be
        interpolated over later, and we want to remove all NaN/infs).
        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["NUMNANS"] = numNans
        if numNans > 0:
            self.log.warning("There were %d unmasked NaNs.", numNans)
 

maskNegativeVariance()

lsst.ip.isr.isrTask.IsrTask.maskNegativeVariance	(		self,
			exposure )

Identify and mask pixels with negative variance values.

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

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

Definition at line 2264 of file isrTask.py.

    def maskNegativeVariance(self, exposure):
        """Identify and mask pixels with negative variance values.
 
        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to process.
 
        See Also
        --------
        lsst.ip.isr.isrFunctions.updateVariance
        """
        maskPlane = exposure.getMask().getPlaneBitMask(self.config.negativeVarianceMaskName)
        bad = numpy.where(exposure.getVariance().getArray() <= 0.0)
        exposure.mask.array[bad] |= maskPlane
 

measureBackground()

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

Measure the image background in subgrids, for quality control.

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 2582 of file isrTask.py.

    def measureBackground(self, exposure, IsrQaConfig=None):
        """Measure the image background in subgrids, for quality control.
 
        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["SKYLEVEL"] = skyLevel
            metadata["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["FLATNESS_PP"] = float(flatness_pp)
            metadata["FLATNESS_RMS"] = float(flatness_rms)
            metadata["FLATNESS_NGRIDS"] = '%dx%d' % (nX, nY)
            metadata["FLATNESS_MESHX"] = IsrQaConfig.flatness.meshX
            metadata["FLATNESS_MESHY"] = IsrQaConfig.flatness.meshY
 

overscanCorrection()

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.overscan.OverscanTask`, which is called here
after the amplifier is preprocessed.

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

Returns
-------
overscanResults : `lsst.pipe.base.Struct`
    Result struct with components:

    ``imageFit``
        Value or fit subtracted from the amplifier image data.
        (scalar or `lsst.afw.image.Image`)
    ``overscanFit``
        Value or fit subtracted from the overscan image data.
        (scalar or `lsst.afw.image.Image`)
    ``overscanImage``
        Image of the overscan region with the overscan
        correction applied. This quantity is used to estimate
        the amplifier read noise empirically.
        (`lsst.afw.image.Image`)
    ``edgeMask``
        Mask of the suspect pixels. (`lsst.afw.image.Mask`)
    ``overscanMean``
        Median overscan fit value. (`float`)
    ``overscanSigma``
        Clipped standard deviation of the overscan after
        correction. (`float`)

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

See Also
--------
lsst.ip.isr.overscan.OverscanTask

Definition at line 2139 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.overscan.OverscanTask`, which is called here
        after the amplifier is preprocessed.
 
        Parameters
        ----------
        ccdExposure : `lsst.afw.image.Exposure`
            Exposure to have overscan correction performed.
        amp : `lsst.afw.cameraGeom.Amplifer`
            The amplifier to consider while correcting the overscan.
 
        Returns
        -------
        overscanResults : `lsst.pipe.base.Struct`
            Result struct with components:
 
            ``imageFit``
                Value or fit subtracted from the amplifier image data.
                (scalar or `lsst.afw.image.Image`)
            ``overscanFit``
                Value or fit subtracted from the overscan image data.
                (scalar or `lsst.afw.image.Image`)
            ``overscanImage``
                Image of the overscan region with the overscan
                correction applied. This quantity is used to estimate
                the amplifier read noise empirically.
                (`lsst.afw.image.Image`)
            ``edgeMask``
                Mask of the suspect pixels. (`lsst.afw.image.Mask`)
            ``overscanMean``
                Median overscan fit value. (`float`)
            ``overscanSigma``
                Clipped standard deviation of the overscan after
                correction. (`float`)
 
        Raises
        ------
        RuntimeError
            Raised if the ``amp`` does not contain raw pixel information.
 
        See Also
        --------
        lsst.ip.isr.overscan.OverscanTask
        """
        if amp.getRawHorizontalOverscanBBox().isEmpty():
            self.log.info("ISR_OSCAN: No overscan region.  Not performing overscan correction.")
            return None
 
        # Perform overscan correction on subregions.
        overscanResults = self.overscan.run(ccdExposure, amp)
 
        metadata = ccdExposure.getMetadata()
        ampName = amp.getName()
 
        keyBase = "LSST ISR OVERSCAN"
        # Updated quantities
        if isinstance(overscanResults.overscanMean, float):
            # Serial overscan correction only:
            metadata[f"{keyBase} SERIAL MEAN {ampName}"] = overscanResults.overscanMean
            metadata[f"{keyBase} SERIAL MEDIAN {ampName}"] = overscanResults.overscanMedian
            metadata[f"{keyBase} SERIAL STDEV {ampName}"] = overscanResults.overscanSigma
 
            metadata[f"{keyBase} RESIDUAL SERIAL MEAN {ampName}"] = overscanResults.residualMean
            metadata[f"{keyBase} RESIDUAL SERIAL MEDIAN {ampName}"] = overscanResults.residualMedian
            metadata[f"{keyBase} RESIDUAL SERIAL STDEV {ampName}"] = overscanResults.residualSigma
        elif isinstance(overscanResults.overscanMean, tuple):
            # Both serial and parallel overscan have run:
            metadata[f"{keyBase} SERIAL MEAN {ampName}"] = overscanResults.overscanMean[0]
            metadata[f"{keyBase} SERIAL MEDIAN {ampName}"] = overscanResults.overscanMedian[0]
            metadata[f"{keyBase} SERIAL STDEV {ampName}"] = overscanResults.overscanSigma[0]
 
            metadata[f"{keyBase} PARALLEL MEAN {ampName}"] = overscanResults.overscanMean[1]
            metadata[f"{keyBase} PARALLEL MEDIAN {ampName}"] = overscanResults.overscanMedian[1]
            metadata[f"{keyBase} PARALLEL STDEV {ampName}"] = overscanResults.overscanSigma[1]
 
            metadata[f"{keyBase} RESIDUAL SERIAL MEAN {ampName}"] = overscanResults.residualMean[0]
            metadata[f"{keyBase} RESIDUAL SERIAL MEDIAN {ampName}"] = overscanResults.residualMedian[0]
            metadata[f"{keyBase} RESIDUAL SERIAL STDEV {ampName}"] = overscanResults.residualSigma[0]
 
            metadata[f"{keyBase} RESIDUAL PARALLEL MEAN {ampName}"] = overscanResults.residualMean[1]
            metadata[f"{keyBase} RESIDUAL PARALLEL MEDIAN {ampName}"] = overscanResults.residualMedian[1]
            metadata[f"{keyBase} RESIDUAL PARALLEL STDEV {ampName}"] = overscanResults.residualSigma[1]
        else:
            self.log.warning("Unexpected type for overscan values; none added to header.")
 
        return overscanResults
 

roughZeroPoint()

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 2646 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.
        """
        filterLabel = exposure.getFilter()
        physicalFilter = isrFunctions.getPhysicalFilter(filterLabel, self.log)
 
        if physicalFilter in self.config.fluxMag0T1:
            fluxMag0 = self.config.fluxMag0T1[physicalFilter]
        else:
            self.log.warning("No rough magnitude zero point defined for filter %s.", physicalFilter)
            fluxMag0 = self.config.defaultFluxMag0T1
 
        expTime = exposure.getInfo().getVisitInfo().getExposureTime()
        if not expTime > 0:  # handle NaN as well as <= 0
            self.log.warning("Non-positive exposure time; skipping rough zero point.")
            return
 
        self.log.info("Setting rough magnitude zero point for filter %s: %f",
                      physicalFilter, 2.5*math.log10(fluxMag0*expTime))
        exposure.setPhotoCalib(afwImage.makePhotoCalibFromCalibZeroPoint(fluxMag0*expTime, 0.0))
 

run()

lsst.ip.isr.isrTask.IsrTask.run	(		self,
			ccdExposure,
		*	camera = None,
			bias = None,
			linearizer = None,
			crosstalk = None,
			crosstalkSources = None,
			dark = None,
			flat = None,
			ptc = None,
			bfKernel = None,
			bfGains = None,
			defects = None,
			fringes = pipeBase.Struct(fringes=None),
			opticsTransmission = None,
			filterTransmission = None,
			sensorTransmission = None,
			atmosphereTransmission = None,
			detectorNum = None,
			strayLightData = None,
			illumMaskedImage = None,
			deferredChargeCalib = None )

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. Required if
    one or more of ``ccdExposure``, ``bias``, ``dark``, or
    ``flat`` does not have an associated detector.
bias : `lsst.afw.image.Exposure`, optional
    Bias calibration frame.
linearizer : `lsst.ip.isr.linearize.LinearizeBase`, optional
    Functor for linearization.
crosstalk : `lsst.ip.isr.crosstalk.CrosstalkCalib`, optional
    Calibration for crosstalk.
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.
ptc : `lsst.ip.isr.PhotonTransferCurveDataset`, optional
    Photon transfer curve dataset, with, e.g., gains
    and read noise.
bfKernel : `numpy.ndarray`, optional
    Brighter-fatter kernel.
bfGains : `dict` of `float`, optional
    Gains used to override the detector's nominal gains for the
    brighter-fatter correction. A dict keyed by amplifier name for
    the detector in question.
defects : `lsst.ip.isr.Defects`, optional
    List of defects.
fringes : `lsst.pipe.base.Struct`, optional
    Struct containing the fringe correction data, with
    elements:

    ``fringes``
        fringe calibration frame (`lsst.afw.image.Exposure`)
    ``seed``
        random seed derived from the ``ccdExposureId`` for random
        number generator (`numpy.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.
strayLightData : `object`, optional
    Opaque object containing calibration information for stray-light
    correction.  If `None`, no correction will be performed.
illumMaskedImage : `lsst.afw.image.MaskedImage`, optional
    Illumination correction image.

Returns
-------
result : `lsst.pipe.base.Struct`
    Result struct with component:

    ``exposure``
        The fully ISR corrected exposure.
        (`lsst.afw.image.Exposure`)
    ``outputExposure``
        An alias for ``exposure``. (`lsst.afw.image.Exposure`)
    ``ossThumb``
        Thumbnail image of the exposure after overscan subtraction.
        (`numpy.ndarray`)
    ``flattenedThumb``
        Thumbnail image of the exposure after flat-field correction.
        (`numpy.ndarray`)
    ``outputStatistics``
        Values of the additional statistics calculated.

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 1178 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. Required if
            one or more of ``ccdExposure``, ``bias``, ``dark``, or
            ``flat`` does not have an associated detector.
        bias : `lsst.afw.image.Exposure`, optional
            Bias calibration frame.
        linearizer : `lsst.ip.isr.linearize.LinearizeBase`, optional
            Functor for linearization.
        crosstalk : `lsst.ip.isr.crosstalk.CrosstalkCalib`, optional
            Calibration for crosstalk.
        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.
        ptc : `lsst.ip.isr.PhotonTransferCurveDataset`, optional
            Photon transfer curve dataset, with, e.g., gains
            and read noise.
        bfKernel : `numpy.ndarray`, optional
            Brighter-fatter kernel.
        bfGains : `dict` of `float`, optional
            Gains used to override the detector's nominal gains for the
            brighter-fatter correction. A dict keyed by amplifier name for
            the detector in question.
        defects : `lsst.ip.isr.Defects`, optional
            List of defects.
        fringes : `lsst.pipe.base.Struct`, optional
            Struct containing the fringe correction data, with
            elements:
 
            ``fringes``
                fringe calibration frame (`lsst.afw.image.Exposure`)
            ``seed``
                random seed derived from the ``ccdExposureId`` for random
                number generator (`numpy.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.
        strayLightData : `object`, optional
            Opaque object containing calibration information for stray-light
            correction.  If `None`, no correction will be performed.
        illumMaskedImage : `lsst.afw.image.MaskedImage`, optional
            Illumination correction image.
 
        Returns
        -------
        result : `lsst.pipe.base.Struct`
            Result struct with component:
 
            ``exposure``
                The fully ISR corrected exposure.
                (`lsst.afw.image.Exposure`)
            ``outputExposure``
                An alias for ``exposure``. (`lsst.afw.image.Exposure`)
            ``ossThumb``
                Thumbnail image of the exposure after overscan subtraction.
                (`numpy.ndarray`)
            ``flattenedThumb``
                Thumbnail image of the exposure after flat-field correction.
                (`numpy.ndarray`)
            ``outputStatistics``
                Values of the additional statistics calculated.
 
        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.
        """
 
        ccdExposure = self.ensureExposure(ccdExposure, camera, detectorNum)
        bias = self.ensureExposure(bias, camera, detectorNum)
        dark = self.ensureExposure(dark, camera, detectorNum)
        flat = self.ensureExposure(flat, camera, detectorNum)
 
        ccd = ccdExposure.getDetector()
        filterLabel = ccdExposure.getFilter()
        physicalFilter = isrFunctions.getPhysicalFilter(filterLabel, self.log)
 
        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 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.doFringe and physicalFilter in self.fringe.config.filters
                and fringes.fringes is None):
            # The `fringes` object needs to be a pipeBase.Struct, as
            # we use it as a `dict` for the parameters of
            # `FringeTask.run()`.  The `fringes.fringes` `list` may
            # not be `None` if `doFringe=True`.  Otherwise, raise.
            raise RuntimeError("Must supply fringe exposure as a pipeBase.Struct.")
        if (self.config.doIlluminationCorrection and physicalFilter in self.config.illumFilters
                and illumMaskedImage is None):
            raise RuntimeError("Must supply an illumcor if config.doIlluminationCorrection=True.")
        if (self.config.doDeferredCharge and deferredChargeCalib is None):
            raise RuntimeError("Must supply a deferred charge calibration if config.doDeferredCharge=True.")
        if (self.config.usePtcGains and ptc is None):
            raise RuntimeError("No ptcDataset provided to use PTC gains.")
        if (self.config.usePtcReadNoise and ptc is None):
            raise RuntimeError("No ptcDataset provided to use PTC read noise.")
 
        # Validate that the inputs match the exposure configuration.
        exposureMetadata = ccdExposure.getMetadata()
        if self.config.doBias:
            self.compareCameraKeywords(exposureMetadata, bias, "bias")
        if self.config.doBrighterFatter:
            self.compareCameraKeywords(exposureMetadata, bfKernel, "brighter-fatter")
        if self.config.doCrosstalk:
            self.compareCameraKeywords(exposureMetadata, crosstalk, "crosstalk")
        if self.config.doDark:
            self.compareCameraKeywords(exposureMetadata, dark, "dark")
        if self.config.doDefect:
            self.compareCameraKeywords(exposureMetadata, defects, "defects")
        if self.config.doDeferredCharge:
            self.compareCameraKeywords(exposureMetadata, deferredChargeCalib, "CTI")
        if self.config.doFlat:
            self.compareCameraKeywords(exposureMetadata, flat, "flat")
        if (self.config.doFringe and physicalFilter in self.fringe.config.filters):
            self.compareCameraKeywords(exposureMetadata, fringes.fringes, "fringe")
        if (self.config.doIlluminationCorrection and physicalFilter in self.config.illumFilters):
            self.compareCameraKeywords(exposureMetadata, illumMaskedImage, "illumination")
        if self.doLinearize(ccd):
            self.compareCameraKeywords(exposureMetadata, linearizer, "linearizer")
        if self.config.usePtcGains or self.config.usePtcReadNoise:
            self.compareCameraKeywords(exposureMetadata, ptc, "PTC")
        if self.config.doStrayLight:
            self.compareCameraKeywords(exposureMetadata, strayLightData, "straylight")
 
        # Start in ADU. Update units to electrons when gain is applied:
        # updateVariance, applyGains
        # Check if needed during/after BFE correction, CTI correction.
        exposureMetadata["LSST ISR UNITS"] = "ADU"
 
        # Begin ISR processing.
        if self.config.doConvertIntToFloat:
            self.log.info("Converting exposure to floating point values.")
            ccdExposure = self.convertIntToFloat(ccdExposure)
 
        if self.config.doBias and self.config.doBiasBeforeOverscan:
            self.log.info("Applying bias correction.")
            isrFunctions.biasCorrection(ccdExposure.getMaskedImage(), bias.getMaskedImage(),
                                        trimToFit=self.config.doTrimToMatchCalib)
            self.debugView(ccdExposure, "doBias")
 
        # Amplifier level processing.
        overscans = []
 
        if self.config.doOverscan and self.config.overscan.doParallelOverscan:
            # This will attempt to mask bleed pixels across all amplifiers.
            self.overscan.maskParallelOverscan(ccdExposure, ccd)
 
        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 overscanResults is not None and \
                       self.config.qa is not None and self.config.qa.saveStats is True:
                        if isinstance(overscanResults.overscanMean, float):
                            # Only serial overscan was run
                            mean = overscanResults.overscanMean
                            sigma = overscanResults.overscanSigma
                            residMean = overscanResults.residualMean
                            residSigma = overscanResults.residualSigma
                        else:
                            # Both serial and parallel overscan were
                            # run.  Only report serial here.
                            mean = overscanResults.overscanMean[0]
                            sigma = overscanResults.overscanSigma[0]
                            residMean = overscanResults.residualMean[0]
                            residSigma = overscanResults.residualSigma[0]
 
                        self.metadata[f"FIT MEDIAN {amp.getName()}"] = mean
                        self.metadata[f"FIT STDEV {amp.getName()}"] = sigma
                        self.log.debug("  Overscan stats for amplifer %s: %f +/- %f",
                                       amp.getName(), mean, sigma)
 
                        self.metadata[f"RESIDUAL MEDIAN {amp.getName()}"] = residMean
                        self.metadata[f"RESIDUAL STDEV {amp.getName()}"] = residSigma
                        self.log.debug("  Overscan stats for amplifer %s after correction: %f +/- %f",
                                       amp.getName(), residMean, residSigma)
 
                        ccdExposure.getMetadata().set('OVERSCAN', "Overscan corrected")
                else:
                    if badAmp:
                        self.log.warning("Amplifier %s is bad.", amp.getName())
                    overscanResults = None
 
                overscans.append(overscanResults if overscanResults is not None else None)
            else:
                self.log.info("Skipped OSCAN for %s.", amp.getName())
 
        # Define an effective PTC that will contain the gain and readout
        # noise to be used throughout the ISR task.
        ptc = self.defineEffectivePtc(ptc, ccd, bfGains, overscans, exposureMetadata)
 
        if self.config.doDeferredCharge:
            self.log.info("Applying deferred charge/CTI correction.")
            self.deferredChargeCorrection.run(ccdExposure, deferredChargeCalib)
            self.debugView(ccdExposure, "doDeferredCharge")
 
        if self.config.doCrosstalk and self.config.doCrosstalkBeforeAssemble:
            self.log.info("Applying crosstalk correction.")
            self.crosstalk.run(ccdExposure, crosstalk=crosstalk,
                               crosstalkSources=crosstalkSources, camera=camera)
            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.warning("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 and not self.config.doBiasBeforeOverscan:
            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 in ccd:
                if ccdExposure.getBBox().contains(amp.getBBox()):
                    self.log.debug("Constructing variance map for amplifer %s.", amp.getName())
                    ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
                    self.updateVariance(ampExposure, amp, ptc)
 
                    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[f"ISR VARIANCE {amp.getName()} MEDIAN"] = \
                            qaStats.getValue(afwMath.MEDIAN)
                        self.metadata[f"ISR VARIANCE {amp.getName()} STDEV"] = \
                            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.config.maskNegativeVariance:
                self.maskNegativeVariance(ccdExposure)
 
        if self.doLinearize(ccd):
            self.log.info("Applying linearizer.")
            linearizer.applyLinearity(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, crosstalk=crosstalk,
                               crosstalkSources=crosstalkSources, isTrimmed=True)
            self.debugView(ccdExposure, "doCrosstalk")
 
        # Masking block. Optionally mask known defects, NAN/inf 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.numEdgeSuspect > 0:
                self.log.info("Masking edges as SUSPECT.")
                self.maskEdges(ccdExposure, numEdgePixels=self.config.numEdgeSuspect,
                               maskPlane="SUSPECT", level=self.config.edgeMaskLevel)
 
        if self.config.doNanMasking:
            self.log.info("Masking non-finite (NAN, inf) 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=interpExp.getMaskedImage(),
                    fwhm=self.config.fwhm,
                    growSaturatedFootprints=self.config.growSaturationFootprintSize,
                    maskNameList=list(self.config.brighterFatterMaskListToInterpolate)
                )
            bfExp = interpExp.clone()
 
            self.log.info("Applying brighter-fatter correction using kernel type %s / gains %s.",
                          type(bfKernel), type(bfGains))
            if self.config.doFluxConservingBrighterFatterCorrection:
                bfResults = isrFunctions.fluxConservingBrighterFatterCorrection(
                    bfExp,
                    bfKernel,
                    self.config.brighterFatterMaxIter,
                    self.config.brighterFatterThreshold,
                    self.config.brighterFatterApplyGain,
                    bfGains
                )
            else:
                bfResults = isrFunctions.brighterFatterCorrection(
                    bfExp,
                    bfKernel,
                    self.config.brighterFatterMaxIter,
                    self.config.brighterFatterThreshold,
                    self.config.brighterFatterApplyGain,
                    bfGains
                )
            if bfResults[1] == self.config.brighterFatterMaxIter - 1:
                self.log.warning("Brighter-fatter correction did not converge, final difference %f.",
                                 bfResults[0])
            else:
                self.log.info("Finished brighter-fatter correction in %d iterations.",
                              bfResults[1])
            image = ccdExposure.getMaskedImage().getImage()
            bfCorr = bfExp.getMaskedImage().getImage()
            bfCorr -= interpExp.getMaskedImage().getImage()
            image += bfCorr
 
            # Applying the brighter-fatter correction applies a
            # convolution to the science image. At the edges this
            # convolution may not have sufficient valid pixels to
            # produce a valid correction. Mark pixels within the size
            # of the brighter-fatter kernel as EDGE to warn of this
            # fact.
            self.log.info("Ensuring image edges are masked as EDGE to the brighter-fatter kernel size.")
            self.maskEdges(ccdExposure, numEdgePixels=numpy.max(bfKernel.shape) // 2,
                           maskPlane="EDGE")
 
            if self.config.brighterFatterMaskGrowSize > 0:
                self.log.info("Growing masks to account for brighter-fatter kernel convolution.")
                for maskPlane in self.config.brighterFatterMaskListToInterpolate:
                    isrFunctions.growMasks(ccdExposure.getMask(),
                                           radius=self.config.brighterFatterMaskGrowSize,
                                           maskNameList=maskPlane,
                                           maskValue=maskPlane)
 
            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 and self.strayLight.check(ccdExposure):
            self.log.info("Checking strayLight correction.")
            self.strayLight.run(ccdExposure, strayLightData)
            self.debugView(ccdExposure, "doStrayLight")
 
        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,
                                    ptcGains=ptc.gain)
            exposureMetadata["LSST ISR UNITS"] = "electrons"
 
        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:
            if self.config.doMaskVignettePolygon:
                self.log.info("Constructing, attaching, and masking vignette polygon.")
            else:
                self.log.info("Constructing and attaching vignette polygon.")
            self.vignettePolygon = self.vignette.run(
                exposure=ccdExposure, doUpdateMask=self.config.doMaskVignettePolygon,
                vignetteValue=self.config.vignetteValue, log=self.log)
 
        if self.config.doAttachTransmissionCurve:
            self.log.info("Adding transmission curves.")
            isrFunctions.attachTransmissionCurve(ccdExposure, opticsTransmission=opticsTransmission,
                                                 filterTransmission=filterTransmission,
                                                 sensorTransmission=sensorTransmission,
                                                 atmosphereTransmission=atmosphereTransmission)
 
        flattenedThumb = None
        if self.config.qa.doThumbnailFlattened:
            flattenedThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa)
 
        if self.config.doIlluminationCorrection and physicalFilter in self.config.illumFilters:
            self.log.info("Performing illumination correction.")
            isrFunctions.illuminationCorrection(ccdExposure.getMaskedImage(),
                                                illumMaskedImage, illumScale=self.config.illumScale,
                                                trimToFit=self.config.doTrimToMatchCalib)
 
        preInterpExp = None
        if self.config.doSaveInterpPixels:
            preInterpExp = ccdExposure.clone()
 
        # Reset and interpolate bad pixels.
        #
        # Large contiguous bad regions (which should have the BAD mask
        # bit set) should have their values set to the image median.
        # This group should include defects and bad amplifiers. As the
        # area covered by these defects are large, there's little
        # reason to expect that interpolation would provide a more
        # useful value.
        #
        # Smaller defects can be safely interpolated after the larger
        # regions have had their pixel values reset.  This ensures
        # that the remaining defects adjacent to bad amplifiers (as an
        # example) do not attempt to interpolate extreme values.
        if self.config.doSetBadRegions:
            badPixelCount, badPixelValue = isrFunctions.setBadRegions(ccdExposure)
            if badPixelCount > 0:
                self.log.info("Set %d BAD pixels to %f.", badPixelCount, badPixelValue)
 
        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)
            )
 
        self.roughZeroPoint(ccdExposure)
 
        # correct for amp offsets within the CCD
        if self.config.doAmpOffset:
            self.log.info("Correcting amp offsets.")
            self.ampOffset.run(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[f"ISR BACKGROUND {amp.getName()} MEDIAN"] = qaStats.getValue(afwMath.MEDIAN)
                    self.metadata[f"ISR BACKGROUND {amp.getName()} STDEV"] = \
                        qaStats.getValue(afwMath.STDEVCLIP)
                    self.log.debug("  Background stats for amplifer %s: %f +/- %f",
                                   amp.getName(), qaStats.getValue(afwMath.MEDIAN),
                                   qaStats.getValue(afwMath.STDEVCLIP))
 
        # Calculate standard image quality statistics
        if self.config.doStandardStatistics:
            metadata = ccdExposure.getMetadata()
            for amp in ccd:
                ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
                ampName = amp.getName()
                metadata[f"LSST ISR MASK SAT {ampName}"] = isrFunctions.countMaskedPixels(
                    ampExposure.getMaskedImage(),
                    [self.config.saturatedMaskName]
                )
                metadata[f"LSST ISR MASK BAD {ampName}"] = isrFunctions.countMaskedPixels(
                    ampExposure.getMaskedImage(),
                    ["BAD"]
                )
                qaStats = afwMath.makeStatistics(ampExposure.getImage(),
                                                 afwMath.MEAN | afwMath.MEDIAN | afwMath.STDEVCLIP)
 
                metadata[f"LSST ISR FINAL MEAN {ampName}"] = qaStats.getValue(afwMath.MEAN)
                metadata[f"LSST ISR FINAL MEDIAN {ampName}"] = qaStats.getValue(afwMath.MEDIAN)
                metadata[f"LSST ISR FINAL STDEV {ampName}"] = qaStats.getValue(afwMath.STDEVCLIP)
 
                k1 = f"LSST ISR FINAL MEDIAN {ampName}"
                k2 = f"LSST ISR OVERSCAN SERIAL MEDIAN {ampName}"
                if self.config.doOverscan and k1 in metadata and k2 in metadata:
                    metadata[f"LSST ISR LEVEL {ampName}"] = metadata[k1] - metadata[k2]
                else:
                    metadata[f"LSST ISR LEVEL {ampName}"] = numpy.nan
 
        # calculate additional statistics.
        outputStatistics = None
        if self.config.doCalculateStatistics:
            outputStatistics = self.isrStats.run(ccdExposure, overscanResults=overscans,
                                                 bias=bias, dark=dark, flat=flat, ptc=ptc).results
 
        # do any binning.
        outputBin1Exposure = None
        outputBin2Exposure = None
        if self.config.doBinnedExposures:
            outputBin1Exposure, outputBin2Exposure = self.makeBinnedImages(ccdExposure)
 
        self.debugView(ccdExposure, "postISRCCD")
 
        return pipeBase.Struct(
            exposure=ccdExposure,
            ossThumb=ossThumb,
            flattenedThumb=flattenedThumb,
 
            outputBin1Exposure=outputBin1Exposure,
            outputBin2Exposure=outputBin2Exposure,
 
            preInterpExposure=preInterpExp,
            outputExposure=ccdExposure,
            outputOssThumbnail=ossThumb,
            outputFlattenedThumbnail=flattenedThumb,
            outputStatistics=outputStatistics,
        )
 

runQuantum()

lsst.ip.isr.isrTask.IsrTask.runQuantum	(		self,
			butlerQC,
			inputRefs,
			outputRefs )

Definition at line 1024 of file isrTask.py.

    def runQuantum(self, butlerQC, inputRefs, outputRefs):
        inputs = butlerQC.get(inputRefs)
 
        try:
            inputs['detectorNum'] = inputRefs.ccdExposure.dataId['detector']
        except Exception as e:
            raise ValueError("Failure to find valid detectorNum value for Dataset %s: %s." %
                             (inputRefs, e))
 
        detector = inputs['ccdExposure'].getDetector()
 
        # This is use for header provenance.
        additionalInputDates = {}
 
        if self.config.doCrosstalk is True:
            # Crosstalk sources need to be defined by the pipeline
            # yaml if they exist.
            if 'crosstalk' in inputs and inputs['crosstalk'] is not None:
                if not isinstance(inputs['crosstalk'], CrosstalkCalib):
                    inputs['crosstalk'] = CrosstalkCalib.fromTable(inputs['crosstalk'])
            else:
                coeffVector = (self.config.crosstalk.crosstalkValues
                               if self.config.crosstalk.useConfigCoefficients else None)
                crosstalkCalib = CrosstalkCalib().fromDetector(detector, coeffVector=coeffVector)
                inputs['crosstalk'] = crosstalkCalib
            if inputs['crosstalk'].interChip and len(inputs['crosstalk'].interChip) > 0:
                if 'crosstalkSources' not in inputs:
                    self.log.warning("No crosstalkSources found for chip with interChip terms!")
 
        if self.doLinearize(detector) is True:
            if 'linearizer' in inputs:
                if isinstance(inputs['linearizer'], dict):
                    linearizer = linearize.Linearizer(detector=detector, log=self.log)
                    linearizer.fromYaml(inputs['linearizer'])
                    self.log.warning("Dictionary linearizers will be deprecated in DM-28741.")
                elif isinstance(inputs['linearizer'], numpy.ndarray):
                    linearizer = linearize.Linearizer(table=inputs.get('linearizer', None),
                                                      detector=detector,
                                                      log=self.log)
                    self.log.warning("Bare lookup table linearizers will be deprecated in DM-28741.")
                else:
                    linearizer = inputs['linearizer']
                    self.log.info("Loading linearizer from the Butler.")
                    linearizer.log = self.log
                inputs['linearizer'] = linearizer
            else:
                inputs['linearizer'] = linearize.Linearizer(detector=detector, log=self.log)
                self.log.info("Constructing linearizer from cameraGeom information.")
 
        if self.config.doDefect is True:
            if "defects" in inputs and inputs['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(inputs["defects"], Defects):
                    inputs["defects"] = Defects.fromTable(inputs["defects"])
 
        # Load the correct style of brighter-fatter kernel, and repack
        # the information as a numpy array.
        brighterFatterSource = None
        if self.config.doBrighterFatter:
            brighterFatterKernel = inputs.pop('newBFKernel', None)
            if brighterFatterKernel is None:
                # This type of kernel must be in (y, x) index
                # ordering, as it used directly as the .array
                # component of the afwImage kernel.
                brighterFatterKernel = inputs.get('bfKernel', None)
                brighterFatterSource = 'bfKernel'
                additionalInputDates[brighterFatterSource] = self.extractCalibDate(brighterFatterKernel)
 
            if brighterFatterKernel is None:
                # This was requested by the config, but none were found.
                raise RuntimeError("No brighter-fatter kernel was supplied.")
            elif not isinstance(brighterFatterKernel, numpy.ndarray):
                # This is a ISR calib kernel.  These kernels are
                # generated in (x, y) index ordering, and need to be
                # transposed to be used directly as the .array
                # component of the afwImage kernel.  This is done
                # explicitly below when setting the ``bfKernel``
                # input.
                brighterFatterSource = 'newBFKernel'
                additionalInputDates[brighterFatterSource] = self.extractCalibDate(brighterFatterKernel)
 
                detName = detector.getName()
                level = brighterFatterKernel.level
 
                # This is expected to be a dictionary of amp-wise gains.
                inputs['bfGains'] = brighterFatterKernel.gain
                if self.config.brighterFatterLevel == 'DETECTOR':
                    kernel = None
                    if level == 'DETECTOR':
                        if detName in brighterFatterKernel.detKernels:
                            kernel = brighterFatterKernel.detKernels[detName]
                        else:
                            raise RuntimeError("Failed to extract kernel from new-style BF kernel.")
                    elif level == 'AMP':
                        self.log.warning("Making DETECTOR level kernel from AMP based brighter "
                                         "fatter kernels.")
                        brighterFatterKernel.makeDetectorKernelFromAmpwiseKernels(detName)
                        kernel = brighterFatterKernel.detKernels[detName]
                    if kernel is None:
                        raise RuntimeError("Could not identify brighter-fatter kernel!")
                    # Do the one single transpose here so the kernel
                    # can be directly loaded into the afwImage .array
                    # component.
                    inputs['bfKernel'] = numpy.transpose(kernel)
                elif self.config.brighterFatterLevel == 'AMP':
                    raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented")
 
        if self.config.doFringe is True and self.fringe.checkFilter(inputs['ccdExposure']):
            expId = inputs['ccdExposure'].info.id
            inputs['fringes'] = self.fringe.loadFringes(inputs['fringes'],
                                                        expId=expId,
                                                        assembler=self.assembleCcd
                                                        if self.config.doAssembleIsrExposures else None)
        else:
            inputs['fringes'] = pipeBase.Struct(fringes=None)
 
        if self.config.doStrayLight is True and self.strayLight.checkFilter(inputs['ccdExposure']):
            if 'strayLightData' not in inputs:
                inputs['strayLightData'] = None
 
        if self.config.doHeaderProvenance:
            # Add calibration provenanace info to header.
            exposureMetadata = inputs['ccdExposure'].getMetadata()
 
            # These inputs change name during this step.  These should
            # have matching entries in the additionalInputDates dict.
            additionalInputs = []
            if self.config.doBrighterFatter:
                additionalInputs.append(brighterFatterSource)
 
            for inputName in sorted(list(inputs.keys()) + additionalInputs):
                reference = getattr(inputRefs, inputName, None)
                if reference is not None and hasattr(reference, "run"):
                    runKey = f"LSST CALIB RUN {inputName.upper()}"
                    runValue = reference.run
                    idKey = f"LSST CALIB UUID {inputName.upper()}"
                    idValue = str(reference.id)
                    dateKey = f"LSST CALIB DATE {inputName.upper()}"
 
                    if inputName in additionalInputDates:
                        dateValue = additionalInputDates[inputName]
                    else:
                        dateValue = self.extractCalibDate(inputs[inputName])
 
                    exposureMetadata[runKey] = runValue
                    exposureMetadata[idKey] = idValue
                    exposureMetadata[dateKey] = dateValue
 
        outputs = self.run(**inputs)
        butlerQC.put(outputs, outputRefs)
 

saturationDetection()

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

Detect and mask saturated pixels in config.saturatedMaskName.

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

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

Definition at line 2367 of file isrTask.py.

    def saturationDetection(self, exposure, amp):
        """Detect and mask saturated pixels in config.saturatedMaskName.
 
        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to process.  Only the amplifier DataSec is processed.
        amp : `lsst.afw.cameraGeom.Amplifier`
            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()

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 2391 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),
        )
 

suspectDetection()

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

Detect and mask suspect pixels in config.suspectMaskName.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure to process.  Only the amplifier DataSec is processed.
amp : `lsst.afw.cameraGeom.Amplifier`
    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 2416 of file isrTask.py.

    def suspectDetection(self, exposure, amp):
        """Detect and mask suspect pixels in config.suspectMaskName.
 
        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to process.  Only the amplifier DataSec is processed.
        amp : `lsst.afw.cameraGeom.Amplifier`
            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()

lsst.ip.isr.isrTask.IsrTask.updateVariance	(		self,
			ampExposure,
			amp,
			ptcDataset )

Set the variance plane using the 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.cameraGeom.Amplifier` or `FakeAmp`
    Amplifier detector data.
ptcDataset : `lsst.ip.isr.PhotonTransferCurveDataset`
    Effective PTC dataset containing the gains and read noise.

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

Definition at line 2232 of file isrTask.py.

    def updateVariance(self, ampExposure, amp, ptcDataset):
        """Set the variance plane using the 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.cameraGeom.Amplifier` or `FakeAmp`
            Amplifier detector data.
        ptcDataset : `lsst.ip.isr.PhotonTransferCurveDataset`
            Effective PTC dataset containing the gains and read noise.
 
        See also
        --------
        lsst.ip.isr.isrFunctions.updateVariance
        """
        ampName = amp.getName()
        # At this point, the effective PTC should have
        # gain and noise values.
        gain = ptcDataset.gain[ampName]
        readNoise = ptcDataset.noise[ampName]
 
        isrFunctions.updateVariance(
            maskedImage=ampExposure.getMaskedImage(),
            gain=gain,
            readNoise=readNoise,
        )
 

Member Data Documentation

_DefaultName

str lsst.ip.isr.isrTask.IsrTask._DefaultName = "isr"

staticprotected

Definition at line 1009 of file isrTask.py.

ConfigClass

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

static

Definition at line 1008 of file isrTask.py.

vignettePolygon

lsst.ip.isr.isrTask.IsrTask.vignettePolygon

Definition at line 1651 of file isrTask.py.

---

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

• /j/snowflake/release/lsstsw/stack/lsst-scipipe-8.0.0/Linux64/ip_isr/g02d81e74bb+f5613e8b4f/python/lsst/ip/isr/isrTask.py