isrTaskLSST.py

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__all__ = ["IsrTaskLSST", "IsrTaskLSSTConfig"]
 
import numpy
import math
 
from . import isrFunctions
from . import isrQa
from .defects import Defects
 
from contextlib import contextmanager
from deprecated.sphinx import deprecated
import lsst.pex.config as pexConfig
import lsst.afw.math as afwMath
import lsst.pipe.base as pipeBase
import lsst.afw.image as afwImage
import lsst.pipe.base.connectionTypes as cT
from lsst.pipe.base import UnprocessableDataError
from lsst.meas.algorithms.detection import SourceDetectionTask
import lsst.afw.detection as afwDetection
 
from .ampOffset import AmpOffsetTask
from .binImageDataTask import BinImageDataTask
from .overscan import SerialOverscanCorrectionTask, ParallelOverscanCorrectionTask
from .overscanAmpConfig import OverscanCameraConfig
from .assembleCcdTask import AssembleCcdTask
from .deferredCharge import DeferredChargeTask
from .crosstalk import CrosstalkTask
from .masking import MaskingTask
from .isrStatistics import IsrStatisticsTask
from .isr import maskNans
from .ptcDataset import PhotonTransferCurveDataset
from .isrFunctions import isTrimmedExposure, compareCameraKeywords
 
 
class IsrTaskLSSTConnections(pipeBase.PipelineTaskConnections,
                             dimensions={"instrument", "exposure", "detector"},
                             defaultTemplates={}):
    ccdExposure = cT.Input(
        name="raw",
        doc="Input exposure to process.",
        storageClass="Exposure",
        dimensions=["instrument", "exposure", "detector"],
    )
    camera = cT.PrerequisiteInput(
        name="camera",
        storageClass="Camera",
        doc="Input camera to construct complete exposures.",
        dimensions=["instrument"],
        isCalibration=True,
    )
    dnlLUT = cT.PrerequisiteInput(
        name="dnlLUT",
        doc="Look-up table for differential non-linearity.",
        storageClass="IsrCalib",
        dimensions=["instrument", "exposure", "detector"],
        isCalibration=True,
        # TODO DM 36636
    )
    bias = cT.PrerequisiteInput(
        name="bias",
        doc="Input bias calibration.",
        storageClass="ExposureF",
        dimensions=["instrument", "detector"],
        isCalibration=True,
    )
    deferredChargeCalib = cT.PrerequisiteInput(
        name="cti",
        doc="Deferred charge/CTI correction dataset.",
        storageClass="IsrCalib",
        dimensions=["instrument", "detector"],
        isCalibration=True,
    )
    linearizer = cT.PrerequisiteInput(
        name='linearizer',
        storageClass="Linearizer",
        doc="Linearity correction calibration.",
        dimensions=["instrument", "detector"],
        isCalibration=True,
    )
    ptc = cT.PrerequisiteInput(
        name="ptc",
        doc="Input Photon Transfer Curve dataset",
        storageClass="PhotonTransferCurveDataset",
        dimensions=["instrument", "detector"],
        isCalibration=True,
    )
    gainCorrection = cT.PrerequisiteInput(
        name="gain_correction",
        doc="Gain correction dataset",
        storageClass="IsrCalib",
        dimensions=["instrument", "detector"],
        isCalibration=True,
        minimum=0,
    )
    crosstalk = cT.PrerequisiteInput(
        name="crosstalk",
        doc="Input crosstalk object",
        storageClass="CrosstalkCalib",
        dimensions=["instrument", "detector"],
        isCalibration=True,
    )
    defects = cT.PrerequisiteInput(
        name='defects',
        doc="Input defect tables.",
        storageClass="Defects",
        dimensions=["instrument", "detector"],
        isCalibration=True,
    )
    bfKernel = cT.PrerequisiteInput(
        name="bfk",
        doc="Complete kernel + gain solutions.",
        storageClass="BrighterFatterKernel",
        dimensions=["instrument", "detector"],
        isCalibration=True,
    )
    dark = cT.PrerequisiteInput(
        name='dark',
        doc="Input dark calibration.",
        storageClass="ExposureF",
        dimensions=["instrument", "detector"],
        isCalibration=True,
    )
    flat = cT.PrerequisiteInput(
        name="flat",
        doc="Input flat calibration.",
        storageClass="ExposureF",
        dimensions=["instrument", "detector", "physical_filter"],
        isCalibration=True,
    )
    outputExposure = cT.Output(
        name='postISRCCD',
        doc="Output ISR processed exposure.",
        storageClass="Exposure",
        dimensions=["instrument", "exposure", "detector"],
    )
    preInterpExposure = cT.Output(
        name='preInterpISRCCD',
        doc="Output ISR processed exposure, with pixels left uninterpolated.",
        storageClass="ExposureF",
        dimensions=["instrument", "exposure", "detector"],
    )
    outputBin1Exposure = cT.Output(
        name="postIsrBin1",
        doc="First binned image.",
        storageClass="ExposureF",
        dimensions=["instrument", "exposure", "detector"],
    )
    outputBin2Exposure = cT.Output(
        name="postIsrBin2",
        doc="Second binned image.",
        storageClass="ExposureF",
        dimensions=["instrument", "exposure", "detector"],
    )
 
    outputStatistics = cT.Output(
        name="isrStatistics",
        doc="Output of additional statistics table.",
        storageClass="StructuredDataDict",
        dimensions=["instrument", "exposure", "detector"],
    )
 
    def __init__(self, *, config=None):
        super().__init__(config=config)
 
        doApplyGains = config.doApplyGains
        useLinearizerGains = config.useGainsFrom == "LINEARIZER"
 
        if config.doBootstrap or (doApplyGains and useLinearizerGains):
            del self.ptc
        if not config.doCorrectGains:
            del self.gainCorrection
        if config.doDiffNonLinearCorrection is not True:
            del self.dnlLUT
        if config.doBias is not True:
            del self.bias
        if config.doDeferredCharge is not True:
            del self.deferredChargeCalib
        if (config.doLinearize or (doApplyGains and useLinearizerGains)) is not True:
            del self.linearizer
 
        if not config.doCrosstalk:
            del self.crosstalk
        if config.doDefect is not True:
            del self.defects
        if config.doBrighterFatter is not True:
            del self.bfKernel
        if config.doDark is not True:
            del self.dark
        if config.doFlat is not True:
            del self.flat
 
        if config.doBinnedExposures is not True:
            del self.outputBin1Exposure
            del self.outputBin2Exposure
        if config.doSaveInterpPixels is not True:
            del self.preInterpExposure
 
        if config.doCalculateStatistics is not True:
            del self.outputStatistics
 
 
class IsrTaskLSSTConfig(pipeBase.PipelineTaskConfig,
                        pipelineConnections=IsrTaskLSSTConnections):
    """Configuration parameters for IsrTaskLSST.
 
    Items are grouped in the order in which they are executed by the task.
    """
    expectWcs = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Expect input science images to have a WCS (set False for e.g. spectrographs)."
    )
    qa = pexConfig.ConfigField(
        dtype=isrQa.IsrQaConfig,
        doc="QA related configuration options.",
    )
    doHeaderProvenance = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Write calibration identifiers into output exposure header.",
    )
 
    # Calib checking configuration:
    doRaiseOnCalibMismatch = pexConfig.Field(
        dtype=bool,
        default=False,
        doc="Should IsrTaskLSST halt if exposure and calibration header values do not match?",
    )
    cameraKeywordsToCompare = pexConfig.ListField(
        dtype=str,
        doc="List of header keywords to compare between exposure and calibrations.",
        default=[],
    )
 
    # Differential non-linearity correction.
    doDiffNonLinearCorrection = pexConfig.Field(
        dtype=bool,
        doc="Do differential non-linearity correction?",
        default=False,
    )
 
    doBootstrap = pexConfig.Field(
        dtype=bool,
        default=False,
        doc="Is this task to be run in a ``bootstrap`` fashion that does not require "
            "a PTC or full calibrations?",
    )
 
    doCheckUnprocessableData = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Check if this image is completely unprocessable due to all bad amps.",
    )
 
    overscanCamera = pexConfig.ConfigField(
        dtype=OverscanCameraConfig,
        doc="Per-detector and per-amplifier overscan configurations.",
    )
 
    serialOverscanMedianShiftSigmaThreshold = pexConfig.Field(
        dtype=float,
        default=numpy.inf,
        doc="Number of sigma difference from per-amp overscan median (as compared to PTC) to "
            "check if an amp is in a different state than the baseline PTC calib and should "
            "be marked BAD. Set to np.inf/np.nan to turn off overscan median checking.",
    )
 
    ampNoiseThreshold = pexConfig.Field(
        dtype=float,
        default=25.0,
        doc="Maximum amplifier noise (e-) that is allowed before an amp is masked as bad. "
            "Set to np.inf/np.nan to turn off noise checking.",
    )
 
    bssVoltageMinimum = pexConfig.Field(
        dtype=float,
        default=5.0,
        doc="Minimum back-side bias voltage.  Below this the detector is ``off`` and an "
            "UnprocessableDataError will be logged. Check will be skipped if doCheckUnprocessableData "
            "is False or if value is less than or equal to 0.",
    )
    bssVoltageKeyword = pexConfig.Field(
        dtype=str,
        default="BSSVBS",
        doc="Back-side bias voltage header keyword. Only checked if doCheckUnprocessableData is True "
            "and bssVoltageMinimum is greater than 0.",
    )
    hvBiasKeyword = pexConfig.Field(
        dtype=str,
        default="HVBIAS",
        doc="Back-side bias voltage on/off header keyword. Only checked if doCheckUnprocessableData is True "
            "and bssVoltageMinimum is greater than 0.",
    )
 
    # Amplifier to CCD assembly configuration.
    doAssembleCcd = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Assemble amp-level exposures into a ccd-level exposure?"
    )
    assembleCcd = pexConfig.ConfigurableField(
        target=AssembleCcdTask,
        doc="CCD assembly task.",
    )
 
    # Bias subtraction.
    doBias = pexConfig.Field(
        dtype=bool,
        doc="Apply bias frame correction?",
        default=True,
    )
 
    # Deferred charge correction.
    doDeferredCharge = pexConfig.Field(
        dtype=bool,
        doc="Apply deferred charge correction?",
        default=True,
    )
    deferredChargeCorrection = pexConfig.ConfigurableField(
        target=DeferredChargeTask,
        doc="Deferred charge correction task.",
    )
 
    # Linearization.
    doLinearize = pexConfig.Field(
        dtype=bool,
        doc="Correct for nonlinearity of the detector's response?",
        default=True,
    )
 
    # Gains.
    doCorrectGains = pexConfig.Field(
        dtype=bool,
        doc="Apply gain corrections from detector restarts?",
        default=True,
    )
    doApplyGains = pexConfig.Field(
        dtype=bool,
        doc="Apply gains to the image?",
        default=True,
    )
    useGainsFrom = pexConfig.ChoiceField(
        dtype=str,
        doc="Where to retrieve the gains. Unused if doBootstrap is True.",
        allowed={
            "PTC": "Use the gains from the inputPtc calibration.",
            "LINEARIZER": "Use the gains from the linearizer calibration.",
        },
        default="PTC",
    )
 
    # Variance construction.
    doVariance = pexConfig.Field(
        dtype=bool,
        doc="Calculate variance?",
        default=True
    )
    maskNegativeVariance = pexConfig.Field(
        dtype=bool,
        doc="Mask pixels that claim a negative variance.  This likely indicates a failure "
        "in the measurement of the overscan at an edge due to the data falling off faster "
        "than the overscan model can account for it.",
        default=True,
    )
    negativeVarianceMaskName = pexConfig.Field(
        dtype=str,
        doc="Mask plane to use to mark pixels with negative variance, if `maskNegativeVariance` is True.",
        default="BAD",
    )
    doSaturation = pexConfig.Field(
        dtype=bool,
        doc="Mask saturated pixels? NB: this is totally independent of the"
        " interpolation option - this is ONLY setting the bits in the mask."
        " To have them interpolated make sure doInterpolate=True and"
        " maskListToInterpolate includes SAT.",
        default=True,
    )
    saturatedMaskName = pexConfig.Field(
        dtype=str,
        doc="Name of mask plane to use in saturation detection and interpolation.",
        default="SAT",
    )
    defaultSaturationSource = pexConfig.ChoiceField(
        dtype=str,
        doc="Source to retrieve default amp-level saturation values.",
        allowed={
            "NONE": "No default saturation values; only config overrides will be used.",
            "CAMERAMODEL": "Use the default from the camera model (old defaults).",
            "PTCTURNOFF": "Use the ptcTurnoff value as the saturation level.",
        },
        default="PTCTURNOFF",
    )
    doSuspect = pexConfig.Field(
        dtype=bool,
        doc="Mask suspect pixels?",
        default=True,
    )
    suspectMaskName = pexConfig.Field(
        dtype=str,
        doc="Name of mask plane to use for suspect pixels.",
        default="SUSPECT",
    )
    defaultSuspectSource = pexConfig.ChoiceField(
        dtype=str,
        doc="Source to retrieve default amp-level suspect values.",
        allowed={
            "NONE": "No default suspect values; only config overrides will be used.",
            "CAMERAMODEL": "Use the default from the camera model (old defaults).",
            "PTCTURNOFF": "Use the ptcTurnoff value as the suspect level.",
        },
        default="PTCTURNOFF",
    )
 
    # Crosstalk.
    doCrosstalk = pexConfig.Field(
        dtype=bool,
        doc="Apply intra-CCD crosstalk correction?",
        default=True,
    )
    crosstalk = pexConfig.ConfigurableField(
        target=CrosstalkTask,
        doc="Intra-CCD crosstalk correction.",
    )
 
    # Masking options.
    doITLDipMask = pexConfig.Field(
        dtype=bool,
        doc="Apply ``ITL dip`` masking. The ``itlDipMaskPlane`` mask plane "
            "will be added even if this configuration is False.",
        default=True,
    )
    itlDipMaskPlanes = pexConfig.ListField(
        dtype=str,
        doc="Mask plane to use for ITL dip pixels.",
        default=["SUSPECT", "ITL_DIP"],
    )
    doDefect = pexConfig.Field(
        dtype=bool,
        doc="Apply correction for CCD defects, e.g. hot pixels?",
        default=True,
    )
    badAmps = pexConfig.ListField(
        dtype=str,
        doc="List of bad amps that should be masked as BAD in the defect code. "
            "Value should be of form {detector_name}_{amp_name}, e.g. ``R42_S21_C07``. "
            "Only used if doDefect is True.",
        default=[],
    )
    doNanMasking = pexConfig.Field(
        dtype=bool,
        doc="Mask non-finite (NAN, inf) pixels. The UNMASKEDNAN mask plane "
            "will be added even if this configuration is False.",
        default=True,
    )
    doWidenSaturationTrails = pexConfig.Field(
        dtype=bool,
        doc="Widen bleed trails based on their width.",
        default=False,
    )
    masking = pexConfig.ConfigurableField(
        target=MaskingTask,
        doc="Masking task."
    )
    doE2VEdgeBleedMask = pexConfig.Field(
        dtype=bool,
        doc="Mask flag-like edge bleeds from saturated columns "
            "in E2V amplifiers.",
        default=True,
    )
    e2vEdgeBleedSatMinArea = pexConfig.Field(
        dtype=int,
        doc="Minimum limit of saturated cores footprint area to apply edge"
            "bleed masking in E2V amplifiers.",
        default=10000,
    )
    e2vEdgeBleedSatMaxArea = pexConfig.Field(
        dtype=int,
        doc="Maximum limit of saturated cores footprint area to apply edge"
            "bleed masking in E2V amplifiers.",
        default=100000,
    )
    e2vEdgeBleedYMax = pexConfig.Field(
        dtype=int,
        doc="Height in pixels of edge bleed masking in E2V amplifiers (width"
            "is the width of the amplifier).",
        default=350,
    )
    doITLEdgeBleedMask = pexConfig.Field(
        dtype=bool,
        doc="Mask edge bleeds from saturated columns in ITL amplifiers.",
        default=True,
    )
    doITLSatSagMask = pexConfig.Field(
        dtype=bool,
        doc="Mask columns presenting saturation sag.",
        default=True,
    )
    itlEdgeBleedSatMinArea = pexConfig.Field(
        dtype=int,
        doc="Minimum limit for saturated cores footprint area.",
        default=10000,
    )
    itlEdgeBleedSatMaxArea = pexConfig.Field(
        dtype=int,
        doc="Maximum limit for saturated cores footprint area.",
        default=100000,
    )
    itlEdgeBleedThreshold = pexConfig.Field(
        dtype=float,
        doc="Sky background threshold for edge bleed detection.",
        default=5000.,
    )
    itlEdgeBleedModelConstant = pexConfig.Field(
        dtype=float,
        doc="Constant in the edge bleed exponential decay model.",
        default=0.02,
    )
 
    # Interpolation options.
    doInterpolate = pexConfig.Field(
        dtype=bool,
        doc="Interpolate masked pixels?",
        default=True,
    )
    maskListToInterpolate = pexConfig.ListField(
        dtype=str,
        doc="List of mask planes that should be interpolated.",
        default=["SAT", "BAD", "UNMASKEDNAN"],
    )
    doSaveInterpPixels = pexConfig.Field(
        dtype=bool,
        doc="Save a copy of the pre-interpolated pixel values?",
        default=False,
    )
    useLegacyInterp = pexConfig.Field(
        dtype=bool,
        doc="Use the legacy interpolation algorithm. If False use Gaussian Process.",
        default=True,
    )
 
    # Amp offset correction.
    doAmpOffset = pexConfig.Field(
        doc="Calculate amp offset corrections?",
        dtype=bool,
        default=False,
    )
    ampOffset = pexConfig.ConfigurableField(
        doc="Amp offset correction task.",
        target=AmpOffsetTask,
    )
 
    # Initial masking options.
    doSetBadRegions = pexConfig.Field(
        dtype=bool,
        doc="Should we set the level of all BAD patches of the chip to the chip's average value?",
        default=True,
    )
 
    # Brighter-Fatter correction.
    doBrighterFatter = pexConfig.Field(
        dtype=bool,
        doc="Apply the brighter-fatter correction?",
        default=True,
    )
    brighterFatterLevel = pexConfig.ChoiceField(
        dtype=str,
        doc="The level at which to correct for brighter-fatter.",
        allowed={
            "AMP": "Every amplifier treated separately.",
            "DETECTOR": "One kernel per detector.",
        },
        default="DETECTOR",
    )
    brighterFatterMaxIter = pexConfig.Field(
        dtype=int,
        doc="Maximum number of iterations for the brighter-fatter correction.",
        default=10,
    )
    brighterFatterThreshold = pexConfig.Field(
        dtype=float,
        doc="Threshold used to stop iterating the brighter-fatter correction.  It is the "
        "absolute value of the difference between the current corrected image and the one "
        "from the previous iteration summed over all the pixels.",
        default=1000,
    )
    brighterFatterMaskListToInterpolate = pexConfig.ListField(
        dtype=str,
        doc="List of mask planes that should be interpolated over when applying the brighter-fatter "
        "correction.",
        default=["SAT", "BAD", "NO_DATA", "UNMASKEDNAN"],
    )
    brighterFatterMaskGrowSize = pexConfig.Field(
        dtype=int,
        doc="Number of pixels to grow the masks listed in config.brighterFatterMaskListToInterpolate "
        "when brighter-fatter correction is applied.",
        default=2,
    )
    brighterFatterFwhmForInterpolation = pexConfig.Field(
        dtype=float,
        doc="FWHM of PSF in arcseconds used for interpolation in brighter-fatter correction "
        "(currently unused).",
        default=1.0,
    )
    growSaturationFootprintSize = pexConfig.Field(
        dtype=int,
        doc="Number of pixels by which to grow the saturation footprints.",
        default=1,
    )
 
    # Dark subtraction.
    doDark = pexConfig.Field(
        dtype=bool,
        doc="Apply dark frame correction.",
        default=True,
    )
 
    # Flat correction.
    doFlat = pexConfig.Field(
        dtype=bool,
        doc="Apply flat field correction.",
        default=True,
    )
    flatScalingType = pexConfig.ChoiceField(
        dtype=str,
        doc="The method for scaling the flat on the fly.",
        default='USER',
        allowed={
            "USER": "Scale by flatUserScale",
            "MEAN": "Scale by the inverse of the mean",
            "MEDIAN": "Scale by the inverse of the median",
        },
    )
    flatUserScale = pexConfig.Field(
        dtype=float,
        doc="If flatScalingType is 'USER' then scale flat by this amount; ignored otherwise.",
        default=1.0,
    )
 
    # Calculate image quality statistics?
    doStandardStatistics = pexConfig.Field(
        dtype=bool,
        doc="Should standard image quality statistics be calculated?",
        default=True,
    )
    # Calculate additional statistics?
    doCalculateStatistics = pexConfig.Field(
        dtype=bool,
        doc="Should additional ISR statistics be calculated?",
        default=True,
    )
    isrStats = pexConfig.ConfigurableField(
        target=IsrStatisticsTask,
        doc="Task to calculate additional statistics.",
    )
 
    # Make binned images?
    doBinnedExposures = pexConfig.Field(
        dtype=bool,
        doc="Should binned exposures be calculated?",
        default=False,
    )
    binning = pexConfig.ConfigurableField(
        target=BinImageDataTask,
        doc="Task to bin the exposure.",
    )
    binFactor1 = pexConfig.Field(
        dtype=int,
        doc="Binning factor for first binned exposure. This is intended for a finely binned output.",
        default=8,
        check=lambda x: x > 1,
    )
    binFactor2 = pexConfig.Field(
        dtype=int,
        doc="Binning factor for second binned exposure. This is intended for a coarsely binned output.",
        default=64,
        check=lambda x: x > 1,
    )
 
    def validate(self):
        super().validate()
 
        if self.doBootstrap:
            # Additional checks in bootstrap (no gains) mode.
            if self.doApplyGains:
                raise ValueError("Cannot run task with doBootstrap=True and doApplyGains=True.")
            if self.doCorrectGains:
                raise ValueError("Cannot run task with doBootstrap=True and doCorrectGains=True.")
            if self.doCrosstalk and self.crosstalk.doQuadraticCrosstalkCorrection:
                raise ValueError("Cannot apply quadratic crosstalk correction with doBootstrap=True.")
            if numpy.isfinite(self.serialOverscanMedianShiftSigmaThreshold):
                raise ValueError("Cannot do amp overscan level checks with doBootstrap=True.")
            if numpy.isfinite(self.ampNoiseThreshold):
                raise ValueError("Cannot do amp noise thresholds with doBootstrap=True.")
 
        if self.doITLEdgeBleedMask and not self.doSaturation:
            raise ValueError("Cannot do ITL edge bleed masking when doSaturation=False.")
        if self.doE2VEdgeBleedMask and not self.doSaturation:
            raise ValueError("Cannot do e2v edge bleed masking when doSaturation=False.")
 
    def setDefaults(self):
        super().setDefaults()
 
 
class IsrTaskLSST(pipeBase.PipelineTask):
    ConfigClass = IsrTaskLSSTConfig
    _DefaultName = "isrLSST"
 
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.makeSubtask("assembleCcd")
        self.makeSubtask("deferredChargeCorrection")
        self.makeSubtask("crosstalk")
        self.makeSubtask("masking")
        self.makeSubtask("isrStats")
        self.makeSubtask("ampOffset")
        self.makeSubtask("binning")
 
    def runQuantum(self, butlerQC, inputRefs, outputRefs):
 
        inputs = butlerQC.get(inputRefs)
        self.validateInput(inputs)
 
        if self.config.doHeaderProvenance:
            # Add calibration provenanace info to header.
            exposureMetadata = inputs['ccdExposure'].metadata
            for inputName in sorted(list(inputs.keys())):
                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()}"
                    dateValue = self.extractCalibDate(inputs[inputName])
                    if dateValue != "Unknown Unknown":
                        butlerQC.add_additional_provenance(reference, {"calib date": dateValue})
 
                    exposureMetadata[runKey] = runValue
                    exposureMetadata[idKey] = idValue
                    exposureMetadata[dateKey] = dateValue
 
        outputs = self.run(**inputs)
        butlerQC.put(outputs, outputRefs)
 
    def validateInput(self, inputs):
        """
        This is a check that all the inputs required by the config
        are available.
        """
 
        doApplyGains = self.config.doApplyGains
        useLinearizerGains = self.config.useGainsFrom == "LINEARIZER"
        usePtcGains = not useLinearizerGains
 
        inputMap = {
            'dnlLUT': self.config.doDiffNonLinearCorrection,
            'bias': self.config.doBias,
            'deferredChargeCalib': self.config.doDeferredCharge,
            # Some tasks require gains in order to be
            # supplied regardless of whether
            # self.config.doApplyGains is True or False.
            'linearizer': (self.config.doLinearize or (doApplyGains and useLinearizerGains)),
            'ptc': self.config.doApplyGains and usePtcGains,
            'crosstalk': self.config.doCrosstalk,
            'defects': self.config.doDefect,
            'bfKernel': self.config.doBrighterFatter,
            'dark': self.config.doDark,
        }
 
        for calibrationFile, configValue in inputMap.items():
            if configValue and inputs[calibrationFile] is None:
                raise RuntimeError("Must supply ", calibrationFile)
 
    def diffNonLinearCorrection(self, ccdExposure, dnlLUT, **kwargs):
        # TODO DM 36636
        # isrFunctions.diffNonLinearCorrection
        pass
 
    def maskFullAmplifiers(self, ccdExposure, detector, defects, gains=None):
        """
        Check for fully masked bad amplifiers and mask them.
 
        This includes defects which cover full amplifiers, as well
        as amplifiers with nan gain values which should be used
        if self.config.doApplyGains=True.
 
        Full defect masking happens later to allow for defects which
        cross amplifier boundaries.
 
        Parameters
        ----------
        ccdExposure : `lsst.afw.image.Exposure`
            Input exposure to be masked.
        detector : `lsst.afw.cameraGeom.Detector`
            Detector object.
        defects : `lsst.ip.isr.Defects`
            List of defects.  Used to determine if an entire
            amplifier is bad.
        gains : `dict` [`str`, `float`], optional
            Dictionary of gains to check if
            self.config.doApplyGains=True.
 
        Returns
        -------
        badAmpDict : `str`[`bool`]
            Dictionary of amplifiers, keyed by name, value is True if
            amplifier is fully masked.
        """
        badAmpDict = {}
 
        maskedImage = ccdExposure.getMaskedImage()
 
        for amp in detector:
            ampName = amp.getName()
            badAmpDict[ampName] = 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:
                badAmpDict[ampName] = bool(sum([v.getBBox().contains(amp.getBBox()) for v in defects]))
 
                if badAmpDict[ampName]:
                    self.log.warning("Amplifier %s is bad (completely covered with defects)", ampName)
 
            if gains is not None and self.config.doApplyGains:
                if not math.isfinite(gains[ampName]):
                    badAmpDict[ampName] = True
 
                    self.log.warning("Amplifier %s is bad (non-finite gain)", ampName)
 
            # 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 badAmpDict[ampName]:
                dataView = afwImage.MaskedImageF(maskedImage, amp.getRawBBox(),
                                                 afwImage.PARENT)
                maskView = dataView.getMask()
                maskView |= maskView.getPlaneBitMask("BAD")
                del maskView
 
        return badAmpDict
 
    def checkAllBadAmps(self, badAmpDict, detector):
        """Check if all amps are marked as bad.
 
        Parameters
        ----------
        badAmpDict : `str`[`bool`]
            Dictionary of amplifiers, keyed by name, value is True if
            amplifier is fully masked.
        detector : `lsst.afw.cameraGeom.Detector`
            Detector object.
 
        Raises
        ------
        UnprocessableDataError if all amps are bad and doCheckUnprocessableData
        configuration is True.
        """
        if not self.config.doCheckUnprocessableData:
            return
 
        for amp in detector:
            if not badAmpDict.get(amp.getName(), False):
                return
 
        raise UnprocessableDataError(f"All amps in the exposure {detector.getName()} are bad; skipping ISR.")
 
    def checkAmpOverscanLevel(self, badAmpDict, exposure, ptc):
        """Check if the amplifier overscan levels have changed.
 
        Any amplifier that has an overscan median level that has changed
        significantly will be masked as BAD and added to toe badAmpDict.
 
        Parameters
        ----------
        badAmpDict : `str` [`bool`]
            Dictionary of amplifiers, keyed by name, value is True if
            amplifier is fully masked.
        exposure : `lsst.afw.image.Exposure`
            Input exposure to be masked (untrimmed).
        ptc : `lsst.ip.isr.PhotonTransferCurveDataset`
            PTC dataset with gains/read noises.
 
        Returns
        -------
        badAmpDict : `str`[`bool`]
            Dictionary of amplifiers, keyed by name.
 
        """
        if isTrimmedExposure(exposure):
            raise RuntimeError("checkAmpOverscanLevel must be run on an untrimmed exposure.")
 
        # We want to consolidate all the amps into one warning if necessary.
        # This config should not be set to a finite threshold if the necessary
        # data is not in the PTC.
        missingWarnString = "No PTC overscan information for amplifier "
        missingWarnFlag = False
        for amp in exposure.getDetector():
            ampName = amp.getName()
 
            if not numpy.isfinite(ptc.overscanMedian[ampName]) or \
               not numpy.isfinite(ptc.overscanMedianSigma[ampName]):
                missingWarnString += f"{ampName},"
                missingWarnFlag = True
            else:
                key = f"LSST ISR OVERSCAN SERIAL MEDIAN {ampName}"
                # If it is missing, just return the PTC value and it
                # will be skipped.
                overscanLevel = exposure.metadata.get(key, ptc.overscanMedian[ampName])
                pull = (overscanLevel - ptc.overscanMedian[ampName])/ptc.overscanMedianSigma[ampName]
                if numpy.abs(pull) > self.config.serialOverscanMedianShiftSigmaThreshold:
                    self.log.warning(
                        "Amplifier %s has an overscan level that is %.2f sigma from the expected level; "
                        "masking it as BAD.",
                        ampName,
                        pull,
                    )
 
                    badAmpDict[ampName] = True
                    exposure.mask[amp.getRawBBox()] |= exposure.mask.getPlaneBitMask("BAD")
 
        if missingWarnFlag:
            self.log.warning(missingWarnString)
 
        return badAmpDict
 
    def checkAmpNoise(self, badAmpDict, exposure, ptc):
        """Check if amplifier noise levels are above threshold.
 
        Any amplifier that is above the noise level will be masked as BAD
        and added to the badAmpDict.
 
        Parameters
        ----------
        badAmpDict : `str` [`bool`]
            Dictionary of amplifiers, keyed by name, value is True if
            amplifier is fully masked.
        exposure : `lsst.afw.image.Exposure`
            Input exposure to be masked (untrimmed).
        ptc : `lsst.ip.isr.PhotonTransferCurveDataset`
            PTC dataset with gains/read noises.
 
        Returns
        -------
        badAmpDict : `str`[`bool`]
            Dictionary of amplifiers, keyed by name.
        """
 
        if isTrimmedExposure(exposure):
            raise RuntimeError("checkAmpNoise must be run on an untrimmed exposure.")
 
        for amp in exposure.getDetector():
            ampName = amp.getName()
 
            doMask = False
            if ptc.noise[ampName] > self.config.ampNoiseThreshold:
                self.log.info(
                    "Amplifier %s has a PTC noise level of %.2f e-, above threshold.",
                    ampName,
                    ptc.noise[ampName],
                )
                doMask = True
            else:
                key = f"LSST ISR OVERSCAN RESIDUAL SERIAL STDEV {ampName}"
                overscanNoise = exposure.metadata.get(key, numpy.nan)
                if overscanNoise * ptc.gain[ampName] > self.config.ampNoiseThreshold:
                    self.log.warning(
                        "Amplifier %s has an overscan read noise level of %.2f e-, above threshold.",
                        ampName,
                        overscanNoise * ptc.gain[ampName],
                    )
                    doMask = True
 
            if doMask:
                badAmpDict[ampName] = True
 
                exposure.mask[amp.getRawBBox()] |= exposure.mask.getPlaneBitMask("BAD")
 
        return badAmpDict
 
    def maskSaturatedPixels(self, badAmpDict, ccdExposure, detector, detectorConfig, ptc=None):
        """
        Mask SATURATED and SUSPECT pixels and check if any amplifiers
        are fully masked.
 
        Parameters
        ----------
        badAmpDict : `str` [`bool`]
            Dictionary of amplifiers, keyed by name, value is True if
            amplifier is fully masked.
        ccdExposure : `lsst.afw.image.Exposure`
            Input exposure to be masked.
        detector : `lsst.afw.cameraGeom.Detector`
            Detector object.
        defects : `lsst.ip.isr.Defects`
            List of defects.  Used to determine if an entire
            amplifier is bad.
        detectorConfig : `lsst.ip.isr.OverscanDetectorConfig`
            Per-amplifier configurations.
        ptc : `lsst.ip.isr.PhotonTransferCurveDataset`, optional
            PTC dataset (used if configured to use PTCTURNOFF).
 
        Returns
        -------
        badAmpDict : `str`[`bool`]
            Dictionary of amplifiers, keyed by name.
        """
        maskedImage = ccdExposure.getMaskedImage()
 
        metadata = ccdExposure.metadata
 
        if self.config.doSaturation and self.config.defaultSaturationSource == "PTCTURNOFF" and ptc is None:
            raise RuntimeError("Must provide ptc if using PTCTURNOFF as saturation source.")
        if self.config.doSuspect and self.config.defaultSuspectSource == "PTCTURNOFF" and ptc is None:
            raise RuntimeError("Must provide ptc if using PTCTURNOFF as suspect source.")
 
        for amp in detector:
            ampName = amp.getName()
 
            ampConfig = detectorConfig.getOverscanAmpConfig(amp)
 
            if badAmpDict[ampName]:
                # No need to check fully bad amplifiers.
                continue
 
            # Mask saturated and suspect pixels.
            limits = {}
            if self.config.doSaturation:
                if self.config.defaultSaturationSource == "PTCTURNOFF":
                    limits.update({self.config.saturatedMaskName: ptc.ptcTurnoff[amp.getName()]})
                elif self.config.defaultSaturationSource == "CAMERAMODEL":
                    # Set to the default from the camera model.
                    limits.update({self.config.saturatedMaskName: amp.getSaturation()})
                elif self.config.defaultSaturationSource == "NONE":
                    limits.update({self.config.saturatedMaskName: numpy.inf})
 
                # And update if it is set in the config.
                if math.isfinite(ampConfig.saturation):
                    limits.update({self.config.saturatedMaskName: ampConfig.saturation})
                metadata[f"LSST ISR SATURATION LEVEL {ampName}"] = limits[self.config.saturatedMaskName]
 
            if self.config.doSuspect:
                if self.config.defaultSuspectSource == "PTCTURNOFF":
                    limits.update({self.config.suspectMaskName: ptc.ptcTurnoff[amp.getName()]})
                elif self.config.defaultSuspectSource == "CAMERAMODEL":
                    # Set to the default from the camera model.
                    limits.update({self.config.suspectMaskName: amp.getSuspectLevel()})
                elif self.config.defaultSuspectSource == "NONE":
                    limits.update({self.config.suspectMaskName: numpy.inf})
 
                # And update if it set in the config.
                if math.isfinite(ampConfig.suspectLevel):
                    limits.update({self.config.suspectMaskName: ampConfig.suspectLevel})
                metadata[f"LSST ISR SUSPECT LEVEL {ampName}"] = limits[self.config.suspectMaskName]
 
            for maskName, maskThreshold in limits.items():
                if not math.isnan(maskThreshold):
                    dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
                    toMask = (dataView.image.array >= maskThreshold)
                    dataView.mask.array[toMask] |= dataView.mask.getPlaneBitMask(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):
                self.log.warning("Amplifier %s is bad (completely SATURATED or SUSPECT)", ampName)
                badAmpDict[ampName] = True
                maskView |= maskView.getPlaneBitMask("BAD")
 
        return badAmpDict
 
    def maskITLSatEdgesAndColumns(self, exposure, badAmpDict):
 
        # The following steps will rely on the footprint of saturated
        # cores with large areas.
        thresh = afwDetection.Threshold(exposure.mask.getPlaneBitMask("SAT"),
                                        afwDetection.Threshold.BITMASK
                                        )
        fpList = afwDetection.FootprintSet(exposure.mask, thresh).getFootprints()
 
        satAreas = numpy.asarray([fp.getArea() for fp in fpList])
        largeAreas, = numpy.where((satAreas >= self.config.itlEdgeBleedSatMinArea)
                                  & (satAreas < self.config.itlEdgeBleedSatMaxArea))
 
        for largeAreasIndex in largeAreas:
 
            fpCore = fpList[largeAreasIndex]
 
            # Edge bleed masking
            if self.config.doITLEdgeBleedMask:
                isrFunctions.maskITLEdgeBleed(ccdExposure=exposure,
                                              badAmpDict=badAmpDict,
                                              fpCore=fpCore,
                                              itlEdgeBleedSatMinArea=self.config.itlEdgeBleedSatMinArea,
                                              itlEdgeBleedSatMaxArea=self.config.itlEdgeBleedSatMaxArea,
                                              itlEdgeBleedThreshold=self.config.itlEdgeBleedThreshold,
                                              itlEdgeBleedModelConstant=self.config.itlEdgeBleedModelConstant,
                                              saturatedMaskName=self.config.saturatedMaskName,
                                              log=self.log
                                              )
            if self.config.doITLSatSagMask:
                isrFunctions.maskITLSatSag(ccdExposure=exposure, fpCore=fpCore,
                                           saturatedMaskName=self.config.saturatedMaskName)
 
    def overscanCorrection(self, mode, detectorConfig, detector, badAmpDict, ccdExposure):
        """Apply serial overscan correction in place to all amps.
 
        The actual overscan subtraction is performed by the
        `lsst.ip.isr.overscan.OverscanTask`, which is called here.
 
        Parameters
        ----------
        mode : `str`
            Must be `SERIAL` or `PARALLEL`.
        detectorConfig : `lsst.ip.isr.OverscanDetectorConfig`
            Per-amplifier configurations.
        detector : `lsst.afw.cameraGeom.Detector`
            Detector object.
        badAmpDict : `dict`
            Dictionary of amp name to whether it is a bad amp.
        ccdExposure : `lsst.afw.image.Exposure`
            Exposure to have overscan correction performed.
 
        Returns
        -------
        overscans : `list` [`lsst.pipe.base.Struct` or None]
            Overscan measurements (always in adu).
            Each result struct has 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`)
            ``overscanMean``
                Mean overscan fit value. (`float`)
            ``overscanMedian``
                Median overscan fit value. (`float`)
            ``overscanSigma``
                Clipped standard deviation of the overscan fit. (`float`)
            ``residualMean``
                Mean of the overscan after fit subtraction. (`float`)
            ``residualMedian``
                Median of the overscan after fit subtraction. (`float`)
            ``residualSigma``
                Clipped standard deviation of the overscan after fit
                subtraction. (`float`)
 
        See Also
        --------
        lsst.ip.isr.overscan.OverscanTask
        """
        if mode not in ["SERIAL", "PARALLEL"]:
            raise ValueError("Mode must be SERIAL or PARALLEL")
 
        # This returns a list in amp order, with None for uncorrected amps.
        overscans = []
 
        for i, amp in enumerate(detector):
            ampName = amp.getName()
 
            ampConfig = detectorConfig.getOverscanAmpConfig(amp)
 
            if mode == "SERIAL" and not ampConfig.doSerialOverscan:
                self.log.debug(
                    "ISR_OSCAN: Amplifier %s/%s configured to skip serial overscan.",
                    detector.getName(),
                    ampName,
                )
                results = None
            elif mode == "PARALLEL" and not ampConfig.doParallelOverscan:
                self.log.debug(
                    "ISR_OSCAN: Amplifier %s configured to skip parallel overscan.",
                    detector.getName(),
                    ampName,
                )
                results = None
            elif badAmpDict[ampName] or not ccdExposure.getBBox().contains(amp.getBBox()):
                results = None
            else:
                # This check is to confirm that we are not trying to run
                # overscan on an already trimmed image.
                if isTrimmedExposure(ccdExposure):
                    self.log.warning(
                        "ISR_OSCAN: No overscan region for amp %s. Not performing overscan correction.",
                        ampName,
                    )
                    results = None
                else:
                    if mode == "SERIAL":
                        # We need to set up the subtask here with a custom
                        # configuration.
                        serialOverscan = SerialOverscanCorrectionTask(config=ampConfig.serialOverscanConfig)
                        results = serialOverscan.run(ccdExposure, amp)
                    else:
                        config = ampConfig.parallelOverscanConfig
                        parallelOverscan = ParallelOverscanCorrectionTask(
                            config=config,
                        )
 
                        metadata = ccdExposure.metadata
 
                        # We need to know the saturation level that was used
                        # for the parallel overscan masking. If it isn't set
                        # then the configured parallelOverscanSaturationLevel
                        # will be used instead (assuming
                        # doParallelOverscanSaturation is True). Note that
                        # this will have the correct units (adu or electron)
                        # depending on whether the gain has been applied.
                        if self.config.doSaturation:
                            saturationLevel = metadata[f"LSST ISR SATURATION LEVEL {amp.getName()}"]
                            saturationLevel *= config.parallelOverscanSaturationLevelAdjustmentFactor
                        else:
                            saturationLevel = config.parallelOverscanSaturationLevel
                            if ccdExposure.metadata["LSST ISR UNITS"] == "electron":
                                # Need to convert to electron from adu.
                                saturationLevel *= metadata[f"LSST ISR GAIN {amp.getName()}"]
 
                        self.log.debug(
                            "Using saturation level of %.2f for parallel overscan amp %s",
                            saturationLevel,
                            amp.getName(),
                        )
 
                        parallelOverscan.maskParallelOverscanAmp(
                            ccdExposure,
                            amp,
                            saturationLevel=saturationLevel,
                        )
 
                        results = parallelOverscan.run(ccdExposure, amp)
 
                    metadata = ccdExposure.metadata
                    keyBase = "LSST ISR OVERSCAN"
 
                    # The overscan is always in adu for the serial mode,
                    # but, it may be electron in the parallel mode if
                    # doApplyGains==True. If doApplyGains==True, then the
                    # gains are applied to the untrimmed image, so the
                    # overscan statistics units here will always match the
                    # units of the image at this point.
                    metadata[f"{keyBase} {mode} UNITS"] = ccdExposure.metadata["LSST ISR UNITS"]
                    metadata[f"{keyBase} {mode} MEAN {ampName}"] = results.overscanMean
                    metadata[f"{keyBase} {mode} MEDIAN {ampName}"] = results.overscanMedian
                    metadata[f"{keyBase} {mode} STDEV {ampName}"] = results.overscanSigma
 
                    metadata[f"{keyBase} RESIDUAL {mode} MEAN {ampName}"] = results.residualMean
                    metadata[f"{keyBase} RESIDUAL {mode} MEDIAN {ampName}"] = results.residualMedian
                    metadata[f"{keyBase} RESIDUAL {mode} STDEV {ampName}"] = results.residualSigma
 
            overscans.append(results)
 
        # Question: should this be finer grained?
        ccdExposure.metadata.set("OVERSCAN", "Overscan corrected")
 
        return overscans
 
    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
 
    def addVariancePlane(self, exposure, detector):
        """Add the variance plane to the image.
 
        The gain and read noise per amp must have been set in the
        exposure metadata as ``LSST ISR GAIN ampName`` and
        ``LSST ISR READNOISE ampName`` with the units of the image.
        Unit conversions for the variance plane will be done as
        necessary based on the exposure units.
 
        The units of the variance plane will always be of the same
        type as the units of the input image itself
        (``LSST ISR UNITS``^2).
 
        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            The exposure to add the variance plane.
        detector : `lsst.afw.cameraGeom.Detector`
            Detector with geometry info.
        """
        # NOTE: this will fail if the exposure is not trimmed.
        if not isTrimmedExposure(exposure):
            raise RuntimeError("Exposure must be trimmed to add variance plane.")
 
        isElectrons = (exposure.metadata["LSST ISR UNITS"] == "electron")
 
        for amp in detector:
            if exposure.getBBox().contains(amp.getBBox()):
                self.log.debug("Constructing variance map for amplifer %s.", amp.getName())
                ampExposure = exposure.Factory(exposure, amp.getBBox())
 
                # The effective gain is 1.0 if we are in electron units.
                # The metadata read noise is in the same units as the image.
                gain = exposure.metadata[f"LSST ISR GAIN {amp.getName()}"] if not isElectrons else 1.0
                readNoise = exposure.metadata[f"LSST ISR READNOISE {amp.getName()}"]
 
                isrFunctions.updateVariance(
                    maskedImage=ampExposure.maskedImage,
                    gain=gain,
                    readNoise=readNoise,
                    replace=False,
                )
 
                if self.config.qa is not None and self.config.qa.saveStats is True:
                    qaStats = afwMath.makeStatistics(ampExposure.getVariance(),
                                                     afwMath.MEDIAN | 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(exposure)
 
    def maskDefects(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`.
        """
        maskedImage = exposure.getMaskedImage()
        if not isinstance(defectBaseList, Defects):
            # Promotes DefectBase to Defect
            defectList = Defects(defectBaseList)
        else:
            defectList = defectBaseList
        defectList.maskPixels(maskedImage, maskName="BAD")
 
        if len(self.config.badAmps) == 0:
            return
 
        detector = exposure.getDetector()
        mask = maskedImage.mask
        for badAmp in self.config.badAmps:
            if badAmp.startswith(detector.getName()):
                # Split on the full detector name plus _, which
                # gives us an empty string and the amp name.
                ampName = badAmp.split(detector.getName() + "_")[-1]
                self.log.info("Masking amplifier %s as bad via config.", ampName)
                mask[detector[ampName].getBBox()].array[:, :] |= mask.getPlaneBitMask("BAD")
 
    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)
 
    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
        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)
 
    def setBadRegions(self, exposure):
        """Set bad regions from large contiguous regions.
 
        Parameters
        ----------
        exposure : `lsst.afw.Exposure`
            Exposure to set bad regions.
 
        Notes
        -----
        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.
        """
        badPixelCount, badPixelValue = isrFunctions.setBadRegions(exposure)
        if badPixelCount > 0:
            self.log.info("Set %d BAD pixels to %f.", badPixelCount, badPixelValue)
 
    @contextmanager
    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 and flat is not None:
            self.flatCorrection(exp, flat)
        try:
            yield exp
        finally:
            if self.config.doFlat and flat is not None:
                self.flatCorrection(exp, flat, invert=True)
            if self.config.doDark and dark is not None:
                self.darkCorrection(exp, dark, invert=True)
 
    def getBrighterFatterKernel(self, detector, bfKernel):
        detName = detector.getName()
 
        # This is expected to be a dictionary of amp-wise gains.
        bfGains = bfKernel.gain
        if bfKernel.level == 'DETECTOR':
            if detName in bfKernel.detKernels:
                bfKernelOut = bfKernel.detKernels[detName]
                return bfKernelOut, bfGains
            else:
                raise RuntimeError("Failed to extract kernel from new-style BF kernel.")
        elif bfKernel.level == 'AMP':
            self.log.info("Making DETECTOR level kernel from AMP based brighter "
                          "fatter kernels.")
            bfKernel.makeDetectorKernelFromAmpwiseKernels(detName)
            bfKernelOut = bfKernel.detKernels[detName]
            return bfKernelOut, bfGains
 
    def applyBrighterFatterCorrection(self, ccdExposure, flat, dark, bfKernel, brighterFatterApplyGain,
                                      bfGains):
        """Apply a brighter fatter correction to the image using the
        method defined in Coulton et al. 2019.
 
        Note that this correction requires that the image is in units
        electrons.
 
        Parameters
        ----------
        ccdExposure : `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``.
        bfKernel : `lsst.ip.isr.BrighterFatterKernel`
            The brighter-fatter kernel.
        brighterFatterApplyGain : `bool`
            Apply the gain to convert the image to electrons?
        bfGains : `dict`
            The gains to use if brighterFatterApplyGain = True.
 
        Yields
        ------
        exp : `lsst.afw.image.Exposure`
            The flat and dark corrected exposure.
        """
        interpExp = ccdExposure.clone()
 
        # We need to interpolate before we do B-F. Note that
        # brighterFatterFwhmForInterpolation is currently unused.
        isrFunctions.interpolateFromMask(
            maskedImage=interpExp.getMaskedImage(),
            fwhm=self.config.brighterFatterFwhmForInterpolation,
            growSaturatedFootprints=self.config.growSaturationFootprintSize,
            maskNameList=list(self.config.brighterFatterMaskListToInterpolate),
            useLegacyInterp=self.config.useLegacyInterp,
        )
        bfExp = interpExp.clone()
        bfResults = isrFunctions.brighterFatterCorrection(bfExp, bfKernel,
                                                          self.config.brighterFatterMaxIter,
                                                          self.config.brighterFatterThreshold,
                                                          brighterFatterApplyGain,
                                                          bfGains)
        bfCorrIters = bfResults[1]
        if bfCorrIters == self.config.brighterFatterMaxIter:
            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)
 
        return ccdExposure, bfCorrIters
 
    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.visitInfo.darkTime
        if math.isnan(expScale):
            raise RuntimeError("Exposure darktime is NAN.")
        if darkExposure.visitInfo is not None \
                and not math.isnan(darkExposure.visitInfo.darkTime):
            darkScale = darkExposure.visitInfo.darkTime
        else:
            # DM-17444: darkExposure.visitInfo is None
            #           so darkTime does not exist.
            self.log.warning("darkExposure.visitInfo does not exist. Using darkScale = 1.0.")
            darkScale = 1.0
 
        isrFunctions.darkCorrection(
            maskedImage=exposure.maskedImage,
            darkMaskedImage=darkExposure.maskedImage,
            expScale=expScale,
            darkScale=darkScale,
            invert=invert,
        )
 
    @staticmethod
    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.metadata:
                return " ".join((calib.metadata.get("CALIB_CREATION_DATE", "Unknown"),
                                 calib.metadata.get("CALIB_CREATION_TIME", "Unknown")))
            else:
                return " ".join((calib.metadata.get("CALIB_CREATE_DATE", "Unknown"),
                                 calib.metadata.get("CALIB_CREATE_TIME", "Unknown")))
        else:
            return "Unknown Unknown"
 
    def compareUnits(self, calibMetadata, calibName):
        """Compare units from calibration to ISR units.
 
        This compares calibration units (adu or electron) to whether
        doApplyGain is set.
 
        Parameters
        ----------
        calibMetadata : `lsst.daf.base.PropertyList`
            Calibration metadata from header.
        calibName : `str`
            Calibration name for log message.
        """
        calibUnits = calibMetadata.get("LSST ISR UNITS", "adu")
        isrUnits = "electron" if self.config.doApplyGains else "adu"
        if calibUnits != isrUnits:
            if self.config.doRaiseOnCalibMismatch:
                raise RuntimeError(
                    "Unit mismatch: isr has %s units but %s has %s units",
                    isrUnits,
                    calibName,
                    calibUnits,
                )
            else:
                self.log.warning(
                    "Unit mismatch: isr has %s units but %s has %s units",
                    isrUnits,
                    calibName,
                    calibUnits,
                )
 
    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
 
    def ditherCounts(self, exposure, detectorConfig, fallbackSeed=12345):
        """Dither the counts in the exposure.
 
        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            The raw exposure to be dithered.
        detectorConfig : `lsst.ip.isr.OverscanDetectorConfig`
            Configuration for overscan/etc for this detector.
        fallbackSeed : `int`, optional
            Random seed to fall back to if exposure.getInfo().getId() is
            not set.
        """
        if detectorConfig.integerDitherMode == "NONE":
            # Nothing to do here.
            return
 
        # This ID is a unique combination of {exposure, detector} for a raw
        # image as we have here. We additionally need to take the lower
        # 32 bits to be used as a random seed.
        if exposure.info.id is not None:
            seed = exposure.info.id & 0xFFFFFFFF
        else:
            seed = fallbackSeed
            self.log.warning("No exposure ID found; using fallback random seed.")
 
        self.log.info("Seeding dithering random number generator with %d.", seed)
        rng = numpy.random.RandomState(seed=seed)
 
        if detectorConfig.integerDitherMode == "POSITIVE":
            low = 0.0
            high = 1.0
        elif detectorConfig.integerDitherMode == "NEGATIVE":
            low = -1.0
            high = 0.0
        elif detectorConfig.integerDitherMode == "SYMMETRIC":
            low = -0.5
            high = 0.5
        else:
            raise RuntimeError("Invalid config")
 
        exposure.image.array[:, :] += rng.uniform(low=low, high=high, size=exposure.image.array.shape)
 
    def checkBssVoltage(self, exposure):
        """Check the back-side bias voltage to see if the detector is on.
 
        Parameters
        ----------
        exposure : `lsst.afw.image.ExposureF`
            Input exposure.
 
        Raises
        ------
        `UnprocessableDataError` if voltage is off.
        """
        voltage = exposure.metadata.get(self.config.bssVoltageKeyword, None)
        if voltage is None or not numpy.isfinite(voltage):
            self.log.warning(
                "Back-side bias voltage %s not found in metadata.",
                self.config.bssVoltageKeyword,
            )
            return
 
        hv = exposure.metadata.get(self.config.hvBiasKeyword, None)
        if hv is None:
            self.log.warning(
                "HV bias on %s not found in metadata.",
                self.config.hvBiasKeyword,
            )
            return
 
        if voltage < self.config.bssVoltageMinimum or hv == "OFF":
            detector = exposure.getDetector()
            raise UnprocessableDataError(
                f"Back-side bias voltage is turned off for {detector.getName()}; skipping ISR.",
            )
 
    @deprecated(
        reason=(
            "makeBinnedImages is no longer used. "
            "Please subtask lsst.ip.isr.BinImageDataTask instead."
        ),
        version="v28", category=FutureWarning
    )
    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)
 
        bin1 = afwImage.makeExposure(bin1)
        bin2 = afwImage.makeExposure(bin2)
 
        bin1.setInfo(exposure.getInfo())
        bin2.setInfo(exposure.getInfo())
 
        return bin1, bin2
 
    def run(
        self,
        ccdExposure,
        *,
        dnlLUT=None,
        bias=None,
        deferredChargeCalib=None,
        linearizer=None,
        ptc=None,
        gainCorrection=None,
        crosstalk=None,
        defects=None,
        bfKernel=None,
        dark=None,
        flat=None,
        camera=None,
    ):
        """Run the IsrTaskLSST task.
 
        Parameters
        ----------
        ccdExposure : `lsst.afw.image.Exposure`
            Exposure to run ISR.
        dnlLUT : `None`, optional
            DNL lookup table; placeholder, unused.
        bias : `lsst.afw.image.Exposure`, optional
            Bias frame.
        deferredChargeCalib : `lsst.ip.isr.DeferredChargeCalib`, optional
            Deferred charge calibration.
        linearizer : `lsst.ip.isr.Linearizer`, optional
            Linearizer calibration.
        ptc : `lsst.ip.isr.PhotonTransferCurveDataset`, optional
            PTC dataset.
        gainCorrection : `lsst.ip.isr.GainCorrection`, optional
            Gain correction dataset.
        crosstalk : `lsst.ip.isr.CrosstalkCalib`, optional
            Crosstalk calibration dataset.
        defects : `lsst.ip.isr.Defects`, optional
            Defects dataset.
        bfKernel : `lsst.ip.isr.BrighterFatterKernel`, optional
            Brighter-fatter kernel dataset.
        dark : `lsst.afw.image.Exposure`, optional
            Dark frame.
        flat : `lsst.afw.image.Exposure`, optional
            Flat-field frame.
        camera : `lsst.afw.cameraGeom.Camera`, optional
            Camera object.
 
        Returns
        -------
        result : `lsst.pipe.base.Struct`
            Struct with fields:
                ``exposure``: `lsst.afw.image.Exposure`
                    Calibrated exposure.
                ``outputBin1Exposure``: `lsst.afw.image.Exposure`
                    Binned exposure (bin1 config).
                ``outputBin2Exposure``: `lsst.afw.image.Exposure`
                    Binned exposure (bin2 config).
                ``outputExposure``: `lsst.afw.image.Exposure`
                    Calibrated exposure (same as ``exposure``).
                ``outputStatistics``: `lsst.ip.isr.isrStatistics`
                    Calibrated exposure statistics.
        """
        detector = ccdExposure.getDetector()
 
        overscanDetectorConfig = self.config.overscanCamera.getOverscanDetectorConfig(detector)
 
        if self.config.doBootstrap:
            if ptc is not None:
                self.log.warning("Task configured with doBootstrap=True. Ignoring provided PTC.")
                ptc = None
        else:
            if self.config.useGainsFrom == "LINEARIZER":
                if linearizer is None:
                    raise RuntimeError("doBootstrap==False and useGainsFrom == 'LINEARIZER' but "
                                       "no linearizer provided.")
            elif self.config.useGainsFrom == "PTC":
                if ptc is None:
                    raise RuntimeError("doBootstrap==False and useGainsFrom == 'PTC' but no PTC provided.")
 
        # Validation step: check inputs match exposure configuration.
        exposureMetadata = ccdExposure.metadata
        doRaise = self.config.doRaiseOnCalibMismatch
        keywords = self.config.cameraKeywordsToCompare
        if not self.config.doBootstrap:
            if self.config.useGainsFrom == "LINEARIZER":
                compareCameraKeywords(doRaise, keywords, exposureMetadata, linearizer,
                                      "LINEARIZER", log=self.log)
            elif self.config.useGainsFrom == "PTC":
                compareCameraKeywords(doRaise, keywords, exposureMetadata, ptc, "PTC",
                                      log=self.log)
            # Note that doCorrectGains can be True without a gainCorrection.
            if self.config.doCorrectGains and gainCorrection is not None:
                compareCameraKeywords(
                    doRaise,
                    keywords,
                    exposureMetadata,
                    gainCorrection,
                    "gain_correction",
                    log=self.log,
                )
        else:
            if self.config.doCorrectGains:
                raise RuntimeError("doCorrectGains is True but no ptc provided.")
        if self.config.doDiffNonLinearCorrection:
            if dnlLUT is None:
                raise RuntimeError("doDiffNonLinearCorrection is True but no dnlLUT provided.")
            compareCameraKeywords(doRaise, keywords, exposureMetadata, dnlLUT, "dnlLUT", log=self.log)
        if self.config.doLinearize:
            if linearizer is None:
                raise RuntimeError("doLinearize is True but no linearizer provided.")
            compareCameraKeywords(doRaise, keywords, exposureMetadata, linearizer, "linearizer", log=self.log)
        if self.config.doBias:
            if bias is None:
                raise RuntimeError("doBias is True but no bias provided.")
            compareCameraKeywords(doRaise, keywords, exposureMetadata, bias, "bias", log=self.log)
            self.compareUnits(bias.metadata, "bias")
        if self.config.doCrosstalk:
            if crosstalk is None:
                raise RuntimeError("doCrosstalk is True but no crosstalk provided.")
            compareCameraKeywords(doRaise, keywords, exposureMetadata, crosstalk, "crosstalk", log=self.log)
        if self.config.doDeferredCharge:
            if deferredChargeCalib is None:
                raise RuntimeError("doDeferredCharge is True but no deferredChargeCalib provided.")
            compareCameraKeywords(
                doRaise,
                keywords,
                exposureMetadata,
                deferredChargeCalib,
                "CTI",
                log=self.log,
            )
        if self.config.doDefect:
            if defects is None:
                raise RuntimeError("doDefect is True but no defects provided.")
            compareCameraKeywords(doRaise, keywords, exposureMetadata, defects, "defects", log=self.log)
        if self.config.doDark:
            if dark is None:
                raise RuntimeError("doDark is True but no dark frame provided.")
            compareCameraKeywords(doRaise, keywords, exposureMetadata, dark, "dark", log=self.log)
            self.compareUnits(bias.metadata, "dark")
        if self.config.doBrighterFatter:
            if bfKernel is None:
                raise RuntimeError("doBrighterFatter is True not no bfKernel provided.")
            compareCameraKeywords(doRaise, keywords, exposureMetadata, bfKernel, "bf", log=self.log)
        if self.config.doFlat:
            if flat is None:
                raise RuntimeError("doFlat is True but no flat provided.")
            compareCameraKeywords(doRaise, keywords, exposureMetadata, flat, "flat", log=self.log)
 
        if self.config.doSaturation:
            if self.config.defaultSaturationSource in ["PTCTURNOFF",]:
                if ptc is None:
                    raise RuntimeError(
                        "doSaturation is True and defaultSaturationSource is "
                        f"{self.config.defaultSaturationSource}, but no ptc provided."
                    )
        if self.config.doSuspect:
            if self.config.defaultSuspectSource in ["PTCTURNOFF",]:
                if ptc is None:
                    raise RuntimeError(
                        "doSuspect is True and defaultSuspectSource is "
                        f"{self.config.defaultSuspectSource}, but no ptc provided."
                    )
 
        if self.config.doCheckUnprocessableData and self.config.bssVoltageMinimum > 0.0:
            self.checkBssVoltage(ccdExposure)
 
        # FIXME: Make sure that if linearity is done then it is matched
        # with the right PTC.
 
        # We keep track of units: start in adu.
        exposureMetadata["LSST ISR UNITS"] = "adu"
        exposureMetadata["LSST ISR GAINCORRECTION APPLIED"] = False
        exposureMetadata["LSST ISR CROSSTALK APPLIED"] = False
        exposureMetadata["LSST ISR OVERSCANLEVEL CHECKED"] = False
        exposureMetadata["LSST ISR NOISE CHECKED"] = False
        exposureMetadata["LSST ISR LINEARIZER APPLIED"] = False
        exposureMetadata["LSST ISR CTI APPLIED"] = False
        exposureMetadata["LSST ISR BIAS APPLIED"] = False
        exposureMetadata["LSST ISR DARK APPLIED"] = False
        exposureMetadata["LSST ISR BF APPLIED"] = False
        exposureMetadata["LSST ISR FLAT APPLIED"] = False
        exposureMetadata["LSST ISR DEFECTS APPLIED"] = False
 
        if self.config.doBootstrap:
            self.log.info("Configured using doBootstrap=True; using gain of 1.0 (adu units)")
            ptc = PhotonTransferCurveDataset([amp.getName() for amp in detector], "NOMINAL_PTC", 1)
            for amp in detector:
                ptc.gain[amp.getName()] = 1.0
                ptc.noise[amp.getName()] = 0.0
        elif self.config.useGainsFrom == "LINEARIZER":
            self.log.info("Using gains from linearizer.")
            # Create a dummy ptc object to hold the gains from the linearizer.
            ptc = PhotonTransferCurveDataset([amp.getName() for amp in detector], "NOMINAL_PTC", 1)
            for amp in detector:
                ptc.gain[amp.getName()] = linearizer.inputGain[amp.getName()]
                ptc.noise[amp.getName()] = 0.0
 
        exposureMetadata["LSST ISR BOOTSTRAP"] = self.config.doBootstrap
 
        # Choose the gains to use
        gains = ptc.gain
 
        # And check if we have configured gains to override. This is
        # also a warning, since it should not be typical usage.
        for amp in detector:
            if not math.isnan(gain := overscanDetectorConfig.getOverscanAmpConfig(amp).gain):
                gains[amp.getName()] = gain
                self.log.warning(
                    "Overriding gain for amp %s with configured value of %.3f.",
                    amp.getName(),
                    gain,
                )
 
        # First we convert the exposure to floating point values
        # (if necessary).
        self.log.debug("Converting exposure to floating point values.")
        ccdExposure = self.convertIntToFloat(ccdExposure)
 
        # Then we mark which amplifiers are completely bad from defects.
        badAmpDict = self.maskFullAmplifiers(ccdExposure, detector, defects, gains=gains)
 
        self.checkAllBadAmps(badAmpDict, detector)
 
        # Now we go through ISR steps.
 
        # Differential non-linearity correction.
        # Units: adu
        if self.config.doDiffNonLinearCorrection:
            self.diffNonLinearCorrection(ccdExposure, dnlLUT)
 
        # Dither the integer counts.
        # Input units: integerized adu
        # Output units: floating-point adu
        self.ditherCounts(ccdExposure, overscanDetectorConfig)
 
        # Serial overscan correction.
        # Input units: adu
        # Output units: adu
        if overscanDetectorConfig.doAnySerialOverscan:
            serialOverscans = self.overscanCorrection(
                "SERIAL",
                overscanDetectorConfig,
                detector,
                badAmpDict,
                ccdExposure,
            )
 
            if self.config.doBootstrap or self.config.useGainsFrom == "LINEARIZER":
                # Get the empirical read noise
                for amp, serialOverscan in zip(detector, serialOverscans):
                    if serialOverscan is None:
                        ptc.noise[amp.getName()] = 0.0
                    else:
                        # All PhotonTransferCurveDataset objects should contain
                        # noise attributes in units of electrons. The read
                        # noise measured from overscans is always in adu, so we
                        # scale it by the gain.
                        # Note that in bootstrap mode, these gains will always
                        # be 1.0, but we put this conversion here for clarity.
                        ptc.noise[amp.getName()] = serialOverscan.residualSigma * gains[amp.getName()]
        else:
            serialOverscans = [None]*len(detector)
 
        # After serial overscan correction, we can mask SATURATED and
        # SUSPECT pixels. This updates badAmpDict if any amplifier
        # is fully saturated after serial overscan correction.
 
        # The saturation is currently assumed to be recorded in
        # overscan-corrected adu.
        badAmpDict = self.maskSaturatedPixels(
            badAmpDict,
            ccdExposure,
            detector,
            overscanDetectorConfig,
            ptc=ptc,
        )
 
        self.checkAllBadAmps(badAmpDict, detector)
 
        if self.config.doCorrectGains and gainCorrection is not None:
            self.log.info("Correcting gains based on input GainCorrection.")
            gainCorrection.correctGains(gains, exposure=ccdExposure)
            exposureMetadata["LSST ISR GAINCORRECTION APPLIED"] = True
        elif self.config.doCorrectGains:
            self.log.info("Skipping gain correction because no GainCorrection available.")
 
        # Do gain normalization.
        # Input units: adu
        # Output units: electron
        if self.config.doApplyGains:
            self.log.info("Using gain values to convert from adu to electron units.")
            isrFunctions.applyGains(ccdExposure, normalizeGains=False, ptcGains=gains, isTrimmed=False)
            # The units are now electron.
            exposureMetadata["LSST ISR UNITS"] = "electron"
 
            # Update the saturation units in the metadata if there.
            # These will always have the same units as the image.
            for amp in detector:
                ampName = amp.getName()
                if (key := f"LSST ISR SATURATION LEVEL {ampName}") in exposureMetadata:
                    exposureMetadata[key] *= gains[ampName]
                if (key := f"LSST ISR SUSPECT LEVEL {ampName}") in exposureMetadata:
                    exposureMetadata[key] *= gains[ampName]
 
        # Record gain and read noise in header.
        metadata = ccdExposure.metadata
        metadata["LSST ISR READNOISE UNITS"] = "electron"
        metadata["LSST ISR GAIN SOURCE"] = self.config.useGainsFrom
        for amp in detector:
            # This includes any gain correction (if applied).
            metadata[f"LSST ISR GAIN {amp.getName()}"] = gains[amp.getName()]
 
            # At this stage, the read noise is always in electrons.
            noise = ptc.noise[amp.getName()]
            metadata[f"LSST ISR READNOISE {amp.getName()}"] = noise
 
        # Do crosstalk correction in the full region.
        # Output units: electron (adu if doBootstrap=True)
        if self.config.doCrosstalk:
            self.log.info("Applying crosstalk corrections to full amplifier region.")
            if self.config.doBootstrap and numpy.any(crosstalk.fitGains != 0):
                crosstalkGains = None
            else:
                crosstalkGains = gains
            self.crosstalk.run(
                ccdExposure,
                crosstalk=crosstalk,
                gains=crosstalkGains,
                fullAmplifier=True,
                badAmpDict=badAmpDict,
                ignoreVariance=True,
            )
            ccdExposure.metadata["LSST ISR CROSSTALK APPLIED"] = True
 
        # After crosstalk, we check for amplifier noise and state changes.
        if numpy.isfinite(self.config.serialOverscanMedianShiftSigmaThreshold):
            badAmpDict = self.checkAmpOverscanLevel(badAmpDict, ccdExposure, ptc)
            ccdExposure.metadata["LSST ISR OVERSCANLEVEL CHECKED"] = True
 
        if numpy.isfinite(self.config.ampNoiseThreshold):
            badAmpDict = self.checkAmpNoise(badAmpDict, ccdExposure, ptc)
            ccdExposure.metadata["LSST ISR NOISE CHECKED"] = True
 
        if numpy.isfinite(self.config.serialOverscanMedianShiftSigmaThreshold) or \
           numpy.isfinite(self.config.ampNoiseThreshold):
            self.checkAllBadAmps(badAmpDict, detector)
 
        # Parallel overscan correction.
        # Output units: electron (adu if doBootstrap=True)
        parallelOverscans = None
        if overscanDetectorConfig.doAnyParallelOverscan:
            # At the moment we do not use the return values from this task.
            parallelOverscans = self.overscanCorrection(
                "PARALLEL",
                overscanDetectorConfig,
                detector,
                badAmpDict,
                ccdExposure,
            )
 
        # Linearity correction
        # Output units: electron (adu if doBootstrap=True)
        if self.config.doLinearize:
            self.log.info("Applying linearizer.")
            # The linearizer is in units of adu.
            # If our units are electron, then pass in the gains
            # for conversion.
            if exposureMetadata["LSST ISR UNITS"] == "electron":
                linearityGains = gains
            else:
                linearityGains = None
            linearizer.applyLinearity(
                image=ccdExposure.image,
                detector=detector,
                log=self.log,
                gains=linearityGains,
            )
            ccdExposure.metadata["LSST ISR LINEARIZER APPLIED"] = True
 
        # Serial CTI (deferred charge) correction
        # This will be performed in electron units
        # Output units: same as input units
        if self.config.doDeferredCharge:
            if self.config.doBootstrap:
                self.log.info("Applying deferred charge correction with doBootstrap=True: "
                              "will need to use deferredChargeCalib.inputGain to apply "
                              "CTI correction in electron units.")
                deferredChargeGains = deferredChargeCalib.inputGain
                if numpy.all(numpy.isnan(list(deferredChargeGains.values()))):
                    self.log.warning("All gains contained in the deferredChargeCalib are "
                                     "NaN, approximating with gain of 1.0.")
                    deferredChargeGains = gains
            else:
                deferredChargeGains = gains
            self.deferredChargeCorrection.run(
                ccdExposure,
                deferredChargeCalib,
                gains=deferredChargeGains,
            )
            ccdExposure.metadata["LSST ISR CTI APPLIED"] = True
 
        # Save the untrimmed version for later statistics,
        # which still contains the overscan information
        untrimmedCcdExposure = None
        if self.config.isrStats.doCtiStatistics:
            untrimmedCcdExposure = ccdExposure.clone()
 
        # Assemble/trim
        # Output units: electron (adu if doBootstrap=True)
        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.")
 
        # E2V edge bleed
        if self.config.doE2VEdgeBleedMask and detector.getPhysicalType() == "E2V":
            isrFunctions.maskE2VEdgeBleed(
                exposure=ccdExposure,
                e2vEdgeBleedSatMinArea=self.config.e2vEdgeBleedSatMinArea,
                e2vEdgeBleedSatMaxArea=self.config.e2vEdgeBleedSatMaxArea,
                e2vEdgeBleedYMax=self.config.e2vEdgeBleedYMax,
                saturatedMaskName=self.config.saturatedMaskName,
                log=self.log,
            )
 
        # ITL Dip Masking
        for maskPlane in self.config.itlDipMaskPlanes:
            if maskPlane not in ccdExposure.mask.getMaskPlaneDict():
                self.log.info("Adding %s mask plane to image.", maskPlane)
                ccdExposure.mask.addMaskPlane(maskPlane)
 
        if self.config.doITLDipMask:
            isrFunctions.maskITLDip(
                exposure=ccdExposure,
                detectorConfig=overscanDetectorConfig,
                log=self.log,
                maskPlaneNames=self.config.itlDipMaskPlanes,
            )
 
        if (self.config.doITLSatSagMask or self.config.doITLEdgeBleedMask) \
                and detector.getPhysicalType() == 'ITL':
            self.maskITLSatEdgesAndColumns(exposure=ccdExposure,
                                           badAmpDict=badAmpDict)
 
        # Bias subtraction
        # Output units: electron (adu if doBootstrap=True)
        if self.config.doBias:
            self.log.info("Applying bias correction.")
            # Bias frame and ISR unit consistency is checked at the top of
            # the run method.
            isrFunctions.biasCorrection(ccdExposure.maskedImage, bias.maskedImage)
            ccdExposure.metadata["LSST ISR BIAS APPLIED"] = True
 
        # Dark subtraction
        # Output units: electron (adu if doBootstrap=True)
        if self.config.doDark:
            self.log.info("Applying dark subtraction.")
            # Dark frame and ISR unit consistency is checked at the top of
            # the run method.
            self.darkCorrection(ccdExposure, dark)
            ccdExposure.metadata["LSST ISR DARK APPLIED"] = True
 
        # Defect masking
        # Masking block (defects, NAN pixels and trails).
        # Saturated and suspect pixels have already been masked.
        # Output units: electron (adu if doBootstrap=True)
        if self.config.doDefect:
            self.log.info("Applying defect masking.")
            self.maskDefects(ccdExposure, defects)
            ccdExposure.metadata["LSST ISR DEFECTS APPLIED"] = True
 
        self.log.info("Adding UNMASKEDNAN mask plane to image.")
        ccdExposure.mask.addMaskPlane("UNMASKEDNAN")
        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())
 
        # Brighter-Fatter
        # Output units: electron (adu if doBootstrap=True)
        if self.config.doBrighterFatter:
            self.log.info("Applying brighter-fatter correction.")
 
            bfKernelOut, bfGains = self.getBrighterFatterKernel(detector, bfKernel)
 
            # Needs to be done in electrons; applyBrighterFatterCorrection
            # will convert the image if necessary.
            if exposureMetadata["LSST ISR UNITS"] == "electron":
                brighterFatterApplyGain = False
            else:
                brighterFatterApplyGain = True
 
            if brighterFatterApplyGain and (ptc is not None) and (bfGains != gains):
                # The supplied ptc should be the same as the ptc used to
                # generate the bfKernel, in which case they will have the
                # same stored amp-keyed dictionary of gains. If not, there
                # is a mismatch in the calibrations being used. This should
                # not be always be a fatal error, but ideally, everything
                # should to be consistent.
                self.log.warning("Need to apply gain for brighter-fatter, but the stored"
                                 "gains in the kernel are not the same as the gains used"
                                 f"by {self.config.useGainsFrom}. Using the gains stored"
                                 "in the kernel.")
 
            ccdExposure, bfCorrIters = self.applyBrighterFatterCorrection(
                ccdExposure,
                flat,
                dark,
                bfKernelOut,
                brighterFatterApplyGain,
                bfGains,
            )
 
            ccdExposure.metadata["LSST ISR BF APPLIED"] = True
            metadata["LSST ISR BF ITERS"] = bfCorrIters
 
        # Variance plane creation
        # Output units: electron (adu if doBootstrap=True)
        if self.config.doVariance:
            self.addVariancePlane(ccdExposure, detector)
 
        # Flat-fielding
        # This may move elsewhere, but this is the most convenient
        # location for simple flat-fielding for attractive backgrounds.
        # Output units: electron (adu if doBootstrap=True)
        if self.config.doFlat:
            self.log.info("Applying flat correction.")
            isrFunctions.flatCorrection(
                maskedImage=ccdExposure.maskedImage,
                flatMaskedImage=flat.maskedImage,
                scalingType=self.config.flatScalingType,
                userScale=self.config.flatUserScale,
            )
 
            # Copy over valid polygon from flat if it is
            # available, and set NO_DATA to 0.0 (which may
            # be inherited from the flat in the fully
            # vignetted region).
            if (validPolygon := flat.info.getValidPolygon()) is not None:
                ccdExposure.info.setValidPolygon(validPolygon)
 
                noData = (ccdExposure.mask.array & ccdExposure.mask.getPlaneBitMask("NO_DATA")) > 0
                ccdExposure.image.array[noData] = 0.0
                ccdExposure.variance.array[noData] = 0.0
 
            ccdExposure.metadata["LSST ISR FLAT APPLIED"] = True
            ccdExposure.metadata["LSST ISR FLAT SOURCE"] = flat.metadata.get("FLATSRC", "UNKNOWN")
 
        # Pixel values for masked regions are set here
        preInterpExp = None
        if self.config.doSaveInterpPixels:
            preInterpExp = ccdExposure.clone()
 
        if self.config.doSetBadRegions:
            self.log.info('Setting values in large contiguous bad regions.')
            self.setBadRegions(ccdExposure)
 
        if self.config.doInterpolate:
            self.log.info("Interpolating masked pixels.")
            isrFunctions.interpolateFromMask(
                maskedImage=ccdExposure.getMaskedImage(),
                fwhm=self.config.brighterFatterFwhmForInterpolation,
                growSaturatedFootprints=self.config.growSaturationFootprintSize,
                maskNameList=list(self.config.maskListToInterpolate),
                useLegacyInterp=self.config.useLegacyInterp,
            )
 
        # Calculate amp offset corrections within the CCD.
        if self.config.doAmpOffset:
            if self.config.ampOffset.doApplyAmpOffset:
                self.log.info("Measuring and applying amp offset corrections.")
            else:
                self.log.info("Measuring amp offset corrections only, without applying them.")
            self.ampOffset.run(ccdExposure)
 
        # Calculate standard image quality statistics
        if self.config.doStandardStatistics:
            metadata = ccdExposure.metadata
            for amp in detector:
                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"]
                )
                metadata[f"LSST ISR MASK SUSPECT {ampName}"] = isrFunctions.countMaskedPixels(
                    ampExposure.getMaskedImage(),
                    ["SUSPECT"],
                )
                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 overscanDetectorConfig.doAnySerialOverscan 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,
                                                 untrimmedInputExposure=untrimmedCcdExposure,
                                                 serialOverscanResults=serialOverscans,
                                                 parallelOverscanResults=parallelOverscans,
                                                 bias=bias, dark=dark, flat=flat,
                                                 ptc=ptc, defects=defects).results
 
        # do image binning.
        outputBin1Exposure = None
        outputBin2Exposure = None
        if self.config.doBinnedExposures:
            self.log.info("Creating binned exposures.")
            outputBin1Exposure = self.binning.run(
                ccdExposure,
                binFactor=self.config.binFactor1,
            ).outputData
            outputBin2Exposure = self.binning.run(
                ccdExposure,
                binFactor=self.config.binFactor2,
            ).outputData
 
        return pipeBase.Struct(
            exposure=ccdExposure,
 
            outputBin1Exposure=outputBin1Exposure,
            outputBin2Exposure=outputBin2Exposure,
 
            preInterpExposure=preInterpExp,
            outputExposure=ccdExposure,
            outputStatistics=outputStatistics,
        )