detectAndMeasure.py

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# This file is part of ip_diffim.
#
# Developed for the LSST Data Management System.
# This product includes software developed by the LSST Project
# (https://www.lsst.org).
# See the COPYRIGHT file at the top-level directory of this distribution
# for details of code ownership.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <https://www.gnu.org/licenses/>.
 
import dataclasses
import numpy as np
import requests
import os
 
 
import lsst.afw.detection as afwDetection
import lsst.afw.image as afwImage
import lsst.afw.math as afwMath
import lsst.afw.table as afwTable
import lsst.daf.base as dafBase
import lsst.geom
from lsst.images import Mask, get_legacy_difference_image_mask_planes
from lsst.images.fields import field_from_legacy_background
from lsst.ip.diffim.utils import (evaluateMaskFraction, computeDifferenceImageMetrics,
                                  populate_sattle_visit_cache,
                                  get_difference_image_provenance)
from lsst.meas.algorithms import SkyObjectsTask, SourceDetectionTask, SetPrimaryFlagsTask, MaskStreaksTask
from lsst.meas.algorithms import FindGlintTrailsTask, FindCosmicRaysConfig, findCosmicRays
from lsst.meas.base import ForcedMeasurementTask, ApplyApCorrTask, DetectorVisitIdGeneratorConfig
import lsst.meas.deblender
import lsst.meas.extensions.trailedSources  # noqa: F401
import lsst.meas.extensions.shapeHSM
import lsst.pex.config as pexConfig
from lsst.pex.exceptions import InvalidParameterError, LengthError
import lsst.pipe.base as pipeBase
import lsst.utils
from lsst.utils.timer import timeMethod
 
from . import DipoleFitTask
 
__all__ = ["DetectAndMeasureConfig", "DetectAndMeasureTask",
           "DetectAndMeasureScoreConfig", "DetectAndMeasureScoreTask"]
 
_TEMPLATE_EPOCH_NAMES = {
    "TEMPLATE MJD MIN": "templateMjdMin",
    "TEMPLATE MJD MAX": "templateMjdMax",
    "TEMPLATE MJD MEAN": "templateMjdMean",
}
"""Image metadata key of each summary of the template epochs of the
diaSources, mapped to the name of the diaSource field and task metadata
entry it summarizes.
"""
 
 
class BadSubtractionError(pipeBase.AlgorithmError):
    """Raised when the residuals in footprints of stars used to compute the
    psf-matching kernel exceeds the configured maximum.
    """
    def __init__(self, *, ratio, threshold):
        msg = ("The ratio of residual power in source footprints on the"
               " difference image to the power in the footprints on the"
               f" science image was {ratio}, which exceeds the maximum"
               f" threshold of {threshold}")
        super().__init__(msg)
        self.ratio = ratio
        self.threshold = threshold
 
    @property
    def metadata(self):
        return {"ratio": self.ratio,
                "threshold": self.threshold
                }
 
 
class NoDiaSourcesError(pipeBase.AlgorithmError):
    """Raised when there are no diaSources detected on an image difference.
    """
    def __init__(self):
        msg = ("No diaSources detected!")
        super().__init__(msg)
 
    @property
    def metadata(self):
        return {}
 
 
class TooManyCosmicRays(pipeBase.AlgorithmError):
    """Raised if the cosmic ray task fails with too many cosmics.
 
    Parameters
    ----------
    maxCosmicRays : `int`
        Maximum number of cosmic rays allowed.
    """
    def __init__(self, maxCosmicRays, **kwargs):
        msg = f"Cosmic ray task found more than {maxCosmicRays} cosmics."
        self.msg = msg
        self._metadata = kwargs
        super().__init__(msg, **kwargs)
        self._metadata["maxCosmicRays"] = maxCosmicRays
 
    def __str__(self):
        # Exception doesn't handle **kwargs, so we need a custom str.
        return f"{self.msg}: {self.metadata}"
 
    @property
    def metadata(self):
        for key, value in self._metadata.items():
            if not (isinstance(value, int) or isinstance(value, float) or isinstance(value, str)):
                raise TypeError(f"{key} is of type {type(value)}, but only (int, float, str) are allowed.")
        return self._metadata
 
 
class DetectAndMeasureConnections(pipeBase.PipelineTaskConnections,
                                  dimensions=("instrument", "visit", "detector"),
                                  defaultTemplates={"coaddName": "deep",
                                                    "warpTypeSuffix": "",
                                                    "fakesType": ""}):
    science = pipeBase.connectionTypes.Input(
        doc="Input science exposure.",
        dimensions=("instrument", "visit", "detector"),
        storageClass="ExposureF",
        name="{fakesType}calexp"
    )
    matchedTemplate = pipeBase.connectionTypes.Input(
        doc="Warped and PSF-matched template used to create the difference image.",
        dimensions=("instrument", "visit", "detector"),
        storageClass="ExposureF",
        name="{fakesType}{coaddName}Diff_matchedExp",
    )
    difference = pipeBase.connectionTypes.Input(
        doc="Result of subtracting template from science.",
        dimensions=("instrument", "visit", "detector"),
        storageClass="ExposureF",
        name="{fakesType}{coaddName}Diff_differenceTempExp",
    )
    kernelSources = pipeBase.connectionTypes.Input(
        doc="Final selection of sources used for psf matching.",
        dimensions=("instrument", "visit", "detector"),
        storageClass="SourceCatalog",
        name="{fakesType}{coaddName}Diff_psfMatchSources"
    )
    outputSchema = pipeBase.connectionTypes.InitOutput(
        doc="Schema (as an example catalog) for output DIASource catalog.",
        storageClass="SourceCatalog",
        name="{fakesType}{coaddName}Diff_diaSrc_schema",
    )
    diaSources = pipeBase.connectionTypes.Output(
        doc="Detected diaSources on the difference image.",
        dimensions=("instrument", "visit", "detector"),
        storageClass="SourceCatalog",
        name="{fakesType}{coaddName}Diff_diaSrc",
    )
    subtractedMeasuredExposure = pipeBase.connectionTypes.Output(
        doc="Difference image with detection mask plane filled in.",
        dimensions=("instrument", "visit", "detector"),
        storageClass="ExposureF",
        name="{fakesType}{coaddName}Diff_differenceExp",
    )
    differenceBackground = pipeBase.connectionTypes.Output(
        doc="Background model that was subtracted from the difference image.",
        dimensions=("instrument", "visit", "detector"),
        storageClass="Background",
        name="difference_background",
    )
    maskedStreaks = pipeBase.connectionTypes.Output(
        doc='Catalog of streak fit parameters for the difference image.',
        storageClass="ArrowNumpyDict",
        dimensions=("instrument", "visit", "detector"),
        name="{fakesType}{coaddName}Diff_streaks",
    )
    glintTrailInfo = pipeBase.connectionTypes.Output(
        doc='Dict of fit parameters for glint trails in the catalog.',
        storageClass="ArrowNumpyDict",
        dimensions=("instrument", "visit", "detector"),
        name="trailed_glints",
    )
 
    def __init__(self, *, config):
        super().__init__(config=config)
        if not (self.config.doCalculateResidualMetics):
            self.inputs.remove("kernelSources")
        if not (self.config.writeStreakInfo and self.config.doMaskStreaks):
            self.outputs.remove("maskedStreaks")
        # In future mode the background is recorded on the output images.
        if (self.config.image_type == "future"
                or not (self.config.doSubtractBackground and self.config.doWriteBackground)):
            self.outputs.remove("differenceBackground")
        if not (self.config.writeGlintInfo):
            self.outputs.remove("glintTrailInfo")
        if self.config.image_type == "future":
            self.difference = dataclasses.replace(self.difference, storageClass="DifferenceImage")
            self.subtractedMeasuredExposure = dataclasses.replace(self.subtractedMeasuredExposure,
                                                                  storageClass="DifferenceImage")
 
 
class DetectAndMeasureConfig(pipeBase.PipelineTaskConfig,
                             pipelineConnections=DetectAndMeasureConnections):
    """Config for DetectAndMeasureTask
    """
    doMerge = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Merge positive and negative diaSources with grow radius "
            "set by growFootprint"
    )
    doForcedMeasurement = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Force photometer diaSource locations on PVI?"
    )
    doAddMetrics = pexConfig.Field(
        dtype=bool,
        default=False,
        doc="Add columns to the source table to hold analysis metrics?"
    )
    doSubtractBackground = pexConfig.Field(
        dtype=bool,
        doc="Subtract a background model from the image before detection?",
        default=True,
    )
    doWriteBackground = pexConfig.Field(
        dtype=bool,
        doc="Persist the fitted background model? Ignored when"
        " image_type='future', which records it on the output images.",
        default=False,
        deprecated="Use image_type='future', which records the background on the output images."
        " Will be removed after v31.",
    )
    doCalculateResidualMetics = pexConfig.Field(
        dtype=bool,
        doc="Calculate metrics to assess image subtraction quality for the task"
        "metadata?",
        default=True,
    )
    subtractInitialBackground = pexConfig.ConfigurableField(
        target=lsst.meas.algorithms.SubtractBackgroundTask,
        doc="Task to perform intial background subtraction, before first detection pass.",
    )
    subtractFinalBackground = pexConfig.ConfigurableField(
        target=lsst.meas.algorithms.SubtractBackgroundTask,
        doc="Task to perform final background subtraction, after first detection pass.",
    )
    doFindCosmicRays = pexConfig.Field(
        dtype=bool,
        doc="Detect and mask cosmic rays on the difference image?"
            "CRs can be interpolated over by setting cosmicray.keepCRs=False",
        default=True,
    )
    cosmicray = pexConfig.ConfigField(
        dtype=FindCosmicRaysConfig,
        doc="Options for finding and masking cosmic rays",
    )
    detection = pexConfig.ConfigurableField(
        target=SourceDetectionTask,
        doc="Final source detection for diaSource measurement",
    )
    streakDetection = pexConfig.ConfigurableField(
        target=SourceDetectionTask,
        doc="Separate source detection used only for streak masking",
    )
    doDeblend = pexConfig.Field(
        dtype=bool,
        default=False,
        doc="Deblend DIASources after detection?"
    )
    deblend = pexConfig.ConfigurableField(
        target=lsst.meas.deblender.SourceDeblendTask,
        doc="Task to split blended sources into their components."
    )
    measurement = pexConfig.ConfigurableField(
        target=DipoleFitTask,
        doc="Task to measure sources on the difference image.",
    )
    doApCorr = lsst.pex.config.Field(
        dtype=bool,
        default=True,
        doc="Run subtask to apply aperture corrections"
    )
    applyApCorr = lsst.pex.config.ConfigurableField(
        target=ApplyApCorrTask,
        doc="Task to apply aperture corrections"
    )
    forcedMeasurement = pexConfig.ConfigurableField(
        target=ForcedMeasurementTask,
        doc="Task to force photometer science image at diaSource locations.",
    )
    growFootprint = pexConfig.Field(
        dtype=int,
        default=2,
        doc="Grow positive and negative footprints by this many pixels before merging"
    )
    diaSourceMatchRadius = pexConfig.Field(
        dtype=float,
        default=0.5,
        doc="Match radius (in arcseconds) for DiaSource to Source association"
    )
    doSkySources = pexConfig.Field(
        dtype=bool,
        default=False,
        doc="Generate sky sources?",
    )
    skySources = pexConfig.ConfigurableField(
        target=SkyObjectsTask,
        doc="Generate sky sources",
    )
    doMaskStreaks = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Turn on streak masking",
    )
    maskStreaks = pexConfig.ConfigurableField(
        target=MaskStreaksTask,
        doc="Subtask for masking streaks. Only used if doMaskStreaks is True. "
            "Adds a mask plane to an exposure, with the mask plane name set by streakMaskName.",
    )
    streakBinFactor = pexConfig.Field(
        dtype=int,
        default=4,
        doc="Bin scale factor to use when rerunning detection for masking streaks. "
            "Only used if doMaskStreaks is True.",
    )
    writeStreakInfo = pexConfig.Field(
        dtype=bool,
        default=False,
        doc="Record the parameters of any detected streaks. For LSST, this should be turned off except for "
            "development work."
    )
    findGlints = pexConfig.ConfigurableField(
        target=FindGlintTrailsTask,
        doc="Subtask for finding glint trails, usually caused by satellites or debris."
    )
    writeGlintInfo = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Record the parameters of any detected glint trails."
    )
    setPrimaryFlags = pexConfig.ConfigurableField(
        target=SetPrimaryFlagsTask,
        doc="Task to add isPrimary and deblending-related flags to the catalog."
    )
    badSourceFlags = lsst.pex.config.ListField(
        dtype=str,
        doc="Sources with any of these flags set are removed before writing the output catalog.",
        default=("base_PixelFlags_flag_offimage",
                 "base_PixelFlags_flag_interpolatedCenterAll",
                 "base_PixelFlags_flag_badCenter",
                 "base_PixelFlags_flag_edgeCenter",
                 "base_PixelFlags_flag_nodataCenter",
                 "base_PixelFlags_flag_saturatedCenter",
                 "base_PixelFlags_flag_sat_templateCenter",
                 "base_PixelFlags_flag_spikeCenter",
                 ),
    )
    clearMaskPlanes = lsst.pex.config.ListField(
        dtype=str,
        doc="Mask planes to clear before running detection.",
        default=("DETECTED", "DETECTED_NEGATIVE", "NOT_DEBLENDED", "STREAK"),
    )
    doRejectBadMaskPlaneDetections = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Reject any peaks detected on ``badMaskPlanes`` before measurement."
            "These should all be rejected downstream by ``badSourceFlags``, "
            "but filtering earlier can save time."
    )
    badMaskPlanes = lsst.pex.config.ListField(
        dtype=str,
        doc="Detections whose footprint peak lies on a pixel with any of these"
            " mask planes set will be rejected before measurement."
            " Any missing mask planes will be silently ignored.",
        default=("NO_DATA", "BAD", "SAT", "EDGE"),
    )
    raiseOnBadSubtractionRatio = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Raise an error if the ratio of power in detected footprints"
            " on the difference image to the power in footprints on the science"
            " image exceeds ``badSubtractionRatioThreshold``",
    )
    badSubtractionRatioThreshold = pexConfig.Field(
        dtype=float,
        default=0.2,
        doc="Maximum ratio of power in footprints on the difference image to"
            " the same footprints on the science image."
            "Only used if ``raiseOnBadSubtractionRatio`` is set",
    )
    badSubtractionVariationThreshold = pexConfig.Field(
        dtype=float,
        default=0.4,
        doc="Maximum standard deviation of the ratio of power in footprints on"
            " the difference image to the same footprints on the science image."
            "Only used if ``raiseOnBadSubtractionRatio`` is set",
    )
    raiseOnNoDiaSources = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Raise an algorithm error if no diaSources are detected.",
    )
    run_sattle = pexConfig.Field(
        dtype=bool,
        default=False,
        doc="If true, dia source bounding boxes will be sent for verification"
            "to the sattle service."
    )
    sattle_historical = pexConfig.Field(
        dtype=bool,
        default=False,
        doc="If re-running a pipeline that requires sattle, this should be set "
            "to True. This will populate sattle's cache with the historic data "
            "closest in time to the exposure."
    )
    idGenerator = DetectorVisitIdGeneratorConfig.make_field()
    image_type = pexConfig.ChoiceField[str](
        "Image type of the input and measured difference images (and, for"
        " DetectAndMeasureScoreTask, the input and measured score images).",
        allowed={
            "legacy": "Read and write lsst.afw.image.ExposureF.",
            "future": "Read and write lsst.images.DifferenceImage.",
        },
        optional=False,
        default="legacy",
    )
 
    def setDefaults(self):
        # Background subtraction
        # Use a small binsize for the first pass to reduce detections on glints
        #  and extended structures. Should not affect the detectability of
        #  faint diaSources
        self.subtractInitialBackground.binSize = 8
        self.subtractInitialBackground.useApprox = False
        self.subtractInitialBackground.statisticsProperty = "MEDIAN"
        self.subtractInitialBackground.doFilterSuperPixels = True
        self.subtractInitialBackground.ignoredPixelMask = ["BAD",
                                                           "EDGE",
                                                           "DETECTED",
                                                           "DETECTED_NEGATIVE",
                                                           "NO_DATA",
                                                           ]
        # Use a larger binsize for the final background subtraction, to reduce
        #  over-subtraction of bright objects.
        self.subtractFinalBackground.binSize = 40
        self.subtractFinalBackground.useApprox = False
        self.subtractFinalBackground.statisticsProperty = "MEDIAN"
        self.subtractFinalBackground.doFilterSuperPixels = True
        self.subtractFinalBackground.ignoredPixelMask = ["BAD",
                                                         "EDGE",
                                                         "DETECTED",
                                                         "DETECTED_NEGATIVE",
                                                         "NO_DATA",
                                                         ]
        # Cosmic ray detection
        self.cosmicray.keepCRs = True  # do not interpolate over detected CRs
        # DiaSource Detection
        self.detection.thresholdPolarity = "both"
        self.detection.thresholdValue = 5.0
        self.detection.reEstimateBackground = False
        self.detection.thresholdType = "pixel_stdev"
        self.detection.excludeMaskPlanes = []
 
        # Copy configs for binned streak detection from the base detection task
        self.streakDetection.thresholdType = self.detection.thresholdType
        self.streakDetection.reEstimateBackground = False
        self.streakDetection.excludeMaskPlanes = self.detection.excludeMaskPlanes
        self.streakDetection.thresholdValue = self.detection.thresholdValue
        # Only detect positive streaks
        self.streakDetection.thresholdPolarity = "positive"
        # Do not grow detected mask for streaks
        self.streakDetection.nSigmaToGrow = 0
        # Set the streak mask along the entire fit line, not only where the
        # detected mask is set.
        self.maskStreaks.onlyMaskDetected = False
        # Restrict streak masking from growing too large
        self.maskStreaks.maxStreakWidth = 100
        # Restrict the number of iterations allowed for fitting streaks
        # When the fit is good it should solve quickly, and exit a bad fit quickly
        self.maskStreaks.maxFitIter = 10
        # Only mask to 2 sigma in width
        self.maskStreaks.nSigmaMask = 2
        # Threshold for including streaks after the Hough Transform.
        # A lower value will detect more features that are less linear.
        self.maskStreaks.absMinimumKernelHeight = 2
 
        self.measurement.plugins.names |= ["ext_trailedSources_Naive",
                                           "base_LocalPhotoCalib",
                                           "base_LocalWcs",
                                           "ext_shapeHSM_HsmSourceMoments",
                                           "ext_shapeHSM_HsmPsfMoments",
                                           "base_ClassificationSizeExtendedness",
                                           ]
        self.measurement.slots.psfShape = "ext_shapeHSM_HsmPsfMoments"
        self.measurement.slots.shape = "ext_shapeHSM_HsmSourceMoments"
        self.measurement.plugins["base_SdssCentroid"].maxDistToPeak = 5.0
        self.forcedMeasurement.plugins = ["base_TransformedCentroid", "base_PsfFlux"]
        self.forcedMeasurement.copyColumns = {
            "id": "objectId", "parent": "parentObjectId", "coord_ra": "coord_ra", "coord_dec": "coord_dec"}
        self.forcedMeasurement.slots.centroid = "base_TransformedCentroid"
        self.forcedMeasurement.slots.shape = None
 
        # Keep track of which footprints contain streaks
        self.measurement.plugins["base_PixelFlags"].masksFpAnywhere = [
            "STREAK", "INJECTED", "INJECTED_TEMPLATE", "HIGH_VARIANCE", "SAT_TEMPLATE", "SPIKE"]
        self.measurement.plugins["base_PixelFlags"].masksFpCenter = [
            "STREAK", "INJECTED", "INJECTED_TEMPLATE", "HIGH_VARIANCE", "SAT_TEMPLATE", "SPIKE"]
        self.skySources.avoidMask = ["DETECTED", "DETECTED_NEGATIVE", "BAD", "NO_DATA", "EDGE"]
 
    def validate(self):
        super().validate()
 
        if self.run_sattle:
            if not os.getenv("SATTLE_URI_BASE"):
                raise pexConfig.FieldValidationError(DetectAndMeasureConfig.run_sattle, self,
                                                     "Sattle requested but SATTLE_URI_BASE "
                                                     "environment variable not set.")
 
 
class DetectAndMeasureTask(lsst.pipe.base.PipelineTask):
    """Detect and measure sources on a difference image.
    """
    ConfigClass = DetectAndMeasureConfig
    _DefaultName = "detectAndMeasure"
 
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.schema = afwTable.SourceTable.makeMinimalSchema()
 
        self.algMetadata = dafBase.PropertyList()
        if self.config.doSubtractBackground:
            self.makeSubtask("subtractInitialBackground")
            self.makeSubtask("subtractFinalBackground")
        self.makeSubtask("detection", schema=self.schema)
        if self.config.doDeblend:
            self.makeSubtask("deblend", schema=self.schema)
        self.makeSubtask("setPrimaryFlags", schema=self.schema, isSingleFrame=True)
        self.makeSubtask("measurement", schema=self.schema,
                         algMetadata=self.algMetadata)
        if self.config.doApCorr:
            self.makeSubtask("applyApCorr", schema=self.measurement.schema)
        if self.config.doForcedMeasurement:
            self.schema.addField(
                "ip_diffim_forced_PsfFlux_instFlux", "D",
                "Forced PSF flux measured on the direct image.",
                units="count")
            self.schema.addField(
                "ip_diffim_forced_PsfFlux_instFluxErr", "D",
                "Forced PSF flux error measured on the direct image.",
                units="count")
            self.schema.addField(
                "ip_diffim_forced_PsfFlux_area", "F",
                "Forced PSF flux effective area of PSF.",
                units="pixel")
            self.schema.addField(
                "ip_diffim_forced_PsfFlux_flag", "Flag",
                "Forced PSF flux general failure flag.")
            self.schema.addField(
                "ip_diffim_forced_PsfFlux_flag_noGoodPixels", "Flag",
                "Forced PSF flux not enough non-rejected pixels in data to attempt the fit.")
            self.schema.addField(
                "ip_diffim_forced_PsfFlux_flag_edge", "Flag",
                "Forced PSF flux object was too close to the edge of the image to use the full PSF model.")
            self.schema.addField(
                "ip_diffim_forced_template_PsfFlux_instFlux", "D",
                "Forced PSF flux measured on the template image.",
                units="count")
            self.schema.addField(
                "ip_diffim_forced_template_PsfFlux_instFluxErr", "D",
                "Forced PSF flux error measured on the template image.",
                units="count")
            self.schema.addField(
                "ip_diffim_forced_template_PsfFlux_area", "F",
                "Forced template PSF flux effective area of PSF.",
                units="pixel")
            self.schema.addField(
                "ip_diffim_forced_template_PsfFlux_flag", "Flag",
                "Forced template PSF flux general failure flag.")
            self.schema.addField(
                "ip_diffim_forced_template_PsfFlux_flag_noGoodPixels", "Flag",
                "Forced template PSF flux not enough non-rejected pixels in data to attempt the fit.")
            self.schema.addField(
                "ip_diffim_forced_template_PsfFlux_flag_edge", "Flag",
                """Forced template PSF flux object was too close to the edge of the image """
                """to use the full PSF model.""")
            self.makeSubtask("forcedMeasurement", refSchema=self.schema)
 
        self.schema.addField("refMatchId", "L", "unique id of reference catalog match")
        self.schema.addField("srcMatchId", "L", "unique id of source match")
        # Create the sky source task for use by metrics,
        # even if sky sources are not added to the diaSource catalog
        self.makeSubtask("skySources", schema=self.schema)
        if self.config.doMaskStreaks:
            self.makeSubtask("maskStreaks")
            self.makeSubtask("streakDetection")
        self.makeSubtask("findGlints")
        self.schema.addField("glint_trail", "Flag", "DiaSource is part of a glint trail.")
        self.schema.addField("reliability", type="F", doc="Reliability score of the DiaSource")
        self.schema.addField("reliabilityVersion", type=str, size=7, doc="Version of the reliability model")
        self.schema.addField("templateMjdMean", type="D", units="d",
                             doc="Weighted mean time (MJD, TAI) of the template inputs that"
                                 " cover the diaSource centroid.")
        self.schema.addField("templateMjdMin", type="D", units="d",
                             doc="Earliest time (MJD, TAI) of the template inputs that"
                                 " cover the diaSource centroid.")
        self.schema.addField("templateMjdMax", type="D", units="d",
                             doc="Latest time (MJD, TAI) of the template inputs that"
                                 " cover the diaSource centroid.")
 
        # To get the "merge_*" fields in the schema; have to re-initialize
        # this later, once we have a peak schema post-detection.
        lsst.afw.detection.FootprintMergeList(self.schema, ["positive", "negative"])
 
        # Check that the schema and config are consistent
        for flag in self.config.badSourceFlags:
            if flag not in self.schema:
                raise pipeBase.InvalidQuantumError("Field %s not in schema" % flag)
 
        # initialize InitOutputs
        self.outputSchema = afwTable.SourceCatalog(self.schema)
        self.outputSchema.getTable().setMetadata(self.algMetadata)
 
    def runQuantum(self, butlerQC: pipeBase.QuantumContext,
                   inputRefs: pipeBase.InputQuantizedConnection,
                   outputRefs: pipeBase.OutputQuantizedConnection):
        inputs = butlerQC.get(inputRefs)
        idGenerator = self.config.idGenerator.apply(butlerQC.quantum.dataId)
        idFactory = idGenerator.make_table_id_factory()
 
        # Input image that each output is measured from, keyed by output name.
        inputImages = {}
        templateInfo = None
        if self.config.image_type == "future":
            dataId = butlerQC.quantum.dataId
            for output, connection in (("subtractedMeasuredExposure", "difference"),
                                       ("scoreMeasuredExposure", "scoreExposure")):
                if connection not in inputs:
                    continue
                image = inputs[connection]
                # The output is written back into this image, so it must
                # already carry the kernel and the template records.
                get_difference_image_provenance(image, "kernel", connection, dataId)
                imageTemplates = get_difference_image_provenance(image, "templates", connection, dataId)
                if connection == "difference":
                    templateInfo = imageTemplates
                inputImages[output] = image
                inputs[connection] = image.to_legacy()
 
        # Specify the fields that `annotate` and `convert_outputs_to_future`
        # need below, to ensure they exist, even as None.
        measurementResults = pipeBase.Struct(
            subtractedMeasuredExposure=None,
            scoreMeasuredExposure=None,
            diaSources=None,
            maskedStreaks=None,
            differenceBackground=None,
            templateEpochs=None,
        )
        try:
            self.run(**inputs, idFactory=idFactory, measurementResults=measurementResults,
                     templateInfo=templateInfo)
        except pipeBase.AlgorithmError as e:
            # Annotate before converting
            error = pipeBase.AnnotatedPartialOutputsError.annotate(
                e,
                self,
                measurementResults.subtractedMeasuredExposure,
                measurementResults.scoreMeasuredExposure,
                measurementResults.diaSources,
                measurementResults.maskedStreaks,
                log=self.log
            )
            if self.config.image_type == "future":
                self.convert_outputs_to_future(measurementResults, inputImages)
            butlerQC.put(measurementResults, outputRefs)
            raise error from e
        if self.config.image_type == "future":
            self.convert_outputs_to_future(measurementResults, inputImages)
        butlerQC.put(measurementResults, outputRefs)
 
    def convert_outputs_to_future(self, results, inputImages):
        """Write the output images in a result struct back into the
        `lsst.images` images they were measured from.
 
        The image, variance, and mask planes of each input image are replaced
        by those of the corresponding legacy output, and the background
        subtracted by this task is attached. The kernel, template records,
        and observation metadata of the input are kept. Outputs that are
        absent or `None` are skipped.
 
        Parameters
        ----------
        results : `lsst.pipe.base.Struct`
            Output struct to read and modify in place.
        inputImages : `dict` [`str`, `lsst.images.DifferenceImage`]
            Input image each output was measured from, keyed by the name of
            the output. Each is modified in place.
 
        Notes
        -----
        This uses the default legacy mask plane map of
        `lsst.images.get_legacy_difference_image_mask_planes` and adds any
        planes from `~lsst.images.get_legacy_optional_mask_planes` that are set
        for any pixels in the image.
        """
        background = getattr(results, "differenceBackground", None)
        templateEpochs = getattr(results, "templateEpochs", None) or {}
        for name, image in inputImages.items():
            exposure = getattr(results, name, None)
            if exposure is None:
                continue
            # These are no-ops when the legacy output is a view of the input,
            # as returned by `~lsst.images.DifferenceImage.to_legacy`.
            image.image.array[...] = exposure.image.array
            image.variance.array[...] = exposure.variance.array
            image.mask = Mask.from_legacy(exposure.mask, get_legacy_difference_image_mask_planes(),
                                          sky_projection=image.sky_projection)
            image.metadata.update(templateEpochs)
            if background is not None and len(background) > 0:
                # The same background is subtracted from the difference image
                # and the score image, so it applies to both.
                image.backgrounds.add(
                    "subtracted",
                    field_from_legacy_background(background, bounds=image.bbox, unit=image.unit),
                    description="Background subtracted from this image before detection.",
                    is_subtracted=True,
                )
            setattr(results, name, image)
        if background is not None and len(background) == 0:
            # An empty BackgroundList cannot be converted to a field, and means
            # that no background was subtracted.
            self.log.debug("No background model to attach to the output images.")
 
    @timeMethod
    def run(self, science, matchedTemplate, difference, kernelSources=None,
            idFactory=None, measurementResults=None, templateInfo=None):
        """Detect and measure sources on a difference image.
 
        The difference image will be convolved with a gaussian approximation of
        the PSF to form a maximum likelihood image for detection.
        Close positive and negative detections will optionally be merged into
        dipole diaSources.
        Sky sources, or forced detections in background regions, will optionally
        be added, and the configured measurement algorithm will be run on all
        detections.
 
        Parameters
        ----------
        science : `lsst.afw.image.ExposureF`
            Science exposure that the template was subtracted from.
        matchedTemplate : `lsst.afw.image.ExposureF`
            Warped and PSF-matched template that was used produce the
            difference image.
        difference : `lsst.afw.image.ExposureF`
            Result of subtracting template from the science image.
        kernelSources : `lsst.afw.table.SourceCatalog`, optional
            Final selection of sources that was used for psf matching.
        idFactory : `lsst.afw.table.IdFactory`, optional
            Generator object used to assign ids to detected sources in the
            difference image. Ids from this generator are not set until after
            deblending and merging positive/negative peaks.
        measurementResults : `lsst.pipe.base.Struct`, optional
            Result struct that is modified to allow saving of partial outputs
            for some failure conditions. If the task completes successfully,
            this is also returned.
        templateInfo : `list` [`lsst.images.DifferenceImageTemplateInfo`], \
                optional
            Information about the coadds and their input images that the
            template was built from. Without it the template epochs of every
            diaSource are NaN.
 
        Returns
        -------
        measurementResults : `lsst.pipe.base.Struct`
 
            ``subtractedMeasuredExposure`` : `lsst.afw.image.ExposureF`
                Subtracted exposure with detection mask applied.
            ``diaSources``  : `lsst.afw.table.SourceCatalog`
                The catalog of detected sources.
            ``differenceBackground`` : `lsst.afw.math.BackgroundList`
                Background that was subtracted from the difference image.
            ``templateEpochs`` : `dict` [`str`, `float`]
                Summary of the template epochs of the diaSources, keyed by
                image metadata key; empty if they are not known.
        """
        if measurementResults is None:
            measurementResults = pipeBase.Struct()
        if idFactory is None:
            idFactory = lsst.meas.base.IdGenerator().make_table_id_factory()
 
        # Check image properties and clear detection mask planes.
        self._prepareInputs(difference)
        if self.config.doSubtractBackground:
            background = self._fitAndSubtractBackground(differenceExposure=difference)
        else:
            background = afwMath.BackgroundList()
 
        if self.config.doFindCosmicRays:
            self.findAndMaskCosmicRays(difference)
 
        # Don't use the idFactory until after deblend+merge, so that we aren't
        # generating ids that just get thrown away (footprint merge doesn't
        # know about past ids).
        table = afwTable.SourceTable.make(self.schema)
        results = self.detection.run(
            table=table,
            exposure=difference,
            doSmooth=True,
            background=background,
            clearMask=True,
        )
        measurementResults.differenceBackground = background
 
        if self.config.doDeblend:
            sources, positives, negatives = self._deblend(difference,
                                                          results.positive,
                                                          results.negative)
 
        else:
            positives = afwTable.SourceCatalog(self.schema)
            results.positive.makeSources(positives)
            negatives = afwTable.SourceCatalog(self.schema)
            results.negative.makeSources(negatives)
            sources = results.sources
 
        self.processResults(science, matchedTemplate, difference,
                            sources, idFactory, kernelSources,
                            positives=positives,
                            negatives=negatives,
                            measurementResults=measurementResults,
                            templateInfo=templateInfo)
        return measurementResults
 
    def _prepareInputs(self, difference):
        """Ensure that we start with an empty detection and deblended mask.
 
        Parameters
        ----------
        difference : `lsst.afw.image.ExposureF`
            The difference image that will be used for detecting diaSources.
            The mask plane will be modified in place.
 
        Raises
        ------
        lsst.pipe.base.UpstreamFailureNoWorkFound
            If the PSF is not usable for measurement.
        """
        # Check that we have a valid PSF now before we do more work
        sigma = difference.psf.computeShape(difference.psf.getAveragePosition()).getDeterminantRadius()
        if np.isnan(sigma):
            raise pipeBase.UpstreamFailureNoWorkFound("Invalid PSF detected! PSF width evaluates to NaN.")
        # Ensure that we start with an empty detection and deblended mask.
        mask = difference.mask
        for mp in self.config.clearMaskPlanes:
            if mp not in mask.getMaskPlaneDict():
                mask.addMaskPlane(mp)
        mask &= ~mask.getPlaneBitMask(self.config.clearMaskPlanes)
 
    def _fitAndSubtractBackground(self, differenceExposure, scoreExposure=None):
        """Fit the final background to ``differenceExposure``.
 
        A rough background is first fit on the difference and an internal
        first-pass detection is run on the (background-subtracted) clone; the
        resulting detection mask is then transferred to ``differenceExposure``
        so the final background fit can mask out real sources. This two-pass
        scheme is what suppresses spurious detections from glints and
        extended structures: the small-binsize initial fit aggressively
        over-subtracts those features, and the resulting peak mask lets the
        large-binsize final fit produce a clean background. When
        ``scoreExposure`` is supplied (score-image variant), the final
        background is also subtracted from its image so the caller's final
        detection runs on a properly background-subtracted score.
 
        The first-pass detection's `Struct` is discarded; only the mask it
        leaves on the clone is used downstream.
 
        Parameters
        ----------
        differenceExposure : `lsst.afw.image.ExposureF`
            Difference image. The final background is subtracted from its
            image in place but its mask is not modified.
        scoreExposure : `lsst.afw.image.ExposureF`, optional
            Maximum-likelihood score image. When supplied, the final
            background is also subtracted from its image.
 
        Returns
        -------
        background : `lsst.afw.math.BackgroundList`
            The fit final background.
        """
        if scoreExposure is None:
            detectionExposure = differenceExposure.clone()
            background = self.subtractInitialBackground.run(detectionExposure).background
            doSmooth = True
        else:
            # Use a clone of differenceExposure because the background is
            # subtracted in place.
            background = self.subtractInitialBackground.run(differenceExposure.clone()).background
            detectionExposure = scoreExposure.clone()
            detectionExposure.image -= background.getImage()
            doSmooth = False
        table = afwTable.SourceTable.make(self.schema)
        self.detection.run(
            table=table,
            exposure=detectionExposure,
            doSmooth=doSmooth,
            background=background,
            clearMask=True,
        )
        # Use the temporary detection mask for the final background subtraction,
        # then restore the original mask. The score image variant never
        # clears the detection planes of the difference image.
        mask = differenceExposure.mask
        originalMask = mask.array.copy()
        detectedBit = mask.getPlaneBitMask(["DETECTED"])
        detectedPix = detectionExposure.mask.array & detectedBit > 0
        detectedNegativeBit = mask.getPlaneBitMask(["DETECTED_NEGATIVE"])
        detectedNegativePix = detectionExposure.mask.array & detectedNegativeBit > 0
        mask.array[detectedPix] |= detectedBit
        mask.array[detectedNegativePix] |= detectedNegativeBit
        background = self.subtractFinalBackground.run(differenceExposure).background
        mask.array[...] = originalMask
        if scoreExposure is not None:
            # The preconvolution kernel is normalized to 1, so the same
            # background level applies to the difference and score images.
            scoreExposure.image -= background.getImage()
        return background
 
    def processResults(self, science, matchedTemplate, difference, sources, idFactory,
                       kernelSources=None, positives=None, negatives=None, measurementResults=None,
                       templateInfo=None):
        """Measure and process the results of source detection.
 
        Parameters
        ----------
        science : `lsst.afw.image.ExposureF`
            Science exposure that the template was subtracted from.
        matchedTemplate : `lsst.afw.image.ExposureF`
            Warped and PSF-matched template that was used produce the
            difference image.
        difference : `lsst.afw.image.ExposureF`
            Result of subtracting template from the science image.
        sources : `lsst.afw.table.SourceCatalog`
            Detected sources on the difference exposure.
        idFactory : `lsst.afw.table.IdFactory`
            Generator object used to assign ids to detected sources in the
            difference image.
        kernelSources : `lsst.afw.table.SourceCatalog`, optional
            Final selection of sources that was used for psf matching.
        positives : `lsst.afw.table.SourceCatalog`, optional
            Positive polarity footprints.
        negatives : `lsst.afw.table.SourceCatalog`, optional
            Negative polarity footprints.
        measurementResults : `lsst.pipe.base.Struct`, optional
            Result struct that is modified to allow saving of partial outputs
            for some failure conditions. If the task completes successfully,
            this is also returned.
        templateInfo : `list` [`lsst.images.DifferenceImageTemplateInfo`], \
                optional
            Information about the coadds and their input images that the
            template was built from.
 
        Returns
        -------
        measurementResults : `lsst.pipe.base.Struct`
 
            ``subtractedMeasuredExposure`` : `lsst.afw.image.ExposureF`
                Subtracted exposure with detection mask applied.
            ``diaSources``  : `lsst.afw.table.SourceCatalog`
                The catalog of detected sources.
            ``templateEpochs`` : `dict` [`str`, `float`]
                Summary of the template epochs of the diaSources, keyed by
                image metadata key; empty if they are not known.
        """
        if measurementResults is None:
            measurementResults = pipeBase.Struct()
        self.metadata["nUnmergedDiaSources"] = len(sources)
        if self.config.doMerge:
            # preserve peak schema, if there are any footprints
            if len(positives) > 0:
                peakSchema = positives[0].getFootprint().peaks.schema
            elif len(negatives) > 0:
                peakSchema = negatives[0].getFootprint().peaks.schema
            else:
                peakSchema = afwDetection.PeakTable.makeMinimalSchema()
            mergeList = afwDetection.FootprintMergeList(self.schema,
                                                        ["positive", "negative"], peakSchema)
            initialDiaSources = afwTable.SourceCatalog(self.schema)
            # Start with positive, as FootprintMergeList will self-merge the
            # subsequent added catalogs, and we want to try to preserve
            # deblended positive sources.
            mergeList.addCatalog(initialDiaSources.table, positives, "positive", minNewPeakDist=0)
            mergeList.addCatalog(initialDiaSources.table, negatives, "negative", minNewPeakDist=0)
            mergeList.getFinalSources(initialDiaSources)
            # Flag as negative those sources that *only* came from the negative
            # footprint set.
            initialDiaSources["is_negative"] = initialDiaSources["merge_footprint_negative"] & \
                ~initialDiaSources["merge_footprint_positive"]
            self.log.info("Merging detections into %d sources", len(initialDiaSources))
            # All positive peaks were added before any negative peaks, so
            # re-order the peaks in each footprint by significance.
            self._reorderPeaksBySignificance(initialDiaSources)
        else:
            initialDiaSources = sources
 
        # Assign source ids at the end: deblend/merge mean that we don't keep
        # track of parents and children, we only care about the final ids.
        for source in initialDiaSources:
            source.setId(idFactory())
        # Ensure sources added after this get correct ids.
        initialDiaSources.getTable().setIdFactory(idFactory)
        initialDiaSources.setMetadata(self.algMetadata)
 
        self.metadata["nMergedDiaSources"] = len(initialDiaSources)
 
        if self.config.doMaskStreaks:
            streakInfo = self._runStreakMasking(difference)
 
        if self.config.doSkySources:
            self.addSkySources(initialDiaSources, difference.mask, difference.info.id)
 
        if self.config.doRejectBadMaskPlaneDetections:
            # Save time by rejecting peaks on bad mask planes prior to
            # measurement.
            initialDiaSources = self._rejectBadMaskedDetections(initialDiaSources, difference.mask)
 
        if not initialDiaSources.isContiguous():
            initialDiaSources = initialDiaSources.copy(deep=True)
 
        self.measureDiaSources(initialDiaSources, science, difference, matchedTemplate)
 
        # Remove unphysical diaSources per config.badSourceFlags
        diaSources = self._removeBadSources(initialDiaSources)
 
        if self.config.run_sattle:
            diaSources = self.filterSatellites(diaSources, science)
 
        # Flag diaSources in glint trails, but do not remove them
        diaSources, trail_parameters = self._find_glint_trails(diaSources)
        if self.config.writeGlintInfo:
            measurementResults.mergeItems(trail_parameters, 'glintTrailInfo')
 
        self._measureTemplateEpochs(diaSources, templateInfo)
        measurementResults.templateEpochs = self._summarizeTemplateEpochs(diaSources)
 
        if self.config.doForcedMeasurement:
            self.measureForcedSources(diaSources, science, difference.getWcs())
            self.measureForcedSources(diaSources, matchedTemplate, difference.getWcs(),
                                      template=True)
 
        # Clear the image plane for regions with NO_DATA.
        # These regions are most often caused by insufficient template coverage.
        # Do this for the final difference image after detection and measurement
        # since the subtasks should all be configured to handle NO_DATA properly
        difference.image.array[difference.mask.array & difference.mask.getPlaneBitMask('NO_DATA') > 0] = 0
 
        measurementResults.subtractedMeasuredExposure = difference
 
        if self.config.doMaskStreaks and self.config.writeStreakInfo:
            measurementResults.maskedStreaks = streakInfo.maskedStreaks
 
        if kernelSources is not None:
            self.calculateMetrics(science, difference, diaSources, kernelSources)
 
        if np.count_nonzero(~diaSources["sky_source"]) > 0:
            measurementResults.diaSources = diaSources
        elif self.config.raiseOnNoDiaSources:
            raise NoDiaSourcesError()
        elif len(diaSources) > 0:
            # This option allows returning sky sources,
            # even if there are no diaSources
            measurementResults.diaSources = diaSources
        self.log.info("Measured %d diaSources and %d sky sources",
                      np.count_nonzero(~diaSources["sky_source"]),
                      np.count_nonzero(diaSources["sky_source"])
                      )
        return measurementResults
 
    def _reorderPeaksBySignificance(self, diaSources):
        """Sort each merged source's footprint peaks by significance.
 
        Parameters
        ----------
        diaSources : `lsst.afw.table.SourceCatalog`
            Merged sources whose footprint peaks are re-ordered in place.
        """
        for source in diaSources:
            peaks = source.getFootprint().getPeaks()
            if len(peaks) < 2 or "significance" not in peaks.schema.getNames():
                continue
            ordered = sorted(peaks, key=lambda peak: peak["significance"], reverse=True)
            newPeaks = afwDetection.PeakCatalog(peaks.table)
            newPeaks.extend(ordered)
            peaks.clear()
            source.getFootprint().setPeakCatalog(newPeaks)
 
    def _deblend(self, difference, positiveFootprints, negativeFootprints):
        """Deblend the positive and negative footprints and return a catalog
        containing just the children, and the deblended footprints.
 
        Parameters
        ----------
        difference : `lsst.afw.image.Exposure`
            Result of subtracting template from the science image.
        positiveFootprints, negativeFootprints : `lsst.afw.detection.FootprintSet`
            Positive and negative polarity footprints measured on
            ``difference`` to be deblended separately.
 
        Returns
        -------
        sources : `lsst.afw.table.SourceCatalog`
            Positive and negative deblended children.
        positives, negatives : `lsst.afw.table.SourceCatalog`
            Deblended positive and negative polarity sources with footprints
            detected on ``difference``.
        """
 
        def deblend(footprints, negative=False):
            """Deblend a positive or negative footprint set,
            and return the deblended children.
 
            Parameters
            ----------
            footprints : `lsst.afw.detection.FootprintSet`
            negative : `bool`
                Set True if the footprints contain negative fluxes
 
            Returns
            -------
            sources : `lsst.afw.table.SourceCatalog`
            """
            sources = afwTable.SourceCatalog(self.schema)
            footprints.makeSources(sources)
            if negative:
                # Invert the image so the deblender can run on positive peaks
                difference_inverted = difference.clone()
                difference_inverted.image *= -1
                self.deblend.run(exposure=difference_inverted, sources=sources)
                children = sources[sources["parent"] != 0]
                # Set the heavy footprint pixel values back to reality
                for child in children:
                    footprint = child.getFootprint()
                    array = footprint.getImageArray()
                    array *= -1
            else:
                self.deblend.run(exposure=difference, sources=sources)
            self.setPrimaryFlags.run(sources)
            children = sources["detect_isDeblendedSource"] == 1
            sources = sources[children].copy(deep=True)
            # Clear parents, so that measurement plugins behave correctly.
            sources['parent'] = 0
            return sources.copy(deep=True)
 
        positives = deblend(positiveFootprints)
        negatives = deblend(negativeFootprints, negative=True)
 
        sources = afwTable.SourceCatalog(self.schema)
        sources.reserve(len(positives) + len(negatives))
        sources.extend(positives, deep=True)
        sources.extend(negatives, deep=True)
        if len(negatives) > 0:
            sources[-len(negatives):]["is_negative"] = True
        return sources, positives, negatives
 
    def _rejectBadMaskedDetections(self, initialDiaSources, mask):
        """Remove detections whose footprint peak lies on a bad-masked pixel.
 
        Detections are rejected if any peak of the source footprint falls on a
        pixel with any of the ``config.badMaskPlanes`` bits set.
 
        Parameters
        ----------
        initialDiaSources : `lsst.afw.table.SourceCatalog`
            The catalog of detected peaks.
        mask : `lsst.afw.image.Mask`
            Mask of the image used for detection.
 
        Returns
        -------
        initialDiaSources : `lsst.afw.table.SourceCatalog`
            The catalog with bad-masked detections removed.
        """
        if not self.config.badMaskPlanes or len(initialDiaSources) == 0:
            self.metadata["nRejectedBadMaskPlanes"] = 0
            return initialDiaSources
 
        presentPlanes = [mp for mp in self.config.badMaskPlanes
                         if mp in mask.getMaskPlaneDict()]
        if not presentPlanes:
            self.metadata["nRejectedBadMaskPlanes"] = 0
            return initialDiaSources
 
        badBitMask = mask.getPlaneBitMask(presentPlanes)
        x0, y0 = mask.getXY0()
 
        selector = np.ones(len(initialDiaSources), dtype=bool)
        for i, source in enumerate(initialDiaSources):
            peaks = source.getFootprint().getPeaks()
            for peak in peaks:
                ix = peak.getIx() - x0
                iy = peak.getIy() - y0
                if mask.array[iy, ix] & badBitMask:
                    selector[i] = False
                    break
        nRejected = np.count_nonzero(~selector)
        self.metadata["nRejectedBadMaskPlanes"] = nRejected
        if nRejected > 0:
            self.log.info("Rejected %d detections on pixels with bad mask "
                          "planes %s before measurement.",
                          nRejected, presentPlanes)
        return initialDiaSources[selector].copy(deep=True)
 
    def _removeBadSources(self, diaSources):
        """Remove unphysical diaSources from the catalog.
 
        Parameters
        ----------
        diaSources : `lsst.afw.table.SourceCatalog`
            The catalog of detected sources.
 
        Returns
        -------
        diaSources : `lsst.afw.table.SourceCatalog`
            The updated catalog of detected sources, with any source that has a
            flag in ``config.badSourceFlags`` set removed.
        """
        selector = np.ones(len(diaSources), dtype=bool)
        for flag in self.config.badSourceFlags:
            flags = diaSources[flag]
            nBad = np.count_nonzero(flags)
            if nBad > 0:
                self.log.debug("Found %d unphysical sources with flag %s.", nBad, flag)
                selector &= ~flags
        nBadTotal = np.count_nonzero(~selector)
        self.metadata["nRemovedBadFlaggedSources"] = nBadTotal
        self.log.info("Removed %d unphysical sources.", nBadTotal)
        return diaSources[selector].copy(deep=True)
 
    def _find_glint_trails(self, diaSources):
        """Define a new flag column for diaSources that are in a glint trail.
 
        Parameters
        ----------
        diaSources : `lsst.afw.table.SourceCatalog`
            The catalog of detected sources.
 
        Returns
        -------
        diaSources : `lsst.afw.table.SourceCatalog`
            The updated catalog of detected sources, with a new bool column
            called 'glint_trail' added.
 
        trail_parameters : `dict`
            Parameters of all the trails that were found.
        """
        if self.config.doSkySources:
            # Do not include sky sources in glint detection
            candidateDiaSources = diaSources[~diaSources["sky_source"]].copy(deep=True)
        else:
            candidateDiaSources = diaSources
        trailed_glints = self.findGlints.run(candidateDiaSources)
        glint_mask = [True if id in trailed_glints.trailed_ids else False for id in diaSources['id']]
        if np.any(glint_mask):
            diaSources['glint_trail'] = np.array(glint_mask)
 
        slopes = np.array([trail.slope for trail in trailed_glints.parameters])
        intercepts = np.array([trail.intercept for trail in trailed_glints.parameters])
        stderrs = np.array([trail.stderr for trail in trailed_glints.parameters])
        lengths = np.array([trail.length for trail in trailed_glints.parameters])
        angles = np.array([trail.angle for trail in trailed_glints.parameters])
        center_xs = np.array([trail.center_x for trail in trailed_glints.parameters])
        center_ys = np.array([trail.center_y for trail in trailed_glints.parameters])
        parameters = {'slopes': slopes, 'intercepts': intercepts, 'stderrs': stderrs, 'lengths': lengths,
                      'angles': angles, 'center_xs': center_xs, 'center_ys': center_ys}
 
        trail_parameters = pipeBase.Struct(glintTrailInfo=parameters)
 
        return diaSources, trail_parameters
 
    def _measureTemplateEpochs(self, diaSources, templateInfo):
        """Determine the observed epochs of the template for each diaSource.
 
        Parameters
        ----------
        diaSources : `lsst.afw.table.SourceCatalog`
            Contiguous catalog to fill in place.
        templateInfo : `list` [`lsst.images.DifferenceImageTemplateInfo`] or \
                `None`
            Information about the coadds and their input images that the
            template was built from.
 
        Notes
        -----
        An input covers a diaSource if both its coadd's bounds and its own
        bounds contain the centroid. Each coadd's inputs give a mean weighted
        by their coadd weights, and where coadds overlap their means are
        averaged with weights ``sqrt(W)``, ``W`` being the total weight of a
        coadd's inputs there. `GetTemplateTask` merges the patches of a tract
        with weights ``variance**-0.5``, and the variance of a coadd is about
        ``1/W``.
        """
        x = diaSources.getX()
        y = diaSources.getY()
        numerator = np.zeros(len(diaSources))
        denominator = np.zeros(len(diaSources))
        epochMin = np.full(len(diaSources), np.inf)
        epochMax = np.full(len(diaSources), -np.inf)
        for info in templateInfo or []:
            inCoadd = info.bounds.contains(x=x, y=y)
            if not inCoadd.any():
                continue
            weightSum = np.zeros(len(diaSources))
            weightedSum = np.zeros(len(diaSources))
            for record in info.inputs:
                covers = inCoadd & record.bounds.contains(x=x, y=y)
                weightSum[covers] += record.weight
                weightedSum[covers] += record.weight*record.mid_mjd_tai
                epochMin[covers] = np.minimum(epochMin[covers], record.mid_mjd_tai)
                epochMax[covers] = np.maximum(epochMax[covers], record.mid_mjd_tai)
            inputs = weightSum > 0
            numerator[inputs] += weightedSum[inputs]/np.sqrt(weightSum[inputs])
            denominator[inputs] += np.sqrt(weightSum[inputs])
        known = denominator > 0
        epoch = np.full(len(diaSources), np.nan)
        epoch[known] = numerator[known]/denominator[known]
        epochMin[~known] = np.nan
        epochMax[~known] = np.nan
        diaSources["templateMjdMean"] = epoch
        diaSources["templateMjdMin"] = epochMin
        diaSources["templateMjdMax"] = epochMax
 
    def _summarizeTemplateEpochs(self, diaSources):
        """Summarize the template epochs of a catalog in the task metadata.
 
        The earliest, latest and mean values over the rows with a template
        epoch, sky sources included, are set in the task metadata under the
        names of the diaSource fields they summarize. They are NaN if no row
        has a template epoch.
 
        Parameters
        ----------
        diaSources : `lsst.afw.table.SourceCatalog`
            Contiguous catalog with the template epoch fields set.
 
        Returns
        -------
        templateEpochs : `dict` [`str`, `float`]
            The same values keyed by their image metadata key in
            ``_TEMPLATE_EPOCH_NAMES``, or empty if no row has a template epoch.
        """
        known = np.isfinite(diaSources["templateMjdMean"])
        if not known.any():
            for name in _TEMPLATE_EPOCH_NAMES.values():
                self.metadata[name] = np.nan
            return {}
        values = {"templateMjdMin": float(np.min(diaSources["templateMjdMin"][known])),
                  "templateMjdMax": float(np.max(diaSources["templateMjdMax"][known])),
                  "templateMjdMean": float(np.mean(diaSources["templateMjdMean"][known]))}
        for name, value in values.items():
            self.metadata[name] = value
        return {key: values[name] for key, name in _TEMPLATE_EPOCH_NAMES.items()}
 
    def addSkySources(self, diaSources, mask, seed,
                      subtask=None):
        """Add sources in empty regions of the difference image
        for measuring the background.
 
        Parameters
        ----------
        diaSources : `lsst.afw.table.SourceCatalog`
            The catalog of detected sources.
        mask : `lsst.afw.image.Mask`
            Mask plane for determining regions where Sky sources can be added.
        seed : `int`
            Seed value to initialize the random number generator.
        """
        if subtask is None:
            subtask = self.skySources
        if subtask.config.nSources <= 0:
            self.metadata[f"n_{subtask.getName()}"] = 0
            return
        skySourceFootprints = subtask.run(mask=mask, seed=seed, catalog=diaSources)
        self.metadata[f"n_{subtask.getName()}"] = len(skySourceFootprints)
 
    def measureDiaSources(self, diaSources, science, difference, matchedTemplate):
        """Use (matched) template and science image to constrain dipole fitting.
 
        Parameters
        ----------
        diaSources : `lsst.afw.table.SourceCatalog`
            The catalog of detected sources.
        science : `lsst.afw.image.ExposureF`
            Science exposure that the template was subtracted from.
        difference : `lsst.afw.image.ExposureF`
            Result of subtracting template from the science image.
        matchedTemplate : `lsst.afw.image.ExposureF`
            Warped and PSF-matched template that was used produce the
            difference image.
        """
        # Ensure that the required mask planes are present
        for mp in self.config.measurement.plugins["base_PixelFlags"].masksFpAnywhere:
            difference.mask.addMaskPlane(mp)
        # Note that this may not be correct if we convolved the science image.
        # In the future we may wish to persist the matchedScience image.
        self.measurement.run(diaSources, difference, science, matchedTemplate)
        if self.config.doApCorr:
            apCorrMap = difference.getInfo().getApCorrMap()
            if apCorrMap is None:
                self.log.warning("Difference image does not have valid aperture correction; skipping.")
            else:
                self.applyApCorr.run(
                    catalog=diaSources,
                    apCorrMap=apCorrMap,
                )
 
    def measureForcedSources(self, diaSources, image, wcs, template=False):
        """Perform forced measurement of the diaSources on a direct image.
 
        Parameters
        ----------
        diaSources : `lsst.afw.table.SourceCatalog`
            The catalog of detected sources.
        image: `lsst.afw.image.ExposureF`
            Exposure that the forced measurement is being performed on
        wcs : `lsst.afw.geom.SkyWcs`
            Coordinate system definition (wcs) for the exposure.
        template : `bool`
            Is the forced measurement being performed on the template?
        """
        # Run forced psf photometry on the image at the diaSource locations.
        # Copy the measured flux and error into the diaSource.
        forcedSources = self.forcedMeasurement.generateMeasCat(image, diaSources, wcs)
        self.forcedMeasurement.run(forcedSources, image, diaSources, wcs)
 
        if template:
            base_key = 'ip_diffim_forced_template_PsfFlux'
        else:
            base_key = 'ip_diffim_forced_PsfFlux'
        mapper = afwTable.SchemaMapper(forcedSources.schema, diaSources.schema)
        mapper.addMapping(forcedSources.schema.find("base_PsfFlux_instFlux")[0],
                          f"{base_key}_instFlux", True)
        mapper.addMapping(forcedSources.schema.find("base_PsfFlux_instFluxErr")[0],
                          f"{base_key}_instFluxErr", True)
        mapper.addMapping(forcedSources.schema.find("base_PsfFlux_area")[0],
                          f"{base_key}_area", True)
        mapper.addMapping(forcedSources.schema.find("base_PsfFlux_flag")[0],
                          f"{base_key}_flag", True)
        mapper.addMapping(forcedSources.schema.find("base_PsfFlux_flag_noGoodPixels")[0],
                          f"{base_key}_flag_noGoodPixels", True)
        mapper.addMapping(forcedSources.schema.find("base_PsfFlux_flag_edge")[0],
                          f"{base_key}_flag_edge", True)
        for diaSource, forcedSource in zip(diaSources, forcedSources):
            diaSource.assign(forcedSource, mapper)
 
    def findAndMaskCosmicRays(self, difference):
        """Detect and mask cosmic rays on the difference image.
 
        Parameters
        ----------
        difference : `lsst.afw.image.Exposure`
            The background-subtracted difference image.
            The mask plane will be modified in place.
        """
        # This is run on a background-subtracted difference image, so the
        # remaining background should be ~0.
        medianBg = 0.
        try:
            crs = findCosmicRays(
                difference.maskedImage,
                difference.psf,
                medianBg,
                pexConfig.makePropertySet(self.config.cosmicray),
                self.config.cosmicray.keepCRs,
            )
        except LengthError:
            raise TooManyCosmicRays(self.config.cosmicray.nCrPixelMax) from None
        num = 0
        if crs is not None:
            mask = difference.maskedImage.mask
            crBit = mask.getPlaneBitMask("CR")
            afwDetection.setMaskFromFootprintList(mask, crs, crBit)
            num = len(crs)
 
        text = "masked" if self.config.cosmicray.keepCRs else "interpolated over"
        self.log.info("Identified and %s %s cosmic rays.", text, num)
        self.metadata["cosmic_ray_count"] = num
 
    def calculateMetrics(self, science, difference, diaSources, kernelSources):
        """Add difference image QA metrics to the Task metadata.
 
        This may be used to produce corresponding metrics (see
        lsst.analysis.tools.tasks.diffimTaskDetectorVisitMetricAnalysis).
 
        Parameters
        ----------
        science : `lsst.afw.image.ExposureF`
            Science exposure that was subtracted.
        difference : `lsst.afw.image.Exposure`
            The target difference image to calculate metrics for.
        diaSources : `lsst.afw.table.SourceCatalog`
            The catalog of detected sources.
        kernelSources : `lsst.afw.table.SourceCatalog`
            Final selection of sources that was used for psf matching.
        """
        mask = difference.mask
        badPix = (mask.array & mask.getPlaneBitMask(self.config.detection.excludeMaskPlanes)) > 0
        self.metadata["nGoodPixels"] = np.sum(~badPix)
        self.metadata["nBadPixels"] = np.sum(badPix)
        detPosPix = (mask.array & mask.getPlaneBitMask("DETECTED")) > 0
        detNegPix = (mask.array & mask.getPlaneBitMask("DETECTED_NEGATIVE")) > 0
        self.metadata["nPixelsDetectedPositive"] = np.sum(detPosPix)
        self.metadata["nPixelsDetectedNegative"] = np.sum(detNegPix)
        detPosPix &= badPix
        detNegPix &= badPix
        self.metadata["nBadPixelsDetectedPositive"] = np.sum(detPosPix)
        self.metadata["nBadPixelsDetectedNegative"] = np.sum(detNegPix)
 
        metricsMaskPlanes = list(mask.getMaskPlaneDict().keys())
        for maskPlane in metricsMaskPlanes:
            try:
                self.metadata["%s_mask_fraction"%maskPlane.lower()] = evaluateMaskFraction(mask, maskPlane)
            except InvalidParameterError:
                self.metadata["%s_mask_fraction"%maskPlane.lower()] = -1
                self.log.info("Unable to calculate metrics for mask plane %s: not in image"%maskPlane)
 
        if self.config.doSkySources:
            skySources = diaSources[diaSources["sky_source"]]
        else:
            skySources = None
        metrics = computeDifferenceImageMetrics(science, difference, kernelSources, sky_sources=skySources)
 
        self.metadata["residualFootprintRatioMean"] = metrics.differenceFootprintRatioMean
        self.metadata["residualFootprintRatioStdev"] = metrics.differenceFootprintRatioStdev
        self.metadata["differenceFootprintSkyRatioMean"] = metrics.differenceFootprintSkyRatioMean
        self.metadata["differenceFootprintSkyRatioStdev"] = metrics.differenceFootprintSkyRatioStdev
        self.log.info("Mean, stdev of ratio of difference to science "
                      "pixels in star footprints: %5.4f, %5.4f",
                      self.metadata["residualFootprintRatioMean"],
                      self.metadata["residualFootprintRatioStdev"])
        if self.config.raiseOnBadSubtractionRatio:
            if metrics.differenceFootprintRatioMean > self.config.badSubtractionRatioThreshold:
                raise BadSubtractionError(ratio=metrics.differenceFootprintRatioMean,
                                          threshold=self.config.badSubtractionRatioThreshold)
            if metrics.differenceFootprintRatioStdev > self.config.badSubtractionVariationThreshold:
                raise BadSubtractionError(ratio=metrics.differenceFootprintRatioStdev,
                                          threshold=self.config.badSubtractionVariationThreshold)
 
    def getSattleDiaSourceAllowlist(self, diaSources, science):
        """Query the sattle service and determine which diaSources are allowed.
 
        Parameters
        ----------
        diaSources : `lsst.afw.table.SourceCatalog`
            The catalog of detected sources.
        science : `lsst.afw.image.ExposureF`
            Science exposure that was subtracted.
 
        Returns
        ----------
        allow_list : `list` of `int`
            diaSourceIds of diaSources that can be made public.
 
        Raises
        ------
        requests.HTTPError
            Raised if sattle call does not return success.
        """
        wcs = science.getWcs()
        visit_info = science.getInfo().getVisitInfo()
        visit_id = visit_info.getId()
        sattle_uri_base = os.getenv('SATTLE_URI_BASE')
 
        dia_sources_json = []
        for source in diaSources:
            source_bbox = source.getFootprint().getBBox()
            corners = wcs.pixelToSky([lsst.geom.Point2D(c) for c in source_bbox.getCorners()])
            bbox_radec = [[pt.getRa().asDegrees(), pt.getDec().asDegrees()] for pt in corners]
            dia_sources_json.append({"diasource_id": source["id"], "bbox": bbox_radec})
 
        payload = {"visit_id": visit_id, "detector_id": science.getDetector().getId(),
                   "diasources": dia_sources_json, "historical": self.config.sattle_historical}
 
        sattle_output = requests.put(f'{sattle_uri_base}/diasource_allow_list',
                                     json=payload)
 
        # retry once if visit cache is not populated
        if sattle_output.status_code == 404:
            self.log.warning(f'Visit {visit_id} not found in sattle cache, re-sending')
            populate_sattle_visit_cache(visit_info, historical=self.config.sattle_historical)
            sattle_output = requests.put(f'{sattle_uri_base}/diasource_allow_list', json=payload)
 
        sattle_output.raise_for_status()
 
        return sattle_output.json()['allow_list']
 
    @timeMethod
    def filterSatellites(self, diaSources, science):
        """Remove diaSources overlapping predicted satellite positions.
 
        Parameters
        ----------
        diaSources : `lsst.afw.table.SourceCatalog`
            The catalog of detected sources.
        science : `lsst.afw.image.ExposureF`
            Science exposure that was subtracted.
 
        Returns
        ----------
        filterdDiaSources : `lsst.afw.table.SourceCatalog`
            Filtered catalog of diaSources
        """
 
        allow_list = self.getSattleDiaSourceAllowlist(diaSources, science)
 
        if allow_list:
            allow_set = set(allow_list)
            allowed_ids = [source['id'] in allow_set for source in diaSources]
            diaSources = diaSources[np.array(allowed_ids)].copy(deep=True)
        else:
            self.log.warning('Sattle allowlist is empty, all diaSources removed')
            diaSources = diaSources[0:0].copy(deep=True)
        return diaSources
 
    def _runStreakMasking(self, difference):
        """Do streak masking and optionally save the resulting streak
        fit parameters in a catalog.
 
        Only returns non-empty streakInfo if self.config.writeStreakInfo
        is set. The difference image is binned by self.config.streakBinFactor
        (and detection is run a second time) so that regions with lower
        surface brightness streaks are more likely to fall above the
        detection threshold.
 
        Parameters
        ----------
        difference: `lsst.afw.image.Exposure`
            The exposure in which to search for streaks. Must have a detection
            mask.
 
        Returns
        -------
        streakInfo: `lsst.pipe.base.Struct`
            ``rho`` : `np.ndarray`
                Angle of detected streak.
            ``theta`` : `np.ndarray`
                Distance from center of detected streak.
            ``sigma`` : `np.ndarray`
                Width of streak profile.
            ``reducedChi2`` : `np.ndarray`
                Reduced chi2 of the best-fit streak profile.
            ``modelMaximum`` : `np.ndarray`
                Peak value of the fit line profile.
        """
        maskedImage = difference.maskedImage
        # Bin the diffim to enhance low surface brightness streaks
        binnedMaskedImage = afwMath.binImage(maskedImage,
                                             self.config.streakBinFactor,
                                             self.config.streakBinFactor)
        binnedExposure = afwImage.ExposureF(binnedMaskedImage.getBBox())
        binnedExposure.setMaskedImage(binnedMaskedImage)
        # Clear the DETECTED mask plane before streak detection
        binnedExposure.mask &= ~binnedExposure.mask.getPlaneBitMask('DETECTED')
        # Rerun detection to set the DETECTED mask plane on binnedExposure
        sigma = difference.psf.computeShape(difference.psf.getAveragePosition()).getDeterminantRadius()
        _table = afwTable.SourceTable.make(afwTable.SourceTable.makeMinimalSchema())
        self.streakDetection.run(table=_table, exposure=binnedExposure, doSmooth=True,
                                 sigma=sigma/self.config.streakBinFactor)
        binnedDetectedMaskPlane = binnedExposure.mask.array & binnedExposure.mask.getPlaneBitMask('DETECTED')
        rescaledDetectedMaskPlane = binnedDetectedMaskPlane.repeat(self.config.streakBinFactor,
                                                                   axis=0).repeat(self.config.streakBinFactor,
                                                                                  axis=1)
        # Create new version of a diffim with DETECTED based on binnedExposure
        streakMaskedImage = maskedImage.clone()
        ysize, xsize = rescaledDetectedMaskPlane.shape
        streakMaskedImage.mask.array[:ysize, :xsize] |= rescaledDetectedMaskPlane
        # Detect streaks on this new version of the diffim
        streaks = self.maskStreaks.run(streakMaskedImage)
        streakMaskPlane = streakMaskedImage.mask.array & streakMaskedImage.mask.getPlaneBitMask('STREAK')
        # Apply the new STREAK mask to the original diffim
        maskedImage.mask.array |= streakMaskPlane
 
        if self.config.writeStreakInfo:
            rhos = np.array([line.rho for line in streaks.lines])
            thetas = np.array([line.theta for line in streaks.lines])
            sigmas = np.array([line.sigma for line in streaks.lines])
            chi2s = np.array([line.reducedChi2 for line in streaks.lines])
            modelMaximums = np.array([line.modelMaximum for line in streaks.lines])
            streakInfo = {'rho': rhos, 'theta': thetas, 'sigma': sigmas, 'reducedChi2': chi2s,
                          'modelMaximum': modelMaximums}
        else:
            streakInfo = {'rho': np.array([]), 'theta': np.array([]), 'sigma': np.array([]),
                          'reducedChi2': np.array([]), 'modelMaximum': np.array([])}
        return pipeBase.Struct(maskedStreaks=streakInfo)
 
 
class DetectAndMeasureScoreConnections(DetectAndMeasureConnections):
    scoreExposure = pipeBase.connectionTypes.Input(
        doc="Maximum likelihood image for detection.",
        dimensions=("instrument", "visit", "detector"),
        storageClass="ExposureF",
        name="{fakesType}{coaddName}Diff_scoreTempExp",
    )
    scoreMeasuredExposure = pipeBase.connectionTypes.Output(
        doc="Score image after backgrond subtraction and detection.",
        dimensions=("instrument", "visit", "detector"),
        storageClass="ExposureF",
        name="{fakesType}{coaddName}Diff_scoreExp",
    )
 
    def __init__(self, *, config):
        super().__init__(config=config)
        if self.config.image_type == "future":
            self.scoreExposure = dataclasses.replace(self.scoreExposure, storageClass="DifferenceImage")
            self.scoreMeasuredExposure = dataclasses.replace(self.scoreMeasuredExposure,
                                                             storageClass="DifferenceImage")
 
 
class DetectAndMeasureScoreConfig(DetectAndMeasureConfig,
                                  pipelineConnections=DetectAndMeasureScoreConnections):
    pass
 
 
class DetectAndMeasureScoreTask(DetectAndMeasureTask):
    """Detect DIA sources using a score image,
    and measure the detections on the difference image.
 
    Source detection is run on the supplied score, or maximum likelihood,
    image. Note that no additional convolution will be done in this case.
    Close positive and negative detections will optionally be merged into
    dipole diaSources.
    Sky sources, or forced detections in background regions, will optionally
    be added, and the configured measurement algorithm will be run on all
    detections.
    """
    ConfigClass = DetectAndMeasureScoreConfig
    _DefaultName = "detectAndMeasureScore"
 
    @timeMethod
    def run(self, science, matchedTemplate, difference, scoreExposure, kernelSources,
            idFactory=None, measurementResults=None, templateInfo=None):
        """Detect and measure sources on a score image.
 
        Parameters
        ----------
        science : `lsst.afw.image.ExposureF`
            Science exposure that the template was subtracted from.
        matchedTemplate : `lsst.afw.image.ExposureF`
            Warped and PSF-matched template that was used produce the
            difference image.
        difference : `lsst.afw.image.ExposureF`
            Result of subtracting template from the science image.
        scoreExposure : `lsst.afw.image.ExposureF`
            Score or maximum likelihood difference image
        kernelSources : `lsst.afw.table.SourceCatalog`
            Final selection of sources that was used for psf matching.
        idFactory : `lsst.afw.table.IdFactory`, optional
            Generator object used to assign ids to detected sources in the
            difference image. Ids from this generator are not set until after
            deblending and merging positive/negative peaks.
        measurementResults : `lsst.pipe.base.Struct`, optional
            Result struct that is modified to allow saving of partial outputs
            for some failure conditions. If the task completes successfully,
            this is also returned.
        templateInfo : `list` [`lsst.images.DifferenceImageTemplateInfo`], \
                optional
            Information about the coadds and their input images that the
            template was built from. Without it the template epochs of every
            diaSource are NaN.
 
        Returns
        -------
        measurementResults : `lsst.pipe.base.Struct`
 
            ``subtractedMeasuredExposure`` : `lsst.afw.image.ExposureF`
                Subtracted exposure with detection mask applied.
            ``scoreMeasuredExposure`` : `lsst.afw.image.ExposureF`
                Score exposure with detection mask applied.
            ``diaSources``  : `lsst.afw.table.SourceCatalog`
                The catalog of detected sources.
            ``differenceBackground`` : `lsst.afw.math.BackgroundList`
                Background that was subtracted from the difference image.
            ``templateEpochs`` : `dict` [`str`, `float`]
                Summary of the template epochs of the diaSources, keyed by
                image metadata key; empty if they are not known.
        """
        if measurementResults is None:
            measurementResults = pipeBase.Struct()
        if idFactory is None:
            idFactory = lsst.meas.base.IdGenerator().make_table_id_factory()
 
        # Check image properties and clear detection mask planes.
        self._prepareInputs(difference)
        self._prepareInputs(scoreExposure)
        if self.config.doSubtractBackground:
            background = self._fitAndSubtractBackground(
                differenceExposure=difference, scoreExposure=scoreExposure,
            )
        else:
            background = afwMath.BackgroundList()
 
        if self.config.doFindCosmicRays:
            # Cosmic rays are detected on the difference and the resulting
            # mask is propagated to the score image used for detection.
            self.findAndMaskCosmicRays(difference)
            scoreExposure.mask.array |= difference.mask.array
 
        # Don't use the idFactory until after deblend+merge, so that we aren't
        # generating ids that just get thrown away (footprint merge doesn't
        # know about past ids).
        table = afwTable.SourceTable.make(self.schema)
        detectResults = self.detection.run(
            table=table,
            exposure=scoreExposure,
            doSmooth=False,
            background=background,
            clearMask=True,
        )
        measurementResults.differenceBackground = background
        measurementResults.scoreMeasuredExposure = scoreExposure
 
        # Copy the final detection masks from the Score image to the difference.
        # Do not copy all mask planes, since the convolution will grow them by
        # the size of the convolution kernel.
        detectedBit = difference.mask.getPlaneBitMask(["DETECTED"])
        detectedPix = scoreExposure.mask.array & detectedBit > 0
        detectedNegativeBit = difference.mask.getPlaneBitMask(["DETECTED_NEGATIVE"])
        detectedNegativePix = scoreExposure.mask.array & detectedNegativeBit > 0
        difference.mask.array[detectedPix] |= detectedBit
        difference.mask.array[detectedNegativePix] |= detectedNegativeBit
        # Also set the EDGE mask, since the science image was convolved
        edgeBit = difference.mask.getPlaneBitMask(["EDGE"])
        edgePix = scoreExposure.mask.array & edgeBit > 0
        difference.mask.array[edgePix] |= edgeBit
 
        if self.config.doDeblend:
            sources, positives, negatives = self._deblend(difference,
                                                          detectResults.positive,
                                                          detectResults.negative)
 
            self.processResults(science, matchedTemplate, difference,
                                sources, idFactory, kernelSources,
                                positives=positives,
                                negatives=negatives,
                                measurementResults=measurementResults,
                                templateInfo=templateInfo)
 
        else:
            positives = afwTable.SourceCatalog(self.schema)
            detectResults.positive.makeSources(positives)
            negatives = afwTable.SourceCatalog(self.schema)
            detectResults.negative.makeSources(negatives)
            self.processResults(science, matchedTemplate, difference,
                                detectResults.sources, idFactory, kernelSources,
                                positives=positives,
                                negatives=negatives,
                                measurementResults=measurementResults,
                                templateInfo=templateInfo)
        return measurementResults