characterizeImage.py

Go to the documentation of this file.

#
# LSST Data Management System
# Copyright 2008-2015 AURA/LSST.
#
# This product includes software developed by the
# LSST Project (http://www.lsst.org/).
#
# 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 LSST License Statement and
# the GNU General Public License along with this program.  If not,
# see <https://www.lsstcorp.org/LegalNotices/>.
#
import numpy as np
 
from lsstDebug import getDebugFrame
import lsst.afw.table as afwTable
import lsst.pex.config as pexConfig
import lsst.pipe.base as pipeBase
import lsst.daf.base as dafBase
import lsst.pipe.base.connectionTypes as cT
from lsst.afw.math import BackgroundList
from lsst.afw.table import SourceTable, SourceCatalog
from lsst.meas.algorithms import SubtractBackgroundTask, SourceDetectionTask, MeasureApCorrTask
from lsst.meas.algorithms.installGaussianPsf import InstallGaussianPsfTask
from lsst.meas.astrom import RefMatchTask, displayAstrometry
from lsst.meas.algorithms import LoadIndexedReferenceObjectsTask
from lsst.obs.base import ExposureIdInfo
from lsst.meas.base import SingleFrameMeasurementTask, ApplyApCorrTask, CatalogCalculationTask
from lsst.meas.deblender import SourceDeblendTask
from .measurePsf import MeasurePsfTask
from .repair import RepairTask
from .computeExposureSummaryStats import ComputeExposureSummaryStatsTask
from lsst.pex.exceptions import LengthError
 
__all__ = ["CharacterizeImageConfig", "CharacterizeImageTask"]
 
 
class CharacterizeImageConnections(pipeBase.PipelineTaskConnections,
                                   dimensions=("instrument", "visit", "detector")):
    exposure = cT.Input(
        doc="Input exposure data",
        name="postISRCCD",
        storageClass="Exposure",
        dimensions=["instrument", "exposure", "detector"],
    )
    characterized = cT.Output(
        doc="Output characterized data.",
        name="icExp",
        storageClass="ExposureF",
        dimensions=["instrument", "visit", "detector"],
    )
    sourceCat = cT.Output(
        doc="Output source catalog.",
        name="icSrc",
        storageClass="SourceCatalog",
        dimensions=["instrument", "visit", "detector"],
    )
    backgroundModel = cT.Output(
        doc="Output background model.",
        name="icExpBackground",
        storageClass="Background",
        dimensions=["instrument", "visit", "detector"],
    )
    outputSchema = cT.InitOutput(
        doc="Schema of the catalog produced by CharacterizeImage",
        name="icSrc_schema",
        storageClass="SourceCatalog",
    )
 
    def adjustQuantum(self, datasetRefMap: pipeBase.InputQuantizedConnection):
        # Docstring inherited from PipelineTaskConnections
        try:
            return super().adjustQuantum(datasetRefMap)
        except pipeBase.ScalarError as err:
            raise pipeBase.ScalarError(
                "CharacterizeImageTask can at present only be run on visits that are associated with "
                "exactly one exposure.  Either this is not a valid exposure for this pipeline, or the "
                "snap-combination step you probably want hasn't been configured to run between ISR and "
                "this task (as of this writing, that would be because it hasn't been implemented yet)."
            ) from err
 
 
class CharacterizeImageConfig(pipeBase.PipelineTaskConfig,
                              pipelineConnections=CharacterizeImageConnections):
 
    """!Config for CharacterizeImageTask"""
    doMeasurePsf = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Measure PSF? If False then for all subsequent operations use either existing PSF "
            "model when present, or install simple PSF model when not (see installSimplePsf "
            "config options)"
    )
    doWrite = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Persist results?",
    )
    doWriteExposure = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Write icExp and icExpBackground in addition to icSrc? Ignored if doWrite False.",
    )
    psfIterations = pexConfig.RangeField(
        dtype=int,
        default=2,
        min=1,
        doc="Number of iterations of detect sources, measure sources, "
            "estimate PSF. If useSimplePsf is True then 2 should be plenty; "
            "otherwise more may be wanted.",
    )
    background = pexConfig.ConfigurableField(
        target=SubtractBackgroundTask,
        doc="Configuration for initial background estimation",
    )
    detection = pexConfig.ConfigurableField(
        target=SourceDetectionTask,
        doc="Detect sources"
    )
    doDeblend = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Run deblender input exposure"
    )
    deblend = pexConfig.ConfigurableField(
        target=SourceDeblendTask,
        doc="Split blended source into their components"
    )
    measurement = pexConfig.ConfigurableField(
        target=SingleFrameMeasurementTask,
        doc="Measure sources"
    )
    doApCorr = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Run subtasks to measure and apply aperture corrections"
    )
    measureApCorr = pexConfig.ConfigurableField(
        target=MeasureApCorrTask,
        doc="Subtask to measure aperture corrections"
    )
    applyApCorr = pexConfig.ConfigurableField(
        target=ApplyApCorrTask,
        doc="Subtask to apply aperture corrections"
    )
    # If doApCorr is False, and the exposure does not have apcorrections already applied, the
    # active plugins in catalogCalculation almost certainly should not contain the characterization plugin
    catalogCalculation = pexConfig.ConfigurableField(
        target=CatalogCalculationTask,
        doc="Subtask to run catalogCalculation plugins on catalog"
    )
    doComputeSummaryStats = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Run subtask to measure exposure summary statistics"
    )
    computeSummaryStats = pexConfig.ConfigurableField(
        target=ComputeExposureSummaryStatsTask,
        doc="Subtask to run computeSummaryStats on exposure"
    )
    useSimplePsf = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Replace the existing PSF model with a simplified version that has the same sigma "
        "at the start of each PSF determination iteration? Doing so makes PSF determination "
        "converge more robustly and quickly.",
    )
    installSimplePsf = pexConfig.ConfigurableField(
        target=InstallGaussianPsfTask,
        doc="Install a simple PSF model",
    )
    refObjLoader = pexConfig.ConfigurableField(
        target=LoadIndexedReferenceObjectsTask,
        doc="reference object loader",
    )
    ref_match = pexConfig.ConfigurableField(
        target=RefMatchTask,
        doc="Task to load and match reference objects. Only used if measurePsf can use matches. "
        "Warning: matching will only work well if the initial WCS is accurate enough "
        "to give good matches (roughly: good to 3 arcsec across the CCD).",
    )
    measurePsf = pexConfig.ConfigurableField(
        target=MeasurePsfTask,
        doc="Measure PSF",
    )
    repair = pexConfig.ConfigurableField(
        target=RepairTask,
        doc="Remove cosmic rays",
    )
    requireCrForPsf = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Require cosmic ray detection and masking to run successfully before measuring the PSF."
    )
    checkUnitsParseStrict = pexConfig.Field(
        doc="Strictness of Astropy unit compatibility check, can be 'raise', 'warn' or 'silent'",
        dtype=str,
        default="raise",
    )
 
    def setDefaults(self):
        super().setDefaults()
        # just detect bright stars; includeThresholdMultipler=10 seems large,
        # but these are the values we have been using
        self.detectiondetection.thresholdValue = 5.0
        self.detectiondetection.includeThresholdMultiplier = 10.0
        self.detectiondetection.doTempLocalBackground = False
        # do not deblend, as it makes a mess
        self.doDeblenddoDeblend = False
        # measure and apply aperture correction; note: measuring and applying aperture
        # correction are disabled until the final measurement, after PSF is measured
        self.doApCorrdoApCorr = True
        # minimal set of measurements needed to determine PSF
        self.measurementmeasurement.plugins.names = [
            "base_PixelFlags",
            "base_SdssCentroid",
            "base_SdssShape",
            "base_GaussianFlux",
            "base_PsfFlux",
            "base_CircularApertureFlux",
        ]
 
    def validate(self):
        if self.doApCorrdoApCorr and not self.measurePsfmeasurePsf:
            raise RuntimeError("Must measure PSF to measure aperture correction, "
                               "because flags determined by PSF measurement are used to identify "
                               "sources used to measure aperture correction")
 
 
 
 
class CharacterizeImageTask(pipeBase.PipelineTask, pipeBase.CmdLineTask):
    r"""!Measure bright sources and use this to estimate background and PSF of an exposure
 
    @anchor CharacterizeImageTask_
 
    @section pipe_tasks_characterizeImage_Contents  Contents
 
     - @ref pipe_tasks_characterizeImage_Purpose
     - @ref pipe_tasks_characterizeImage_Initialize
     - @ref pipe_tasks_characterizeImage_IO
     - @ref pipe_tasks_characterizeImage_Config
     - @ref pipe_tasks_characterizeImage_Debug
 
 
    @section pipe_tasks_characterizeImage_Purpose  Description
 
    Given an exposure with defects repaired (masked and interpolated over, e.g. as output by IsrTask):
    - detect and measure bright sources
    - repair cosmic rays
    - measure and subtract background
    - measure PSF
 
    @section pipe_tasks_characterizeImage_Initialize  Task initialisation
 
    @copydoc \_\_init\_\_
 
    @section pipe_tasks_characterizeImage_IO  Invoking the Task
 
    If you want this task to unpersist inputs or persist outputs, then call
    the `runDataRef` method (a thin wrapper around the `run` method).
 
    If you already have the inputs unpersisted and do not want to persist the output
    then it is more direct to call the `run` method:
 
    @section pipe_tasks_characterizeImage_Config  Configuration parameters
 
    See @ref CharacterizeImageConfig
 
    @section pipe_tasks_characterizeImage_Debug  Debug variables
 
    The @link lsst.pipe.base.cmdLineTask.CmdLineTask command line task@endlink interface supports a flag
    `--debug` to import `debug.py` from your `$PYTHONPATH`; see @ref baseDebug for more about `debug.py`.
 
    CharacterizeImageTask has a debug dictionary with the following keys:
    <dl>
    <dt>frame
    <dd>int: if specified, the frame of first debug image displayed (defaults to 1)
    <dt>repair_iter
    <dd>bool; if True display image after each repair in the measure PSF loop
    <dt>background_iter
    <dd>bool; if True display image after each background subtraction in the measure PSF loop
    <dt>measure_iter
    <dd>bool; if True display image and sources at the end of each iteration of the measure PSF loop
        See @ref lsst.meas.astrom.displayAstrometry for the meaning of the various symbols.
    <dt>psf
    <dd>bool; if True display image and sources after PSF is measured;
        this will be identical to the final image displayed by measure_iter if measure_iter is true
    <dt>repair
    <dd>bool; if True display image and sources after final repair
    <dt>measure
    <dd>bool; if True display image and sources after final measurement
    </dl>
 
    For example, put something like:
    @code{.py}
        import lsstDebug
        def DebugInfo(name):
            di = lsstDebug.getInfo(name)  # N.b. lsstDebug.Info(name) would call us recursively
            if name == "lsst.pipe.tasks.characterizeImage":
                di.display = dict(
                    repair = True,
                )
 
            return di
 
        lsstDebug.Info = DebugInfo
    @endcode
    into your `debug.py` file and run `calibrateTask.py` with the `--debug` flag.
 
    Some subtasks may have their own debug variables; see individual Task documentation.
    """
 
    # Example description used to live here, removed 2-20-2017 by MSSG
 
    ConfigClass = CharacterizeImageConfig
    _DefaultName = "characterizeImage"
    RunnerClass = pipeBase.ButlerInitializedTaskRunner
 
    def runQuantum(self, butlerQC, inputRefs, outputRefs):
        inputs = butlerQC.get(inputRefs)
        if 'exposureIdInfo' not in inputs.keys():
            inputs['exposureIdInfo'] = ExposureIdInfo.fromDataId(butlerQC.quantum.dataId, "visit_detector")
        outputs = self.runrun(**inputs)
        butlerQC.put(outputs, outputRefs)
 
    def __init__(self, butler=None, refObjLoader=None, schema=None, **kwargs):
        """!Construct a CharacterizeImageTask
 
        @param[in] butler  A butler object is passed to the refObjLoader constructor in case
            it is needed to load catalogs.  May be None if a catalog-based star selector is
            not used, if the reference object loader constructor does not require a butler,
            or if a reference object loader is passed directly via the refObjLoader argument.
        @param[in] refObjLoader  An instance of LoadReferenceObjectsTasks that supplies an
            external reference catalog to a catalog-based star selector.  May be None if a
            catalog star selector is not used or the loader can be constructed from the
            butler argument.
        @param[in,out] schema  initial schema (an lsst.afw.table.SourceTable), or None
        @param[in,out] kwargs  other keyword arguments for lsst.pipe.base.CmdLineTask
        """
        super().__init__(**kwargs)
 
        if schema is None:
            schema = SourceTable.makeMinimalSchema()
        self.schemaschema = schema
        self.makeSubtask("background")
        self.makeSubtask("installSimplePsf")
        self.makeSubtask("repair")
        self.makeSubtask("measurePsf", schema=self.schemaschema)
        if self.config.doMeasurePsf and self.measurePsf.usesMatches:
            if not refObjLoader:
                self.makeSubtask('refObjLoader', butler=butler)
                refObjLoader = self.refObjLoader
            self.makeSubtask("ref_match", refObjLoader=refObjLoader)
        self.algMetadataalgMetadata = dafBase.PropertyList()
        self.makeSubtask('detection', schema=self.schemaschema)
        if self.config.doDeblend:
            self.makeSubtask("deblend", schema=self.schemaschema)
        self.makeSubtask('measurement', schema=self.schemaschema, algMetadata=self.algMetadataalgMetadata)
        if self.config.doApCorr:
            self.makeSubtask('measureApCorr', schema=self.schemaschema)
            self.makeSubtask('applyApCorr', schema=self.schemaschema)
        self.makeSubtask('catalogCalculation', schema=self.schemaschema)
        if self.config.doComputeSummaryStats:
            self.makeSubtask('computeSummaryStats')
        self._initialFrame_initialFrame = getDebugFrame(self._display, "frame") or 1
        self._frame_frame = self._initialFrame_initialFrame
        self.schemaschema.checkUnits(parse_strict=self.config.checkUnitsParseStrict)
        self.outputSchemaoutputSchema = afwTable.SourceCatalog(self.schemaschema)
 
    def getInitOutputDatasets(self):
        outputCatSchema = afwTable.SourceCatalog(self.schemaschema)
        outputCatSchema.getTable().setMetadata(self.algMetadataalgMetadata)
        return {'outputSchema': outputCatSchema}
 
    @pipeBase.timeMethod
    def runDataRef(self, dataRef, exposure=None, background=None, doUnpersist=True):
        """!Characterize a science image and, if wanted, persist the results
 
        This simply unpacks the exposure and passes it to the characterize method to do the work.
 
        @param[in] dataRef: butler data reference for science exposure
        @param[in,out] exposure  exposure to characterize (an lsst.afw.image.ExposureF or similar).
            If None then unpersist from "postISRCCD".
            The following changes are made, depending on the config:
            - set psf to the measured PSF
            - set apCorrMap to the measured aperture correction
            - subtract background
            - interpolate over cosmic rays
            - update detection and cosmic ray mask planes
        @param[in,out] background  initial model of background already subtracted from exposure
            (an lsst.afw.math.BackgroundList). May be None if no background has been subtracted,
            which is typical for image characterization.
            A refined background model is output.
        @param[in] doUnpersist  if True the exposure is read from the repository
            and the exposure and background arguments must be None;
            if False the exposure must be provided.
            True is intended for running as a command-line task, False for running as a subtask
 
        @return same data as the characterize method
        """
        self._frame_frame = self._initialFrame_initialFrame  # reset debug display frame
        self.log.info("Processing %s" % (dataRef.dataId))
 
        if doUnpersist:
            if exposure is not None or background is not None:
                raise RuntimeError("doUnpersist true; exposure and background must be None")
            exposure = dataRef.get("postISRCCD", immediate=True)
        elif exposure is None:
            raise RuntimeError("doUnpersist false; exposure must be provided")
 
        exposureIdInfo = dataRef.get("expIdInfo")
 
        charRes = self.runrun(
            exposure=exposure,
            exposureIdInfo=exposureIdInfo,
            background=background,
        )
 
        if self.config.doWrite:
            dataRef.put(charRes.sourceCat, "icSrc")
            if self.config.doWriteExposure:
                dataRef.put(charRes.exposure, "icExp")
                dataRef.put(charRes.background, "icExpBackground")
 
        return charRes
 
    @pipeBase.timeMethod
    def run(self, exposure, exposureIdInfo=None, background=None):
        """!Characterize a science image
 
        Peforms the following operations:
        - Iterate the following config.psfIterations times, or once if config.doMeasurePsf false:
            - detect and measure sources and estimate PSF (see detectMeasureAndEstimatePsf for details)
        - interpolate over cosmic rays
        - perform final measurement
 
        @param[in,out] exposure  exposure to characterize (an lsst.afw.image.ExposureF or similar).
            The following changes are made:
            - update or set psf
            - set apCorrMap
            - update detection and cosmic ray mask planes
            - subtract background and interpolate over cosmic rays
        @param[in] exposureIdInfo  ID info for exposure (an lsst.obs.base.ExposureIdInfo).
            If not provided, returned SourceCatalog IDs will not be globally unique.
        @param[in,out] background  initial model of background already subtracted from exposure
            (an lsst.afw.math.BackgroundList). May be None if no background has been subtracted,
            which is typical for image characterization.
 
        @return pipe_base Struct containing these fields, all from the final iteration
        of detectMeasureAndEstimatePsf:
        - exposure: characterized exposure; image is repaired by interpolating over cosmic rays,
            mask is updated accordingly, and the PSF model is set
        - sourceCat: detected sources (an lsst.afw.table.SourceCatalog)
        - background: model of background subtracted from exposure (an lsst.afw.math.BackgroundList)
        - psfCellSet: spatial cells of PSF candidates (an lsst.afw.math.SpatialCellSet)
        """
        self._frame_frame = self._initialFrame_initialFrame  # reset debug display frame
 
        if not self.config.doMeasurePsf and not exposure.hasPsf():
            self.log.warn("Source catalog detected and measured with placeholder or default PSF")
            self.installSimplePsf.run(exposure=exposure)
 
        if exposureIdInfo is None:
            exposureIdInfo = ExposureIdInfo()
 
        # subtract an initial estimate of background level
        background = self.background.run(exposure).background
 
        psfIterations = self.config.psfIterations if self.config.doMeasurePsf else 1
        for i in range(psfIterations):
            dmeRes = self.detectMeasureAndEstimatePsfdetectMeasureAndEstimatePsf(
                exposure=exposure,
                exposureIdInfo=exposureIdInfo,
                background=background,
            )
 
            psf = dmeRes.exposure.getPsf()
            psfSigma = psf.computeShape().getDeterminantRadius()
            psfDimensions = psf.computeImage().getDimensions()
            medBackground = np.median(dmeRes.background.getImage().getArray())
            self.log.info("iter %s; PSF sigma=%0.2f, dimensions=%s; median background=%0.2f" %
                          (i + 1, psfSigma, psfDimensions, medBackground))
 
        self.displaydisplay("psf", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat)
 
        # perform final repair with final PSF
        self.repair.run(exposure=dmeRes.exposure)
        self.displaydisplay("repair", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat)
 
        # perform final measurement with final PSF, including measuring and applying aperture correction,
        # if wanted
        self.measurement.run(measCat=dmeRes.sourceCat, exposure=dmeRes.exposure,
                             exposureId=exposureIdInfo.expId)
        if self.config.doApCorr:
            apCorrMap = self.measureApCorr.run(exposure=dmeRes.exposure, catalog=dmeRes.sourceCat).apCorrMap
            dmeRes.exposure.getInfo().setApCorrMap(apCorrMap)
            self.applyApCorr.run(catalog=dmeRes.sourceCat, apCorrMap=exposure.getInfo().getApCorrMap())
        self.catalogCalculation.run(dmeRes.sourceCat)
        if self.config.doComputeSummaryStats:
            summary = self.computeSummaryStats.run(exposure=dmeRes.exposure,
                                                   sources=dmeRes.sourceCat,
                                                   background=dmeRes.background)
            dmeRes.exposure.getInfo().setSummaryStats(summary)
 
        self.displaydisplay("measure", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat)
 
        return pipeBase.Struct(
            exposure=dmeRes.exposure,
            sourceCat=dmeRes.sourceCat,
            background=dmeRes.background,
            psfCellSet=dmeRes.psfCellSet,
 
            characterized=dmeRes.exposure,
            backgroundModel=dmeRes.background
        )
 
    @pipeBase.timeMethod
    def detectMeasureAndEstimatePsf(self, exposure, exposureIdInfo, background):
        """!Perform one iteration of detect, measure and estimate PSF
 
        Performs the following operations:
        - if config.doMeasurePsf or not exposure.hasPsf():
            - install a simple PSF model (replacing the existing one, if need be)
        - interpolate over cosmic rays with keepCRs=True
        - estimate background and subtract it from the exposure
        - detect, deblend and measure sources, and subtract a refined background model;
        - if config.doMeasurePsf:
            - measure PSF
 
        @param[in,out] exposure  exposure to characterize (an lsst.afw.image.ExposureF or similar)
            The following changes are made:
            - update or set psf
            - update detection and cosmic ray mask planes
            - subtract background
        @param[in] exposureIdInfo  ID info for exposure (an lsst.obs_base.ExposureIdInfo)
        @param[in,out] background  initial model of background already subtracted from exposure
            (an lsst.afw.math.BackgroundList).
 
        @return pipe_base Struct containing these fields, all from the final iteration
        of detect sources, measure sources and estimate PSF:
        - exposure  characterized exposure; image is repaired by interpolating over cosmic rays,
            mask is updated accordingly, and the PSF model is set
        - sourceCat  detected sources (an lsst.afw.table.SourceCatalog)
        - background  model of background subtracted from exposure (an lsst.afw.math.BackgroundList)
        - psfCellSet  spatial cells of PSF candidates (an lsst.afw.math.SpatialCellSet)
        """
        # install a simple PSF model, if needed or wanted
        if not exposure.hasPsf() or (self.config.doMeasurePsf and self.config.useSimplePsf):
            self.log.warn("Source catalog detected and measured with placeholder or default PSF")
            self.installSimplePsf.run(exposure=exposure)
 
        # run repair, but do not interpolate over cosmic rays (do that elsewhere, with the final PSF model)
        if self.config.requireCrForPsf:
            self.repair.run(exposure=exposure, keepCRs=True)
        else:
            try:
                self.repair.run(exposure=exposure, keepCRs=True)
            except LengthError:
                self.log.warn("Skipping cosmic ray detection: Too many CR pixels (max %0.f)" %
                              self.config.repair.cosmicray.nCrPixelMax)
 
        self.displaydisplay("repair_iter", exposure=exposure)
 
        if background is None:
            background = BackgroundList()
 
        sourceIdFactory = exposureIdInfo.makeSourceIdFactory()
        table = SourceTable.make(self.schemaschema, sourceIdFactory)
        table.setMetadata(self.algMetadataalgMetadata)
 
        detRes = self.detection.run(table=table, exposure=exposure, doSmooth=True)
        sourceCat = detRes.sources
        if detRes.fpSets.background:
            for bg in detRes.fpSets.background:
                background.append(bg)
 
        if self.config.doDeblend:
            self.deblend.run(exposure=exposure, sources=sourceCat)
 
        self.measurement.run(measCat=sourceCat, exposure=exposure, exposureId=exposureIdInfo.expId)
 
        measPsfRes = pipeBase.Struct(cellSet=None)
        if self.config.doMeasurePsf:
            if self.measurePsf.usesMatches:
                matches = self.ref_match.loadAndMatch(exposure=exposure, sourceCat=sourceCat).matches
            else:
                matches = None
            measPsfRes = self.measurePsf.run(exposure=exposure, sources=sourceCat, matches=matches,
                                             expId=exposureIdInfo.expId)
        self.displaydisplay("measure_iter", exposure=exposure, sourceCat=sourceCat)
 
        return pipeBase.Struct(
            exposure=exposure,
            sourceCat=sourceCat,
            background=background,
            psfCellSet=measPsfRes.cellSet,
        )
 
    def getSchemaCatalogs(self):
        """Return a dict of empty catalogs for each catalog dataset produced by this task.
        """
        sourceCat = SourceCatalog(self.schemaschema)
        sourceCat.getTable().setMetadata(self.algMetadataalgMetadata)
        return {"icSrc": sourceCat}
 
    def display(self, itemName, exposure, sourceCat=None):
        """Display exposure and sources on next frame, if display of itemName has been requested
 
        @param[in] itemName  name of item in debugInfo
        @param[in] exposure  exposure to display
        @param[in] sourceCat  source catalog to display
        """
        val = getDebugFrame(self._display, itemName)
        if not val:
            return
 
        displayAstrometry(exposure=exposure, sourceCat=sourceCat, frame=self._frame_frame, pause=False)
        self._frame_frame += 1