finalizeCharacterization.py

Go to the documentation of this file.

# This file is part of pipe_tasks.
#
# 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/>.
 
"""Task to run a finalized image characterization, using additional data.
"""
 
__all__ = ['FinalizeCharacterizationConnections',
           'FinalizeCharacterizationConfig',
           'FinalizeCharacterizationTask']
 
import astropy.table
import numpy as np
import esutil
 
 
import lsst.pex.config as pexConfig
import lsst.pipe.base as pipeBase
import lsst.daf.base as dafBase
import lsst.afw.table as afwTable
import lsst.meas.algorithms as measAlg
import lsst.meas.extensions.piff.piffPsfDeterminer  # noqa: F401
from lsst.meas.algorithms import MeasureApCorrTask
from lsst.meas.base import SingleFrameMeasurementTask, ApplyApCorrTask
from lsst.meas.algorithms.sourceSelector import sourceSelectorRegistry
 
from .reserveIsolatedStars import ReserveIsolatedStarsTask
 
 
class FinalizeCharacterizationConnections(pipeBase.PipelineTaskConnections,
                                          dimensions=('instrument', 'visit',),
                                          defaultTemplates={}):
    src_schema = pipeBase.connectionTypes.InitInput(
        doc='Input schema used for src catalogs.',
        name='src_schema',
        storageClass='SourceCatalog',
    )
    srcs = pipeBase.connectionTypes.Input(
        doc='Source catalogs for the visit',
        name='src',
        storageClass='SourceCatalog',
        dimensions=('instrument', 'visit', 'detector'),
        deferLoad=True,
        multiple=True,
        deferGraphConstraint=True,
    )
    calexps = pipeBase.connectionTypes.Input(
        doc='Calexps for the visit',
        name='calexp',
        storageClass='ExposureF',
        dimensions=('instrument', 'visit', 'detector'),
        deferLoad=True,
        multiple=True,
    )
    isolated_star_cats = pipeBase.connectionTypes.Input(
        doc=('Catalog of isolated stars with average positions, number of associated '
             'sources, and indexes to the isolated_star_sources catalogs.'),
        name='isolated_star_presource_associations',
        storageClass='ArrowAstropy',
        dimensions=('instrument', 'tract', 'skymap'),
        deferLoad=True,
        multiple=True,
    )
    isolated_star_sources = pipeBase.connectionTypes.Input(
        doc=('Catalog of isolated star sources with sourceIds, and indexes to the '
             'isolated_star_cats catalogs.'),
        name='isolated_star_presources',
        storageClass='ArrowAstropy',
        dimensions=('instrument', 'tract', 'skymap'),
        deferLoad=True,
        multiple=True,
    )
    finalized_psf_ap_corr_cat = pipeBase.connectionTypes.Output(
        doc=('Per-visit finalized psf models and aperture corrections.  This '
             'catalog uses detector id for the id and are sorted for fast '
             'lookups of a detector.'),
        name='finalized_psf_ap_corr_catalog',
        storageClass='ExposureCatalog',
        dimensions=('instrument', 'visit'),
    )
    finalized_src_table = pipeBase.connectionTypes.Output(
        doc=('Per-visit catalog of measurements for psf/flag/etc.'),
        name='finalized_src_table',
        storageClass='ArrowAstropy',
        dimensions=('instrument', 'visit'),
    )
 
 
class FinalizeCharacterizationConfig(pipeBase.PipelineTaskConfig,
                                     pipelineConnections=FinalizeCharacterizationConnections):
    """Configuration for FinalizeCharacterizationTask."""
    source_selector = sourceSelectorRegistry.makeField(
        doc="How to select sources",
        default="science"
    )
    id_column = pexConfig.Field(
        doc='Name of column in isolated_star_sources with source id.',
        dtype=str,
        default='sourceId',
    )
    reserve_selection = pexConfig.ConfigurableField(
        target=ReserveIsolatedStarsTask,
        doc='Task to select reserved stars',
    )
    make_psf_candidates = pexConfig.ConfigurableField(
        target=measAlg.MakePsfCandidatesTask,
        doc='Task to make psf candidates from selected stars.',
    )
    psf_determiner = measAlg.psfDeterminerRegistry.makeField(
        'PSF Determination algorithm',
        default='piff'
    )
    measurement = pexConfig.ConfigurableField(
        target=SingleFrameMeasurementTask,
        doc='Measure sources for aperture corrections'
    )
    measure_ap_corr = pexConfig.ConfigurableField(
        target=MeasureApCorrTask,
        doc="Subtask to measure aperture corrections"
    )
    apply_ap_corr = pexConfig.ConfigurableField(
        target=ApplyApCorrTask,
        doc="Subtask to apply aperture corrections"
    )
 
    def setDefaults(self):
        super().setDefaults()
 
        source_selector = self.source_selector['science']
        source_selector.setDefaults()
 
        # We use the source selector only to select out flagged objects
        # and signal-to-noise.  Isolated, unresolved sources are handled
        # by the isolated star catalog.
 
        source_selector.doFlags = True
        source_selector.doSignalToNoise = True
        source_selector.doFluxLimit = False
        source_selector.doUnresolved = False
        source_selector.doIsolated = False
 
        source_selector.signalToNoise.minimum = 50.0
        source_selector.signalToNoise.maximum = 1000.0
 
        source_selector.signalToNoise.fluxField = 'base_GaussianFlux_instFlux'
        source_selector.signalToNoise.errField = 'base_GaussianFlux_instFluxErr'
 
        source_selector.flags.bad = ['base_PixelFlags_flag_edge',
                                     'base_PixelFlags_flag_nodata',
                                     'base_PixelFlags_flag_interpolatedCenter',
                                     'base_PixelFlags_flag_saturatedCenter',
                                     'base_PixelFlags_flag_crCenter',
                                     'base_PixelFlags_flag_bad',
                                     'base_PixelFlags_flag_interpolated',
                                     'base_PixelFlags_flag_saturated',
                                     'slot_Centroid_flag',
                                     'base_GaussianFlux_flag']
 
        # Configure aperture correction to select only high s/n sources (that
        # were used in the psf modeling) to avoid background problems when
        # computing the aperture correction map.
        self.measure_ap_corr.sourceSelector = 'science'
 
        ap_selector = self.measure_ap_corr.sourceSelector['science']
        # We do not need to filter flags or unresolved because we have used
        # the filtered isolated stars as an input
        ap_selector.doFlags = False
        ap_selector.doUnresolved = False
 
        import lsst.meas.modelfit  # noqa: F401
        import lsst.meas.extensions.photometryKron  # noqa: F401
        import lsst.meas.extensions.convolved  # noqa: F401
        import lsst.meas.extensions.gaap  # noqa: F401
        import lsst.meas.extensions.shapeHSM  # noqa: F401
 
        # Set up measurement defaults
        self.measurement.plugins.names = [
            'base_FPPosition',
            'base_PsfFlux',
            'base_GaussianFlux',
            'modelfit_DoubleShapeletPsfApprox',
            'modelfit_CModel',
            'ext_photometryKron_KronFlux',
            'ext_convolved_ConvolvedFlux',
            'ext_gaap_GaapFlux',
            'ext_shapeHSM_HsmShapeRegauss',
            'ext_shapeHSM_HsmSourceMoments',
            'ext_shapeHSM_HsmPsfMoments',
            'ext_shapeHSM_HsmSourceMomentsRound',
            'ext_shapeHSM_HigherOrderMomentsSource',
            'ext_shapeHSM_HigherOrderMomentsPSF',
        ]
        self.measurement.slots.modelFlux = 'modelfit_CModel'
        self.measurement.plugins['ext_convolved_ConvolvedFlux'].seeing.append(8.0)
        self.measurement.plugins['ext_gaap_GaapFlux'].sigmas = [
            0.5,
            0.7,
            1.0,
            1.5,
            2.5,
            3.0
        ]
        self.measurement.plugins['ext_gaap_GaapFlux'].doPsfPhotometry = True
        self.measurement.slots.shape = 'ext_shapeHSM_HsmSourceMoments'
        self.measurement.slots.psfShape = 'ext_shapeHSM_HsmPsfMoments'
        self.measurement.plugins['ext_shapeHSM_HsmShapeRegauss'].deblendNChild = ""
 
        # TODO: Remove in DM-44658, streak masking to happen only in ip_diffim
        # Keep track of which footprints contain streaks
        self.measurement.plugins['base_PixelFlags'].masksFpAnywhere = ['STREAK']
        self.measurement.plugins['base_PixelFlags'].masksFpCenter = ['STREAK']
 
        # Turn off slot setting for measurement for centroid and shape
        # (for which we use the input src catalog measurements)
        self.measurement.slots.centroid = None
        self.measurement.slots.apFlux = None
        self.measurement.slots.calibFlux = None
 
        names = self.measurement.plugins['ext_convolved_ConvolvedFlux'].getAllResultNames()
        self.measure_ap_corr.allowFailure += names
        names = self.measurement.plugins["ext_gaap_GaapFlux"].getAllGaapResultNames()
        self.measure_ap_corr.allowFailure += names
 
 
class FinalizeCharacterizationTask(pipeBase.PipelineTask):
    """Run final characterization on exposures."""
    ConfigClass = FinalizeCharacterizationConfig
    _DefaultName = 'finalize_characterization'
 
    def __init__(self, initInputs=None, **kwargs):
        super().__init__(initInputs=initInputs, **kwargs)
 
        self.schema_mapper, self.schema = self._make_output_schema_mapper(
            initInputs['src_schema'].schema
        )
 
        self.makeSubtask('reserve_selection')
        self.makeSubtask('source_selector')
        self.makeSubtask('make_psf_candidates')
        self.makeSubtask('psf_determiner')
        self.makeSubtask('measurement', schema=self.schema)
        self.makeSubtask('measure_ap_corr', schema=self.schema)
        self.makeSubtask('apply_ap_corr', schema=self.schema)
 
        # Only log warning and fatal errors from the source_selector
        self.source_selector.log.setLevel(self.source_selector.log.WARN)
 
    def runQuantum(self, butlerQC, inputRefs, outputRefs):
        input_handle_dict = butlerQC.get(inputRefs)
 
        band = butlerQC.quantum.dataId['band']
        visit = butlerQC.quantum.dataId['visit']
 
        src_dict_temp = {handle.dataId['detector']: handle
                         for handle in input_handle_dict['srcs']}
        calexp_dict_temp = {handle.dataId['detector']: handle
                            for handle in input_handle_dict['calexps']}
        isolated_star_cat_dict_temp = {handle.dataId['tract']: handle
                                       for handle in input_handle_dict['isolated_star_cats']}
        isolated_star_source_dict_temp = {handle.dataId['tract']: handle
                                          for handle in input_handle_dict['isolated_star_sources']}
        # TODO: Sort until DM-31701 is done and we have deterministic
        # dataset ordering.
        src_dict = {detector: src_dict_temp[detector] for
                    detector in sorted(src_dict_temp.keys())}
        calexp_dict = {detector: calexp_dict_temp[detector] for
                       detector in sorted(calexp_dict_temp.keys())}
        isolated_star_cat_dict = {tract: isolated_star_cat_dict_temp[tract] for
                                  tract in sorted(isolated_star_cat_dict_temp.keys())}
        isolated_star_source_dict = {tract: isolated_star_source_dict_temp[tract] for
                                     tract in sorted(isolated_star_source_dict_temp.keys())}
 
        struct = self.run(visit,
                          band,
                          isolated_star_cat_dict,
                          isolated_star_source_dict,
                          src_dict,
                          calexp_dict)
 
        butlerQC.put(struct.psf_ap_corr_cat,
                     outputRefs.finalized_psf_ap_corr_cat)
        butlerQC.put(astropy.table.Table(struct.output_table),
                     outputRefs.finalized_src_table)
 
    def run(self, visit, band, isolated_star_cat_dict, isolated_star_source_dict, src_dict, calexp_dict):
        """
        Run the FinalizeCharacterizationTask.
 
        Parameters
        ----------
        visit : `int`
            Visit number.  Used in the output catalogs.
        band : `str`
            Band name.  Used to select reserved stars.
        isolated_star_cat_dict : `dict`
            Per-tract dict of isolated star catalog handles.
        isolated_star_source_dict : `dict`
            Per-tract dict of isolated star source catalog handles.
        src_dict : `dict`
            Per-detector dict of src catalog handles.
        calexp_dict : `dict`
            Per-detector dict of calibrated exposure handles.
 
        Returns
        -------
        struct : `lsst.pipe.base.struct`
            Struct with outputs for persistence.
 
        Raises
        ------
        NoWorkFound
            Raised if the selector returns no good sources.
        """
        # Check if we have the same inputs for each of the
        # src_dict and calexp_dict.
        src_detectors = set(src_dict.keys())
        calexp_detectors = set(calexp_dict.keys())
 
        if src_detectors != calexp_detectors:
            detector_keys = sorted(src_detectors.intersection(calexp_detectors))
            self.log.warning(
                "Input src and calexp have mismatched detectors; "
                "running intersection of %d detectors.",
                len(detector_keys),
            )
        else:
            detector_keys = sorted(src_detectors)
 
        # We do not need the isolated star table in this task.
        # However, it is used in tests to confirm consistency of indexes.
        _, isolated_source_table = self.concat_isolated_star_cats(
            band,
            isolated_star_cat_dict,
            isolated_star_source_dict
        )
 
        if isolated_source_table is None:
            raise pipeBase.NoWorkFound(f'No good isolated sources found for any detectors in visit {visit}')
 
        exposure_cat_schema = afwTable.ExposureTable.makeMinimalSchema()
        exposure_cat_schema.addField('visit', type='L', doc='Visit number')
 
        metadata = dafBase.PropertyList()
        metadata.add("COMMENT", "Catalog id is detector id, sorted.")
        metadata.add("COMMENT", "Only detectors with data have entries.")
 
        psf_ap_corr_cat = afwTable.ExposureCatalog(exposure_cat_schema)
        psf_ap_corr_cat.setMetadata(metadata)
 
        measured_src_tables = []
        measured_src_table = None
 
        self.log.info("Running finalizeCharacterization on %d detectors.", len(detector_keys))
        for detector in detector_keys:
            self.log.info("Starting finalizeCharacterization on detector ID %d.", detector)
            src = src_dict[detector].get()
            exposure = calexp_dict[detector].get()
 
            psf, ap_corr_map, measured_src = self.compute_psf_and_ap_corr_map(
                visit,
                detector,
                exposure,
                src,
                isolated_source_table
            )
 
            # And now we package it together...
            if measured_src is not None:
                record = psf_ap_corr_cat.addNew()
                record['id'] = int(detector)
                record['visit'] = visit
                if psf is not None:
                    record.setPsf(psf)
                if ap_corr_map is not None:
                    record.setApCorrMap(ap_corr_map)
 
                measured_src['visit'][:] = visit
                measured_src['detector'][:] = detector
 
                measured_src_tables.append(measured_src.asAstropy().as_array())
 
        if len(measured_src_tables) > 0:
            measured_src_table = np.concatenate(measured_src_tables)
 
        if measured_src_table is None:
            raise pipeBase.NoWorkFound(f'No good sources found for any detectors in visit {visit}')
 
        return pipeBase.Struct(psf_ap_corr_cat=psf_ap_corr_cat,
                               output_table=measured_src_table)
 
    def _make_output_schema_mapper(self, input_schema):
        """Make the schema mapper from the input schema to the output schema.
 
        Parameters
        ----------
        input_schema : `lsst.afw.table.Schema`
            Input schema.
 
        Returns
        -------
        mapper : `lsst.afw.table.SchemaMapper`
            Schema mapper
        output_schema : `lsst.afw.table.Schema`
            Output schema (with alias map)
        """
        mapper = afwTable.SchemaMapper(input_schema)
        mapper.addMinimalSchema(afwTable.SourceTable.makeMinimalSchema())
        mapper.addMapping(input_schema['slot_Centroid_x'].asKey())
        mapper.addMapping(input_schema['slot_Centroid_y'].asKey())
 
        # The aperture fields may be used by the psf determiner.
        aper_fields = input_schema.extract('base_CircularApertureFlux_*')
        for field, item in aper_fields.items():
            mapper.addMapping(item.key)
 
        # The following two may be redundant, but then the mapping is a no-op.
        # Note that the slot_CalibFlux mapping will copy over any
        # normalized compensated fluxes that are used for calibration.
        apflux_fields = input_schema.extract('slot_ApFlux_*')
        for field, item in apflux_fields.items():
            mapper.addMapping(item.key)
 
        calibflux_fields = input_schema.extract('slot_CalibFlux_*')
        for field, item in calibflux_fields.items():
            mapper.addMapping(item.key)
 
        mapper.addMapping(
            input_schema[self.config.source_selector.active.signalToNoise.fluxField].asKey(),
            'calib_psf_selection_flux')
        mapper.addMapping(
            input_schema[self.config.source_selector.active.signalToNoise.errField].asKey(),
            'calib_psf_selection_flux_err')
 
        output_schema = mapper.getOutputSchema()
 
        output_schema.addField(
            'calib_psf_candidate',
            type='Flag',
            doc=('set if the source was a candidate for PSF determination, '
                 'as determined from FinalizeCharacterizationTask.'),
        )
        output_schema.addField(
            'calib_psf_reserved',
            type='Flag',
            doc=('set if source was reserved from PSF determination by '
                 'FinalizeCharacterizationTask.'),
        )
        output_schema.addField(
            'calib_psf_used',
            type='Flag',
            doc=('set if source was used in the PSF determination by '
                 'FinalizeCharacterizationTask.'),
        )
        output_schema.addField(
            'visit',
            type=np.int64,
            doc='Visit number for the sources.',
        )
        output_schema.addField(
            'detector',
            type=np.int32,
            doc='Detector number for the sources.',
        )
 
        alias_map = input_schema.getAliasMap()
        alias_map_output = afwTable.AliasMap()
        alias_map_output.set('slot_Centroid', alias_map.get('slot_Centroid'))
        alias_map_output.set('slot_ApFlux', alias_map.get('slot_ApFlux'))
        alias_map_output.set('slot_CalibFlux', alias_map.get('slot_CalibFlux'))
 
        output_schema.setAliasMap(alias_map_output)
 
        return mapper, output_schema
 
    def _make_selection_schema_mapper(self, input_schema):
        """Make the schema mapper from the input schema to the selection schema.
 
        Parameters
        ----------
        input_schema : `lsst.afw.table.Schema`
            Input schema.
 
        Returns
        -------
        mapper : `lsst.afw.table.SchemaMapper`
            Schema mapper
        selection_schema : `lsst.afw.table.Schema`
            Selection schema (with alias map)
        """
        mapper = afwTable.SchemaMapper(input_schema)
        mapper.addMinimalSchema(input_schema)
 
        selection_schema = mapper.getOutputSchema()
 
        selection_schema.setAliasMap(input_schema.getAliasMap())
 
        return mapper, selection_schema
 
    def concat_isolated_star_cats(self, band, isolated_star_cat_dict, isolated_star_source_dict):
        """
        Concatenate isolated star catalogs and make reserve selection.
 
        Parameters
        ----------
        band : `str`
            Band name.  Used to select reserved stars.
        isolated_star_cat_dict : `dict`
            Per-tract dict of isolated star catalog handles.
        isolated_star_source_dict : `dict`
            Per-tract dict of isolated star source catalog handles.
 
        Returns
        -------
        isolated_table : `np.ndarray` (N,)
            Table of isolated stars, with indexes to isolated sources.
            Returns None if there are no usable isolated catalogs.
        isolated_source_table : `np.ndarray` (M,)
            Table of isolated sources, with indexes to isolated stars.
            Returns None if there are no usable isolated catalogs.
        """
        isolated_tables = []
        isolated_sources = []
        merge_cat_counter = 0
        merge_source_counter = 0
 
        for tract in isolated_star_cat_dict:
            astropy_cat = isolated_star_cat_dict[tract].get()
            table_cat = np.asarray(astropy_cat)
 
            astropy_source = isolated_star_source_dict[tract].get(
                parameters={'columns': [self.config.id_column, 'obj_index']}
            )
            table_source = np.asarray(astropy_source)
 
            # Cut isolated star table to those observed in this band, and adjust indexes
            (use_band,) = (table_cat[f'nsource_{band}'] > 0).nonzero()
 
            if len(use_band) == 0:
                # There are no sources in this band in this tract.
                self.log.info("No sources found in %s band in tract %d.", band, tract)
                continue
 
            # With the following matching:
            #   table_source[b] <-> table_cat[use_band[a]]
            obj_index = table_source['obj_index'][:]
            a, b = esutil.numpy_util.match(use_band, obj_index)
 
            # Update indexes and cut to band-selected stars/sources
            table_source['obj_index'][b] = a
            _, index_new = np.unique(a, return_index=True)
            table_cat[f'source_cat_index_{band}'][use_band] = index_new
 
            # After the following cuts, the catalogs have the following properties:
            # - table_cat only contains isolated stars that have at least one source
            #   in ``band``.
            # - table_source only contains ``band`` sources.
            # - The slice table_cat["source_cat_index_{band}"]: table_cat["source_cat_index_{band}"]
            #                                                   + table_cat["nsource_{band}]
            #   applied to table_source will give all the sources associated with the star.
            # - For each source, table_source["obj_index"] points to the index of the associated
            #   isolated star.
            table_source = table_source[b]
            table_cat = table_cat[use_band]
 
            # Add reserved flag column to tables
            table_cat = np.lib.recfunctions.append_fields(
                table_cat,
                'reserved',
                np.zeros(table_cat.size, dtype=bool),
                usemask=False
            )
            table_source = np.lib.recfunctions.append_fields(
                table_source,
                'reserved',
                np.zeros(table_source.size, dtype=bool),
                usemask=False
            )
 
            # Get reserve star flags
            table_cat['reserved'][:] = self.reserve_selection.run(
                len(table_cat),
                extra=f'{band}_{tract}',
            )
            table_source['reserved'][:] = table_cat['reserved'][table_source['obj_index']]
 
            # Offset indexes to account for tract merging
            table_cat[f'source_cat_index_{band}'] += merge_source_counter
            table_source['obj_index'] += merge_cat_counter
 
            isolated_tables.append(table_cat)
            isolated_sources.append(table_source)
 
            merge_cat_counter += len(table_cat)
            merge_source_counter += len(table_source)
 
        if len(isolated_tables) > 0:
            isolated_table = np.concatenate(isolated_tables)
            isolated_source_table = np.concatenate(isolated_sources)
        else:
            isolated_table = None
            isolated_source_table = None
 
        return isolated_table, isolated_source_table
 
    def compute_psf_and_ap_corr_map(self, visit, detector, exposure, src, isolated_source_table):
        """Compute psf model and aperture correction map for a single exposure.
 
        Parameters
        ----------
        visit : `int`
            Visit number (for logging).
        detector : `int`
            Detector number (for logging).
        exposure : `lsst.afw.image.ExposureF`
        src : `lsst.afw.table.SourceCatalog`
        isolated_source_table : `np.ndarray`
 
        Returns
        -------
        psf : `lsst.meas.algorithms.ImagePsf`
            PSF Model
        ap_corr_map : `lsst.afw.image.ApCorrMap`
            Aperture correction map.
        measured_src : `lsst.afw.table.SourceCatalog`
            Updated source catalog with measurements, flags and aperture corrections.
        """
        # Extract footprints from the input src catalog for noise replacement.
        footprints = SingleFrameMeasurementTask.getFootprintsFromCatalog(src)
 
        # Apply source selector (s/n, flags, etc.)
        good_src = self.source_selector.selectSources(src)
        if sum(good_src.selected) == 0:
            self.log.warning('No good sources remain after cuts for visit %d, detector %d',
                             visit, detector)
            return None, None, None
 
        # Cut down input src to the selected sources
        # We use a separate schema/mapper here than for the output/measurement catalog because of
        # clashes between fields that were previously run and those that need to be rerun with
        # the new psf model.  This may be slightly inefficient but keeps input
        # and output values cleanly separated.
        selection_mapper, selection_schema = self._make_selection_schema_mapper(src.schema)
 
        selected_src = afwTable.SourceCatalog(selection_schema)
        selected_src.reserve(good_src.selected.sum())
        selected_src.extend(src[good_src.selected], mapper=selection_mapper)
 
        # The calib flags have been copied from the input table,
        # and we reset them here just to ensure they aren't propagated.
        selected_src['calib_psf_candidate'] = np.zeros(len(selected_src), dtype=bool)
        selected_src['calib_psf_used'] = np.zeros(len(selected_src), dtype=bool)
        selected_src['calib_psf_reserved'] = np.zeros(len(selected_src), dtype=bool)
 
        # Find the isolated sources and set flags
        matched_src, matched_iso = esutil.numpy_util.match(
            selected_src['id'],
            isolated_source_table[self.config.id_column]
        )
        if len(matched_src) == 0:
            self.log.warning(
                "No candidates from matched isolate stars for visit=%s, detector=%s "
                "(this is probably the result of an earlier astrometry failure).",
                visit, detector,
            )
            return None, None, None
 
        matched_arr = np.zeros(len(selected_src), dtype=bool)
        matched_arr[matched_src] = True
        selected_src['calib_psf_candidate'] = matched_arr
 
        reserved_arr = np.zeros(len(selected_src), dtype=bool)
        reserved_arr[matched_src] = isolated_source_table['reserved'][matched_iso]
        selected_src['calib_psf_reserved'] = reserved_arr
 
        selected_src = selected_src[selected_src['calib_psf_candidate']].copy(deep=True)
 
        # Make the measured source catalog as well, based on the selected catalog.
        measured_src = afwTable.SourceCatalog(self.schema)
        measured_src.reserve(len(selected_src))
        measured_src.extend(selected_src, mapper=self.schema_mapper)
 
        # We need to copy over the calib_psf flags because they were not in the mapper
        measured_src['calib_psf_candidate'] = selected_src['calib_psf_candidate']
        measured_src['calib_psf_reserved'] = selected_src['calib_psf_reserved']
 
        # Select the psf candidates from the selection catalog
        try:
            psf_selection_result = self.make_psf_candidates.run(selected_src, exposure=exposure)
        except Exception as e:
            self.log.exception('Failed to make PSF candidates for visit %d, detector %d: %s',
                               visit, detector, e)
            return None, None, measured_src
 
        psf_cand_cat = psf_selection_result.goodStarCat
 
        # Make list of psf candidates to send to the determiner
        # (omitting those marked as reserved)
        psf_determiner_list = [cand for cand, use
                               in zip(psf_selection_result.psfCandidates,
                                      ~psf_cand_cat['calib_psf_reserved']) if use]
        flag_key = psf_cand_cat.schema['calib_psf_used'].asKey()
        try:
            psf, cell_set = self.psf_determiner.determinePsf(exposure,
                                                             psf_determiner_list,
                                                             self.metadata,
                                                             flagKey=flag_key)
        except Exception as e:
            self.log.exception('Failed to determine PSF for visit %d, detector %d: %s',
                               visit, detector, e)
            return None, None, measured_src
        # Verify that the PSF is usable by downstream tasks
        sigma = psf.computeShape(psf.getAveragePosition()).getDeterminantRadius()
        if np.isnan(sigma):
            self.log.warning('Failed to determine psf for visit %d, detector %d: '
                             'Computed final PSF size is NAN.',
                             visit, detector)
            return None, None, measured_src
 
        # Set the psf in the exposure for measurement/aperture corrections.
        exposure.setPsf(psf)
 
        # At this point, we need to transfer the psf used flag from the selection
        # catalog to the measurement catalog.
        matched_selected, matched_measured = esutil.numpy_util.match(
            selected_src['id'],
            measured_src['id']
        )
        measured_used = np.zeros(len(measured_src), dtype=bool)
        measured_used[matched_measured] = selected_src['calib_psf_used'][matched_selected]
        measured_src['calib_psf_used'] = measured_used
 
        # Next, we do the measurement on all the psf candidate, used, and reserved stars.
        # We use the full footprint list from the input src catalog for noise replacement.
        try:
            self.measurement.run(measCat=measured_src, exposure=exposure, footprints=footprints)
        except Exception as e:
            self.log.warning('Failed to make measurements for visit %d, detector %d: %s',
                             visit, detector, e)
            return psf, None, measured_src
 
        # And finally the ap corr map.
        try:
            ap_corr_map = self.measure_ap_corr.run(exposure=exposure,
                                                   catalog=measured_src).apCorrMap
        except Exception as e:
            self.log.warning('Failed to compute aperture corrections for visit %d, detector %d: %s',
                             visit, detector, e)
            return psf, None, measured_src
 
        # Need to merge the original normalization aperture correction map.
        ap_corr_map_input = exposure.apCorrMap
        for key in ap_corr_map_input:
            if key not in ap_corr_map:
                ap_corr_map[key] = ap_corr_map_input[key]
 
        self.apply_ap_corr.run(catalog=measured_src, apCorrMap=ap_corr_map)
 
        return psf, ap_corr_map, measured_src