forcedPhotCcd.py

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# This file is part of meas_base.
#
# 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 astropy.table
import pandas as pd
import numpy as np
 
import lsst.pex.config
import lsst.pex.exceptions
import lsst.pipe.base
import lsst.geom
import lsst.afw.detection
import lsst.afw.geom
import lsst.afw.image
import lsst.afw.table
import lsst.sphgeom
 
from lsst.pipe.base import PipelineTaskConnections, NoWorkFound
import lsst.pipe.base.connectionTypes as cT
 
import lsst.pipe.base as pipeBase
from lsst.skymap import BaseSkyMap
 
from .forcedMeasurement import ForcedMeasurementTask
from .applyApCorr import ApplyApCorrTask
from .catalogCalculation import CatalogCalculationTask
from ._id_generator import DetectorVisitIdGeneratorConfig
 
__all__ = ("ForcedPhotCcdConfig", "ForcedPhotCcdTask",
           "ForcedPhotCcdFromDataFrameTask", "ForcedPhotCcdFromDataFrameConfig")
 
 
class ForcedPhotCcdConnections(PipelineTaskConnections,
                               dimensions=("instrument", "visit", "detector", "skymap", "tract"),
                               defaultTemplates={"inputCoaddName": "deep",
                                                 "inputName": "calexp"}):
    inputSchema = cT.InitInput(
        doc="Schema for the input measurement catalogs.",
        name="{inputCoaddName}Coadd_ref_schema",
        storageClass="SourceCatalog",
    )
    outputSchema = cT.InitOutput(
        doc="Schema for the output forced measurement catalogs.",
        name="forced_src_schema",
        storageClass="SourceCatalog",
    )
    exposure = cT.Input(
        doc="Input exposure to perform photometry on.",
        name="{inputName}",
        storageClass="ExposureF",
        dimensions=["instrument", "visit", "detector"],
    )
    refCat = cT.Input(
        doc="Catalog of shapes and positions at which to force photometry.",
        name="{inputCoaddName}Coadd_ref",
        storageClass="SourceCatalog",
        dimensions=["skymap", "tract", "patch"],
        multiple=True,
        deferLoad=True,
    )
    skyMap = cT.Input(
        doc="SkyMap dataset that defines the coordinate system of the reference catalog.",
        name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
        storageClass="SkyMap",
        dimensions=["skymap"],
    )
    skyCorr = cT.Input(
        doc="Input Sky Correction to be subtracted from the calexp if doApplySkyCorr=True",
        name="skyCorr",
        storageClass="Background",
        dimensions=("instrument", "visit", "detector"),
    )
    visitSummary = cT.Input(
        doc="Input visit-summary catalog with updated calibration objects.",
        name="finalVisitSummary",
        storageClass="ExposureCatalog",
        dimensions=("instrument", "visit"),
    )
    measCat = cT.Output(
        doc="Output forced photometry catalog.",
        name="forced_src",
        storageClass="SourceCatalog",
        dimensions=["instrument", "visit", "detector", "skymap", "tract"],
    )
 
    def __init__(self, *, config=None):
        super().__init__(config=config)
        if not config.doApplySkyCorr:
            del self.skyCorr
        if not config.useVisitSummary:
            del self.visitSummary
        if config.refCatStorageClass != "SourceCatalog":
            del self.inputSchema
            # Connections are immutable, so we have to replace them entirely
            # rather than edit them in-place.
            self.refCat = dataclasses.replace(self.refCat, storageClass=config.refCatStorageClass)
 
 
class ForcedPhotCcdConfig(pipeBase.PipelineTaskConfig,
                          pipelineConnections=ForcedPhotCcdConnections):
    """Config class for forced measurement driver task."""
    measurement = lsst.pex.config.ConfigurableField(
        target=ForcedMeasurementTask,
        doc="subtask to do forced measurement"
    )
    coaddName = lsst.pex.config.Field(
        doc="coadd name: typically one of deep or goodSeeing",
        dtype=str,
        default="deep",
    )
    doApCorr = lsst.pex.config.Field(
        dtype=bool,
        default=True,
        doc="Run subtask to apply aperture corrections"
    )
    applyApCorr = lsst.pex.config.ConfigurableField(
        target=ApplyApCorrTask,
        doc="Subtask to apply aperture corrections"
    )
    catalogCalculation = lsst.pex.config.ConfigurableField(
        target=CatalogCalculationTask,
        doc="Subtask to run catalogCalculation plugins on catalog"
    )
    doApplySkyCorr = lsst.pex.config.Field(
        dtype=bool,
        default=False,
        doc="Apply sky correction?",
    )
    useVisitSummary = lsst.pex.config.Field(
        dtype=bool,
        default=True,
        doc=(
            "Use updated WCS, PhotoCalib, ApCorr, and PSF from visit summary? "
            "This should be False if and only if the input image already has the best-available calibration "
            "objects attached."
        ),
    )
    refCatStorageClass = lsst.pex.config.ChoiceField(
        dtype=str,
        allowed={
            "SourceCatalog": "Read an lsst.afw.table.SourceCatalog.",
            "DataFrame": "Read a pandas.DataFrame.",
            "ArrowAstropy": "Read an astropy.table.Table saved to Parquet.",
        },
        default="SourceCatalog",
        doc=(
            "The butler storage class for the refCat connection. "
            "If set to something other than 'SourceCatalog', the "
            "'inputSchema'  connection will be ignored."
        )
    )
    refCatIdColumn = lsst.pex.config.Field(
        dtype=str,
        default="diaObjectId",
        doc=(
            "Name of the column that provides the object ID from the refCat connection. "
            "measurement.copyColumns['id'] must be set to this value as well."
            "Ignored if refCatStorageClass='SourceCatalog'."
        )
    )
    refCatRaColumn = lsst.pex.config.Field(
        dtype=str,
        default="ra",
        doc=(
            "Name of the column that provides the right ascension (in floating-point degrees) from the "
            "refCat connection. "
            "Ignored if refCatStorageClass='SourceCatalog'."
        )
    )
    refCatDecColumn = lsst.pex.config.Field(
        dtype=str,
        default="dec",
        doc=(
            "Name of the column that provides the declination (in floating-point degrees) from the "
            "refCat connection. "
            "Ignored if refCatStorageClass='SourceCatalog'."
        )
    )
    includePhotoCalibVar = lsst.pex.config.Field(
        dtype=bool,
        default=False,
        doc="Add photometric calibration variance to warp variance plane?",
    )
    footprintSource = lsst.pex.config.ChoiceField(
        dtype=str,
        doc="Where to obtain footprints to install in the measurement catalog, prior to measurement.",
        allowed={
            "transformed": "Transform footprints from the reference catalog (downgrades HeavyFootprints).",
            "psf": ("Use the scaled shape of the PSF at the position of each source (does not generate "
                    "HeavyFootprints)."),
        },
        optional=True,
        default="transformed",
    )
    psfFootprintScaling = lsst.pex.config.Field(
        dtype=float,
        doc="Scaling factor to apply to the PSF shape when footprintSource='psf' (ignored otherwise).",
        default=3.0,
    )
    idGenerator = DetectorVisitIdGeneratorConfig.make_field()
 
    def setDefaults(self):
        # Docstring inherited.
        super().setDefaults()
        # Footprints here will not be entirely correct, so don't try to make
        # a biased correction for blended neighbors.
        self.measurement.doReplaceWithNoise = False
        # Only run a minimal set of plugins, as these measurements are only
        # needed for PSF-like sources.
        self.measurement.plugins.names = ["base_PixelFlags",
                                          "base_TransformedCentroid",
                                          "base_PsfFlux",
                                          "base_LocalBackground",
                                          "base_LocalPhotoCalib",
                                          "base_LocalWcs",
                                          ]
        self.measurement.slots.psfFlux = "base_PsfFlux"
        self.measurement.slots.shape = None
        # Make catalogCalculation a no-op by default as no modelFlux is setup
        # by default in ForcedMeasurementTask.
        self.catalogCalculation.plugins.names = []
 
    def validate(self):
        super().validate()
        if self.refCatStorageClass != "SourceCatalog":
            if self.footprintSource == "transformed":
                raise ValueError("Cannot transform footprints from reference catalog, because "
                                 f"{self.config.refCatStorageClass} datasets can't hold footprints.")
            if self.measurement.copyColumns["id"] != self.refCatIdColumn:
                raise ValueError(
                    f"measurement.copyColumns['id'] should be set to {self.refCatIdColumn} "
                    f"(refCatIdColumn) when refCatStorageClass={self.refCatStorageClass}."
                )
 
    def configureParquetRefCat(self, refCatStorageClass: str = "ArrowAstropy"):
        """Set the refCatStorageClass option to a Parquet-based type, and
        reconfigure the measurement subtask and footprintSources accordingly.
        """
        self.refCatStorageClass = refCatStorageClass
        self.footprintSource = "psf"
        self.measurement.doReplaceWithNoise = False
        self.measurement.plugins.names -= {"base_TransformedCentroid"}
        self.measurement.plugins.names |= {"base_TransformedCentroidFromCoord"}
        self.measurement.copyColumns["id"] = self.refCatIdColumn
        self.measurement.copyColumns.pop("deblend_nChild", None)
        self.measurement.slots.centroid = "base_TransformedCentroidFromCoord"
 
 
class ForcedPhotCcdTask(pipeBase.PipelineTask):
    """A pipeline task for performing forced measurement on CCD images.
 
    Parameters
    ----------
    refSchema : `lsst.afw.table.Schema`, optional
        The schema of the reference catalog, passed to the constructor of the
        references subtask. Optional, but must be specified if ``initInputs``
        is not; if both are specified, ``initInputs`` takes precedence.
    initInputs : `dict`
        Dictionary that can contain a key ``inputSchema`` containing the
        schema. If present will override the value of ``refSchema``.
    **kwargs
        Keyword arguments are passed to the supertask constructor.
    """
 
    ConfigClass = ForcedPhotCcdConfig
    _DefaultName = "forcedPhotCcd"
    dataPrefix = ""
 
    def __init__(self, refSchema=None, initInputs=None, **kwargs):
        super().__init__(**kwargs)
 
        if initInputs:
            refSchema = initInputs['inputSchema'].schema
 
        if refSchema is None:
            refSchema = lsst.afw.table.SourceTable.makeMinimalSchema()
 
        self.makeSubtask("measurement", refSchema=refSchema)
        # It is necessary to get the schema internal to the forced measurement
        # task until such a time that the schema is not owned by the
        # measurement task, but is passed in by an external caller.
        if self.config.doApCorr:
            self.makeSubtask("applyApCorr", schema=self.measurement.schema)
        self.makeSubtask('catalogCalculation', schema=self.measurement.schema)
        self.outputSchema = lsst.afw.table.SourceCatalog(self.measurement.schema)
 
    def runQuantum(self, butlerQC, inputRefs, outputRefs):
        inputs = butlerQC.get(inputRefs)
 
        tract = butlerQC.quantum.dataId['tract']
        skyMap = inputs.pop('skyMap')
        inputs['refWcs'] = skyMap[tract].getWcs()
 
        # Connections only exist if they are configured to be used.
        skyCorr = inputs.pop('skyCorr', None)
 
        inputs['exposure'] = self.prepareCalibratedExposure(
            inputs['exposure'],
            skyCorr=skyCorr,
            visitSummary=inputs.pop("visitSummary", None),
        )
 
        if inputs["exposure"].getWcs() is None:
            raise NoWorkFound("Exposure has no WCS.")
 
        match self.config.refCatStorageClass:
            case "SourceCatalog":
                prepFunc = self._prepSourceCatalogRefCat
            case "DataFrame":
                prepFunc = self._prepDataFrameRefCat
            case "ArrowAstropy":
                prepFunc = self._prepArrowAstropyRefCat
            case _:
                raise AssertionError("Configuration should not have passed validation.")
        self.log.info("Filtering ref cats: %s", ','.join([str(i.dataId) for i in inputs['refCat']]))
        inputs['refCat'] = prepFunc(
            inputs['refCat'],
            inputs['exposure'].getBBox(),
            inputs['exposure'].getWcs(),
        )
        # generateMeasCat does not actually use the refWcs; parameter is
        # passed for signature backwards compatibility.
        inputs['measCat'], inputs['exposureId'] = self.generateMeasCat(
            inputRefs.exposure.dataId, inputs['exposure'], inputs['refCat'], inputs['refWcs']
        )
        # attachFootprints only uses refWcs in ``transformed`` mode, which is
        # not supported unless refCatStorageClass='SourceCatalog'.
        self.attachFootprints(inputs["measCat"], inputs["refCat"], inputs["exposure"], inputs["refWcs"])
        outputs = self.run(**inputs)
        butlerQC.put(outputs, outputRefs)
 
    def prepareCalibratedExposure(self, exposure, skyCorr=None, visitSummary=None):
        """Prepare a calibrated exposure and apply external calibrations
        and sky corrections if so configured.
 
        Parameters
        ----------
        exposure : `lsst.afw.image.exposure.Exposure`
            Input exposure to adjust calibrations.
        skyCorr : `lsst.afw.math.backgroundList`, optional
            Sky correction frame to apply if doApplySkyCorr=True.
        visitSummary : `lsst.afw.table.ExposureCatalog`, optional
            Exposure catalog with update calibrations; any not-None calibration
            objects attached will be used.  These are applied first and may be
            overridden by other arguments.
 
        Returns
        -------
        exposure : `lsst.afw.image.exposure.Exposure`
            Exposure with adjusted calibrations.
        """
        detectorId = exposure.getInfo().getDetector().getId()
 
        if visitSummary is not None:
            row = visitSummary.find(detectorId)
            if row is None:
                raise RuntimeError(f"Detector id {detectorId} not found in visitSummary.")
            if (photoCalib := row.getPhotoCalib()) is not None:
                exposure.setPhotoCalib(photoCalib)
            if (skyWcs := row.getWcs()) is not None:
                exposure.setWcs(skyWcs)
            if (psf := row.getPsf()) is not None:
                exposure.setPsf(psf)
            if (apCorrMap := row.getApCorrMap()) is not None:
                exposure.info.setApCorrMap(apCorrMap)
 
        if skyCorr is not None:
            exposure.maskedImage -= skyCorr.getImage()
 
        return exposure
 
    def generateMeasCat(self, dataId, exposure, refCat, refWcs):
        """Generate a measurement catalog.
 
        Parameters
        ----------
        dataId : `lsst.daf.butler.DataCoordinate`
            Butler data ID for this image, with ``{visit, detector}`` keys.
        exposure : `lsst.afw.image.exposure.Exposure`
            Exposure to generate the catalog for.
        refCat : `lsst.afw.table.SourceCatalog`
            Catalog of shapes and positions at which to force photometry.
        refWcs : `lsst.afw.image.SkyWcs`
            Reference world coordinate system.
            This parameter is not currently used.
 
        Returns
        -------
        measCat : `lsst.afw.table.SourceCatalog`
            Catalog of forced sources to measure.
        expId : `int`
            Unique binary id associated with the input exposure
        """
        id_generator = self.config.idGenerator.apply(dataId)
        measCat = self.measurement.generateMeasCat(exposure, refCat, refWcs,
                                                   idFactory=id_generator.make_table_id_factory())
        return measCat, id_generator.catalog_id
 
    def run(self, measCat, exposure, refCat, refWcs, exposureId=None):
        """Perform forced measurement on a single exposure.
 
        Parameters
        ----------
        measCat : `lsst.afw.table.SourceCatalog`
            The measurement catalog, based on the sources listed in the
            reference catalog.
        exposure : `lsst.afw.image.Exposure`
            The measurement image upon which to perform forced detection.
        refCat : `lsst.afw.table.SourceCatalog`
            The reference catalog of sources to measure.
        refWcs : `lsst.afw.image.SkyWcs`
            The WCS for the references.
        exposureId : `int`
            Optional unique exposureId used for random seed in measurement
            task.
 
        Returns
        -------
        result : `lsst.pipe.base.Struct`
            Structure with fields:
 
            ``measCat``
                Catalog of forced measurement results
                (`lsst.afw.table.SourceCatalog`).
        """
        self.measurement.run(measCat, exposure, refCat, refWcs, exposureId=exposureId)
        if self.config.doApCorr:
            apCorrMap = exposure.getInfo().getApCorrMap()
            if apCorrMap is None:
                self.log.warning("Forced exposure image does not have valid aperture correction; skipping.")
            else:
                self.applyApCorr.run(
                    catalog=measCat,
                    apCorrMap=apCorrMap,
                )
        self.catalogCalculation.run(measCat)
 
        return pipeBase.Struct(measCat=measCat)
 
    def attachFootprints(self, sources, refCat, exposure, refWcs):
        """Attach footprints to blank sources prior to measurements.
 
        Notes
        -----
        `~lsst.afw.detection.Footprint` objects for forced photometry must
        be in the pixel coordinate system of the image being measured, while
        the actual detections may start out in a different coordinate system.
 
        Subclasses of this class may implement this method to define how
        those `~lsst.afw.detection.Footprint` objects should be generated.
 
        This default implementation transforms depends on the
        ``footprintSource`` configuration parameter.
        """
        if self.config.footprintSource == "transformed":
            return self.measurement.attachTransformedFootprints(sources, refCat, exposure, refWcs)
        elif self.config.footprintSource == "psf":
            return self.measurement.attachPsfShapeFootprints(sources, exposure,
                                                             scaling=self.config.psfFootprintScaling)
 
    def _prepSourceCatalogRefCat(self, refCatHandles, exposureBBox, exposureWcs):
        """Prepare a merged, filtered reference catalog from SourceCatalog
        inputs.
 
        Parameters
        ----------
        refCatHandles : sequence of `lsst.daf.butler.DeferredDatasetHandle`
            Handles for catalogs of shapes and positions at which to force
            photometry.
        exposureBBox :   `lsst.geom.Box2I`
            Bounding box on which to select rows that overlap
        exposureWcs : `lsst.afw.geom.SkyWcs`
            World coordinate system to convert sky coords in ref cat to
            pixel coords with which to compare with exposureBBox
 
        Returns
        -------
        refSources : `lsst.afw.table.SourceCatalog`
            Filtered catalog of forced sources to measure.
 
        Notes
        -----
        The majority of this code is based on the methods of
        lsst.meas.algorithms.loadReferenceObjects.ReferenceObjectLoader
 
        """
        mergedRefCat = None
 
        # Step 1: Determine bounds of the exposure photometry will
        # be performed on.
        expBBox = lsst.geom.Box2D(exposureBBox)
        expBoxCorners = expBBox.getCorners()
        expSkyCorners = [exposureWcs.pixelToSky(corner).getVector() for corner in expBoxCorners]
        expPolygon = lsst.sphgeom.ConvexPolygon(expSkyCorners)
 
        # Step 2: Filter out reference catalog sources that are
        # not contained within the exposure boundaries, or whose
        # parents are not within the exposure boundaries.  Note
        # that within a single input refCat, the parents always
        # appear before the children.
        for refCat in refCatHandles:
            refCat = refCat.get()
            if mergedRefCat is None:
                mergedRefCat = lsst.afw.table.SourceCatalog(refCat.table)
                containedIds = {0}  # zero as a parent ID means "this is a parent"
            for record in refCat:
                if (
                    expPolygon.contains(record.getCoord().getVector()) and record.getParent()
                    in containedIds
                ):
                    record.setFootprint(record.getFootprint())
                    mergedRefCat.append(record)
                    containedIds.add(record.getId())
        if mergedRefCat is None:
            raise RuntimeError("No reference objects for forced photometry.")
        mergedRefCat.sort(lsst.afw.table.SourceTable.getParentKey())
        return mergedRefCat
 
    def _prepDataFrameRefCat(self, refCatHandles, exposureBBox, exposureWcs):
        """Prepare a merged, filtered reference catalog from DataFrame
        inputs.
 
        Parameters
        ----------
        refCatHandles : sequence of `lsst.daf.butler.DeferredDatasetHandle`
            Handles for catalogs of shapes and positions at which to force
            photometry.
        exposureBBox :   `lsst.geom.Box2I`
            Bounding box on which to select rows that overlap
        exposureWcs : `lsst.afw.geom.SkyWcs`
            World coordinate system to convert sky coords in ref cat to
            pixel coords with which to compare with exposureBBox
 
        Returns
        -------
        refCat : `lsst.afw.table.SourceTable`
            Source Catalog with minimal schema that overlaps exposureBBox
        """
        dfList = [
            i.get(
                parameters={
                    "columns": [
                        self.config.refCatIdColumn,
                        self.config.refCatRaColumn,
                        self.config.refCatDecColumn,
                    ]
                }
            )
            for i in refCatHandles
        ]
        df = pd.concat(dfList)
        # translate ra/dec coords in dataframe to detector pixel coords
        # to down select rows that overlap the detector bbox
        mapping = exposureWcs.getTransform().getMapping()
        x, y = mapping.applyInverse(
            np.array(df[[self.config.refCatRaColumn, self.config.refCatDecColumn]].values*2*np.pi/360).T
        )
        inBBox = np.atleast_1d(lsst.geom.Box2D(exposureBBox).contains(x, y))
        refCat = self._makeMinimalSourceCatalogFromDataFrame(df[inBBox])
        return refCat
 
    def _prepArrowAstropyRefCat(self, refCatHandles, exposureBBox, exposureWcs):
        """Prepare a merged, filtered reference catalog from ArrowAstropy
        inputs.
 
        Parameters
        ----------
        refCatHandles : sequence of `lsst.daf.butler.DeferredDatasetHandle`
            Handles for catalogs of shapes and positions at which to force
            photometry.
        exposureBBox :   `lsst.geom.Box2I`
            Bounding box on which to select rows that overlap
        exposureWcs : `lsst.afw.geom.SkyWcs`
            World coordinate system to convert sky coords in ref cat to
            pixel coords with which to compare with exposureBBox
 
        Returns
        -------
        refCat : `lsst.afw.table.SourceTable`
            Source Catalog with minimal schema that overlaps exposureBBox
        """
        table_list = [
            i.get(
                parameters={
                    "columns": [
                        self.config.refCatIdColumn,
                        self.config.refCatRaColumn,
                        self.config.refCatDecColumn,
                    ]
                }
            )
            for i in refCatHandles
        ]
        full_table = astropy.table.vstack(table_list)
        # translate ra/dec coords in table to detector pixel coords
        # to down-select rows that overlap the detector bbox
        mapping = exposureWcs.getTransform().getMapping()
        ra_dec_rad = np.zeros((2, len(full_table)), dtype=float)
        ra_dec_rad[0, :] = full_table[self.config.refCatRaColumn]
        ra_dec_rad[1, :] = full_table[self.config.refCatDecColumn]
        ra_dec_rad *= np.pi/180.0
        x, y = mapping.applyInverse(ra_dec_rad)
        inBBox = lsst.geom.Box2D(exposureBBox).contains(x, y)
        refCat = self._makeMinimalSourceCatalogFromAstropy(full_table[inBBox])
        return refCat
 
    def _makeMinimalSourceCatalogFromDataFrame(self, df):
        """Create minimal schema SourceCatalog from a pandas DataFrame.
 
        The forced measurement subtask expects this as input.
 
        Parameters
        ----------
        df : `pandas.DataFrame`
            Table with locations and ids.
 
        Returns
        -------
        outputCatalog : `lsst.afw.table.SourceTable`
            Output catalog with minimal schema.
        """
        schema = lsst.afw.table.SourceTable.makeMinimalSchema()
        outputCatalog = lsst.afw.table.SourceCatalog(schema)
        outputCatalog.reserve(len(df))
 
        for objectId, ra, dec in df[['ra', 'dec']].itertuples():
            outputRecord = outputCatalog.addNew()
            outputRecord.setId(objectId)
            outputRecord.setCoord(lsst.geom.SpherePoint(ra, dec, lsst.geom.degrees))
        return outputCatalog
 
    def _makeMinimalSourceCatalogFromAstropy(self, table):
        """Create minimal schema SourceCatalog from an Astropy Table.
 
        The forced measurement subtask expects this as input.
 
        Parameters
        ----------
        table : `astropy.table.Table`
            Table with locations and ids.
 
        Returns
        -------
        outputCatalog : `lsst.afw.table.SourceTable`
            Output catalog with minimal schema.
        """
        schema = lsst.afw.table.SourceTable.makeMinimalSchema()
        outputCatalog = lsst.afw.table.SourceCatalog(schema)
        outputCatalog.reserve(len(table))
 
        for objectId, ra, dec in table.iterrows():
            outputRecord = outputCatalog.addNew()
            outputRecord.setId(objectId)
            outputRecord.setCoord(lsst.geom.SpherePoint(ra, dec, lsst.geom.degrees))
        return outputCatalog
 
 
class ForcedPhotCcdFromDataFrameConnections(ForcedPhotCcdConnections,
                                            dimensions=("instrument", "visit", "detector", "skymap", "tract"),
                                            defaultTemplates={"inputCoaddName": "goodSeeing",
                                                              "inputName": "calexp",
                                                              }):
    pass
 
 
class ForcedPhotCcdFromDataFrameConfig(ForcedPhotCcdConfig,
                                       pipelineConnections=ForcedPhotCcdFromDataFrameConnections):
    def setDefaults(self):
        super().setDefaults()
        self.configureParquetRefCat("DataFrame")
        self.connections.refCat = "{inputCoaddName}Diff_fullDiaObjTable"
        self.connections.outputSchema = "forced_src_diaObject_schema"
        self.connections.measCat = "forced_src_diaObject"
 
 
class ForcedPhotCcdFromDataFrameTask(ForcedPhotCcdTask):
    """Force Photometry on a per-detector exposure with coords from a DataFrame
 
    Uses input from a DataFrame instead of SourceCatalog
    like the base class ForcedPhotCcd does.
    Writes out a SourceCatalog so that the downstream
    WriteForcedSourceTableTask can be reused with output from this Task.
    """
    _DefaultName = "forcedPhotCcdFromDataFrame"
    ConfigClass = ForcedPhotCcdFromDataFrameConfig