getTemplate.py

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#
# LSST Data Management System
# Copyright 2016 LSST Corporation.
#
# 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
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.    See the
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# see <http://www.lsstcorp.org/LegalNotices/>.
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import numpy as np
 
import lsst.afw.image as afwImage
import lsst.geom as geom
import lsst.afw.geom as afwGeom
import lsst.afw.table as afwTable
import lsst.afw.math as afwMath
import lsst.pex.config as pexConfig
import lsst.pipe.base as pipeBase
from lsst.skymap import BaseSkyMap
from lsst.daf.butler import DeferredDatasetHandle
from lsst.ip.diffim.dcrModel import DcrModel
from lsst.meas.algorithms import CoaddPsf, CoaddPsfConfig
 
__all__ = ["GetCoaddAsTemplateTask", "GetCoaddAsTemplateConfig",
           "GetCalexpAsTemplateTask", "GetCalexpAsTemplateConfig",
           "GetMultiTractCoaddTemplateTask", "GetMultiTractCoaddTemplateConfig"]
 
 
class GetCoaddAsTemplateConfig(pexConfig.Config):
    templateBorderSize = pexConfig.Field(
        dtype=int,
        default=10,
        doc="Number of pixels to grow the requested template image to account for warping"
    )
    coaddName = pexConfig.Field(
        doc="coadd name: typically one of 'deep', 'goodSeeing', or 'dcr'",
        dtype=str,
        default="deep",
    )
    numSubfilters = pexConfig.Field(
        doc="Number of subfilters in the DcrCoadd. Used only if ``coaddName``='dcr'",
        dtype=int,
        default=3,
    )
    effectiveWavelength = pexConfig.Field(
        doc="Effective wavelength of the filter. Used only if ``coaddName``='dcr'",
        optional=True,
        dtype=float,
    )
    bandwidth = pexConfig.Field(
        doc="Bandwidth of the physical filter. Used only if ``coaddName``='dcr'",
        optional=True,
        dtype=float,
    )
    warpType = pexConfig.Field(
        doc="Warp type of the coadd template: one of 'direct' or 'psfMatched'",
        dtype=str,
        default="direct",
    )
 
    def validate(self):
        if self.coaddNamecoaddName == 'dcr':
            if self.effectiveWavelengtheffectiveWavelength is None or self.bandwidthbandwidth is None:
                raise ValueError("The effective wavelength and bandwidth of the physical filter "
                                 "must be set in the getTemplate config for DCR coadds. "
                                 "Required until transmission curves are used in DM-13668.")
 
 
class GetCoaddAsTemplateTask(pipeBase.Task):
    """Subtask to retrieve coadd for use as an image difference template.
 
    This is the default getTemplate Task to be run as a subtask by
    ``pipe.tasks.ImageDifferenceTask``. The main methods are ``run()`` and
    ``runGen3()``.
 
    Notes
    -----
    From the given skymap, the closest tract is selected;  multiple tracts  are
    not supported. The assembled template inherits the WCS of the selected
    skymap tract and the resolution of the template exposures. Overlapping box
    regions of the input template patches are pixel by pixel copied into the
    assembled template image. There is no warping or pixel resampling.
 
    Pixels with no overlap of any available input patches are set to ``nan`` value
    and ``NO_DATA`` flagged.
    """
 
    ConfigClass = GetCoaddAsTemplateConfig
    _DefaultName = "GetCoaddAsTemplateTask"
 
    def runDataRef(self, exposure, sensorRef, templateIdList=None):
        """Gen2 task entry point. Retrieve and mosaic a template coadd exposure
        that overlaps the science exposure.
 
        Parameters
        ----------
        exposure: `lsst.afw.image.Exposure`
            an exposure for which to generate an overlapping template
        sensorRef : TYPE
            a Butler data reference that can be used to obtain coadd data
        templateIdList : TYPE, optional
            list of data ids, unused here, in the case of coadd template
 
        Returns
        -------
        result : `lsst.pipe.base.Struct`
            - ``exposure`` : `lsst.afw.image.ExposureF`
                a template coadd exposure assembled out of patches
            - ``sources`` :  None for this subtask
        """
        skyMap = sensorRef.get(datasetType=self.config.coaddName + "Coadd_skyMap")
        tractInfo, patchList, skyCorners = self.getOverlapPatchListgetOverlapPatchList(exposure, skyMap)
 
        availableCoaddRefs = dict()
        for patchInfo in patchList:
            patchNumber = tractInfo.getSequentialPatchIndex(patchInfo)
            patchArgDict = dict(
                datasetType=self.getCoaddDatasetNamegetCoaddDatasetName() + "_sub",
                bbox=patchInfo.getOuterBBox(),
                tract=tractInfo.getId(),
                patch="%s,%s" % (patchInfo.getIndex()[0], patchInfo.getIndex()[1]),
                subfilter=0,
                numSubfilters=self.config.numSubfilters,
            )
 
            if sensorRef.datasetExists(**patchArgDict):
                self.log.info("Reading patch %s", patchArgDict)
                availableCoaddRefs[patchNumber] = patchArgDict
 
        templateExposure = self.runrun(
            tractInfo, patchList, skyCorners, availableCoaddRefs,
            sensorRef=sensorRef, visitInfo=exposure.getInfo().getVisitInfo()
        )
        return pipeBase.Struct(exposure=templateExposure, sources=None)
 
    def runQuantum(self, exposure, butlerQC, skyMapRef, coaddExposureRefs):
        """Gen3 task entry point. Retrieve and mosaic a template coadd exposure
        that overlaps the science exposure.
 
        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            The science exposure to define the sky region of the template coadd.
        butlerQC : `lsst.pipe.base.ButlerQuantumContext`
            Butler like object that supports getting data by DatasetRef.
        skyMapRef : `lsst.daf.butler.DatasetRef`
            Reference to SkyMap object that corresponds to the template coadd.
        coaddExposureRefs : iterable of `lsst.daf.butler.DeferredDatasetRef`
            Iterable of references to the available template coadd patches.
 
        Returns
        -------
        result : `lsst.pipe.base.Struct`
            - ``exposure`` : `lsst.afw.image.ExposureF`
                a template coadd exposure assembled out of patches
            - ``sources`` :  `None` for this subtask
        """
        skyMap = butlerQC.get(skyMapRef)
        coaddExposureRefs = butlerQC.get(coaddExposureRefs)
        tracts = [ref.dataId['tract'] for ref in coaddExposureRefs]
        if tracts.count(tracts[0]) == len(tracts):
            tractInfo = skyMap[tracts[0]]
        else:
            raise RuntimeError("Templates constructed from multiple Tracts not supported by this task. "
                               "Use GetMultiTractCoaddTemplateTask instead.")
 
        detectorBBox = exposure.getBBox()
        detectorWcs = exposure.getWcs()
        detectorCorners = detectorWcs.pixelToSky(geom.Box2D(detectorBBox).getCorners())
        validPolygon = exposure.getInfo().getValidPolygon()
        detectorPolygon = validPolygon if validPolygon else geom.Box2D(detectorBBox)
 
        availableCoaddRefs = dict()
        overlappingArea = 0
        for coaddRef in coaddExposureRefs:
            dataId = coaddRef.dataId
            patchWcs = skyMap[dataId['tract']].getWcs()
            patchBBox = skyMap[dataId['tract']][dataId['patch']].getOuterBBox()
            patchCorners = patchWcs.pixelToSky(geom.Box2D(patchBBox).getCorners())
            patchPolygon = afwGeom.Polygon(detectorWcs.skyToPixel(patchCorners))
            if patchPolygon.intersection(detectorPolygon):
                overlappingArea += patchPolygon.intersectionSingle(detectorPolygon).calculateArea()
                if self.config.coaddName == 'dcr':
                    self.log.info("Using template input tract=%s, patch=%s, subfilter=%s",
                                  dataId['tract'], dataId['patch'], dataId['subfilter'])
                    if dataId['patch'] in availableCoaddRefs:
                        availableCoaddRefs[dataId['patch']].append(coaddRef)
                    else:
                        availableCoaddRefs[dataId['patch']] = [coaddRef, ]
                else:
                    self.log.info("Using template input tract=%s, patch=%s",
                                  dataId['tract'], dataId['patch'])
                    availableCoaddRefs[dataId['patch']] = coaddRef
 
        if overlappingArea == 0:
            templateExposure = None
            pixGood = 0
            self.log.warning("No overlapping template patches found")
        else:
            patchList = [tractInfo[patch] for patch in availableCoaddRefs.keys()]
            templateExposure = self.runrun(tractInfo, patchList, detectorCorners, availableCoaddRefs,
                                        visitInfo=exposure.getInfo().getVisitInfo())
            # Count the number of pixels with the NO_DATA mask bit set
            # counting NaN pixels is insufficient because pixels without data are often intepolated over)
            pixNoData = np.count_nonzero(templateExposure.mask.array
                                         & templateExposure.mask.getPlaneBitMask('NO_DATA'))
            pixGood = templateExposure.getBBox().getArea() - pixNoData
            self.log.info("template has %d good pixels (%.1f%%)", pixGood,
                          100*pixGood/templateExposure.getBBox().getArea())
        return pipeBase.Struct(exposure=templateExposure, sources=None, area=pixGood)
 
    def getOverlapPatchList(self, exposure, skyMap):
        """Select the relevant tract and its patches that overlap with the science exposure.
 
        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            The science exposure to define the sky region of the template coadd.
 
        skyMap : `lsst.skymap.BaseSkyMap`
            SkyMap object that corresponds to the template coadd.
 
        Returns
        -------
        result : `tuple` of
         - ``tractInfo`` : `lsst.skymap.TractInfo`
             The selected tract.
         - ``patchList`` : `list` of `lsst.skymap.PatchInfo`
             List of all overlap patches of the selected tract.
         - ``skyCorners`` : `list` of `lsst.geom.SpherePoint`
             Corners of the exposure in the sky in the order given by `lsst.geom.Box2D.getCorners`.
        """
        expWcs = exposure.getWcs()
        expBoxD = geom.Box2D(exposure.getBBox())
        expBoxD.grow(self.config.templateBorderSize)
        ctrSkyPos = expWcs.pixelToSky(expBoxD.getCenter())
        tractInfo = skyMap.findTract(ctrSkyPos)
        self.log.info("Using skyMap tract %s", tractInfo.getId())
        skyCorners = [expWcs.pixelToSky(pixPos) for pixPos in expBoxD.getCorners()]
        patchList = tractInfo.findPatchList(skyCorners)
 
        if not patchList:
            raise RuntimeError("No suitable tract found")
 
        self.log.info("Assembling %d coadd patches", len(patchList))
        self.log.info("exposure dimensions=%s", exposure.getDimensions())
 
        return (tractInfo, patchList, skyCorners)
 
    def run(self, tractInfo, patchList, skyCorners, availableCoaddRefs,
            sensorRef=None, visitInfo=None):
        """Gen2 and gen3 shared code: determination of exposure dimensions and
        copying of pixels from overlapping patch regions.
 
        Parameters
        ----------
        skyMap : `lsst.skymap.BaseSkyMap`
            SkyMap object that corresponds to the template coadd.
        tractInfo : `lsst.skymap.TractInfo`
            The selected tract.
        patchList : iterable of `lsst.skymap.patchInfo.PatchInfo`
            Patches to consider for making the template exposure.
        skyCorners : list of `lsst.geom.SpherePoint`
            Sky corner coordinates to be covered by the template exposure.
        availableCoaddRefs : `dict` [`int`]
            Dictionary of spatially relevant retrieved coadd patches,
            indexed by their sequential patch number. In Gen3 mode, values are
            `lsst.daf.butler.DeferredDatasetHandle` and ``.get()`` is called,
            in Gen2 mode, ``sensorRef.get(**coaddef)`` is called to retrieve the coadd.
        sensorRef : `lsst.daf.persistence.ButlerDataRef`, Gen2 only
            Butler data reference to get coadd data.
            Must be `None` for Gen3.
        visitInfo : `lsst.afw.image.VisitInfo`, Gen2 only
            VisitInfo to make dcr model.
 
        Returns
        -------
        templateExposure: `lsst.afw.image.ExposureF`
            The created template exposure.
        """
        coaddWcs = tractInfo.getWcs()
 
        # compute coadd bbox
        coaddBBox = geom.Box2D()
        for skyPos in skyCorners:
            coaddBBox.include(coaddWcs.skyToPixel(skyPos))
        coaddBBox = geom.Box2I(coaddBBox)
        self.log.info("coadd dimensions=%s", coaddBBox.getDimensions())
 
        coaddExposure = afwImage.ExposureF(coaddBBox, coaddWcs)
        coaddExposure.maskedImage.set(np.nan, afwImage.Mask.getPlaneBitMask("NO_DATA"), np.nan)
        nPatchesFound = 0
        coaddFilterLabel = None
        coaddPsf = None
        coaddPhotoCalib = None
        for patchInfo in patchList:
            patchNumber = tractInfo.getSequentialPatchIndex(patchInfo)
            patchSubBBox = patchInfo.getOuterBBox()
            patchSubBBox.clip(coaddBBox)
            if patchNumber not in availableCoaddRefs:
                self.log.warning("skip patch=%d; patch does not exist for this coadd", patchNumber)
                continue
            if patchSubBBox.isEmpty():
                if isinstance(availableCoaddRefs[patchNumber], DeferredDatasetHandle):
                    tract = availableCoaddRefs[patchNumber].dataId['tract']
                else:
                    tract = availableCoaddRefs[patchNumber]['tract']
                self.log.info("skip tract=%d patch=%d; no overlapping pixels", tract, patchNumber)
                continue
 
            if self.config.coaddName == 'dcr':
                patchInnerBBox = patchInfo.getInnerBBox()
                patchInnerBBox.clip(coaddBBox)
                if np.min(patchInnerBBox.getDimensions()) <= 2*self.config.templateBorderSize:
                    self.log.info("skip tract=%(tract)s, patch=%(patch)s; too few pixels.",
                                  availableCoaddRefs[patchNumber])
                    continue
                self.log.info("Constructing DCR-matched template for patch %s",
                              availableCoaddRefs[patchNumber])
 
                if sensorRef:
                    dcrModel = DcrModel.fromDataRef(sensorRef,
                                                    self.config.effectiveWavelength,
                                                    self.config.bandwidth,
                                                    **availableCoaddRefs[patchNumber])
                else:
                    dcrModel = DcrModel.fromQuantum(availableCoaddRefs[patchNumber],
                                                    self.config.effectiveWavelength,
                                                    self.config.bandwidth)
                # The edge pixels of the DcrCoadd may contain artifacts due to missing data.
                # Each patch has significant overlap, and the contaminated edge pixels in
                # a new patch will overwrite good pixels in the overlap region from
                # previous patches.
                # Shrink the BBox to remove the contaminated pixels,
                # but make sure it is only the overlap region that is reduced.
                dcrBBox = geom.Box2I(patchSubBBox)
                dcrBBox.grow(-self.config.templateBorderSize)
                dcrBBox.include(patchInnerBBox)
                coaddPatch = dcrModel.buildMatchedExposure(bbox=dcrBBox,
                                                           wcs=coaddWcs,
                                                           visitInfo=visitInfo)
            else:
                if sensorRef is None:
                    # Gen3
                    coaddPatch = availableCoaddRefs[patchNumber].get()
                else:
                    # Gen2
                    coaddPatch = sensorRef.get(**availableCoaddRefs[patchNumber])
            nPatchesFound += 1
 
            # Gen2 get() seems to clip based on bbox kwarg but we removed bbox
            # calculation from caller code. Gen3 also does not do this.
            overlapBox = coaddPatch.getBBox()
            overlapBox.clip(coaddBBox)
            coaddExposure.maskedImage.assign(coaddPatch.maskedImage[overlapBox], overlapBox)
 
            if coaddFilterLabel is None:
                coaddFilterLabel = coaddPatch.getFilterLabel()
 
            # Retrieve the PSF for this coadd tract, if not already retrieved
            if coaddPsf is None and coaddPatch.hasPsf():
                coaddPsf = coaddPatch.getPsf()
 
            # Retrieve the calibration for this coadd tract, if not already retrieved
            if coaddPhotoCalib is None:
                coaddPhotoCalib = coaddPatch.getPhotoCalib()
 
        if coaddPhotoCalib is None:
            raise RuntimeError("No coadd PhotoCalib found!")
        if nPatchesFound == 0:
            raise RuntimeError("No patches found!")
        if coaddPsf is None:
            raise RuntimeError("No coadd Psf found!")
 
        coaddExposure.setPhotoCalib(coaddPhotoCalib)
        coaddExposure.setPsf(coaddPsf)
        coaddExposure.setFilterLabel(coaddFilterLabel)
        return coaddExposure
 
    def getCoaddDatasetName(self):
        """Return coadd name for given task config
 
        Returns
        -------
        CoaddDatasetName : `string`
 
        TODO: This nearly duplicates a method in CoaddBaseTask (DM-11985)
        """
        warpType = self.config.warpType
        suffix = "" if warpType == "direct" else warpType[0].upper() + warpType[1:]
        return self.config.coaddName + "Coadd" + suffix
 
 
class GetCalexpAsTemplateConfig(pexConfig.Config):
    doAddCalexpBackground = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Add background to calexp before processing it."
    )
 
 
class GetCalexpAsTemplateTask(pipeBase.Task):
    """Subtask to retrieve calexp of the same ccd number as the science image SensorRef
    for use as an image difference template. Only gen2 supported.
 
    To be run as a subtask by pipe.tasks.ImageDifferenceTask.
    Intended for use with simulations and surveys that repeatedly visit the same pointing.
    This code was originally part of Winter2013ImageDifferenceTask.
    """
 
    ConfigClass = GetCalexpAsTemplateConfig
    _DefaultName = "GetCalexpAsTemplateTask"
 
    def run(self, exposure, sensorRef, templateIdList):
        """Return a calexp exposure with based on input sensorRef.
 
        Construct a dataId based on the sensorRef.dataId combined
        with the specifications from the first dataId in templateIdList
 
        Parameters
        ----------
        exposure :  `lsst.afw.image.Exposure`
            exposure (unused)
        sensorRef : `list` of `lsst.daf.persistence.ButlerDataRef`
            Data reference of the calexp(s) to subtract from.
        templateIdList : `list` of `lsst.daf.persistence.ButlerDataRef`
            Data reference of the template calexp to be subtraced.
            Can be incomplete, fields are initialized from `sensorRef`.
            If there are multiple items, only the first one is used.
 
        Returns
        -------
        result : `struct`
 
            return a pipeBase.Struct:
 
                - ``exposure`` : a template calexp
                - ``sources`` : source catalog measured on the template
        """
 
        if len(templateIdList) == 0:
            raise RuntimeError("No template data reference supplied.")
        if len(templateIdList) > 1:
            self.log.warning("Multiple template data references supplied. Using the first one only.")
 
        templateId = sensorRef.dataId.copy()
        templateId.update(templateIdList[0])
 
        self.log.info("Fetching calexp (%s) as template.", templateId)
 
        butler = sensorRef.getButler()
        template = butler.get(datasetType="calexp", dataId=templateId)
        if self.config.doAddCalexpBackground:
            templateBg = butler.get(datasetType="calexpBackground", dataId=templateId)
            mi = template.getMaskedImage()
            mi += templateBg.getImage()
 
        if not template.hasPsf():
            raise pipeBase.TaskError("Template has no psf")
 
        templateSources = butler.get(datasetType="src", dataId=templateId)
        return pipeBase.Struct(exposure=template,
                               sources=templateSources)
 
    def runDataRef(self, *args, **kwargs):
        return self.runrun(*args, **kwargs)
 
    def runQuantum(self, **kwargs):
        raise NotImplementedError("Calexp template is not supported with gen3 middleware")
 
 
class GetMultiTractCoaddTemplateConnections(pipeBase.PipelineTaskConnections,
                                            dimensions=("instrument", "visit", "detector", "skymap"),
                                            defaultTemplates={"coaddName": "goodSeeing",
                                                              "warpTypeSuffix": "",
                                                              "fakesType": ""}):
    bbox = pipeBase.connectionTypes.Input(
        doc="BBoxes of calexp used determine geometry of output template",
        name="{fakesType}calexp.bbox",
        storageClass="Box2I",
        dimensions=("instrument", "visit", "detector"),
    )
    wcs = pipeBase.connectionTypes.Input(
        doc="WCSs of calexps that we want to fetch the template for",
        name="{fakesType}calexp.wcs",
        storageClass="Wcs",
        dimensions=("instrument", "visit", "detector"),
    )
    skyMap = pipeBase.connectionTypes.Input(
        doc="Input definition of geometry/bbox and projection/wcs for template exposures",
        name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
        dimensions=("skymap", ),
        storageClass="SkyMap",
    )
    # TODO DM-31292: Add option to use global external wcs from jointcal
    # Needed for DRP HSC
    coaddExposures = pipeBase.connectionTypes.Input(
        doc="Input template to match and subtract from the exposure",
        dimensions=("tract", "patch", "skymap", "band"),
        storageClass="ExposureF",
        name="{fakesType}{coaddName}Coadd{warpTypeSuffix}",
        multiple=True,
        deferLoad=True
    )
    outputExposure = pipeBase.connectionTypes.Output(
        doc="Warped template used to create `subtractedExposure`.",
        dimensions=("instrument", "visit", "detector"),
        storageClass="ExposureF",
        name="{fakesType}{coaddName}Diff_templateExp{warpTypeSuffix}",
    )
 
 
class GetMultiTractCoaddTemplateConfig(pipeBase.PipelineTaskConfig, GetCoaddAsTemplateConfig,
                                       pipelineConnections=GetMultiTractCoaddTemplateConnections):
    warp = pexConfig.ConfigField(
        dtype=afwMath.Warper.ConfigClass,
        doc="warper configuration",
    )
    coaddPsf = pexConfig.ConfigField(
        doc="Configuration for CoaddPsf",
        dtype=CoaddPsfConfig,
    )
 
    def setDefaults(self):
        self.warp.warpingKernelName = 'lanczos5'
        self.coaddPsf.warpingKernelName = 'lanczos5'
 
 
class GetMultiTractCoaddTemplateTask(pipeBase.PipelineTask):
    ConfigClass = GetMultiTractCoaddTemplateConfig
    _DefaultName = "getMultiTractCoaddTemplateTask"
 
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.warper = afwMath.Warper.fromConfig(self.config.warp)
 
    def runQuantum(self, butlerQC, inputRefs, outputRefs):
        # Read in all inputs.
        inputs = butlerQC.get(inputRefs)
        inputs['coaddExposures'] = self.getOverlappingExposures(inputs)
        # SkyMap only needed for filtering without
        inputs.pop('skyMap')
        outputs = self.run(**inputs)
        butlerQC.put(outputs, outputRefs)
 
    def getOverlappingExposures(self, inputs):
        """Return list of coaddExposure DeferredDatasetHandles that overlap detector
 
        The spatial index in the registry has generous padding and often supplies
        patches near, but not directly overlapping the detector.
        Filters inputs so that we don't have to read in all input coadds.
 
        Parameters
        ----------
        inputs : `dict` of task Inputs
 
        Returns
        -------
        coaddExposures : list of elements of type
                         `lsst.daf.butler.DeferredDatasetHandle` of
                         `lsst.afw.image.Exposure`
 
        Raises
        ------
        NoWorkFound
            Raised if no patches overlap the input detector bbox
        """
        # Check that the patches actually overlap the detector
        # Exposure's validPolygon would be more accurate
        detectorPolygon = geom.Box2D(inputs['bbox'])
        overlappingArea = 0
        coaddExposureList = []
        for coaddRef in inputs['coaddExposures']:
            dataId = coaddRef.dataId
            patchWcs = inputs['skyMap'][dataId['tract']].getWcs()
            patchBBox = inputs['skyMap'][dataId['tract']][dataId['patch']].getOuterBBox()
            patchCorners = patchWcs.pixelToSky(geom.Box2D(patchBBox).getCorners())
            patchPolygon = afwGeom.Polygon(inputs['wcs'].skyToPixel(patchCorners))
            if patchPolygon.intersection(detectorPolygon):
                overlappingArea += patchPolygon.intersectionSingle(detectorPolygon).calculateArea()
                self.log.info("Using template input tract=%s, patch=%s" %
                              (dataId['tract'], dataId['patch']))
                coaddExposureList.append(coaddRef)
 
        if not overlappingArea:
            raise pipeBase.NoWorkFound('No patches overlap detector')
 
        return coaddExposureList
 
    def run(self, coaddExposures, bbox, wcs):
        """Warp coadds from multiple tracts to form a template for image diff.
 
        Where the tracts overlap, the resulting template image is averaged.
        The PSF on the template is created by combining the CoaddPsf on each
        template image into a meta-CoaddPsf.
 
        Parameters
        ----------
        coaddExposures: list of DeferredDatasetHandle to `lsst.afw.image.Exposure`
            Coadds to be mosaicked
        bbox : `lsst.geom.Box2I`
            Template Bounding box of the detector geometry onto which to
            resample the coaddExposures
        wcs : `lsst.afw.geom.SkyWcs`
            Template WCS onto which to resample the coaddExposures
 
        Returns
        -------
        result : `struct`
            return a pipeBase.Struct:
            - ``outputExposure`` : a template coadd exposure assembled out of patches
 
 
        Raises
        ------
        NoWorkFound
            Raised if no patches overlatp the input detector bbox
 
        """
        # Table for CoaddPSF
        tractsSchema = afwTable.ExposureTable.makeMinimalSchema()
        tractKey = tractsSchema.addField('tract', type=np.int32, doc='Which tract')
        patchKey = tractsSchema.addField('patch', type=np.int32, doc='Which patch')
        weightKey = tractsSchema.addField('weight', type=float, doc='Weight for each tract, should be 1')
        tractsCatalog = afwTable.ExposureCatalog(tractsSchema)
 
        finalWcs = wcs
        bbox.grow(self.config.templateBorderSize)
        finalBBox = bbox
 
        nPatchesFound = 0
        maskedImageList = []
        weightList = []
 
        for coaddExposure in coaddExposures:
            coaddPatch = coaddExposure.get()
 
            # warp to detector WCS
            warped = self.warper.warpExposure(finalWcs, coaddPatch, maxBBox=finalBBox)
 
            # Check if warped image is viable
            if not np.any(np.isfinite(warped.image.array)):
                self.log.info("No overlap for warped %s. Skipping" % coaddExposure.ref.dataId)
                continue
 
            exp = afwImage.ExposureF(finalBBox, finalWcs)
            exp.maskedImage.set(np.nan, afwImage.Mask.getPlaneBitMask("NO_DATA"), np.nan)
            exp.maskedImage.assign(warped.maskedImage, warped.getBBox())
 
            maskedImageList.append(exp.maskedImage)
            weightList.append(1)
            record = tractsCatalog.addNew()
            record.setPsf(coaddPatch.getPsf())
            record.setWcs(coaddPatch.getWcs())
            record.setPhotoCalib(coaddPatch.getPhotoCalib())
            record.setBBox(coaddPatch.getBBox())
            record.setValidPolygon(afwGeom.Polygon(geom.Box2D(coaddPatch.getBBox()).getCorners()))
            record.set(tractKey, coaddExposure.ref.dataId['tract'])
            record.set(patchKey, coaddExposure.ref.dataId['patch'])
            record.set(weightKey, 1.)
            nPatchesFound += 1
 
        if nPatchesFound == 0:
            raise pipeBase.NoWorkFound("No patches found to overlap detector")
 
        # Combine images from individual patches together
        statsFlags = afwMath.stringToStatisticsProperty('MEAN')
        statsCtrl = afwMath.StatisticsControl()
        statsCtrl.setNanSafe(True)
        statsCtrl.setWeighted(True)
        statsCtrl.setCalcErrorFromInputVariance(True)
 
        templateExposure = afwImage.ExposureF(finalBBox, finalWcs)
        templateExposure.maskedImage.set(np.nan, afwImage.Mask.getPlaneBitMask("NO_DATA"), np.nan)
        xy0 = templateExposure.getXY0()
        # Do not mask any values
        templateExposure.maskedImage = afwMath.statisticsStack(maskedImageList, statsFlags, statsCtrl,
                                                               weightList, clipped=0, maskMap=[])
        templateExposure.maskedImage.setXY0(xy0)
 
        # CoaddPsf centroid not only must overlap image, but must overlap the part of
        # image with data. Use centroid of region with data
        boolmask = templateExposure.mask.array & templateExposure.mask.getPlaneBitMask('NO_DATA') == 0
        maskx = afwImage.makeMaskFromArray(boolmask.astype(afwImage.MaskPixel))
        centerCoord = afwGeom.SpanSet.fromMask(maskx, 1).computeCentroid()
 
        ctrl = self.config.coaddPsf.makeControl()
        coaddPsf = CoaddPsf(tractsCatalog, finalWcs, centerCoord, ctrl.warpingKernelName, ctrl.cacheSize)
        if coaddPsf is None:
            raise RuntimeError("CoaddPsf could not be constructed")
 
        templateExposure.setPsf(coaddPsf)
        templateExposure.setFilterLabel(coaddPatch.getFilterLabel())
        templateExposure.setPhotoCalib(coaddPatch.getPhotoCalib())
        return pipeBase.Struct(outputExposure=templateExposure)