measurePsf.py

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# This file is part of pipe_tasks.
#
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__all__ = ["MeasurePsfConfig", "MeasurePsfTask"]
 
import lsst.afw.display as afwDisplay
import lsst.afw.math as afwMath
import lsst.meas.algorithms as measAlg
import lsst.meas.algorithms.utils as maUtils
import lsst.pex.config as pexConfig
import lsst.pipe.base as pipeBase
import lsst.meas.extensions.psfex.psfexPsfDeterminer  # noqa: F401
from lsst.utils.timer import timeMethod
 
 
class MeasurePsfConfig(pexConfig.Config):
    starSelector = measAlg.sourceSelectorRegistry.makeField(
        "Star selection algorithm",
        default="objectSize"
    )
    makePsfCandidates = pexConfig.ConfigurableField(
        target=measAlg.MakePsfCandidatesTask,
        doc="Task to make psf candidates from selected stars.",
    )
    psfDeterminer = measAlg.psfDeterminerRegistry.makeField(
        "PSF Determination algorithm",
        default="psfex"
    )
    reserve = pexConfig.ConfigurableField(
        target=measAlg.ReserveSourcesTask,
        doc="Reserve sources from fitting"
    )
 
    def setDefaults(self):
        super().setDefaults()
        if self.psfDeterminer.name == "piff" and self.psfDeterminer["piff"].useCoordinates == "sky":
            self.makePsfCandidates.kernelSize = 35
 
    def validate(self):
        super().validate()
 
        match self.psfDeterminer.name:
            # Perform Piff-specific validations.
            case "piff":
                if (
                    self.psfDeterminer["piff"].stampSize
                    and self.psfDeterminer["piff"].stampSize
                    > self.makePsfCandidates.kernelSize
                ):
                    # Allowing this would result in a cutout size globally.
                    msg = (
                        f"PIFF stampSize={self.psfDeterminer['piff'].stampSize}"
                        f" must be >= psf candidate kernelSize={self.makePsfCandidates.kernelSize}."
                    )
                    raise pexConfig.FieldValidationError(
                        MeasurePsfConfig.makePsfCandidates, self, msg
                    )
 
                model_size = self.psfDeterminer["piff"].modelSize
                sampling_size = self.psfDeterminer["piff"].samplingSize
                # Calculate the minimum cutout size for stars, given how large
                # the PSF model is and the sampling size.
                if self.psfDeterminer["piff"].useCoordinates == "sky":
                    min_kernel_size = int(
                        1.415 * model_size / sampling_size
                    )  # 1.415 = sqrt(2)
                else:
                    min_kernel_size = int(model_size / sampling_size)
 
                if self.makePsfCandidates.kernelSize < min_kernel_size:
                    msg = (
                        f"psf candidate kernelSize={self.makePsfCandidates.kernelSize}"
                        f" must be >= {min_kernel_size} for PIFF modelSize={model_size}."
                    )
                    raise pexConfig.FieldValidationError(
                        MeasurePsfConfig.makePsfCandidates, self, msg
                    )
 
 
class MeasurePsfTask(pipeBase.Task):
    """A task that selects stars from a catalog of sources and uses those to measure the PSF.
 
    Parameters
    ----------
    schema : `lsst.sfw.table.Schema`
        An `lsst.afw.table.Schema` used to create the output `lsst.afw.table.SourceCatalog`.
    **kwargs :
        Keyword arguments passed to lsst.pipe.base.task.Task.__init__.
 
    Notes
    -----
    If schema is not None, 'calib_psf_candidate' and 'calib_psf_used' fields will be added to
    identify which stars were employed in the PSF estimation.
 
    This task can add fields to the schema, so any code calling this task must ensure that
    these fields are indeed present in the input table.
 
    The star selector is a subclass of
    ``lsst.meas.algorithms.starSelector.BaseStarSelectorTask`` "lsst.meas.algorithms.BaseStarSelectorTask"
    and the PSF determiner is a sublcass of
    ``lsst.meas.algorithms.psfDeterminer.BasePsfDeterminerTask`` "lsst.meas.algorithms.BasePsfDeterminerTask"
 
    There is no establised set of configuration parameters for these algorithms, so once you start modifying
    parameters (as we do in @ref pipe_tasks_measurePsf_Example) your code is no longer portable.
 
    Debugging:
 
    .. code-block:: none
 
    display
        If True, display debugging plots
    displayExposure
        display the Exposure + spatialCells
    displayPsfCandidates
        show mosaic of candidates
    showBadCandidates
        Include bad candidates
    displayPsfMosaic
        show mosaic of reconstructed PSF(xy)
    displayResiduals
        show residuals
    normalizeResiduals
        Normalise residuals by object amplitude
 
    Additionally you can enable any debug outputs that your chosen star selector and psf determiner support.
    """
    ConfigClass = MeasurePsfConfig
    _DefaultName = "measurePsf"
 
    def __init__(self, schema=None, **kwargs):
        pipeBase.Task.__init__(self, **kwargs)
        if schema is not None:
            self.candidateKey = schema.addField(
                "calib_psf_candidate", type="Flag",
                doc=("Flag set if the source was a candidate for PSF determination, "
                     "as determined by the star selector.")
            )
            self.usedKey = schema.addField(
                "calib_psf_used", type="Flag",
                doc=("Flag set if the source was actually used for PSF determination, "
                     "as determined by the '%s' PSF determiner.") % self.config.psfDeterminer.name
            )
        else:
            self.candidateKey = None
            self.usedKey = None
        self.makeSubtask("starSelector")
        self.makeSubtask("makePsfCandidates")
        self.makeSubtask("psfDeterminer", schema=schema)
        self.makeSubtask("reserve", columnName="calib_psf", schema=schema,
                         doc="set if source was reserved from PSF determination")
 
    @timeMethod
    def run(self, exposure, sources, expId=0, matches=None):
        """Measure the PSF.
 
        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to process; measured PSF will be added.
        sources : `Unknown`
            Measured sources on exposure; flag fields will be set marking
            stars chosen by the star selector and the PSF determiner if a schema
            was passed to the task constructor.
        expId : `int`, optional
            Exposure id used for generating random seed.
        matches : `list`, optional
            A list of ``lsst.afw.table.ReferenceMatch`` objects
            (i.e. of ``lsst.afw.table.Match`` with @c first being
            of type ``lsst.afw.table.SimpleRecord`` and @c second
            type lsst.afw.table.SourceRecord --- the reference object and detected
            object respectively) as returned by @em e.g. the AstrometryTask.
            Used by star selectors that choose to refer to an external catalog.
 
        Returns
        -------
        measurement : `lsst.pipe.base.Struct`
             PSF measurement as a struct with attributes:
 
            ``psf``
                The measured PSF (also set in the input exposure).
            ``cellSet``
                An `lsst.afw.math.SpatialCellSet` containing the PSF candidates
                as returned by the psf determiner.
        """
        self.log.info("Measuring PSF")
 
        import lsstDebug
        display = lsstDebug.Info(__name__).display
        displayExposure = lsstDebug.Info(__name__).displayExposure     # display the Exposure + spatialCells
        displayPsfMosaic = lsstDebug.Info(__name__).displayPsfMosaic  # show mosaic of reconstructed PSF(x,y)
        displayPsfCandidates = lsstDebug.Info(__name__).displayPsfCandidates  # show mosaic of candidates
        displayResiduals = lsstDebug.Info(__name__).displayResiduals   # show residuals
        showBadCandidates = lsstDebug.Info(__name__).showBadCandidates  # include bad candidates
        normalizeResiduals = lsstDebug.Info(__name__).normalizeResiduals  # normalise residuals by object peak
 
        #
        # Run star selector
        #
        stars = self.starSelector.run(sourceCat=sources, matches=matches, exposure=exposure)
        selectionResult = self.makePsfCandidates.run(stars.sourceCat, exposure=exposure)
        self.log.info("PSF star selector found %d candidates", len(selectionResult.psfCandidates))
        reserveResult = self.reserve.run(selectionResult.goodStarCat, expId=expId)
        # Make list of psf candidates to send to the determiner (omitting those marked as reserved)
        psfDeterminerList = [cand for cand, use
                             in zip(selectionResult.psfCandidates, reserveResult.use) if use]
 
        if selectionResult.psfCandidates and self.candidateKey is not None:
            for cand in selectionResult.psfCandidates:
                source = cand.getSource()
                source.set(self.candidateKey, True)
 
        self.log.info("Sending %d candidates to PSF determiner", len(psfDeterminerList))
 
        if display:
            frame = 1
            if displayExposure:
                disp = afwDisplay.Display(frame=frame)
                disp.mtv(exposure, title="psf determination")
                frame += 1
        #
        # Determine PSF
        #
        psf, cellSet = self.psfDeterminer.determinePsf(exposure, psfDeterminerList, self.metadata,
                                                       flagKey=self.usedKey)
        self.log.info("PSF determination using %d/%d stars.",
                      self.metadata.getScalar("numGoodStars"), self.metadata.getScalar("numAvailStars"))
 
        exposure.setPsf(psf)
 
        if display:
            frame = display
            if displayExposure:
                disp = afwDisplay.Display(frame=frame)
                showPsfSpatialCells(exposure, cellSet, showBadCandidates, frame=frame)
                frame += 1
 
            if displayPsfCandidates:    # Show a mosaic of  PSF candidates
                plotPsfCandidates(cellSet, showBadCandidates=showBadCandidates, frame=frame)
                frame += 1
 
            if displayResiduals:
                frame = plotResiduals(exposure, cellSet,
                                      showBadCandidates=showBadCandidates,
                                      normalizeResiduals=normalizeResiduals,
                                      frame=frame)
            if displayPsfMosaic:
                disp = afwDisplay.Display(frame=frame)
                maUtils.showPsfMosaic(exposure, psf, display=disp, showFwhm=True)
                disp.scale("linear", 0, 1)
                frame += 1
 
        return pipeBase.Struct(
            psf=psf,
            cellSet=cellSet,
        )
 
    @property
    def usesMatches(self):
        """Return True if this task makes use of the "matches" argument to the run method"""
        return self.starSelector.usesMatches
 
#
# Debug code
#
 
 
def showPsfSpatialCells(exposure, cellSet, showBadCandidates, frame=1):
    disp = afwDisplay.Display(frame=frame)
    maUtils.showPsfSpatialCells(exposure, cellSet,
                                symb="o", ctype=afwDisplay.CYAN, ctypeUnused=afwDisplay.YELLOW,
                                size=4, display=disp)
    for cell in cellSet.getCellList():
        for cand in cell.begin(not showBadCandidates):  # maybe include bad candidates
            status = cand.getStatus()
            disp.dot('+', *cand.getSource().getCentroid(),
                     ctype=afwDisplay.GREEN if status == afwMath.SpatialCellCandidate.GOOD else
                     afwDisplay.YELLOW if status == afwMath.SpatialCellCandidate.UNKNOWN else afwDisplay.RED)
 
 
def plotPsfCandidates(cellSet, showBadCandidates=False, frame=1):
    stamps = []
    for cell in cellSet.getCellList():
        for cand in cell.begin(not showBadCandidates):  # maybe include bad candidates
            try:
                im = cand.getMaskedImage()
 
                chi2 = cand.getChi2()
                if chi2 < 1e100:
                    chi2 = "%.1f" % chi2
                else:
                    chi2 = float("nan")
 
                stamps.append((im, "%d%s" %
                               (maUtils.splitId(cand.getSource().getId(), True)["objId"], chi2),
                               cand.getStatus()))
            except Exception:
                continue
 
    mos = afwDisplay.utils.Mosaic()
    disp = afwDisplay.Display(frame=frame)
    for im, label, status in stamps:
        im = type(im)(im, True)
        try:
            im /= afwMath.makeStatistics(im, afwMath.MAX).getValue()
        except NotImplementedError:
            pass
 
        mos.append(im, label,
                   afwDisplay.GREEN if status == afwMath.SpatialCellCandidate.GOOD else
                   afwDisplay.YELLOW if status == afwMath.SpatialCellCandidate.UNKNOWN else afwDisplay.RED)
 
    if mos.images:
        disp.mtv(mos.makeMosaic(), title="Psf Candidates")
 
 
def plotResiduals(exposure, cellSet, showBadCandidates=False, normalizeResiduals=True, frame=2):
    psf = exposure.getPsf()
    disp = afwDisplay.Display(frame=frame)
    while True:
        try:
            maUtils.showPsfCandidates(exposure, cellSet, psf=psf, display=disp,
                                      normalize=normalizeResiduals,
                                      showBadCandidates=showBadCandidates)
            frame += 1
            maUtils.showPsfCandidates(exposure, cellSet, psf=psf, display=disp,
                                      normalize=normalizeResiduals,
                                      showBadCandidates=showBadCandidates,
                                      variance=True)
            frame += 1
        except Exception:
            if not showBadCandidates:
                showBadCandidates = True
                continue
        break
 
    return frame