imagePsfMatch.py

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# This file is part of ip_diffim.
#
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# 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
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import numpy as np
 
import lsst.daf.base as dafBase
import lsst.pex.config as pexConfig
import lsst.afw.detection as afwDetect
import lsst.afw.image as afwImage
import lsst.afw.math as afwMath
import lsst.afw.geom as afwGeom
import lsst.afw.table as afwTable
import lsst.geom as geom
import lsst.pipe.base as pipeBase
from lsst.meas.algorithms import SourceDetectionTask, SubtractBackgroundTask, WarpedPsf
from lsst.meas.base import SingleFrameMeasurementTask
from .makeKernelBasisList import makeKernelBasisList
from .psfMatch import PsfMatchTask, PsfMatchConfigDF, PsfMatchConfigAL
from . import utils as diffimUtils
from . import diffimLib
from . import diffimTools
import lsst.afw.display as afwDisplay
 
__all__ = ["ImagePsfMatchConfig", "ImagePsfMatchTask", "subtractAlgorithmRegistry"]
 
sigma2fwhm = 2.*np.sqrt(2.*np.log(2.))
 
 
class ImagePsfMatchConfig(pexConfig.Config):
    """Configuration for image-to-image Psf matching.
    """
    kernel = pexConfig.ConfigChoiceField(
        doc="kernel type",
        typemap=dict(
            AL=PsfMatchConfigAL,
            DF=PsfMatchConfigDF
        ),
        default="AL",
    )
    selectDetection = pexConfig.ConfigurableField(
        target=SourceDetectionTask,
        doc="Initial detections used to feed stars to kernel fitting",
    )
    selectMeasurement = pexConfig.ConfigurableField(
        target=SingleFrameMeasurementTask,
        doc="Initial measurements used to feed stars to kernel fitting",
    )
 
    def setDefaults(self):
        # High sigma detections only
        self.selectDetection.reEstimateBackground = False
        self.selectDetection.thresholdValue = 10.0
 
        # Minimal set of measurments for star selection
        self.selectMeasurement.algorithms.names.clear()
        self.selectMeasurement.algorithms.names = ('base_SdssCentroid', 'base_PsfFlux', 'base_PixelFlags',
                                                   'base_SdssShape', 'base_GaussianFlux', 'base_SkyCoord')
        self.selectMeasurement.slots.modelFlux = None
        self.selectMeasurement.slots.apFlux = None
        self.selectMeasurement.slots.calibFlux = None
 
 
class ImagePsfMatchTask(PsfMatchTask):
    """Psf-match two MaskedImages or Exposures using the sources in the images.
 
    Parameters
    ----------
    args :
        Arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__
    kwargs :
        Keyword arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__
 
    Notes
    -----
    Upon initialization, the kernel configuration is defined by self.config.kernel.active.
    The task creates an lsst.afw.math.Warper from the subConfig self.config.kernel.active.warpingConfig.
    A schema for the selection and measurement of candidate lsst.ip.diffim.KernelCandidates is
    defined, and used to initize subTasks selectDetection (for candidate detection) and selectMeasurement
    (for candidate measurement).
 
    Description
 
    Build a Psf-matching kernel using two input images, either as MaskedImages (in which case they need
    to be astrometrically aligned) or Exposures (in which case astrometric alignment will happen by
    default but may be turned off).  This requires a list of input Sources which may be provided
    by the calling Task; if not, the Task will perform a coarse source detection
    and selection for this purpose. Sources are vetted for signal-to-noise and masked pixels
    (in both the template and science image), and substamps around each acceptable
    source are extracted and used to create an instance of KernelCandidate.
    Each KernelCandidate is then placed within a lsst.afw.math.SpatialCellSet, which is used by an ensemble of
    lsst.afw.math.CandidateVisitor instances to build the Psf-matching kernel.   These visitors include, in
    the order that they are called: BuildSingleKernelVisitor, KernelSumVisitor, BuildSpatialKernelVisitor,
    and AssessSpatialKernelVisitor.
 
    Sigma clipping of KernelCandidates is performed as follows:
 
    - BuildSingleKernelVisitor, using the substamp diffim residuals from the per-source kernel fit,
        if PsfMatchConfig.singleKernelClipping is True
    - KernelSumVisitor, using the mean and standard deviation of the kernel sum from all candidates,
        if PsfMatchConfig.kernelSumClipping is True
    - AssessSpatialKernelVisitor, using the substamp diffim ressiduals from the spatial kernel fit,
        if PsfMatchConfig.spatialKernelClipping is True
 
    The actual solving for the kernel (and differential background model) happens in
    lsst.ip.diffim.PsfMatchTask._solve.  This involves a loop over the SpatialCellSet that first builds the
    per-candidate matching kernel for the requested number of KernelCandidates per cell
    (PsfMatchConfig.nStarPerCell).  The quality of this initial per-candidate difference image is examined,
    using moments of the pixel residuals in the difference image normalized by the square root of the variance
    (i.e. sigma); ideally this should follow a normal (0, 1) distribution,
    but the rejection thresholds are set
    by the config (PsfMatchConfig.candidateResidualMeanMax and PsfMatchConfig.candidateResidualStdMax).
    All candidates that pass this initial build are then examined en masse to find the
    mean/stdev of the kernel sums across all candidates.
    Objects that are significantly above or below the mean,
    typically due to variability or sources that are saturated in one image but not the other,
    are also rejected.This threshold is defined by PsfMatchConfig.maxKsumSigma.
    Finally, a spatial model is built using all currently-acceptable candidates,
    and the spatial model used to derive a second set of (spatial) residuals
    which are again used to reject bad candidates, using the same thresholds as above.
 
    Invoking the Task
 
    There is no run() method for this Task.  Instead there are 4 methods that
    may be used to invoke the Psf-matching.  These are
    `~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.matchMaskedImages`,
    `~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.subtractMaskedImages`,
    `~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.matchExposures`, and
    `~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.subtractExposures`.
 
    The methods that operate on lsst.afw.image.MaskedImage require that the images already be astrometrically
    aligned, and are the same shape.  The methods that operate on lsst.afw.image.Exposure allow for the
    input images to be misregistered and potentially be different sizes; by default a
    lsst.afw.math.LanczosWarpingKernel is used to perform the astrometric alignment.  The methods
    that "match" images return a Psf-matched image, while the methods that "subtract" images
    return a Psf-matched and template subtracted image.
 
    See each method's returned lsst.pipe.base.Struct for more details.
 
    Debug variables
 
    The lsst.pipe.base.cmdLineTask.CmdLineTask command line task interface supports a
    flag -d/--debug to import debug.py from your PYTHONPATH.  The relevant contents of debug.py
    for this Task include:
 
    .. code-block:: py
 
        import sys
        import lsstDebug
        def DebugInfo(name):
            di = lsstDebug.getInfo(name)
            if name == "lsst.ip.diffim.psfMatch":
                di.display = True                 # enable debug output
                di.maskTransparency = 80          # display mask transparency
                di.displayCandidates = True       # show all the candidates and residuals
                di.displayKernelBasis = False     # show kernel basis functions
                di.displayKernelMosaic = True     # show kernel realized across the image
                di.plotKernelSpatialModel = False # show coefficients of spatial model
                di.showBadCandidates = True       # show the bad candidates (red) along with good (green)
            elif name == "lsst.ip.diffim.imagePsfMatch":
                di.display = True                 # enable debug output
                di.maskTransparency = 30          # display mask transparency
                di.displayTemplate = True         # show full (remapped) template
                di.displaySciIm = True            # show science image to match to
                di.displaySpatialCells = True     # show spatial cells
                di.displayDiffIm = True           # show difference image
                di.showBadCandidates = True       # show the bad candidates (red) along with good (green)
            elif name == "lsst.ip.diffim.diaCatalogSourceSelector":
                di.display = False                # enable debug output
                di.maskTransparency = 30          # display mask transparency
                di.displayExposure = True         # show exposure with candidates indicated
                di.pauseAtEnd = False             # pause when done
            return di
        lsstDebug.Info = DebugInfo
        lsstDebug.frame = 1
 
    Note that if you want addional logging info, you may add to your scripts:
 
    .. code-block:: py
 
        import lsst.log.utils as logUtils
        logUtils.traceSetAt("ip.diffim", 4)
 
    Examples
    --------
    A complete example of using ImagePsfMatchTask
 
    This code is imagePsfMatchTask.py in the examples directory, and can be run as e.g.
 
    .. code-block:: none
 
        examples/imagePsfMatchTask.py --debug
        examples/imagePsfMatchTask.py --debug --mode="matchExposures"
        examples/imagePsfMatchTask.py --debug --template /path/to/templateExp.fits
        --science /path/to/scienceExp.fits
 
    Create a subclass of ImagePsfMatchTask that allows us to either match exposures, or subtract exposures:
 
    .. code-block:: none
 
        class MyImagePsfMatchTask(ImagePsfMatchTask):
 
            def __init__(self, args, kwargs):
                ImagePsfMatchTask.__init__(self, args, kwargs)
 
            def run(self, templateExp, scienceExp, mode):
                if mode == "matchExposures":
                    return self.matchExposures(templateExp, scienceExp)
                elif mode == "subtractExposures":
                    return self.subtractExposures(templateExp, scienceExp)
 
    And allow the user the freedom to either run the script in default mode,
    or point to their own images on disk.
    Note that these images must be readable as an lsst.afw.image.Exposure.
 
    We have enabled some minor display debugging in this script via the --debug option.  However, if you
    have an lsstDebug debug.py in your PYTHONPATH you will get additional debugging displays.  The following
    block checks for this script:
 
    .. code-block:: py
 
        if args.debug:
            try:
                import debug
                # Since I am displaying 2 images here, set the starting frame number for the LSST debug LSST
                debug.lsstDebug.frame = 3
            except ImportError as e:
                print(e, file=sys.stderr)
 
    Finally, we call a run method that we define below.
    First set up a Config and modify some of the parameters.
    E.g. use an "Alard-Lupton" sum-of-Gaussian basis,
    fit for a differential background, and use low order spatial
    variation in the kernel and background:
 
    .. code-block:: py
 
        def run(args):
        #
        # Create the Config and use sum of gaussian basis
        #
        config = ImagePsfMatchTask.ConfigClass()
        config.kernel.name = "AL"
        config.kernel.active.fitForBackground = True
        config.kernel.active.spatialKernelOrder = 1
        config.kernel.active.spatialBgOrder = 0
 
    Make sure the images (if any) that were sent to the script exist on disk and are readable.  If no images
    are sent, make some fake data up for the sake of this example script (have a look at the code if you want
    more details on generateFakeImages):
 
    .. code-block:: py
 
        # Run the requested method of the Task
        if args.template is not None and args.science is not None:
            if not os.path.isfile(args.template):
                raise FileNotFoundError("Template image %s does not exist" % (args.template))
            if not os.path.isfile(args.science):
                raise FileNotFoundError("Science image %s does not exist" % (args.science))
            try:
                templateExp = afwImage.ExposureF(args.template)
            except Exception as e:
                raise RuntimeError("Cannot read template image %s" % (args.template))
            try:
                scienceExp = afwImage.ExposureF(args.science)
            except Exception as e:
                raise RuntimeError("Cannot read science image %s" % (args.science))
        else:
            templateExp, scienceExp = generateFakeImages()
            config.kernel.active.sizeCellX = 128
            config.kernel.active.sizeCellY = 128
 
    Create and run the Task:
 
    .. code-block:: py
 
        # Create the Task
        psfMatchTask = MyImagePsfMatchTask(config=config)
        # Run the Task
        result = psfMatchTask.run(templateExp, scienceExp, args.mode)
 
    And finally provide some optional debugging displays:
 
    .. code-block:: py
 
        if args.debug:
        # See if the LSST debug has incremented the frame number; if not start with frame 3
        try:
            frame = debug.lsstDebug.frame + 1
        except Exception:
            frame = 3
        afwDisplay.Display(frame=frame).mtv(result.matchedExposure,
                                            title="Example script: Matched Template Image")
        if "subtractedExposure" in result.getDict():
            afwDisplay.Display(frame=frame + 1).mtv(result.subtractedExposure,
                                                    title="Example script: Subtracted Image")
    """
 
    ConfigClass = ImagePsfMatchConfig
 
    def __init__(self, *args, **kwargs):
        """Create the ImagePsfMatchTask.
        """
        PsfMatchTask.__init__(self, *args, **kwargs)
        self.kConfig = self.config.kernel.active
        self._warper = afwMath.Warper.fromConfig(self.kConfig.warpingConfig)
        # the background subtraction task uses a config from an unusual location,
        # so cannot easily be constructed with makeSubtask
        self.background = SubtractBackgroundTask(config=self.kConfig.afwBackgroundConfig, name="background",
                                                 parentTask=self)
        self.selectSchema = afwTable.SourceTable.makeMinimalSchema()
        self.selectAlgMetadata = dafBase.PropertyList()
        self.makeSubtask("selectDetection", schema=self.selectSchema)
        self.makeSubtask("selectMeasurement", schema=self.selectSchema, algMetadata=self.selectAlgMetadata)
 
    def getFwhmPix(self, psf):
        """Return the FWHM in pixels of a Psf.
        """
        sigPix = psf.computeShape().getDeterminantRadius()
        return sigPix*sigma2fwhm
 
    @pipeBase.timeMethod
    def matchExposures(self, templateExposure, scienceExposure,
                       templateFwhmPix=None, scienceFwhmPix=None,
                       candidateList=None, doWarping=True, convolveTemplate=True):
        """Warp and PSF-match an exposure to the reference.
 
        Do the following, in order:
 
        - Warp templateExposure to match scienceExposure,
            if doWarping True and their WCSs do not already match
        - Determine a PSF matching kernel and differential background model
            that matches templateExposure to scienceExposure
        - Convolve templateExposure by PSF matching kernel
 
        Parameters
        ----------
        templateExposure : `lsst.afw.image.Exposure`
            Exposure to warp and PSF-match to the reference masked image
        scienceExposure : `lsst.afw.image.Exposure`
            Exposure whose WCS and PSF are to be matched to
        templateFwhmPix :`float`
            FWHM (in pixels) of the Psf in the template image (image to convolve)
        scienceFwhmPix : `float`
            FWHM (in pixels) of the Psf in the science image
        candidateList : `list`, optional
            a list of footprints/maskedImages for kernel candidates;
            if `None` then source detection is run.
 
            - Currently supported: list of Footprints or measAlg.PsfCandidateF
 
        doWarping : `bool`
            what to do if ``templateExposure`` and ``scienceExposure`` WCSs do not match:
 
            - if `True` then warp ``templateExposure`` to match ``scienceExposure``
            - if `False` then raise an Exception
 
        convolveTemplate : `bool`
            Whether to convolve the template image or the science image:
 
            - if `True`, ``templateExposure`` is warped if doWarping,
              ``templateExposure`` is convolved
            - if `False`, ``templateExposure`` is warped if doWarping,
              ``scienceExposure`` is convolved
 
        Returns
        -------
        results : `lsst.pipe.base.Struct`
            An `lsst.pipe.base.Struct` containing these fields:
 
            - ``matchedImage`` : the PSF-matched exposure =
                Warped ``templateExposure`` convolved by psfMatchingKernel. This has:
 
                - the same parent bbox, Wcs and PhotoCalib as scienceExposure
                - the same filter as templateExposure
                - no Psf (because the PSF-matching process does not compute one)
 
            - ``psfMatchingKernel`` : the PSF matching kernel
            - ``backgroundModel`` : differential background model
            - ``kernelCellSet`` : SpatialCellSet used to solve for the PSF matching kernel
 
        Raises
        ------
        RuntimeError
           Raised if doWarping is False and ``templateExposure`` and
           ``scienceExposure`` WCSs do not match
        """
        if not self._validateWcs(templateExposure, scienceExposure):
            if doWarping:
                self.log.info("Astrometrically registering template to science image")
                templatePsf = templateExposure.getPsf()
                # Warp PSF before overwriting exposure
                xyTransform = afwGeom.makeWcsPairTransform(templateExposure.getWcs(),
                                                           scienceExposure.getWcs())
                psfWarped = WarpedPsf(templatePsf, xyTransform)
                templateExposure = self._warper.warpExposure(scienceExposure.getWcs(),
                                                             templateExposure,
                                                             destBBox=scienceExposure.getBBox())
                templateExposure.setPsf(psfWarped)
            else:
                self.log.error("ERROR: Input images not registered")
                raise RuntimeError("Input images not registered")
 
        if templateFwhmPix is None:
            if not templateExposure.hasPsf():
                self.log.warn("No estimate of Psf FWHM for template image")
            else:
                templateFwhmPix = self.getFwhmPix(templateExposure.getPsf())
                self.log.info("templateFwhmPix: {}".format(templateFwhmPix))
 
        if scienceFwhmPix is None:
            if not scienceExposure.hasPsf():
                self.log.warn("No estimate of Psf FWHM for science image")
            else:
                scienceFwhmPix = self.getFwhmPix(scienceExposure.getPsf())
                self.log.info("scienceFwhmPix: {}".format(scienceFwhmPix))
 
        if convolveTemplate:
            kernelSize = makeKernelBasisList(self.kConfig, templateFwhmPix, scienceFwhmPix)[0].getWidth()
            candidateList = self.makeCandidateList(
                templateExposure, scienceExposure, kernelSize, candidateList)
            results = self.matchMaskedImages(
                templateExposure.getMaskedImage(), scienceExposure.getMaskedImage(), candidateList,
                templateFwhmPix=templateFwhmPix, scienceFwhmPix=scienceFwhmPix)
        else:
            kernelSize = makeKernelBasisList(self.kConfig, scienceFwhmPix, templateFwhmPix)[0].getWidth()
            candidateList = self.makeCandidateList(
                templateExposure, scienceExposure, kernelSize, candidateList)
            results = self.matchMaskedImages(
                scienceExposure.getMaskedImage(), templateExposure.getMaskedImage(), candidateList,
                templateFwhmPix=scienceFwhmPix, scienceFwhmPix=templateFwhmPix)
 
        psfMatchedExposure = afwImage.makeExposure(results.matchedImage, scienceExposure.getWcs())
        psfMatchedExposure.setFilter(templateExposure.getFilter())
        psfMatchedExposure.setPhotoCalib(scienceExposure.getPhotoCalib())
        results.warpedExposure = templateExposure
        results.matchedExposure = psfMatchedExposure
        return results
 
    @pipeBase.timeMethod
    def matchMaskedImages(self, templateMaskedImage, scienceMaskedImage, candidateList,
                          templateFwhmPix=None, scienceFwhmPix=None):
        """PSF-match a MaskedImage (templateMaskedImage) to a reference MaskedImage (scienceMaskedImage).
 
        Do the following, in order:
 
        - Determine a PSF matching kernel and differential background model
            that matches templateMaskedImage to scienceMaskedImage
        - Convolve templateMaskedImage by the PSF matching kernel
 
        Parameters
        ----------
        templateMaskedImage : `lsst.afw.image.MaskedImage`
            masked image to PSF-match to the reference masked image;
            must be warped to match the reference masked image
        scienceMaskedImage : `lsst.afw.image.MaskedImage`
            maskedImage whose PSF is to be matched to
        templateFwhmPix : `float`
            FWHM (in pixels) of the Psf in the template image (image to convolve)
        scienceFwhmPix : `float`
            FWHM (in pixels) of the Psf in the science image
        candidateList : `list`, optional
            A list of footprints/maskedImages for kernel candidates;
            if `None` then source detection is run.
 
            - Currently supported: list of Footprints or measAlg.PsfCandidateF
 
        Returns
        -------
        result : `callable`
        An `lsst.pipe.base.Struct` containing these fields:
 
        - psfMatchedMaskedImage: the PSF-matched masked image =
            ``templateMaskedImage`` convolved with psfMatchingKernel.
            This has the same xy0, dimensions and wcs as ``scienceMaskedImage``.
        - psfMatchingKernel: the PSF matching kernel
        - backgroundModel: differential background model
        - kernelCellSet: SpatialCellSet used to solve for the PSF matching kernel
 
        Raises
        ------
        RuntimeError
            Raised if input images have different dimensions
        """
        import lsstDebug
        display = lsstDebug.Info(__name__).display
        displayTemplate = lsstDebug.Info(__name__).displayTemplate
        displaySciIm = lsstDebug.Info(__name__).displaySciIm
        displaySpatialCells = lsstDebug.Info(__name__).displaySpatialCells
        maskTransparency = lsstDebug.Info(__name__).maskTransparency
        if not maskTransparency:
            maskTransparency = 0
        if display:
            afwDisplay.setDefaultMaskTransparency(maskTransparency)
 
        if not candidateList:
            raise RuntimeError("Candidate list must be populated by makeCandidateList")
 
        if not self._validateSize(templateMaskedImage, scienceMaskedImage):
            self.log.error("ERROR: Input images different size")
            raise RuntimeError("Input images different size")
 
        if display and displayTemplate:
            disp = afwDisplay.Display(frame=lsstDebug.frame)
            disp.mtv(templateMaskedImage, title="Image to convolve")
            lsstDebug.frame += 1
 
        if display and displaySciIm:
            disp = afwDisplay.Display(frame=lsstDebug.frame)
            disp.mtv(scienceMaskedImage, title="Image to not convolve")
            lsstDebug.frame += 1
 
        kernelCellSet = self._buildCellSet(templateMaskedImage,
                                           scienceMaskedImage,
                                           candidateList)
 
        if display and displaySpatialCells:
            diffimUtils.showKernelSpatialCells(scienceMaskedImage, kernelCellSet,
                                               symb="o", ctype=afwDisplay.CYAN, ctypeUnused=afwDisplay.YELLOW,
                                               ctypeBad=afwDisplay.RED, size=4, frame=lsstDebug.frame,
                                               title="Image to not convolve")
            lsstDebug.frame += 1
 
        if templateFwhmPix and scienceFwhmPix:
            self.log.info("Matching Psf FWHM %.2f -> %.2f pix", templateFwhmPix, scienceFwhmPix)
 
        if self.kConfig.useBicForKernelBasis:
            tmpKernelCellSet = self._buildCellSet(templateMaskedImage,
                                                  scienceMaskedImage,
                                                  candidateList)
            nbe = diffimTools.NbasisEvaluator(self.kConfig, templateFwhmPix, scienceFwhmPix)
            bicDegrees = nbe(tmpKernelCellSet, self.log)
            basisList = makeKernelBasisList(self.kConfig, templateFwhmPix, scienceFwhmPix,
                                            alardDegGauss=bicDegrees[0], metadata=self.metadata)
            del tmpKernelCellSet
        else:
            basisList = makeKernelBasisList(self.kConfig, templateFwhmPix, scienceFwhmPix,
                                            metadata=self.metadata)
 
        spatialSolution, psfMatchingKernel, backgroundModel = self._solve(kernelCellSet, basisList)
 
        psfMatchedMaskedImage = afwImage.MaskedImageF(templateMaskedImage.getBBox())
        convolutionControl = afwMath.ConvolutionControl()
        convolutionControl.setDoNormalize(False)
        afwMath.convolve(psfMatchedMaskedImage, templateMaskedImage, psfMatchingKernel, convolutionControl)
        return pipeBase.Struct(
            matchedImage=psfMatchedMaskedImage,
            psfMatchingKernel=psfMatchingKernel,
            backgroundModel=backgroundModel,
            kernelCellSet=kernelCellSet,
        )
 
    @pipeBase.timeMethod
    def subtractExposures(self, templateExposure, scienceExposure,
                          templateFwhmPix=None, scienceFwhmPix=None,
                          candidateList=None, doWarping=True, convolveTemplate=True):
        """Register, Psf-match and subtract two Exposures.
 
        Do the following, in order:
 
        - Warp templateExposure to match scienceExposure, if their WCSs do not already match
        - Determine a PSF matching kernel and differential background model
            that matches templateExposure to scienceExposure
        - PSF-match templateExposure to scienceExposure
        - Compute subtracted exposure (see return values for equation).
 
        Parameters
        ----------
        templateExposure : `lsst.afw.image.Exposure`
            Exposure to PSF-match to scienceExposure
        scienceExposure : `lsst.afw.image.Exposure`
            Reference Exposure
        templateFwhmPix : `float`
            FWHM (in pixels) of the Psf in the template image (image to convolve)
        scienceFwhmPix : `float`
            FWHM (in pixels) of the Psf in the science image
        candidateList : `list`, optional
            A list of footprints/maskedImages for kernel candidates;
            if `None` then source detection is run.
 
            - Currently supported: list of Footprints or measAlg.PsfCandidateF
 
        doWarping : `bool`
            What to do if ``templateExposure``` and ``scienceExposure`` WCSs do
            not match:
 
            - if `True` then warp ``templateExposure`` to match ``scienceExposure``
            - if `False` then raise an Exception
 
        convolveTemplate : `bool`
            Convolve the template image or the science image
 
            - if `True`, ``templateExposure`` is warped if doWarping,
              ``templateExposure`` is convolved
            - if `False`, ``templateExposure`` is warped if doWarping,
              ``scienceExposure is`` convolved
 
        Returns
        -------
        result : `lsst.pipe.base.Struct`
            An `lsst.pipe.base.Struct` containing these fields:
 
            - ``subtractedExposure`` : subtracted Exposure
                scienceExposure - (matchedImage + backgroundModel)
            - ``matchedImage`` : ``templateExposure`` after warping to match
                                 ``templateExposure`` (if doWarping true),
                                 and convolving with psfMatchingKernel
            - ``psfMatchingKernel`` : PSF matching kernel
            - ``backgroundModel`` : differential background model
            - ``kernelCellSet`` : SpatialCellSet used to determine PSF matching kernel
        """
        results = self.matchExposures(
            templateExposure=templateExposure,
            scienceExposure=scienceExposure,
            templateFwhmPix=templateFwhmPix,
            scienceFwhmPix=scienceFwhmPix,
            candidateList=candidateList,
            doWarping=doWarping,
            convolveTemplate=convolveTemplate
        )
 
        subtractedExposure = afwImage.ExposureF(scienceExposure, True)
        if convolveTemplate:
            subtractedMaskedImage = subtractedExposure.getMaskedImage()
            subtractedMaskedImage -= results.matchedExposure.getMaskedImage()
            subtractedMaskedImage -= results.backgroundModel
        else:
            subtractedExposure.setMaskedImage(results.warpedExposure.getMaskedImage())
            subtractedMaskedImage = subtractedExposure.getMaskedImage()
            subtractedMaskedImage -= results.matchedExposure.getMaskedImage()
            subtractedMaskedImage -= results.backgroundModel
 
            # Preserve polarity of differences
            subtractedMaskedImage *= -1
 
            # Place back on native photometric scale
            subtractedMaskedImage /= results.psfMatchingKernel.computeImage(
                afwImage.ImageD(results.psfMatchingKernel.getDimensions()), False)
 
        import lsstDebug
        display = lsstDebug.Info(__name__).display
        displayDiffIm = lsstDebug.Info(__name__).displayDiffIm
        maskTransparency = lsstDebug.Info(__name__).maskTransparency
        if not maskTransparency:
            maskTransparency = 0
        if display:
            afwDisplay.setDefaultMaskTransparency(maskTransparency)
        if display and displayDiffIm:
            disp = afwDisplay.Display(frame=lsstDebug.frame)
            disp.mtv(templateExposure, title="Template")
            lsstDebug.frame += 1
            disp = afwDisplay.Display(frame=lsstDebug.frame)
            disp.mtv(results.matchedExposure, title="Matched template")
            lsstDebug.frame += 1
            disp = afwDisplay.Display(frame=lsstDebug.frame)
            disp.mtv(scienceExposure, title="Science Image")
            lsstDebug.frame += 1
            disp = afwDisplay.Display(frame=lsstDebug.frame)
            disp.mtv(subtractedExposure, title="Difference Image")
            lsstDebug.frame += 1
 
        results.subtractedExposure = subtractedExposure
        return results
 
    @pipeBase.timeMethod
    def subtractMaskedImages(self, templateMaskedImage, scienceMaskedImage, candidateList,
                             templateFwhmPix=None, scienceFwhmPix=None):
        """Psf-match and subtract two MaskedImages.
 
        Do the following, in order:
 
        - PSF-match templateMaskedImage to scienceMaskedImage
        - Determine the differential background
        - Return the difference: scienceMaskedImage
            ((warped templateMaskedImage convolved with psfMatchingKernel) + backgroundModel)
 
        Parameters
        ----------
        templateMaskedImage : `lsst.afw.image.MaskedImage`
            MaskedImage to PSF-match to ``scienceMaskedImage``
        scienceMaskedImage : `lsst.afw.image.MaskedImage`
            Reference MaskedImage
        templateFwhmPix : `float`
            FWHM (in pixels) of the Psf in the template image (image to convolve)
        scienceFwhmPix : `float`
            FWHM (in pixels) of the Psf in the science image
        candidateList : `list`, optional
            A list of footprints/maskedImages for kernel candidates;
            if `None` then source detection is run.
 
            - Currently supported: list of Footprints or measAlg.PsfCandidateF
 
        Returns
        -------
        results : `lsst.pipe.base.Struct`
            An `lsst.pipe.base.Struct` containing these fields:
 
            - ``subtractedMaskedImage`` : ``scienceMaskedImage`` - (matchedImage + backgroundModel)
            - ``matchedImage`` : templateMaskedImage convolved with psfMatchingKernel
            - `psfMatchingKernel`` : PSF matching kernel
            - ``backgroundModel`` : differential background model
            - ``kernelCellSet`` : SpatialCellSet used to determine PSF matching kernel
 
        """
        if not candidateList:
            raise RuntimeError("Candidate list must be populated by makeCandidateList")
 
        results = self.matchMaskedImages(
            templateMaskedImage=templateMaskedImage,
            scienceMaskedImage=scienceMaskedImage,
            candidateList=candidateList,
            templateFwhmPix=templateFwhmPix,
            scienceFwhmPix=scienceFwhmPix,
        )
 
        subtractedMaskedImage = afwImage.MaskedImageF(scienceMaskedImage, True)
        subtractedMaskedImage -= results.matchedImage
        subtractedMaskedImage -= results.backgroundModel
        results.subtractedMaskedImage = subtractedMaskedImage
 
        import lsstDebug
        display = lsstDebug.Info(__name__).display
        displayDiffIm = lsstDebug.Info(__name__).displayDiffIm
        maskTransparency = lsstDebug.Info(__name__).maskTransparency
        if not maskTransparency:
            maskTransparency = 0
        if display:
            afwDisplay.setDefaultMaskTransparency(maskTransparency)
        if display and displayDiffIm:
            disp = afwDisplay.Display(frame=lsstDebug.frame)
            disp.mtv(subtractedMaskedImage, title="Subtracted masked image")
            lsstDebug.frame += 1
 
        return results
 
    def getSelectSources(self, exposure, sigma=None, doSmooth=True, idFactory=None):
        """Get sources to use for Psf-matching.
 
        This method runs detection and measurement on an exposure.
        The returned set of sources will be used as candidates for
        Psf-matching.
 
        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure on which to run detection/measurement
        sigma : `float`
            Detection threshold
        doSmooth : `bool`
            Whether or not to smooth the Exposure with Psf before detection
        idFactory :
            Factory for the generation of Source ids
 
        Returns
        -------
        selectSources :
            source catalog containing candidates for the Psf-matching
        """
        if idFactory:
            table = afwTable.SourceTable.make(self.selectSchema, idFactory)
        else:
            table = afwTable.SourceTable.make(self.selectSchema)
        mi = exposure.getMaskedImage()
 
        imArr = mi.getImage().getArray()
        maskArr = mi.getMask().getArray()
        miArr = np.ma.masked_array(imArr, mask=maskArr)
        try:
            fitBg = self.background.fitBackground(mi)
            bkgd = fitBg.getImageF(self.background.config.algorithm,
                                   self.background.config.undersampleStyle)
        except Exception:
            self.log.warn("Failed to get background model.  Falling back to median background estimation")
            bkgd = np.ma.extras.median(miArr)
 
        # Take off background for detection
        mi -= bkgd
        try:
            table.setMetadata(self.selectAlgMetadata)
            detRet = self.selectDetection.run(
                table=table,
                exposure=exposure,
                sigma=sigma,
                doSmooth=doSmooth
            )
            selectSources = detRet.sources
            self.selectMeasurement.run(measCat=selectSources, exposure=exposure)
        finally:
            # Put back on the background in case it is needed down stream
            mi += bkgd
            del bkgd
        return selectSources
 
    def makeCandidateList(self, templateExposure, scienceExposure, kernelSize, candidateList=None):
        """Make a list of acceptable KernelCandidates.
 
        Accept or generate a list of candidate sources for
        Psf-matching, and examine the Mask planes in both of the
        images for indications of bad pixels
 
        Parameters
        ----------
        templateExposure : `lsst.afw.image.Exposure`
            Exposure that will be convolved
        scienceExposure : `lsst.afw.image.Exposure`
            Exposure that will be matched-to
        kernelSize : `float`
            Dimensions of the Psf-matching Kernel, used to grow detection footprints
        candidateList : `list`, optional
            List of Sources to examine. Elements must be of type afw.table.Source
            or a type that wraps a Source and has a getSource() method, such as
            meas.algorithms.PsfCandidateF.
 
        Returns
        -------
        candidateList : `list` of `dict`
            A list of dicts having a "source" and "footprint"
            field for the Sources deemed to be appropriate for Psf
            matching
        """
        if candidateList is None:
            candidateList = self.getSelectSources(scienceExposure)
 
        if len(candidateList) < 1:
            raise RuntimeError("No candidates in candidateList")
 
        listTypes = set(type(x) for x in candidateList)
        if len(listTypes) > 1:
            raise RuntimeError("Candidate list contains mixed types: %s" % [l for l in listTypes])
 
        if not isinstance(candidateList[0], afwTable.SourceRecord):
            try:
                candidateList[0].getSource()
            except Exception as e:
                raise RuntimeError(f"Candidate List is of type: {type(candidateList[0])} "
                                   "Can only make candidate list from list of afwTable.SourceRecords, "
                                   f"measAlg.PsfCandidateF or other type with a getSource() method: {e}")
            candidateList = [c.getSource() for c in candidateList]
 
        candidateList = diffimTools.sourceToFootprintList(candidateList,
                                                          templateExposure, scienceExposure,
                                                          kernelSize,
                                                          self.kConfig.detectionConfig,
                                                          self.log)
        if len(candidateList) == 0:
            raise RuntimeError("Cannot find any objects suitable for KernelCandidacy")
 
        return candidateList
 
    def _adaptCellSize(self, candidateList):
        """NOT IMPLEMENTED YET.
        """
        return self.kConfig.sizeCellX, self.kConfig.sizeCellY
 
    def _buildCellSet(self, templateMaskedImage, scienceMaskedImage, candidateList):
        """Build a SpatialCellSet for use with the solve method.
 
        Parameters
        ----------
        templateMaskedImage : `lsst.afw.image.MaskedImage`
            MaskedImage to PSF-matched to scienceMaskedImage
        scienceMaskedImage : `lsst.afw.image.MaskedImage`
            Reference MaskedImage
        candidateList : `list`
            A list of footprints/maskedImages for kernel candidates;
 
            - Currently supported: list of Footprints or measAlg.PsfCandidateF
 
        Returns
        -------
        kernelCellSet : `lsst.afw.math.SpatialCellSet`
            a SpatialCellSet for use with self._solve
        """
        if not candidateList:
            raise RuntimeError("Candidate list must be populated by makeCandidateList")
 
        sizeCellX, sizeCellY = self._adaptCellSize(candidateList)
 
        # Object to store the KernelCandidates for spatial modeling
        kernelCellSet = afwMath.SpatialCellSet(templateMaskedImage.getBBox(),
                                               sizeCellX, sizeCellY)
 
        ps = pexConfig.makePropertySet(self.kConfig)
        # Place candidates within the spatial grid
        for cand in candidateList:
            if isinstance(cand, afwDetect.Footprint):
                bbox = cand.getBBox()
            else:
                bbox = cand['footprint'].getBBox()
            tmi = afwImage.MaskedImageF(templateMaskedImage, bbox)
            smi = afwImage.MaskedImageF(scienceMaskedImage, bbox)
 
            if not isinstance(cand, afwDetect.Footprint):
                if 'source' in cand:
                    cand = cand['source']
            xPos = cand.getCentroid()[0]
            yPos = cand.getCentroid()[1]
            cand = diffimLib.makeKernelCandidate(xPos, yPos, tmi, smi, ps)
 
            self.log.debug("Candidate %d at %f, %f", cand.getId(), cand.getXCenter(), cand.getYCenter())
            kernelCellSet.insertCandidate(cand)
 
        return kernelCellSet
 
    def _validateSize(self, templateMaskedImage, scienceMaskedImage):
        """Return True if two image-like objects are the same size.
        """
        return templateMaskedImage.getDimensions() == scienceMaskedImage.getDimensions()
 
    def _validateWcs(self, templateExposure, scienceExposure):
        """Return True if the WCS of the two Exposures have the same origin and extent.
        """
        templateWcs = templateExposure.getWcs()
        scienceWcs = scienceExposure.getWcs()
        templateBBox = templateExposure.getBBox()
        scienceBBox = scienceExposure.getBBox()
 
        # LLC
        templateOrigin = templateWcs.pixelToSky(geom.Point2D(templateBBox.getBegin()))
        scienceOrigin = scienceWcs.pixelToSky(geom.Point2D(scienceBBox.getBegin()))
 
        # URC
        templateLimit = templateWcs.pixelToSky(geom.Point2D(templateBBox.getEnd()))
        scienceLimit = scienceWcs.pixelToSky(geom.Point2D(scienceBBox.getEnd()))
 
        self.log.info("Template Wcs : %f,%f -> %f,%f",
                      templateOrigin[0], templateOrigin[1],
                      templateLimit[0], templateLimit[1])
        self.log.info("Science Wcs : %f,%f -> %f,%f",
                      scienceOrigin[0], scienceOrigin[1],
                      scienceLimit[0], scienceLimit[1])
 
        templateBBox = geom.Box2D(templateOrigin.getPosition(geom.degrees),
                                  templateLimit.getPosition(geom.degrees))
        scienceBBox = geom.Box2D(scienceOrigin.getPosition(geom.degrees),
                                 scienceLimit.getPosition(geom.degrees))
        if not (templateBBox.overlaps(scienceBBox)):
            raise RuntimeError("Input images do not overlap at all")
 
        if ((templateOrigin != scienceOrigin)
            or (templateLimit != scienceLimit)
                or (templateExposure.getDimensions() != scienceExposure.getDimensions())):
            return False
        return True
 
 
subtractAlgorithmRegistry = pexConfig.makeRegistry(
    doc="A registry of subtraction algorithms for use as a subtask in imageDifference",
)
 
subtractAlgorithmRegistry.register('al', ImagePsfMatchTask)