modelPsfMatch.py

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# This file is part of ip_diffim.
#
# 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
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import numpy as np
 
from . import diffimLib
import lsst.afw.display as afwDisplay
import lsst.afw.image as afwImage
import lsst.afw.math as afwMath
import lsst.geom as geom
import lsst.log as log
import lsst.pex.config as pexConfig
import lsst.pipe.base as pipeBase
from .makeKernelBasisList import makeKernelBasisList
from .psfMatch import PsfMatchTask, PsfMatchConfigAL
from . import utils as dituils
 
__all__ = ("ModelPsfMatchTask", "ModelPsfMatchConfig")
 
sigma2fwhm = 2.*np.sqrt(2.*np.log(2.))
 
 
def nextOddInteger(x):
    nextInt = int(np.ceil(x))
    return nextInt + 1 if nextInt%2 == 0 else nextInt
 
 
class ModelPsfMatchConfig(pexConfig.Config):
    """Configuration for model-to-model Psf matching"""
 
    kernel = pexConfig.ConfigChoiceField(
        doc="kernel type",
        typemap=dict(
            AL=PsfMatchConfigAL,
        ),
        default="AL",
    )
    doAutoPadPsf = pexConfig.Field(
        dtype=bool,
        doc=("If too small, automatically pad the science Psf? "
             "Pad to smallest dimensions appropriate for the matching kernel dimensions, "
             "as specified by autoPadPsfTo. If false, pad by the padPsfBy config."),
        default=True,
    )
    autoPadPsfTo = pexConfig.RangeField(
        dtype=float,
        doc=("Minimum Science Psf dimensions as a fraction of matching kernel dimensions. "
             "If the dimensions of the Psf to be matched are less than the "
             "matching kernel dimensions * autoPadPsfTo, pad Science Psf to this size. "
             "Ignored if doAutoPadPsf=False."),
        default=1.4,
        min=1.0,
        max=2.0
    )
    padPsfBy = pexConfig.Field(
        dtype=int,
        doc="Pixels (even) to pad Science Psf by before matching. Ignored if doAutoPadPsf=True",
        default=0,
    )
 
    def setDefaults(self):
        # No sigma clipping
        self.kernel.active.singleKernelClipping = False
        self.kernel.active.kernelSumClipping = False
        self.kernel.active.spatialKernelClipping = False
        self.kernel.active.checkConditionNumber = False
 
        # Variance is ill defined
        self.kernel.active.constantVarianceWeighting = True
 
        # Do not change specified kernel size
        self.kernel.active.scaleByFwhm = False
 
 
class ModelPsfMatchTask(PsfMatchTask):
    """Matching of two model Psfs, and application of the Psf-matching kernel to an input Exposure
 
    Notes
    -----
 
    This Task differs from ImagePsfMatchTask in that it matches two Psf _models_, by realizing
    them in an Exposure-sized SpatialCellSet and then inserting each Psf-image pair into KernelCandidates.
    Because none of the pairs of sources that are to be matched should be invalid, all sigma clipping is
    turned off in ModelPsfMatchConfig.  And because there is no tracked _variance_ in the Psf images, the
    debugging and logging QA info should be interpreted with caution.
 
    One item of note is that the sizes of Psf models are fixed (e.g. its defined as a 21x21 matrix).  When the
    Psf-matching kernel is being solved for, the Psf "image" is convolved with each kernel basis function,
    leading to a loss of information around the borders.
    This pixel loss will be problematic for the numerical
    stability of the kernel solution if the size of the convolution kernel
    (set by ModelPsfMatchConfig.kernelSize) is much bigger than: psfSize//2.
    Thus the sizes of Psf-model matching kernels are typically smaller
    than their image-matching counterparts.  If the size of the kernel is too small, the convolved stars will
    look "boxy"; if the kernel is too large, the kernel solution will be "noisy".  This is a trade-off that
    needs careful attention for a given dataset.
 
    The primary use case for this Task is in matching an Exposure to a
    constant-across-the-sky Psf model for the purposes of image coaddition.
    It is important to note that in the code, the "template" Psf is the Psf
    that the science image gets matched to.  In this sense the order of template and science image are
    reversed, compared to ImagePsfMatchTask, which operates on the template image.
 
    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                 # global
                di.maskTransparency = 80          # 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.modelPsfMatch":
                di.display = True                 # global
                di.maskTransparency = 30          # mask transparency
                di.displaySpatialCells = True     # show spatial cells before the fit
            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 ModelPsfMatchTask
 
    This code is modelPsfMatchTask.py in the examples directory, and can be run as e.g.
 
    .. code-block :: none
 
        examples/modelPsfMatchTask.py
        examples/modelPsfMatchTask.py --debug
        examples/modelPsfMatchTask.py --debug --template /path/to/templateExp.fits
        --science /path/to/scienceExp.fits
 
    Create a subclass of ModelPsfMatchTask that accepts two exposures.
    Note that the "template" exposure contains the Psf that will get matched to,
    and the "science" exposure is the one that will be convolved:
 
    .. code-block :: none
 
        class MyModelPsfMatchTask(ModelPsfMatchTask):
            def __init__(self, *args, **kwargs):
                ModelPsfMatchTask.__init__(self, *args, **kwargs)
            def run(self, templateExp, scienceExp):
                return ModelPsfMatchTask.run(self, scienceExp, templateExp.getPsf())
 
    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:
 
    .. code-block :: none
 
        if __name__ == "__main__":
            import argparse
            parser = argparse.ArgumentParser(description="Demonstrate the use of ModelPsfMatchTask")
            parser.add_argument("--debug", "-d", action="store_true", help="Load debug.py?", default=False)
            parser.add_argument("--template", "-t", help="Template Exposure to use", default=None)
            parser.add_argument("--science", "-s", help="Science Exposure to use", default=None)
            args = parser.parse_args()
 
    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 :: none
 
        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.
    In particular we don't want to "grow" the sizes of the kernel or KernelCandidates,
    since we are operating with fixed–size images (i.e. the size of the input Psf models).
 
    .. code-block :: none
 
        def run(args):
            #
            # Create the Config and use sum of gaussian basis
            #
            config = ModelPsfMatchTask.ConfigClass()
            config.kernel.active.scaleByFwhm = False
 
    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 generateFakeData):
 
    .. code-block :: none
 
        # 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 Exception("Template image %s does not exist" % (args.template))
            if not os.path.isfile(args.science):
                raise Exception("Science image %s does not exist" % (args.science))
            try:
                templateExp = afwImage.ExposureF(args.template)
            except Exception as e:
                raise Exception("Cannot read template image %s" % (args.template))
            try:
                scienceExp = afwImage.ExposureF(args.science)
            except Exception as e:
                raise Exception("Cannot read science image %s" % (args.science))
        else:
            templateExp, scienceExp = generateFakeData()
            config.kernel.active.sizeCellX = 128
            config.kernel.active.sizeCellY = 128
 
    .. code-block :: none
 
        if args.debug:
            afwDisplay.Display(frame=1).mtv(templateExp, title="Example script: Input Template")
            afwDisplay.Display(frame=2).mtv(scienceExp, title="Example script: Input Science Image")
 
    Create and run the Task:
 
    .. code-block :: none
 
        # Create the Task
        psfMatchTask = MyModelPsfMatchTask(config=config)
        # Run the Task
        result = psfMatchTask.run(templateExp, scienceExp)
 
    And finally provide optional debugging display of the Psf-matched (via the Psf models) science image:
 
    .. code-block :: none
 
        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.psfMatchedExposure,
                                                title="Example script: Matched Science Image")
 
    """
    ConfigClass = ModelPsfMatchConfig
 
    def __init__(self, *args, **kwargs):
        """Create a ModelPsfMatchTask
 
        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.  This Task
        does have a run() method, which is the default way to call the Task.
        """
        PsfMatchTask.__init__(self, *args, **kwargs)
        self.kConfig = self.config.kernel.active
 
    @pipeBase.timeMethod
    def run(self, exposure, referencePsfModel, kernelSum=1.0):
        """Psf-match an exposure to a model Psf
 
        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to Psf-match to the reference Psf model;
            it must return a valid PSF model via exposure.getPsf()
        referencePsfModel : `lsst.afw.detection.Psf`
            The Psf model to match to
        kernelSum : `float`, optional
            A multipicative factor to apply to the kernel sum (default=1.0)
 
        Returns
        -------
        result : `struct`
            - ``psfMatchedExposure`` : the Psf-matched Exposure.
                This has the same parent bbox, Wcs, PhotoCalib and
                Filter as the input Exposure but no Psf.
                In theory the Psf should equal referencePsfModel but
                the match is likely not exact.
            - ``psfMatchingKernel`` : the spatially varying Psf-matching kernel
            - ``kernelCellSet`` : SpatialCellSet used to solve for the Psf-matching kernel
            - ``referencePsfModel`` : Validated and/or modified reference model used
 
        Raises
        ------
        RuntimeError
            if the Exposure does not contain a Psf model
        """
        if not exposure.hasPsf():
            raise RuntimeError("exposure does not contain a Psf model")
 
        maskedImage = exposure.getMaskedImage()
 
        self.log.info("compute Psf-matching kernel")
        result = self._buildCellSet(exposure, referencePsfModel)
        kernelCellSet = result.kernelCellSet
        referencePsfModel = result.referencePsfModel
        fwhmScience = exposure.getPsf().computeShape().getDeterminantRadius()*sigma2fwhm
        fwhmModel = referencePsfModel.computeShape().getDeterminantRadius()*sigma2fwhm
 
        basisList = makeKernelBasisList(self.kConfig, fwhmScience, fwhmModel, metadata=self.metadata)
        spatialSolution, psfMatchingKernel, backgroundModel = self._solve(kernelCellSet, basisList)
 
        if psfMatchingKernel.isSpatiallyVarying():
            sParameters = np.array(psfMatchingKernel.getSpatialParameters())
            sParameters[0][0] = kernelSum
            psfMatchingKernel.setSpatialParameters(sParameters)
        else:
            kParameters = np.array(psfMatchingKernel.getKernelParameters())
            kParameters[0] = kernelSum
            psfMatchingKernel.setKernelParameters(kParameters)
 
        self.log.info("Psf-match science exposure to reference")
        psfMatchedExposure = afwImage.ExposureF(exposure.getBBox(), exposure.getWcs())
        psfMatchedExposure.setFilter(exposure.getFilter())
        psfMatchedExposure.setPhotoCalib(exposure.getPhotoCalib())
        psfMatchedExposure.getInfo().setVisitInfo(exposure.getInfo().getVisitInfo())
        psfMatchedExposure.setPsf(referencePsfModel)
        psfMatchedMaskedImage = psfMatchedExposure.getMaskedImage()
 
        # Normalize the psf-matching kernel while convolving since its magnitude is meaningless
        # when PSF-matching one model to another.
        doNormalize = True
        afwMath.convolve(psfMatchedMaskedImage, maskedImage, psfMatchingKernel, doNormalize)
 
        self.log.info("done")
        return pipeBase.Struct(psfMatchedExposure=psfMatchedExposure,
                               psfMatchingKernel=psfMatchingKernel,
                               kernelCellSet=kernelCellSet,
                               metadata=self.metadata,
                               )
 
    def _diagnostic(self, kernelCellSet, spatialSolution, spatialKernel, spatialBg):
        """Print diagnostic information on spatial kernel and background fit
 
        The debugging diagnostics are not really useful here, since the images we are matching have
        no variance.  Thus override the _diagnostic method to generate no logging information"""
        return
 
    def _buildCellSet(self, exposure, referencePsfModel):
        """Build a SpatialCellSet for use with the solve method
 
        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            The science exposure that will be convolved; must contain a Psf
        referencePsfModel : `lsst.afw.detection.Psf`
            Psf model to match to
 
        Returns
        -------
        result : `struct`
            - ``kernelCellSet`` : a SpatialCellSet to be used by self._solve
            - ``referencePsfModel`` : Validated and/or modified
                reference model used to populate the SpatialCellSet
 
        Notes
        -----
        If the reference Psf model and science Psf model have different dimensions,
        adjust the referencePsfModel (the model to which the exposure PSF will be matched)
        to match that of the science Psf. If the science Psf dimensions vary across the image,
        as is common with a WarpedPsf, either pad or clip (depending on config.padPsf)
        the dimensions to be constant.
        """
        sizeCellX = self.kConfig.sizeCellX
        sizeCellY = self.kConfig.sizeCellY
 
        scienceBBox = exposure.getBBox()
        # Extend for proper spatial matching kernel all the way to edge, especially for narrow strips
        scienceBBox.grow(geom.Extent2I(sizeCellX, sizeCellY))
 
        sciencePsfModel = exposure.getPsf()
 
        dimenR = referencePsfModel.getLocalKernel().getDimensions()
        psfWidth, psfHeight = dimenR
 
        regionSizeX, regionSizeY = scienceBBox.getDimensions()
        scienceX0, scienceY0 = scienceBBox.getMin()
 
        kernelCellSet = afwMath.SpatialCellSet(geom.Box2I(scienceBBox), sizeCellX, sizeCellY)
 
        nCellX = regionSizeX//sizeCellX
        nCellY = regionSizeY//sizeCellY
 
        if nCellX == 0 or nCellY == 0:
            raise ValueError("Exposure dimensions=%s and sizeCell=(%s, %s). Insufficient area to match" %
                             (scienceBBox.getDimensions(), sizeCellX, sizeCellY))
 
        # Survey the PSF dimensions of the Spatial Cell Set
        # to identify the minimum enclosed or maximum bounding square BBox.
        widthList = []
        heightList = []
        for row in range(nCellY):
            posY = sizeCellY*row + sizeCellY//2 + scienceY0
            for col in range(nCellX):
                posX = sizeCellX*col + sizeCellX//2 + scienceX0
                widthS, heightS = sciencePsfModel.computeBBox(geom.Point2D(posX, posY)).getDimensions()
                widthList.append(widthS)
                heightList.append(heightS)
 
        psfSize = max(max(heightList), max(widthList))
 
        if self.config.doAutoPadPsf:
            minPsfSize = nextOddInteger(self.kConfig.kernelSize*self.config.autoPadPsfTo)
            paddingPix = max(0, minPsfSize - psfSize)
        else:
            if self.config.padPsfBy % 2 != 0:
                raise ValueError("Config padPsfBy (%i pixels) must be even number." %
                                 self.config.padPsfBy)
            paddingPix = self.config.padPsfBy
 
        if paddingPix > 0:
            self.log.info("Padding Science PSF from (%s, %s) to (%s, %s) pixels" %
                          (psfSize, psfSize, paddingPix + psfSize, paddingPix + psfSize))
            psfSize += paddingPix
 
        # Check that PSF is larger than the matching kernel
        maxKernelSize = psfSize - 1
        if maxKernelSize % 2 == 0:
            maxKernelSize -= 1
        if self.kConfig.kernelSize > maxKernelSize:
            message = """
                Kernel size (%d) too big to match Psfs of size %d.
                Please reconfigure by setting one of the following:
                1) kernel size to <= %d
                2) doAutoPadPsf=True
                3) padPsfBy to >= %s
                """ % (self.kConfig.kernelSize, psfSize,
                       maxKernelSize, self.kConfig.kernelSize - maxKernelSize)
            raise ValueError(message)
 
        dimenS = geom.Extent2I(psfSize, psfSize)
 
        if (dimenR != dimenS):
            try:
                referencePsfModel = referencePsfModel.resized(psfSize, psfSize)
                self.log.info("Adjusted dimensions of reference PSF model from %s to %s" % (dimenR, dimenS))
            except Exception as e:
                self.log.warn("Zero padding or clipping the reference PSF model of type %s and dimensions %s"
                              " to the science Psf dimensions %s because: %s",
                              referencePsfModel.__class__.__name__, dimenR, dimenS, e)
            dimenR = dimenS
 
        ps = pexConfig.makePropertySet(self.kConfig)
        for row in range(nCellY):
            # place at center of cell
            posY = sizeCellY*row + sizeCellY//2 + scienceY0
 
            for col in range(nCellX):
                # place at center of cell
                posX = sizeCellX*col + sizeCellX//2 + scienceX0
 
                log.log("TRACE4." + self.log.getName(), log.DEBUG,
                        "Creating Psf candidate at %.1f %.1f", posX, posY)
 
                # reference kernel image, at location of science subimage
                referenceMI = self._makePsfMaskedImage(referencePsfModel, posX, posY, dimensions=dimenR)
 
                # kernel image we are going to convolve
                scienceMI = self._makePsfMaskedImage(sciencePsfModel, posX, posY, dimensions=dimenR)
 
                # The image to convolve is the science image, to the reference Psf.
                kc = diffimLib.makeKernelCandidate(posX, posY, scienceMI, referenceMI, ps)
                kernelCellSet.insertCandidate(kc)
 
        import lsstDebug
        display = lsstDebug.Info(__name__).display
        displaySpatialCells = lsstDebug.Info(__name__).displaySpatialCells
        maskTransparency = lsstDebug.Info(__name__).maskTransparency
        if not maskTransparency:
            maskTransparency = 0
        if display:
            afwDisplay.setDefaultMaskTransparency(maskTransparency)
        if display and displaySpatialCells:
            dituils.showKernelSpatialCells(exposure.getMaskedImage(), kernelCellSet,
                                           symb="o", ctype=afwDisplay.CYAN, ctypeUnused=afwDisplay.YELLOW,
                                           ctypeBad=afwDisplay.RED, size=4, frame=lsstDebug.frame,
                                           title="Image to be convolved")
            lsstDebug.frame += 1
        return pipeBase.Struct(kernelCellSet=kernelCellSet,
                               referencePsfModel=referencePsfModel,
                               )
 
    def _makePsfMaskedImage(self, psfModel, posX, posY, dimensions=None):
        """Return a MaskedImage of the a PSF Model of specified dimensions
        """
        rawKernel = psfModel.computeKernelImage(geom.Point2D(posX, posY)).convertF()
        if dimensions is None:
            dimensions = rawKernel.getDimensions()
        if rawKernel.getDimensions() == dimensions:
            kernelIm = rawKernel
        else:
            # make image of proper size
            kernelIm = afwImage.ImageF(dimensions)
            bboxToPlace = geom.Box2I(geom.Point2I((dimensions.getX() - rawKernel.getWidth())//2,
                                                  (dimensions.getY() - rawKernel.getHeight())//2),
                                     rawKernel.getDimensions())
            kernelIm.assign(rawKernel, bboxToPlace)
 
        kernelMask = afwImage.Mask(dimensions, 0x0)
        kernelVar = afwImage.ImageF(dimensions, 1.0)
        return afwImage.MaskedImageF(kernelIm, kernelMask, kernelVar)