lsst.ip.diffim.zogy.ZogyImagePsfMatchTask Class Reference

Inheritance diagram for lsst.ip.diffim.zogy.ZogyImagePsfMatchTask:

Public Member Functions
def	__init__ (self, *args, **kwargs)
def	run (self, scienceExposure, templateExposure, doWarping=True)
def	subtractExposures (self, templateExposure, scienceExposure, *args)
def	subtractMaskedImages (self, templateExposure, scienceExposure, *args)
def	getFwhmPix (self, psf)
def	matchExposures (self, templateExposure, scienceExposure, templateFwhmPix=None, scienceFwhmPix=None, candidateList=None, doWarping=True, convolveTemplate=True)
def	matchMaskedImages (self, templateMaskedImage, scienceMaskedImage, candidateList, templateFwhmPix=None, scienceFwhmPix=None)
def	subtractExposures (self, templateExposure, scienceExposure, templateFwhmPix=None, scienceFwhmPix=None, candidateList=None, doWarping=True, convolveTemplate=True)
def	subtractMaskedImages (self, templateMaskedImage, scienceMaskedImage, candidateList, templateFwhmPix=None, scienceFwhmPix=None)
def	getSelectSources (self, exposure, sigma=None, doSmooth=True, idFactory=None)
def	makeCandidateList (self, templateExposure, scienceExposure, kernelSize, candidateList=None)
def	makeKernelBasisList (self, targetFwhmPix=None, referenceFwhmPix=None, basisDegGauss=None, basisSigmaGauss=None, metadata=None)

Public Attributes
	kConfig
	background
	selectSchema
	selectAlgMetadata
	useRegularization
	hMat

Static Public Attributes
	ConfigClass = ZogyImagePsfMatchConfig

Detailed Description

Task to perform Zogy PSF matching and image subtraction.

This class inherits from ImagePsfMatchTask to contain the _warper
subtask and related methods.

Definition at line 1269 of file zogy.py.

Constructor & Destructor Documentation

__init__()

def lsst.ip.diffim.zogy.ZogyImagePsfMatchTask.__init__	(		self,
		*	args,
		**	kwargs
	)

Create the ImagePsfMatchTask.

Reimplemented from lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.

Definition at line 1278 of file zogy.py.

    def __init__(self, *args, **kwargs):
        ImagePsfMatchTask.__init__(self, *args, **kwargs)
 

Member Function Documentation

getFwhmPix()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.getFwhmPix (   self,   psf  )

inherited

Return the FWHM in pixels of a Psf.

Definition at line 333 of file imagePsfMatch.py.

    def getFwhmPix(self, psf):
        """Return the FWHM in pixels of a Psf.
        """
        sigPix = psf.computeShape().getDeterminantRadius()
        return sigPix*sigma2fwhm
 

getSelectSources()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.getSelectSources (   self,   exposure,   sigma = None,   doSmooth = True,   idFactory = None  )

inherited

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

Definition at line 759 of file imagePsfMatch.py.

    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.warning("Failed to get background model.  Falling back to median background estimation")
            bkgd = np.ma.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
 

makeCandidateList()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.makeCandidateList (   self,   templateExposure,   scienceExposure,   kernelSize,   candidateList = None  )

inherited

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

Definition at line 817 of file imagePsfMatch.py.

    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" % [t for t 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
 

makeKernelBasisList()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.makeKernelBasisList (   self,   targetFwhmPix = None,   referenceFwhmPix = None,   basisDegGauss = None,   basisSigmaGauss = None,   metadata = None  )

inherited

Wrapper to set log messages for
`lsst.ip.diffim.makeKernelBasisList`.

Parameters
----------
targetFwhmPix : `float`, optional
    Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
    Not used for delta function basis sets.
referenceFwhmPix : `float`, optional
    Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
    Not used for delta function basis sets.
basisDegGauss : `list` of `int`, optional
    Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
    Not used for delta function basis sets.
basisSigmaGauss : `list` of `int`, optional
    Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
    Not used for delta function basis sets.
metadata : `lsst.daf.base.PropertySet`, optional
    Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
    Not used for delta function basis sets.

Returns
-------
basisList: `list` of `lsst.afw.math.kernel.FixedKernel`
    List of basis kernels.

Definition at line 873 of file imagePsfMatch.py.

                            basisDegGauss=None, basisSigmaGauss=None, metadata=None):
        """Wrapper to set log messages for
        `lsst.ip.diffim.makeKernelBasisList`.
 
        Parameters
        ----------
        targetFwhmPix : `float`, optional
            Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
            Not used for delta function basis sets.
        referenceFwhmPix : `float`, optional
            Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
            Not used for delta function basis sets.
        basisDegGauss : `list` of `int`, optional
            Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
            Not used for delta function basis sets.
        basisSigmaGauss : `list` of `int`, optional
            Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
            Not used for delta function basis sets.
        metadata : `lsst.daf.base.PropertySet`, optional
            Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
            Not used for delta function basis sets.
 
        Returns
        -------
        basisList: `list` of `lsst.afw.math.kernel.FixedKernel`
            List of basis kernels.
        """
        basisList = makeKernelBasisList(self.kConfig,
                                        targetFwhmPix=targetFwhmPix,
                                        referenceFwhmPix=referenceFwhmPix,
                                        basisDegGauss=basisDegGauss,
                                        basisSigmaGauss=basisSigmaGauss,
                                        metadata=metadata)
        if targetFwhmPix == referenceFwhmPix:
            self.log.info("Target and reference psf fwhms are equal, falling back to config values")
        elif referenceFwhmPix > targetFwhmPix:
            self.log.info("Reference psf fwhm is the greater, normal convolution mode")
        else:
            self.log.info("Target psf fwhm is the greater, deconvolution mode")
 
        return basisList
 

matchExposures()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.matchExposures (   self,   templateExposure,   scienceExposure,   templateFwhmPix = None,   scienceFwhmPix = None,   candidateList = None,   doWarping = True,   convolveTemplate = True  )

inherited

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

Definition at line 340 of file imagePsfMatch.py.

                       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.warning("No estimate of Psf FWHM for template image")
            else:
                templateFwhmPix = self.getFwhmPix(templateExposure.getPsf())
                self.log.info("templateFwhmPix: %s", templateFwhmPix)
 
        if scienceFwhmPix is None:
            if not scienceExposure.hasPsf():
                self.log.warning("No estimate of Psf FWHM for science image")
            else:
                scienceFwhmPix = self.getFwhmPix(scienceExposure.getPsf())
                self.log.info("scienceFwhmPix: %s", scienceFwhmPix)
 
        if convolveTemplate:
            kernelSize = self.makeKernelBasisList(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 = self.makeKernelBasisList(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.setFilterLabel(templateExposure.getFilterLabel())
        psfMatchedExposure.setPhotoCalib(scienceExposure.getPhotoCalib())
        results.warpedExposure = templateExposure
        results.matchedExposure = psfMatchedExposure
        return results
 

matchMaskedImages()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.matchMaskedImages (   self,   templateMaskedImage,   scienceMaskedImage,   candidateList,   templateFwhmPix = None,   scienceFwhmPix = None  )

inherited

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

Definition at line 458 of file imagePsfMatch.py.

                          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 = self.makeKernelBasisList(templateFwhmPix, scienceFwhmPix,
                                                 basisDegGauss=bicDegrees[0], metadata=self.metadata)
            del tmpKernelCellSet
        else:
            basisList = self.makeKernelBasisList(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,
        )
 

run()

def lsst.ip.diffim.zogy.ZogyImagePsfMatchTask.run	(		self,
			scienceExposure,
			templateExposure,
			doWarping = True
	)

Register, PSF-match, and subtract two Exposures, ``scienceExposure - templateExposure``
using the ZOGY algorithm.

Parameters
----------
templateExposure : `lsst.afw.image.Exposure`
    exposure to be warped to scienceExposure.
scienceExposure : `lsst.afw.image.Exposure`
    reference Exposure.
doWarping : `bool`
    what to do if templateExposure's and scienceExposure's WCSs do not match:
    - if True then warp templateExposure to match scienceExposure
    - if False then raise an Exception

Notes
-----
Do the following, in order:
    - Warp templateExposure to match scienceExposure, if their WCSs do not already match
    - Compute subtracted exposure ZOGY image subtraction algorithm on the two exposures

This is the new entry point of the task as of DM-25115.

Returns
-------
results : `lsst.pipe.base.Struct` containing these fields:
    - subtractedExposure: `lsst.afw.image.Exposure`
        The subtraction result.
    - warpedExposure: `lsst.afw.image.Exposure` or `None`
        templateExposure after warping to match scienceExposure

Definition at line 1281 of file zogy.py.

    def run(self, scienceExposure, templateExposure, doWarping=True):
        """Register, PSF-match, and subtract two Exposures, ``scienceExposure - templateExposure``
        using the ZOGY algorithm.
 
        Parameters
        ----------
        templateExposure : `lsst.afw.image.Exposure`
            exposure to be warped to scienceExposure.
        scienceExposure : `lsst.afw.image.Exposure`
            reference Exposure.
        doWarping : `bool`
            what to do if templateExposure's and scienceExposure's WCSs do not match:
            - if True then warp templateExposure to match scienceExposure
            - if False then raise an Exception
 
        Notes
        -----
        Do the following, in order:
            - Warp templateExposure to match scienceExposure, if their WCSs do not already match
            - Compute subtracted exposure ZOGY image subtraction algorithm on the two exposures
 
        This is the new entry point of the task as of DM-25115.
 
        Returns
        -------
        results : `lsst.pipe.base.Struct` containing these fields:
            - subtractedExposure: `lsst.afw.image.Exposure`
                The subtraction result.
            - warpedExposure: `lsst.afw.image.Exposure` or `None`
                templateExposure after warping to match scienceExposure
        """
 
        if not self._validateWcs(scienceExposure, templateExposure):
            if doWarping:
                self.log.info("Warping templateExposure to scienceExposure")
                xyTransform = afwGeom.makeWcsPairTransform(templateExposure.getWcs(),
                                                           scienceExposure.getWcs())
                psfWarped = measAlg.WarpedPsf(templateExposure.getPsf(), xyTransform)
                templateExposure = self._warper.warpExposure(
                    scienceExposure.getWcs(), templateExposure, destBBox=scienceExposure.getBBox())
                templateExposure.setPsf(psfWarped)
            else:
                raise RuntimeError("Input images are not registered. Consider setting doWarping=True.")
 
        config = self.config.zogyConfig
        task = ZogyTask(config=config)
        results = task.run(scienceExposure, templateExposure)
        results.warpedExposure = templateExposure
        return results
 

subtractExposures() [1/2]

def lsst.ip.diffim.zogy.ZogyImagePsfMatchTask.subtractExposures	(		self,
			templateExposure,
			scienceExposure,
		*	args
	)

Definition at line 1331 of file zogy.py.

    def subtractExposures(self, templateExposure, scienceExposure, *args):
        raise NotImplementedError
 

subtractExposures() [2/2]

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.subtractExposures (   self,   templateExposure,   scienceExposure,   templateFwhmPix = None,   scienceFwhmPix = None,   candidateList = None,   doWarping = True,   convolveTemplate = True  )

inherited

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.ExposureF`
    Exposure to PSF-match to scienceExposure
scienceExposure : `lsst.afw.image.ExposureF`
    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

Definition at line 571 of file imagePsfMatch.py.

                          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.ExposureF`
            Exposure to PSF-match to scienceExposure
        scienceExposure : `lsst.afw.image.ExposureF`
            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
        )
        # Always inherit WCS and photocalib from science exposure
        subtractedExposure = afwImage.ExposureF(scienceExposure, deep=True)
        # Note, the decorrelation afterburner re-calculates the variance plane
        # from the variance planes of the original exposures.
        # That recalculation code must be in accordance with the
        # photometric level set here in ``subtractedMaskedImage``.
        if convolveTemplate:
            subtractedMaskedImage = subtractedExposure.maskedImage
            subtractedMaskedImage -= results.matchedExposure.maskedImage
            subtractedMaskedImage -= results.backgroundModel
        else:
            subtractedMaskedImage = subtractedExposure.maskedImage
            subtractedMaskedImage[:, :] = results.warpedExposure.maskedImage
            subtractedMaskedImage -= results.matchedExposure.maskedImage
            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)
            # We matched to the warped template
            subtractedExposure.setPsf(results.warpedExposure.getPsf())
 
        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
 

subtractMaskedImages() [1/2]

def lsst.ip.diffim.zogy.ZogyImagePsfMatchTask.subtractMaskedImages	(		self,
			templateExposure,
			scienceExposure,
		*	args
	)

Definition at line 1334 of file zogy.py.

    def subtractMaskedImages(self, templateExposure, scienceExposure, *args):
        raise NotImplementedError
 
 
subtractAlgorithmRegistry.register('zogy', ZogyImagePsfMatchTask)

subtractMaskedImages() [2/2]

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.subtractMaskedImages (   self,   templateMaskedImage,   scienceMaskedImage,   candidateList,   templateFwhmPix = None,   scienceFwhmPix = None  )

inherited

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

Definition at line 689 of file imagePsfMatch.py.

                             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
 

Member Data Documentation

background

lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.background

inherited

Definition at line 326 of file imagePsfMatch.py.

ConfigClass

lsst.ip.diffim.zogy.ZogyImagePsfMatchTask.ConfigClass = ZogyImagePsfMatchConfig

static

Definition at line 1276 of file zogy.py.

hMat

lsst.ip.diffim.psfMatch.PsfMatchTask.hMat

inherited

Definition at line 660 of file psfMatch.py.

kConfig

lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.kConfig

inherited

Definition at line 322 of file imagePsfMatch.py.

selectAlgMetadata

lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.selectAlgMetadata

inherited

Definition at line 329 of file imagePsfMatch.py.

selectSchema

lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.selectSchema

inherited

Definition at line 328 of file imagePsfMatch.py.

useRegularization

lsst.ip.diffim.psfMatch.PsfMatchTask.useRegularization

inherited

Definition at line 655 of file psfMatch.py.

---

The documentation for this class was generated from the following file:

• /j/snowflake/release/lsstsw/stack/lsst-scipipe-0.7.0/Linux64/ip_diffim/22.0.1-19-gb93eaa13+aab3ef7709/python/lsst/ip/diffim/zogy.py