lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask Class Reference

Inheritance diagram for lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask:

Public Member Functions
def	__init__ (self, *args, **kwargs)
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 = ImagePsfMatchConfig

Detailed Description

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")

Definition at line 81 of file imagePsfMatch.py.

Constructor & Destructor Documentation

__init__()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.__init__	(		self,
		*	args,
		**	kwargs
	)

Create the ImagePsfMatchTask.

Reimplemented from lsst.ip.diffim.psfMatch.PsfMatchTask.

Reimplemented in lsst.ip.diffim.zogy.ZogyImagePsfMatchTask.

Definition at line 318 of file imagePsfMatch.py.

    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)
 

Member Function Documentation

getFwhmPix()

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

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
	)

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
	)

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
	)

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
	)

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
	)

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,
        )
 

subtractExposures()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.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.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()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.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

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

Definition at line 326 of file imagePsfMatch.py.

ConfigClass

lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.ConfigClass = ImagePsfMatchConfig

static

Definition at line 316 of file imagePsfMatch.py.

hMat

lsst.ip.diffim.psfMatch.PsfMatchTask.hMat

inherited

Definition at line 660 of file psfMatch.py.

kConfig

lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.kConfig

Definition at line 322 of file imagePsfMatch.py.

selectAlgMetadata

lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.selectAlgMetadata

Definition at line 329 of file imagePsfMatch.py.

selectSchema

lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.selectSchema

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/imagePsfMatch.py