lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask Class Reference

Psf-match two MaskedImages or Exposures using the sources in the images. More...

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

Public Member Functions
def	__init__
	Create the ImagePsfMatchTask. More...
def	getFwhmPix
	Return the FWHM in pixels of a Psf. More...
def	matchExposures
	Warp and PSF-match an exposure to the reference. More...
def	matchMaskedImages
	PSF-match a MaskedImage (templateMaskedImage) to a reference MaskedImage (scienceMaskedImage) More...
def	subtractExposures
	Register, Psf-match and subtract two Exposures. More...
def	subtractMaskedImages
	Psf-match and subtract two MaskedImages. More...
def	getSelectSources
	Get sources to use for Psf-matching. More...
def	makeCandidateList
	Make a list of acceptable KernelCandidates. More...

Public Attributes
	kConfig
	selectSchema
	selectAlgMetadata

Static Public Attributes
	ConfigClass = ImagePsfMatchConfig

Private Member Functions
def	_adaptCellSize
	NOT IMPLEMENTED YET. More...
def	_buildCellSet
	Build a SpatialCellSet for use with the solve method. More...
def	_validateSize
	Return True if two image-like objects are the same size. More...
def	_validateWcs
	Return True if the WCS of the two Exposures have the same origin and extent. More...

Private Attributes
	_warper

Detailed Description

Psf-match two MaskedImages or Exposures using the sources in the images.

Contents

• Description
• Task initialization
• Invoking the Task
• Configuration parameters
• Quantities set in Metadata
• Debug variables
• A complete example of using ImagePsfMatchTask

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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.

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Task initialization

Create the ImagePsfMatchTask.

Parameters

*args	arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__
**kwargs	keyword arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__

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).

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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 matchMaskedImages, subtractMaskedImages, matchExposures, and 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.

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Configuration parameters

See ImagePsfMatchConfig

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Quantities set in Metadata

See PsfMatchTask

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Debug variables

The 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:

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          # ds9 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          # ds9 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          # ds9 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:

import lsst.pex.logging as pexLog
pexLog.Trace_setVerbosity('lsst.ip.diffim', 5)

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A complete example of using ImagePsfMatchTask

This code is imagePsfMatchTask.py in the examples directory, and can be run as e.g.

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:

class MyImagePsfMatchTask(ImagePsfMatchTask):
    """An override for 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:

if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser(description="Demonstrate the use of ImagePsfMatchTask")

    parser.add_argument("--debug", "-d", action="store_true", help="Load debug.py?", default=False)
    parser.add_argument("--template", "-t", help="Template Exposure to use", default=None)
    parser.add_argument("--science", "-s", help="Science Exposure to use", default=None)
    parser.add_argument("--mode", choices=["matchExposures", "subtractExposures"], 
        default="subtractExposures", help="Which method of ImagePsfMatchTask to invoke")

    args = parser.parse_args()

We have enabled some minor display debugging in this script via the –debug option. However, if you have an lsstDebug debug.py in your PYTHONPATH you will get additional debugging displays. The following block checks for this script:

    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 >> sys.stderr, e

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:

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):

    # Run the requested method of the Task
    if args.template is not None and args.science is not None:
        if not os.path.isfile(args.template):
                raise Exception, "Template image %s does not exist" % (args.template)
        if not os.path.isfile(args.science):
                raise Exception, "Science image %s does not exist" % (args.science)

        try:
                templateExp = afwImage.ExposureF(args.template)
        except pexExcept.LsstCppException, e:
                raise Exception, "Cannot read template image %s" % (args.template)
        try:
                scienceExp = afwImage.ExposureF(args.science)
        except pexExcept.LsstCppException, e:
                raise Exception, "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:

    # Create the Task
    psfMatchTask = MyImagePsfMatchTask(config=config)

    # Run the Task
    result = psfMatchTask.run(templateExp, scienceExp, args.mode)

And finally provide some optional debugging displays:

    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
        ds9.mtv(result.matchedExposure, frame=frame, title="Example script: Matched Template Image")
        if result.getDict().has_key("subtractedExposure"):
                ds9.mtv(result.subtractedExposure, frame=frame+1, title="Example script: Subtracted Image")

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Definition at line 81 of file imagePsfMatch.py.

Constructor & Destructor Documentation

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

Create the ImagePsfMatchTask.

Parameters

*args	arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__
**kwargs	keyword arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__

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).

Definition at line 269 of file imagePsfMatch.py.

    def __init__(self, *args, **kwargs):
        """!Create the ImagePsfMatchTask

        \param *args arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__
        \param **kwargs keyword arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__

        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).
        """
        PsfMatchTask.__init__(self, *args, **kwargs)
        self.kConfig = self.config.kernel.active
        self._warper = afwMath.Warper.fromConfig(self.kConfig.warpingConfig)
        self.selectSchema = afwTable.SourceTable.makeMinimalSchema()
        self.selectAlgMetadata = dafBase.PropertyList()
        self.makeSubtask("selectDetection", schema=self.selectSchema)
        self.makeSubtask("selectMeasurement", schema=self.selectSchema, algMetadata=self.selectAlgMetadata)