Inheritance diagram for lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelSpatialTask:

Public Member Functions
	__init__ (self, args, *kwargs)

	computeVarianceMean (self, exposure)

	run (self, scienceExposure, templateExposure, subtractedExposure, psfMatchingKernel, spatiallyVarying=True, preConvKernel=None, templateMatched=True, preConvMode=False)

Public Attributes
	statsControl

Static Public Attributes
	ConfigClass = DecorrelateALKernelSpatialConfig

Static Protected Attributes
str	_DefaultName = "ip_diffim_decorrelateALKernelSpatial"

Detailed Description

Decorrelate the effect of convolution by Alard-Lupton matching kernel in image difference

Definition at line 775 of file imageDecorrelation.py.

Constructor & Destructor Documentation

◆ init()

lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelSpatialTask.__init__	(		self,
		*	args,
		**	kwargs )

Create the image decorrelation Task

Parameters
----------
args :
    arguments to be passed to
    `lsst.pipe.base.task.Task.__init__`
kwargs :
    additional keyword arguments to be passed to
    `lsst.pipe.base.task.Task.__init__`

Definition at line 782 of file imageDecorrelation.py.

    def __init__(self, *args, **kwargs):
        """Create the image decorrelation Task
 
        Parameters
        ----------
        args :
            arguments to be passed to
            `lsst.pipe.base.task.Task.__init__`
        kwargs :
            additional keyword arguments to be passed to
            `lsst.pipe.base.task.Task.__init__`
        """
        pipeBase.Task.__init__(self, *args, **kwargs)
 
        self.statsControl = afwMath.StatisticsControl()
        self.statsControl.setNumSigmaClip(3.)
        self.statsControl.setNumIter(3)
        self.statsControl.setAndMask(afwImage.Mask.getPlaneBitMask(self.config.ignoreMaskPlanes))
 

Member Function Documentation

◆ computeVarianceMean()

lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelSpatialTask.computeVarianceMean	(		self,
			exposure )

Compute the mean of the variance plane of `exposure`.

Definition at line 801 of file imageDecorrelation.py.

    def computeVarianceMean(self, exposure):
        """Compute the mean of the variance plane of `exposure`.
        """
        statObj = afwMath.makeStatistics(exposure.variance,
                                         exposure.mask,
                                         afwMath.MEANCLIP, self.statsControl)
        var = statObj.getValue(afwMath.MEANCLIP)
        return var
 

◆ run()

lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelSpatialTask.run	(	self,
		scienceExposure,
		templateExposure,
		subtractedExposure,
		psfMatchingKernel,
		spatiallyVarying = True,
		preConvKernel = None,
		templateMatched = True,
		preConvMode = False )

Perform decorrelation of an image difference exposure.

Decorrelates the diffim due to the convolution of the
templateExposure with the A&L psfMatchingKernel. If
`spatiallyVarying` is True, it utilizes the spatially varying
matching kernel via the `imageMapReduce` framework to perform
spatially-varying decorrelation on a grid of subExposures.

Parameters
----------
scienceExposure : `lsst.afw.image.Exposure`
   the science Exposure used for PSF matching
templateExposure : `lsst.afw.image.Exposure`
   the template Exposure used for PSF matching
subtractedExposure : `lsst.afw.image.Exposure`
   the subtracted Exposure produced by `ip_diffim.ImagePsfMatchTask.subtractExposures()`
psfMatchingKernel : an (optionally spatially-varying) PSF matching kernel produced
   by `ip_diffim.ImagePsfMatchTask.subtractExposures()`
spatiallyVarying : `bool`
   if True, perform the spatially-varying operation
preConvKernel : `lsst.meas.algorithms.Psf`
   if not none, the scienceExposure has been pre-filtered with this kernel. (Currently
   this option is experimental.)
templateMatched : `bool`, optional
   If True, the template exposure was matched (convolved) to the science exposure.
preConvMode : `bool`, optional
    If True, ``subtractedExposure`` is assumed to be a likelihood difference image
    and will be noise corrected as a likelihood image.

Returns
-------
results : `lsst.pipe.base.Struct`
    a structure containing:
    - ``correctedExposure`` : the decorrelated diffim

Definition at line 810 of file imageDecorrelation.py.

            spatiallyVarying=True, preConvKernel=None, templateMatched=True, preConvMode=False):
        """Perform decorrelation of an image difference exposure.
 
        Decorrelates the diffim due to the convolution of the
        templateExposure with the A&L psfMatchingKernel. If
        `spatiallyVarying` is True, it utilizes the spatially varying
        matching kernel via the `imageMapReduce` framework to perform
        spatially-varying decorrelation on a grid of subExposures.
 
        Parameters
        ----------
        scienceExposure : `lsst.afw.image.Exposure`
           the science Exposure used for PSF matching
        templateExposure : `lsst.afw.image.Exposure`
           the template Exposure used for PSF matching
        subtractedExposure : `lsst.afw.image.Exposure`
           the subtracted Exposure produced by `ip_diffim.ImagePsfMatchTask.subtractExposures()`
        psfMatchingKernel : an (optionally spatially-varying) PSF matching kernel produced
           by `ip_diffim.ImagePsfMatchTask.subtractExposures()`
        spatiallyVarying : `bool`
           if True, perform the spatially-varying operation
        preConvKernel : `lsst.meas.algorithms.Psf`
           if not none, the scienceExposure has been pre-filtered with this kernel. (Currently
           this option is experimental.)
        templateMatched : `bool`, optional
           If True, the template exposure was matched (convolved) to the science exposure.
        preConvMode : `bool`, optional
            If True, ``subtractedExposure`` is assumed to be a likelihood difference image
            and will be noise corrected as a likelihood image.
 
        Returns
        -------
        results : `lsst.pipe.base.Struct`
            a structure containing:
            - ``correctedExposure`` : the decorrelated diffim
        """
        self.log.info('Running A&L decorrelation: spatiallyVarying=%r', spatiallyVarying)
 
        svar = self.computeVarianceMean(scienceExposure)
        tvar = self.computeVarianceMean(templateExposure)
        if np.isnan(svar) or np.isnan(tvar):  # Should not happen unless entire image has been masked.
            # Double check that one of the exposures is all NaNs
            if (np.all(np.isnan(scienceExposure.image.array))
                    or np.all(np.isnan(templateExposure.image.array))):
                self.log.warning('Template or science image is entirely NaNs: skipping decorrelation.')
                if np.isnan(svar):
                    svar = 1e-9
                if np.isnan(tvar):
                    tvar = 1e-9
 
        var = self.computeVarianceMean(subtractedExposure)
 
        if spatiallyVarying:
            self.log.info("Variance (science, template): (%f, %f)", svar, tvar)
            self.log.info("Variance (uncorrected diffim): %f", var)
            config = self.config.decorrelateMapReduceConfig
            task = ImageMapReduceTask(config=config)
            results = task.run(subtractedExposure, science=scienceExposure,
                               template=templateExposure, psfMatchingKernel=psfMatchingKernel,
                               preConvKernel=preConvKernel, forceEvenSized=True,
                               templateMatched=templateMatched, preConvMode=preConvMode)
            results.correctedExposure = results.exposure
 
            # Make sure masks of input image are propagated to diffim
            def gm(exp):
                return exp.mask
            gm(results.correctedExposure)[:, :] = gm(subtractedExposure)
 
            var = self.computeVarianceMean(results.correctedExposure)
            self.log.info("Variance (corrected diffim): %f", var)
 
        else:
            config = self.config.decorrelateConfig
            task = DecorrelateALKernelTask(config=config)
            results = task.run(scienceExposure, templateExposure,
                               subtractedExposure, psfMatchingKernel, preConvKernel=preConvKernel,
                               templateMatched=templateMatched, preConvMode=preConvMode)
 
        return results

Member Data Documentation

◆ _DefaultName

str lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelSpatialTask._DefaultName = "ip_diffim_decorrelateALKernelSpatial"

staticprotected

Definition at line 780 of file imageDecorrelation.py.

◆ ConfigClass

lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelSpatialTask.ConfigClass = DecorrelateALKernelSpatialConfig

static

Definition at line 779 of file imageDecorrelation.py.

◆ statsControl

lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelSpatialTask.statsControl

Definition at line 796 of file imageDecorrelation.py.

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

/j/snowflake/release/lsstsw/stack/lsst-scipipe-8.0.0/Linux64/ip_diffim/g180d380827+a53a32eff8/python/lsst/ip/diffim/imageDecorrelation.py

Public Member Functions

Public Attributes

Static Public Attributes

Static Protected Attributes