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

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

def	computeVarianceMean (self, exposure)

def	run (self, scienceExposure, templateExposure, subtractedExposure, psfMatchingKernel, preConvKernel=None, xcen=None, ycen=None, svar=None, tvar=None, templateMatched=True)

def	computeCommonShape (self, *shapes)

def	computeCorrection (self, kappa, svar, tvar, preConvArr=None)

def	computeCorrectedDiffimPsf (self, corrft, psfOld)

def	computeCorrectedImage (self, corrft, imgOld)

Static Public Member Functions
def	padCenterOriginArray (A, tuple newShape, useInverse=False)

Public Attributes
	statsControl

	freqSpaceShape

Static Public Attributes
	ConfigClass = DecorrelateALKernelConfig

Detailed Description

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

Notes
-----

Pipe-task that removes the neighboring-pixel covariance in an
image difference that are added when the template image is
convolved with the Alard-Lupton PSF matching kernel.

The image differencing pipeline task @link
ip.diffim.psfMatch.PsfMatchTask PSFMatchTask@endlink and @link
ip.diffim.psfMatch.PsfMatchConfigAL PSFMatchConfigAL@endlink uses
the Alard and Lupton (1998) method for matching the PSFs of the
template and science exposures prior to subtraction. The
Alard-Lupton method identifies a matching kernel, which is then
(typically) convolved with the template image to perform PSF
matching. This convolution has the effect of adding covariance
between neighboring pixels in the template image, which is then
added to the image difference by subtraction.

The pixel covariance may be corrected by whitening the noise of
the image difference. This task performs such a decorrelation by
computing a decorrelation kernel (based upon the A&L matching
kernel and variances in the template and science images) and
convolving the image difference with it. This process is described
in detail in [DMTN-021](http://dmtn-021.lsst.io).

This task has no standalone example, however it is applied as a
subtask of pipe.tasks.imageDifference.ImageDifferenceTask.

Definition at line 53 of file imageDecorrelation.py.

Constructor & Destructor Documentation

◆ init()

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

Create the image decorrelation Task

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

Reimplemented in lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelMapper.

Definition at line 87 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 :
             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

◆ computeCommonShape()

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeCommonShape	(		self,
		*	shapes
	)

Calculate the common shape for FFT operations. Set `self.freqSpaceShape`
internally.

Parameters
----------
shapes : one or more `tuple` of `int`
    Shapes of the arrays. All must have the same dimensionality.
    At least one shape must be provided.

Returns
-------
None.

Notes
-----
For each dimension, gets the smallest even number greater than or equal to
`N1+N2-1` where `N1` and `N2` are the two largest values.
In case of only one shape given, rounds up to even each dimension value.

Definition at line 300 of file imageDecorrelation.py.

     def computeCommonShape(self, *shapes):
         """Calculate the common shape for FFT operations. Set `self.freqSpaceShape`
         internally.
  
         Parameters
         ----------
         shapes : one or more `tuple` of `int`
             Shapes of the arrays. All must have the same dimensionality.
             At least one shape must be provided.
  
         Returns
         -------
         None.
  
         Notes
         -----
         For each dimension, gets the smallest even number greater than or equal to
         `N1+N2-1` where `N1` and `N2` are the two largest values.
         In case of only one shape given, rounds up to even each dimension value.
         """
         S = np.array(shapes, dtype=int)
         if len(shapes) > 2:
             S.sort(axis=0)
             S = S[-2:]
         if len(shapes) > 1:
             commonShape = np.sum(S, axis=0) - 1
         else:
             commonShape = S[0]
         commonShape[commonShape % 2 != 0] += 1
         self.freqSpaceShape = tuple(commonShape)
         self.log.info(f"Common frequency space shape {self.freqSpaceShape}")
  

◆ computeCorrectedDiffimPsf()

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeCorrectedDiffimPsf	(	self,
		corrft,
		psfOld
	)

Compute the (decorrelated) difference image's new PSF.

Parameters
----------
corrft : `numpy.ndarray`
    The frequency space representation of the correction calculated by
    `computeCorrection`. Shape must be `self.freqSpaceShape`.
psfOld : `numpy.ndarray`
    The psf of the difference image to be corrected.

Returns
-------
psfNew : `numpy.ndarray`
    The corrected psf, same shape as `psfOld`, sum normed to 1.

Notes
----
There is no algorithmic guarantee that the corrected psf can
meaningfully fit to the same size as the original one.

Definition at line 438 of file imageDecorrelation.py.

     def computeCorrectedDiffimPsf(self, corrft, psfOld):
         """Compute the (decorrelated) difference image's new PSF.
  
         Parameters
         ----------
         corrft : `numpy.ndarray`
             The frequency space representation of the correction calculated by
             `computeCorrection`. Shape must be `self.freqSpaceShape`.
         psfOld : `numpy.ndarray`
             The psf of the difference image to be corrected.
  
         Returns
         -------
         psfNew : `numpy.ndarray`
             The corrected psf, same shape as `psfOld`, sum normed to 1.
  
         Notes
         ----
         There is no algorithmic guarantee that the corrected psf can
         meaningfully fit to the same size as the original one.
         """
         psfShape = psfOld.shape
         psfNew = self.padCenterOriginArray(psfOld, self.freqSpaceShape)
         psfNew = np.fft.fft2(psfNew)
         psfNew *= corrft
         psfNew = np.fft.ifft2(psfNew)
         psfNew = psfNew.real
         psfNew = self.padCenterOriginArray(psfNew, psfShape, useInverse=True)
         psfNew = psfNew/psfNew.sum()
         return psfNew
  

◆ computeCorrectedImage()

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeCorrectedImage	(	self,
		corrft,
		imgOld
	)

Compute the decorrelated difference image.

Parameters
----------
corrft : `numpy.ndarray`
    The frequency space representation of the correction calculated by
    `computeCorrection`. Shape must be `self.freqSpaceShape`.
imgOld : `numpy.ndarray`
    The difference image to be corrected.

Returns
-------
imgNew : `numpy.ndarray`
    The corrected image, same size as the input.

Definition at line 469 of file imageDecorrelation.py.

     def computeCorrectedImage(self, corrft, imgOld):
         """Compute the decorrelated difference image.
  
         Parameters
         ----------
         corrft : `numpy.ndarray`
             The frequency space representation of the correction calculated by
             `computeCorrection`. Shape must be `self.freqSpaceShape`.
         imgOld : `numpy.ndarray`
             The difference image to be corrected.
  
         Returns
         -------
         imgNew : `numpy.ndarray`
             The corrected image, same size as the input.
         """
         expShape = imgOld.shape
         imgNew = np.copy(imgOld)
         filtInf = np.isinf(imgNew)
         filtNan = np.isnan(imgNew)
         imgNew[filtInf] = np.nan
         imgNew[filtInf | filtNan] = np.nanmean(imgNew)
         imgNew = self.padCenterOriginArray(imgNew, self.freqSpaceShape)
         imgNew = np.fft.fft2(imgNew)
         imgNew *= corrft
         imgNew = np.fft.ifft2(imgNew)
         imgNew = imgNew.real
         imgNew = self.padCenterOriginArray(imgNew, expShape, useInverse=True)
         imgNew[filtNan] = np.nan
         imgNew[filtInf] = np.inf
         return imgNew
  
  

◆ computeCorrection()

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeCorrection	(	self,
		kappa,
		svar,
		tvar,
		preConvArr = `None`
	)

Compute the Lupton decorrelation post-convolution kernel for decorrelating an
image difference, based on the PSF-matching kernel.

Parameters
----------
kappa : `numpy.ndarray`
    A matching kernel 2-d numpy.array derived from Alard & Lupton PSF matching.
svar : `float`
    Average variance of science image used for PSF matching.
tvar : `float`
    Average variance of the template (matched) image used for PSF matching.
preConvArr : `numpy.ndarray`, optional
    If not None, then pre-filtering was applied
    to science exposure, and this is the pre-convolution kernel.

Returns
-------
corrft : `numpy.ndarray` of `float`
    The frequency space representation of the correction. The array is real (dtype float).
    Shape is `self.freqSpaceShape`.

Notes
-----
The maximum correction factor converges to `sqrt(tvar/svar)` towards high frequencies.
This should be a plausible value.

Definition at line 380 of file imageDecorrelation.py.

     def computeCorrection(self, kappa, svar, tvar, preConvArr=None):
         """Compute the Lupton decorrelation post-convolution kernel for decorrelating an
         image difference, based on the PSF-matching kernel.
  
         Parameters
         ----------
         kappa : `numpy.ndarray`
             A matching kernel 2-d numpy.array derived from Alard & Lupton PSF matching.
         svar : `float`
             Average variance of science image used for PSF matching.
         tvar : `float`
             Average variance of the template (matched) image used for PSF matching.
         preConvArr : `numpy.ndarray`, optional
             If not None, then pre-filtering was applied
             to science exposure, and this is the pre-convolution kernel.
  
         Returns
         -------
         corrft : `numpy.ndarray` of `float`
             The frequency space representation of the correction. The array is real (dtype float).
             Shape is `self.freqSpaceShape`.
  
         Notes
         -----
         The maximum correction factor converges to `sqrt(tvar/svar)` towards high frequencies.
         This should be a plausible value.
         """
         kSum = np.sum(kappa)
         kappa = self.padCenterOriginArray(kappa, self.freqSpaceShape)
         kft = np.fft.fft2(kappa)
         kftAbsSq = np.real(np.conj(kft) * kft)
         # If there is no pre-convolution kernel, use placeholder scalars
         if preConvArr is None:
             preSum = 1.
             preAbsSq = 1.
         else:
             preSum = np.sum(preConvArr)
             preConvArr = self.padCenterOriginArray(preConvArr, self.freqSpaceShape)
             preK = np.fft.fft2(preConvArr)
             preAbsSq = np.real(np.conj(preK)*preK)
  
         denom = svar * preAbsSq + tvar * kftAbsSq
         # Division by zero protection, though we don't expect to hit it
         # (rather we'll have numerical noise)
         tiny = np.finfo(kftAbsSq.dtype).tiny * 1000.
         flt = denom < tiny
         sumFlt = np.sum(flt)
         if sumFlt > 0:
             self.log.warnf("Avoid zero division. Skip decorrelation "
                            "at {} divergent frequencies.", sumFlt)
             denom[flt] = 1.
         kft = np.sqrt((svar * preSum*preSum + tvar * kSum*kSum) / denom)
         # Don't do any correction at these frequencies
         # the difference image should be close to zero anyway, so can't be decorrelated
         if sumFlt > 0:
             kft[flt] = 1.
         return kft
  

◆ computeVarianceMean()

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeVarianceMean	(	self,
		exposure
	)

Definition at line 104 of file imageDecorrelation.py.

     def computeVarianceMean(self, exposure):
         statObj = afwMath.makeStatistics(exposure.getMaskedImage().getVariance(),
                                          exposure.getMaskedImage().getMask(),
                                          afwMath.MEANCLIP, self.statsControl)
         var = statObj.getValue(afwMath.MEANCLIP)
         return var
  

◆ padCenterOriginArray()

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.padCenterOriginArray	(		A,
		tuple	newShape,
			useInverse = `False`
	)

static

Zero pad an image where the origin is at the center and replace the
origin to the corner as required by the periodic input of FFT. Implement also
the inverse operation, crop the padding and re-center data.

Parameters
----------
A : `numpy.ndarray`
    An array to copy from.
newShape : `tuple` of `int`
    The dimensions of the resulting array. For padding, the resulting array
    must be larger than A in each dimension. For the inverse operation this
    must be the original, before padding size of the array.
useInverse : bool, optional
    Selector of forward, add padding, operation (False)
    or its inverse, crop padding, operation (True).

Returns
-------
R : `numpy.ndarray`
    The padded or unpadded array with shape of `newShape` and the same dtype as A.

Notes
-----
For odd dimensions, the splitting is rounded to
put the center pixel into the new corner origin (0,0). This is to be consistent
e.g. for a dirac delta kernel that is originally located at the center pixel.

Definition at line 333 of file imageDecorrelation.py.

     def padCenterOriginArray(A, newShape: tuple, useInverse=False):
         """Zero pad an image where the origin is at the center and replace the
         origin to the corner as required by the periodic input of FFT. Implement also
         the inverse operation, crop the padding and re-center data.
  
         Parameters
         ----------
         A : `numpy.ndarray`
             An array to copy from.
         newShape : `tuple` of `int`
             The dimensions of the resulting array. For padding, the resulting array
             must be larger than A in each dimension. For the inverse operation this
             must be the original, before padding size of the array.
         useInverse : bool, optional
             Selector of forward, add padding, operation (False)
             or its inverse, crop padding, operation (True).
  
         Returns
         -------
         R : `numpy.ndarray`
             The padded or unpadded array with shape of `newShape` and the same dtype as A.
  
         Notes
         -----
         For odd dimensions, the splitting is rounded to
         put the center pixel into the new corner origin (0,0). This is to be consistent
         e.g. for a dirac delta kernel that is originally located at the center pixel.
         """
  
         # The forward and inverse operations should round odd dimension halves at the opposite
         # sides to get the pixels back to their original positions.
         if not useInverse:
             # Forward operation: First and second halves with respect to the axes of A.
             firstHalves = [x//2 for x in A.shape]
             secondHalves = [x-y for x, y in zip(A.shape, firstHalves)]
         else:
             # Inverse operation: Opposite rounding
             secondHalves = [x//2 for x in newShape]
             firstHalves = [x-y for x, y in zip(newShape, secondHalves)]
  
         R = np.zeros_like(A, shape=newShape)
         R[-firstHalves[0]:, -firstHalves[1]:] = A[:firstHalves[0], :firstHalves[1]]
         R[:secondHalves[0], -firstHalves[1]:] = A[-secondHalves[0]:, :firstHalves[1]]
         R[:secondHalves[0], :secondHalves[1]] = A[-secondHalves[0]:, -secondHalves[1]:]
         R[-firstHalves[0]:, :secondHalves[1]] = A[:firstHalves[0], -secondHalves[1]:]
         return R
  

◆ run()

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.run	(	self,
		scienceExposure,
		templateExposure,
		subtractedExposure,
		psfMatchingKernel,
		preConvKernel = `None`,
		xcen = `None`,
		ycen = `None`,
		svar = `None`,
		tvar = `None`,
		templateMatched = `True`
	)

Perform decorrelation of an image difference exposure.

Decorrelates the diffim due to the convolution of the templateExposure with the
A&L PSF matching kernel. Currently can accept a spatially varying matching kernel but in
this case it simply uses a static kernel from the center of the exposure. The decorrelation
is described in [DMTN-021, Equation 1](http://dmtn-021.lsst.io/#equation-1), where
`exposure` is I_1; templateExposure is I_2; `subtractedExposure` is D(k);
`psfMatchingKernel` is kappa; and svar and tvar are their respective
variances (see below).

Parameters
----------
scienceExposure : `lsst.afw.image.Exposure`
The original science exposure (before `preConvKernel` applied).
templateExposure : `lsst.afw.image.Exposure`
The original template exposure warped into the science exposure dimensions.
subtractedExposure : `lsst.afw.iamge.Exposure`
the subtracted exposure produced by
`ip_diffim.ImagePsfMatchTask.subtractExposures()`. The `subtractedExposure` must
inherit its PSF from `exposure`, see notes below.
psfMatchingKernel : `lsst.afw.detection.Psf`
An (optionally spatially-varying) PSF matching kernel produced
by `ip_diffim.ImagePsfMatchTask.subtractExposures()`.
preConvKernel : `lsst.afw.math.Kernel`, optional
if not None, then the `scienceExposure` was pre-convolved with this kernel.
Allowed only if ``templateMatched==True``.
xcen : `float`, optional
X-pixel coordinate to use for computing constant matching kernel to use
If `None` (default), then use the center of the image.
ycen : `float`, optional
Y-pixel coordinate to use for computing constant matching kernel to use
If `None` (default), then use the center of the image.
svar : `float`, optional
Image variance for science image
If `None` (default) then compute the variance over the entire input science image.
tvar : `float`, optional
Image variance for template image
If `None` (default) then compute the variance over the entire input template image.
templateMatched : `bool`, optional
If True, the template exposure was matched (convolved) to the science exposure.
See also notes below.

Returns
-------
result : `lsst.pipe.base.Struct`
- ``correctedExposure`` : the decorrelated diffim

Notes
-----
The `subtractedExposure` is NOT updated. The returned `correctedExposure` has an updated but
spatially fixed PSF. It is calculated as the center of image PSF corrected by the center of
image matching kernel.

If ``templateMatched==True``, the templateExposure was matched (convolved)
to the ``scienceExposure`` by ``psfMatchingKernel``. Otherwise the ``scienceExposure``
was matched (convolved) by ``psfMatchingKernel``.

This task discards the variance plane of ``subtractedExposure`` and re-computes
it from the variance planes of ``scienceExposure`` and ``templateExposure``.
The image plane of ``subtractedExposure`` must be at the photometric level
set by the AL PSF matching in `ImagePsfMatchTask.subtractExposures`.
The assumptions about the photometric level are controlled by the
`templateMatched` option in this task.

Here we currently convert a spatially-varying matching kernel into a constant kernel,
just by computing it at the center of the image (tickets DM-6243, DM-6244).

We are also using a constant accross-the-image measure of sigma (sqrt(variance)) to compute
the decorrelation kernel.

TODO DM-23857 As part of the spatially varying correction implementation
consider whether returning a Struct is still necessary.

Definition at line 112 of file imageDecorrelation.py.

             preConvKernel=None, xcen=None, ycen=None, svar=None, tvar=None, templateMatched=True):
         """Perform decorrelation of an image difference exposure.
  
         Decorrelates the diffim due to the convolution of the templateExposure with the
         A&L PSF matching kernel. Currently can accept a spatially varying matching kernel but in
         this case it simply uses a static kernel from the center of the exposure. The decorrelation
         is described in [DMTN-021, Equation 1](http://dmtn-021.lsst.io/#equation-1), where
         `exposure` is I_1; templateExposure is I_2; `subtractedExposure` is D(k);
         `psfMatchingKernel` is kappa; and svar and tvar are their respective
         variances (see below).
  
         Parameters
         ----------
         scienceExposure : `lsst.afw.image.Exposure`
             The original science exposure (before `preConvKernel` applied).
         templateExposure : `lsst.afw.image.Exposure`
             The original template exposure warped into the science exposure dimensions.
         subtractedExposure : `lsst.afw.iamge.Exposure`
             the subtracted exposure produced by
             `ip_diffim.ImagePsfMatchTask.subtractExposures()`. The `subtractedExposure` must
             inherit its PSF from `exposure`, see notes below.
         psfMatchingKernel : `lsst.afw.detection.Psf`
             An (optionally spatially-varying) PSF matching kernel produced
             by `ip_diffim.ImagePsfMatchTask.subtractExposures()`.
         preConvKernel : `lsst.afw.math.Kernel`, optional
             if not None, then the `scienceExposure` was pre-convolved with this kernel.
             Allowed only if ``templateMatched==True``.
         xcen : `float`, optional
             X-pixel coordinate to use for computing constant matching kernel to use
             If `None` (default), then use the center of the image.
         ycen : `float`, optional
             Y-pixel coordinate to use for computing constant matching kernel to use
             If `None` (default), then use the center of the image.
         svar : `float`, optional
             Image variance for science image
             If `None` (default) then compute the variance over the entire input science image.
         tvar : `float`, optional
             Image variance for template image
             If `None` (default) then compute the variance over the entire input template image.
         templateMatched : `bool`, optional
             If True, the template exposure was matched (convolved) to the science exposure.
             See also notes below.
  
         Returns
         -------
         result : `lsst.pipe.base.Struct`
             - ``correctedExposure`` : the decorrelated diffim
  
         Notes
         -----
         The `subtractedExposure` is NOT updated. The returned `correctedExposure` has an updated but
         spatially fixed PSF. It is calculated as the center of image PSF corrected by the center of
         image matching kernel.
  
         If ``templateMatched==True``, the templateExposure was matched (convolved)
         to the ``scienceExposure`` by ``psfMatchingKernel``. Otherwise the ``scienceExposure``
         was matched (convolved) by ``psfMatchingKernel``.
  
         This task discards the variance plane of ``subtractedExposure`` and re-computes
         it from the variance planes of ``scienceExposure`` and ``templateExposure``.
         The image plane of ``subtractedExposure`` must be at the photometric level
         set by the AL PSF matching in `ImagePsfMatchTask.subtractExposures`.
         The assumptions about the photometric level are controlled by the
         `templateMatched` option in this task.
  
         Here we currently convert a spatially-varying matching kernel into a constant kernel,
         just by computing it at the center of the image (tickets DM-6243, DM-6244).
  
         We are also using a constant accross-the-image measure of sigma (sqrt(variance)) to compute
         the decorrelation kernel.
  
         TODO DM-23857 As part of the spatially varying correction implementation
         consider whether returning a Struct is still necessary.
         """
         if preConvKernel is not None and not templateMatched:
             raise ValueError("Pre-convolution and the matching of the "
                              "science exposure is not supported.")
  
         spatialKernel = psfMatchingKernel
         kimg = afwImage.ImageD(spatialKernel.getDimensions())
         bbox = subtractedExposure.getBBox()
         if xcen is None:
             xcen = (bbox.getBeginX() + bbox.getEndX()) / 2.
         if ycen is None:
             ycen = (bbox.getBeginY() + bbox.getEndY()) / 2.
         self.log.info("Using matching kernel computed at (%d, %d)", xcen, ycen)
         spatialKernel.computeImage(kimg, False, xcen, ycen)
  
         if svar is None:
             svar = self.computeVarianceMean(scienceExposure)
         if tvar is None:
             tvar = self.computeVarianceMean(templateExposure)
         self.log.infof("Variance (science, template): ({:.5e}, {:.5e})", svar, tvar)
  
         if templateMatched:
             # Regular subtraction, we convolved the template
             self.log.info("Decorrelation after template image convolution")
             expVar = svar
             matchedVar = tvar
             exposure = scienceExposure
             matchedExposure = templateExposure
         else:
             # We convolved the science image
             self.log.info("Decorrelation after science image convolution")
             expVar = tvar
             matchedVar = svar
             exposure = templateExposure
             matchedExposure = scienceExposure
  
         # Should not happen unless entire image has been masked, which could happen
         # if this is a small subimage of the main exposure. In this case, just return a full NaN
         # exposure
         if np.isnan(expVar) or np.isnan(matchedVar):
             # Double check that one of the exposures is all NaNs
             if (np.all(np.isnan(exposure.image.array))
                     or np.all(np.isnan(matchedExposure.image.array))):
                 self.log.warn('Template or science image is entirely NaNs: skipping decorrelation.')
                 outExposure = subtractedExposure.clone()
                 return pipeBase.Struct(correctedExposure=outExposure, )
  
         # The maximal correction value converges to sqrt(matchedVar/expVar).
         # Correction divergence warning if the correction exceeds 4 orders of magnitude.
         mOverExpVar = matchedVar/expVar
         if mOverExpVar > 1e8:
             self.log.warn("Diverging correction: matched image variance is "
                           " much larger than the unconvolved one's"
                           f", matchedVar/expVar:{mOverExpVar:.2e}")
  
         oldVarMean = self.computeVarianceMean(subtractedExposure)
         self.log.info("Variance (uncorrected diffim): %f", oldVarMean)
  
         if preConvKernel is not None:
             self.log.info('Using a pre-convolution kernel as part of decorrelation correction.')
             kimg2 = afwImage.ImageD(preConvKernel.getDimensions())
             preConvKernel.computeImage(kimg2, False)
             pckArr = kimg2.array
  
         kArr = kimg.array
         diffExpArr = subtractedExposure.image.array
         psfImg = subtractedExposure.getPsf().computeKernelImage(geom.Point2D(xcen, ycen))
         psfDim = psfImg.getDimensions()
         psfArr = psfImg.array
  
         # Determine the common shape
         kSum = np.sum(kArr)
         kSumSq = kSum*kSum
         self.log.debugf("Matching kernel sum: {:.2e}", kSum)
         preSum = 1.
         if preConvKernel is None:
             self.computeCommonShape(kArr.shape, psfArr.shape, diffExpArr.shape)
             corrft = self.computeCorrection(kArr, expVar, matchedVar)
         else:
             preSum = np.sum(pckArr)
             self.log.debugf("pre-convolution kernel sum: {:.2e}", preSum)
             self.computeCommonShape(pckArr.shape, kArr.shape,
                                     psfArr.shape, diffExpArr.shape)
             corrft = self.computeCorrection(kArr, expVar, matchedVar, preConvArr=pckArr)
  
         diffExpArr = self.computeCorrectedImage(corrft, diffExpArr)
         corrPsfArr = self.computeCorrectedDiffimPsf(corrft, psfArr)
  
         psfcI = afwImage.ImageD(psfDim)
         psfcI.array = corrPsfArr
         psfcK = afwMath.FixedKernel(psfcI)
         psfNew = measAlg.KernelPsf(psfcK)
  
         correctedExposure = subtractedExposure.clone()
         correctedExposure.image.array[...] = diffExpArr  # Allow for numpy type casting
         # The subtracted exposure variance plane is already correlated, we cannot propagate
         # it through another convolution; instead we need to use the uncorrelated originals
         # The whitening should scale it to expVar + matchedVar on average
         varImg = correctedExposure.variance.array
         # Allow for numpy type casting
         varImg[...] = preSum*preSum*exposure.variance.array + kSumSq*matchedExposure.variance.array
         if not templateMatched:
             # ImagePsfMatch.subtractExposures re-scales the difference in
             # the science image convolution mode
             varImg /= kSumSq
         correctedExposure.setPsf(psfNew)
  
         newVarMean = self.computeVarianceMean(correctedExposure)
         self.log.infof("Variance (corrected diffim): {:.5e}", newVarMean)
  
         # TODO DM-23857 As part of the spatially varying correction implementation
         # consider whether returning a Struct is still necessary.
         return pipeBase.Struct(correctedExposure=correctedExposure, )
  

Member Data Documentation

◆ ConfigClass

lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.ConfigClass = DecorrelateALKernelConfig

static

Definition at line 84 of file imageDecorrelation.py.

◆ freqSpaceShape

lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.freqSpaceShape

Definition at line 329 of file imageDecorrelation.py.

◆ statsControl

lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.statsControl

Definition at line 99 of file imageDecorrelation.py.

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

/j/snowflake/release/lsstsw/stack/0.4.3/Linux64/ip_diffim/21.0.0-11-ga42c5b2+86977b0b17/python/lsst/ip/diffim/imageDecorrelation.py

Public Member Functions

Static Public Member Functions

Public Attributes

Static Public Attributes