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def | __init__ (self, *args, **kwargs) |
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def | computeVarianceMean (self, exposure) |
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def | run (self, scienceExposure, templateExposure, subtractedExposure, psfMatchingKernel, preConvKernel=None, xcen=None, ycen=None, svar=None, tvar=None, templateMatched=True, preConvMode=False, **kwargs) |
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def | computeCommonShape (self, *shapes) |
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def | computeDiffimCorrection (self, kappa, svar, tvar) |
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def | computeScoreCorrection (self, kappa, svar, tvar, preConvArr) |
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def | calculateVariancePlane (self, vplane1, vplane2, varMean1, varMean2, c1ft, c2ft) |
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def | computeCorrectedDiffimPsf (self, corrft, psfOld) |
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def | computeCorrectedImage (self, corrft, imgOld) |
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Decorrelate the effect of convolution by Alard-Lupton matching kernel in image difference
Notes
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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 59 of file imageDecorrelation.py.
def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.calculateVariancePlane |
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self, |
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vplane1, |
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vplane2, |
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varMean1, |
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varMean2, |
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c1ft, |
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c2ft |
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) |
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Full propagation of the variance planes of the original exposures.
The original variance planes of independent pixels are convolved with the
image space square of the overall kernels.
Parameters
----------
vplane1, vplane2 : `numpy.ndarray` of `float`
Variance planes of the original (before pre-convolution or matching)
exposures.
varMean1, varMean2 : `float`
Replacement average values for non-finite ``vplane1`` and ``vplane2`` values respectively.
c1ft, c2ft : `numpy.ndarray` of `complex`
The overall convolution that includes the matching and the
afterburner in frequency space. The result of either
``computeScoreCorrection`` or ``computeDiffimCorrection``.
Returns
-------
vplaneD : `numpy.ndarray` of `float`
The variance plane of the difference/score images.
Notes
------
See DMTN-179 Section 5 about the variance plane calculations.
Infs and NaNs are allowed and kept in the returned array.
Definition at line 537 of file imageDecorrelation.py.
def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeDiffimCorrection |
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self, |
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kappa, |
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svar, |
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tvar |
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Compute the Lupton decorrelation post-convolution kernel for decorrelating an
image difference, based on the PSF-matching kernel.
Parameters
----------
kappa : `numpy.ndarray` of `float`
A matching kernel 2-d numpy.array derived from Alard & Lupton PSF matching.
svar : `float` > 0.
Average variance of science image used for PSF matching.
tvar : `float` > 0.
Average variance of the template (matched) image used for PSF matching.
Returns
-------
corrft : `numpy.ndarray` of `float`
The frequency space representation of the correction. The array is real (dtype float).
Shape is `self.freqSpaceShape`.
cnft, crft : `numpy.ndarray` of `complex`
The overall convolution (pre-conv, PSF matching, noise correction) kernel
for the science and template images, respectively for the variance plane
calculations. These are intermediate results in frequency space.
Notes
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The maximum correction factor converges to `sqrt(tvar/svar)` towards high frequencies.
This should be a plausible value.
Definition at line 410 of file imageDecorrelation.py.
def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeScoreCorrection |
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self, |
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kappa, |
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svar, |
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tvar, |
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preConvArr |
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) |
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Compute the correction kernel for a score image.
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 (before pre-convolution).
tvar : `float`
Average variance of the template (matched) image used for PSF matching.
preConvArr : `numpy.ndarray`
The pre-convolution kernel of the science image. It should be the PSF
of the science image or an approximation of it. It must be normed to sum 1.
Returns
-------
corrft : `numpy.ndarray` of `float`
The frequency space representation of the correction. The array is real (dtype float).
Shape is `self.freqSpaceShape`.
cnft, crft : `numpy.ndarray` of `complex`
The overall convolution (pre-conv, PSF matching, noise correction) kernel
for the science and template images, respectively for the variance plane
calculations. These are intermediate results in frequency space.
Notes
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To be precise, the science image should be _correlated_ by ``preConvArray`` but this
does not matter for this calculation.
``cnft``, ``crft`` contain the scaling factor as well.
Definition at line 450 of file imageDecorrelation.py.
def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.padCenterOriginArray |
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A, |
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tuple |
newShape, |
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useInverse = False |
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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
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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
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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 363 of file imageDecorrelation.py.
def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.run |
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self, |
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scienceExposure, |
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templateExposure, |
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subtractedExposure, |
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psfMatchingKernel, |
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preConvKernel = None , |
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xcen = None , |
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ycen = None , |
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svar = None , |
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tvar = None , |
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templateMatched = True , |
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preConvMode = False , |
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** |
kwargs |
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) |
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Perform decorrelation of an image difference or of a score difference exposure.
Corrects the difference or score image due to the convolution of the
templateExposure with the A&L PSF matching kernel.
See [DMTN-021, Equation 1](http://dmtn-021.lsst.io/#equation-1) and
[DMTN-179](http://dmtn-179.lsst.io/) for details.
Parameters
----------
scienceExposure : `lsst.afw.image.Exposure`
The original science exposure (before pre-convolution, if ``preConvMode==True``).
templateExposure : `lsst.afw.image.Exposure`
The original template exposure warped into the science exposure dimensions.
subtractedExposure : `lsst.afw.image.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 (the reflection of)
this kernel. Must be normalized to sum to 1.
Allowed only if ``templateMatched==True`` and ``preConvMode==True``.
Defaults to the PSF of the science exposure at the image center.
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.
preConvMode : `bool`, optional
If True, ``subtractedExposure`` is assumed to be a likelihood difference image
and will be noise corrected as a likelihood image.
**kwargs
Additional keyword arguments propagated from DecorrelateALKernelSpatialTask.
Returns
-------
result : `lsst.pipe.base.Struct`
- ``correctedExposure`` : the decorrelated diffim
Notes
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If ``preConvMode==True``, ``subtractedExposure`` is assumed to be a
score image and the noise correction for likelihood images
is applied. The resulting image is an optimal detection likelihood image
when the templateExposure has noise. (See DMTN-179) If ``preConvKernel`` is
not specified, the PSF of ``scienceExposure`` is assumed as pre-convolution kernel.
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 118 of file imageDecorrelation.py.