lsst.ip.diffim
13.0-28-gf4bc96c+6
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Decorrelate the effect of convolution by Alard-Lupton matching kernel in image difference. More...
Public Member Functions | |
def | __init__ (self, args, kwargs) |
Create the image decorrelation Task. More... | |
def | computeVarianceMean (self, exposure) |
def | run (self, exposure, templateExposure, subtractedExposure, psfMatchingKernel, preConvKernel=None, xcen=None, ycen=None, svar=None, tvar=None) |
Perform decorrelation of an image difference exposure. More... | |
Static Public Member Functions | |
def | computeCorrectedDiffimPsf (kappa, psf, svar=0.04, tvar=0.04) |
Compute the (decorrelated) difference image's new PSF. More... | |
Public Attributes | |
statsControl | |
Static Public Attributes | |
ConfigClass = DecorrelateALKernelConfig | |
Static Private Member Functions | |
def | _computeDecorrelationKernel (kappa, svar=0.04, tvar=0.04, preConvKernel=None) |
Compute the Lupton decorrelation post-conv. More... | |
def | _fixOddKernel (kernel) |
Take a kernel with odd dimensions and make them even for FFT. More... | |
def | _fixEvenKernel (kernel) |
Take a kernel with even dimensions and make them odd, centered correctly. More... | |
def | _doConvolve (exposure, kernel) |
Convolve an Exposure with a decorrelation convolution kernel. More... | |
Static Private Attributes | |
string | _DefaultName = "ip_diffim_decorrelateALKernel" |
Decorrelate the effect of convolution by Alard-Lupton matching kernel in image difference.
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 PSFMatchTask and PSFMatchConfigAL 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.
Create the image decorrelation Task.
*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__ |
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, 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).
[in] | exposure | the science afwImage.Exposure used for PSF matching |
[in] | templateExposure | the template afwImage.Exposure used for PSF matching |
[in] | subtractedExposure | the subtracted exposure produced by ip_diffim.ImagePsfMatchTask.subtractExposures() |
[in] | psfMatchingKernel | an (optionally spatially-varying) PSF matching kernel produced by ip_diffim.ImagePsfMatchTask.subtractExposures() |
[in] | preConvKernel | if not None, then the exposure was pre-convolved with this kernel |
[in] | xcen | X-pixel coordinate to use for computing constant matching kernel to use If None (default), then use the center of the image. |
[in] | ycen | Y-pixel coordinate to use for computing constant matching kernel to use If None (default), then use the center of the image. |
[in] | svar | image variance for science image If None (default) then compute the variance over the entire input science image. |
[in] | tvar | image variance for template image If None (default) then compute the variance over the entire input template image. |
pipeBase.Struct
containing:correctedExposure
: the decorrelated diffimcorrectionKernel
: the decorrelation correction kernel (which may be ignored)subtractedExposure
is NOT updated correctedExposure
has an updated PSF as well. This task has no debug variables
This task has no standalone example, however it is applied as a subtask of pipe.tasks.imageDifference.ImageDifferenceTask.
Definition at line 63 of file imageDecorrelation.py.
def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.__init__ | ( | self, | |
args, | |||
kwargs | |||
) |
Create the image decorrelation Task.
*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__ |
Definition at line 125 of file imageDecorrelation.py.
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staticprivate |
Compute the Lupton decorrelation post-conv.
kernel for decorrelating an image difference, based on the PSF-matching kernel.
kappa | A matching kernel 2-d numpy.array derived from Alard & Lupton PSF matching |
svar | Average variance of science image used for PSF matching |
tvar | Average variance of template image used for PSF matching |
preConvKernel | If not None, then pre-filtering was applied to science exposure, and this is the pre-convolution kernel. |
Definition at line 242 of file imageDecorrelation.py.
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staticprivate |
Convolve an Exposure with a decorrelation convolution kernel.
exposure | Input afw.image.Exposure to be convolved. |
kernel | Input 2-d numpy.array to convolve the image with |
Definition at line 373 of file imageDecorrelation.py.
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staticprivate |
Take a kernel with even dimensions and make them odd, centered correctly.
kernel | a numpy.array |
Definition at line 350 of file imageDecorrelation.py.
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staticprivate |
Take a kernel with odd dimensions and make them even for FFT.
kernel | a numpy.array |
Definition at line 329 of file imageDecorrelation.py.
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static |
Compute the (decorrelated) difference image's new PSF.
new_psf = psf(k) * sqrt((svar + tvar) / (svar + tvar * kappa_ft(k)**2))
kappa | A matching kernel array derived from Alard & Lupton PSF matching |
psf | The uncorrected psf array of the science image (and also of the diffim) |
svar | Average variance of science image used for PSF matching |
tvar | Average variance of template image used for PSF matching |
Definition at line 295 of file imageDecorrelation.py.
def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeVarianceMean | ( | self, | |
exposure | |||
) |
Definition at line 138 of file imageDecorrelation.py.
def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.run | ( | self, | |
exposure, | |||
templateExposure, | |||
subtractedExposure, | |||
psfMatchingKernel, | |||
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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) |
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, 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).
[in] | exposure | the science afwImage.Exposure used for PSF matching |
[in] | templateExposure | the template afwImage.Exposure used for PSF matching |
[in] | subtractedExposure | the subtracted exposure produced by ip_diffim.ImagePsfMatchTask.subtractExposures() |
[in] | psfMatchingKernel | an (optionally spatially-varying) PSF matching kernel produced by ip_diffim.ImagePsfMatchTask.subtractExposures() |
[in] | preConvKernel | if not None, then the exposure was pre-convolved with this kernel |
[in] | xcen | X-pixel coordinate to use for computing constant matching kernel to use If None (default), then use the center of the image. |
[in] | ycen | Y-pixel coordinate to use for computing constant matching kernel to use If None (default), then use the center of the image. |
[in] | svar | image variance for science image If None (default) then compute the variance over the entire input science image. |
[in] | tvar | image variance for template image If None (default) then compute the variance over the entire input template image. |
pipeBase.Struct
containing:correctedExposure
: the decorrelated diffimcorrectionKernel
: the decorrelation correction kernel (which may be ignored)subtractedExposure
is NOT updated correctedExposure
has an updated PSF as well. Definition at line 147 of file imageDecorrelation.py.
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staticprivate |
Definition at line 123 of file imageDecorrelation.py.
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static |
Definition at line 122 of file imageDecorrelation.py.
lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.statsControl |
Definition at line 132 of file imageDecorrelation.py.