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| __init__ (self, *args, **kwargs) |
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| computeVarianceMean (self, exposure) |
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| run (self, scienceExposure, templateExposure, subtractedExposure, psfMatchingKernel, spatiallyVarying=True, preConvKernel=None, templateMatched=True, preConvMode=False) |
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Decorrelate the effect of convolution by Alard-Lupton matching kernel in image difference
Definition at line 775 of file imageDecorrelation.py.
◆ __init__()
lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelSpatialTask.__init__ |
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self, |
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args, |
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kwargs |
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Create the image decorrelation Task
Parameters
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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.
◆ computeVarianceMean()
lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelSpatialTask.computeVarianceMean |
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self, |
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exposure |
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◆ run()
lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelSpatialTask.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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spatiallyVarying = True , |
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preConvKernel = None , |
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templateMatched = True , |
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preConvMode = False |
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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 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
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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.
◆ _DefaultName
str lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelSpatialTask._DefaultName = "ip_diffim_decorrelateALKernelSpatial" |
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staticprotected |
◆ ConfigClass
◆ statsControl
lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelSpatialTask.statsControl |
The documentation for this class was generated from the following file: