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def | __init__ (self, *args, **kwargs) |
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def | run (self, subExposure, expandedSubExposure, fullBBox, template, science, alTaskResult=None, psfMatchingKernel=None, preConvKernel=None, **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) |
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def | computeCommonShape (self, *shapes) |
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def | computeCorrection (self, kappa, svar, tvar, preConvArr=None) |
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def | computeCorrectedDiffimPsf (self, corrft, psfOld) |
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def | computeCorrectedImage (self, corrft, imgOld) |
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def | run (self, subExposure, expandedSubExposure, fullBBox, **kwargs) |
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Task to be used as an ImageMapper for performing
A&L decorrelation on subimages on a grid across a A&L difference image.
This task subclasses DecorrelateALKernelTask in order to implement
all of that task's configuration parameters, as well as its `run` method.
Definition at line 503 of file imageDecorrelation.py.
def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeCorrection |
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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 = None |
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inherited |
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 381 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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) |
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staticinherited |
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
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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 334 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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inherited |
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
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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.
def lsst.ip.diffim.imageMapReduce.ImageMapper.run |
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self, |
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subExposure, |
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expandedSubExposure, |
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fullBBox, |
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** |
kwargs |
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inherited |
Perform operation on `subExposure`.
To be implemented by subclasses. See class docstring for more
details. This method is given the `subExposure` which
is to be operated upon, and an `expandedSubExposure` which
will contain `subExposure` with additional surrounding
pixels. This allows for, for example, convolutions (which
should be performed on `expandedSubExposure`), to prevent the
returned sub-exposure from containing invalid pixels.
This method may return a new, processed sub-exposure which can
be be "stitched" back into a new resulting larger exposure
(depending on the paired, configured `ImageReducer`);
otherwise if it does not return an lsst.afw.image.Exposure, then the
`ImageReducer.config.mapper.reduceOperation`
should be set to 'none' and the result will be propagated
as-is.
Parameters
----------
subExposure : `lsst.afw.image.Exposure`
the sub-exposure upon which to operate
expandedSubExposure : `lsst.afw.image.Exposure`
the expanded sub-exposure upon which to operate
fullBBox : `lsst.geom.Box2I`
the bounding box of the original exposure
kwargs :
additional keyword arguments propagated from
`ImageMapReduceTask.run`.
Returns
-------
result : `lsst.pipe.base.Struct`
A structure containing the result of the `subExposure` processing,
which may itself be of any type. See above for details. If it is an
`lsst.afw.image.Exposure` (processed sub-exposure), then the name in
the Struct should be 'subExposure'. This is implemented here as a
pass-through example only.
Definition at line 109 of file imageMapReduce.py.
def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelMapper.run |
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self, |
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subExposure, |
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expandedSubExposure, |
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fullBBox, |
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template, |
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science, |
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alTaskResult = None , |
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psfMatchingKernel = None , |
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preConvKernel = None , |
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** |
kwargs |
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) |
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Perform decorrelation operation on `subExposure`, using
`expandedSubExposure` to allow for invalid edge pixels arising from
convolutions.
This method performs A&L decorrelation on `subExposure` using
local measures for image variances and PSF. `subExposure` is a
sub-exposure of the non-decorrelated A&L diffim. It also
requires the corresponding sub-exposures of the template
(`template`) and science (`science`) exposures.
Parameters
----------
subExposure : `lsst.afw.image.Exposure`
the sub-exposure of the diffim
expandedSubExposure : `lsst.afw.image.Exposure`
the expanded sub-exposure upon which to operate
fullBBox : `lsst.geom.Box2I`
the bounding box of the original exposure
template : `lsst.afw.image.Exposure`
the corresponding sub-exposure of the template exposure
science : `lsst.afw.image.Exposure`
the corresponding sub-exposure of the science exposure
alTaskResult : `lsst.pipe.base.Struct`
the result of A&L image differencing on `science` and
`template`, importantly containing the resulting
`psfMatchingKernel`. Can be `None`, only if
`psfMatchingKernel` is not `None`.
psfMatchingKernel : Alternative parameter for passing the
A&L `psfMatchingKernel` directly.
preConvKernel : If not None, then pre-filtering was applied
to science exposure, and this is the pre-convolution
kernel.
kwargs :
additional keyword arguments propagated from
`ImageMapReduceTask.run`.
Returns
-------
A `pipeBase.Struct` containing:
- ``subExposure`` : the result of the `subExposure` processing.
- ``decorrelationKernel`` : the decorrelation kernel, currently
not used.
Notes
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This `run` method accepts parameters identical to those of
`ImageMapper.run`, since it is called from the
`ImageMapperTask`. See that class for more information.
Definition at line 517 of file imageDecorrelation.py.