|
| | __init__ (self, *args, **kwargs) |
| |
| | run (self, subExposure, expandedSubExposure, fullBBox, template, science, alTaskResult=None, psfMatchingKernel=None, preConvKernel=None, **kwargs) |
| |
| | computeVarianceMean (self, exposure) |
| |
| | computeCommonShape (self, *shapes) |
| |
| | computeDiffimCorrection (self, kappa, svar, tvar) |
| |
| | computeScoreCorrection (self, kappa, svar, tvar, preConvArr) |
| |
| | calculateVariancePlane (self, vplane1, vplane2, varMean1, varMean2, c1ft, c2ft) |
| |
| | computeCorrectedDiffimPsf (self, corrft, psfOld) |
| |
| | computeCorrectedImage (self, corrft, imgOld) |
| |
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 653 of file imageDecorrelation.py.
| lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.calculateVariancePlane |
( |
| self, |
|
|
| vplane1, |
|
|
| vplane2, |
|
|
| varMean1, |
|
|
| varMean2, |
|
|
| c1ft, |
|
|
| c2ft ) |
|
inherited |
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 523 of file imageDecorrelation.py.
| lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeDiffimCorrection |
( |
| self, |
|
|
| kappa, |
|
|
| svar, |
|
|
| tvar ) |
|
inherited |
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
-----
The maximum correction factor converges to `sqrt(tvar/svar)` towards high frequencies.
This should be a plausible value.
Definition at line 396 of file imageDecorrelation.py.
| lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeScoreCorrection |
( |
| self, |
|
|
| kappa, |
|
|
| svar, |
|
|
| tvar, |
|
|
| preConvArr ) |
|
inherited |
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
-----
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 436 of file imageDecorrelation.py.
| lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.padCenterOriginArray |
( |
| A, |
|
|
tuple | newShape, |
|
|
| useInverse = False ) |
|
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
-----
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 349 of file imageDecorrelation.py.
| lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelMapper.run |
( |
| self, |
|
|
| subExposure, |
|
|
| expandedSubExposure, |
|
|
| fullBBox, |
|
|
| template, |
|
|
| science, |
|
|
| alTaskResult = None, |
|
|
| psfMatchingKernel = None, |
|
|
| preConvKernel = None, |
|
|
** | kwargs ) |
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
-----
This `run` method accepts parameters identical to those of
`ImageMapper.run`, since it is called from the
`ImageMapperTask`. See that class for more information.
Reimplemented from lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.
Definition at line 667 of file imageDecorrelation.py.
1.13.2