lsst.ip.diffim  22.0.1-13-g00a9d746+70a5c35ac4
Public Member Functions | Static Public Member Functions | Public Attributes | Static Public Attributes | List of all members
lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask Class Reference
Inheritance diagram for lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask:
lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelMapper

Public Member Functions

def __init__ (self, *args, **kwargs)
 
def computeVarianceMean (self, exposure)
 
def run (self, scienceExposure, templateExposure, subtractedExposure, psfMatchingKernel, preConvKernel=None, xcen=None, ycen=None, svar=None, tvar=None, templateMatched=True)
 
def computeCommonShape (self, *shapes)
 
def computeCorrection (self, kappa, svar, tvar, preConvArr=None)
 
def computeCorrectedDiffimPsf (self, corrft, psfOld)
 
def computeCorrectedImage (self, corrft, imgOld)
 

Static Public Member Functions

def padCenterOriginArray (A, tuple newShape, useInverse=False)
 

Public Attributes

 statsControl
 
 freqSpaceShape
 

Static Public Attributes

 ConfigClass = DecorrelateALKernelConfig
 

Detailed Description

Decorrelate the effect of convolution by Alard-Lupton matching kernel in image difference

Notes
-----

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 53 of file imageDecorrelation.py.

Constructor & Destructor Documentation

◆ __init__()

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.__init__ (   self,
args,
**  kwargs 
)
Create the image decorrelation Task

Parameters
----------
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__``

Reimplemented in lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelMapper.

Definition at line 87 of file imageDecorrelation.py.

Member Function Documentation

◆ computeCommonShape()

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeCommonShape (   self,
shapes 
)
Calculate the common shape for FFT operations. Set `self.freqSpaceShape`
internally.

Parameters
----------
shapes : one or more `tuple` of `int`
    Shapes of the arrays. All must have the same dimensionality.
    At least one shape must be provided.

Returns
-------
None.

Notes
-----
For each dimension, gets the smallest even number greater than or equal to
`N1+N2-1` where `N1` and `N2` are the two largest values.
In case of only one shape given, rounds up to even each dimension value.

Definition at line 301 of file imageDecorrelation.py.

◆ computeCorrectedDiffimPsf()

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeCorrectedDiffimPsf (   self,
  corrft,
  psfOld 
)
Compute the (decorrelated) difference image's new PSF.

Parameters
----------
corrft : `numpy.ndarray`
    The frequency space representation of the correction calculated by
    `computeCorrection`. Shape must be `self.freqSpaceShape`.
psfOld : `numpy.ndarray`
    The psf of the difference image to be corrected.

Returns
-------
psfNew : `numpy.ndarray`
    The corrected psf, same shape as `psfOld`, sum normed to 1.

Notes
-----
There is no algorithmic guarantee that the corrected psf can
meaningfully fit to the same size as the original one.

Definition at line 439 of file imageDecorrelation.py.

◆ computeCorrectedImage()

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeCorrectedImage (   self,
  corrft,
  imgOld 
)
Compute the decorrelated difference image.

Parameters
----------
corrft : `numpy.ndarray`
    The frequency space representation of the correction calculated by
    `computeCorrection`. Shape must be `self.freqSpaceShape`.
imgOld : `numpy.ndarray`
    The difference image to be corrected.

Returns
-------
imgNew : `numpy.ndarray`
    The corrected image, same size as the input.

Definition at line 470 of file imageDecorrelation.py.

◆ computeCorrection()

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeCorrection (   self,
  kappa,
  svar,
  tvar,
  preConvArr = None 
)
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.

◆ computeVarianceMean()

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeVarianceMean (   self,
  exposure 
)

Definition at line 104 of file imageDecorrelation.py.

◆ padCenterOriginArray()

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.padCenterOriginArray (   A,
tuple  newShape,
  useInverse = False 
)
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
----------
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 334 of file imageDecorrelation.py.

◆ run()

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.run (   self,
  scienceExposure,
  templateExposure,
  subtractedExposure,
  psfMatchingKernel,
  preConvKernel = None,
  xcen = None,
  ycen = None,
  svar = None,
  tvar = None,
  templateMatched = True 
)
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
-----
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.

Member Data Documentation

◆ ConfigClass

lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.ConfigClass = DecorrelateALKernelConfig
static

Definition at line 84 of file imageDecorrelation.py.

◆ freqSpaceShape

lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.freqSpaceShape

Definition at line 330 of file imageDecorrelation.py.

◆ statsControl

lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.statsControl

Definition at line 99 of file imageDecorrelation.py.


The documentation for this class was generated from the following file: