lsst.ip.diffim  17.0-2-g41668cb+2
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, exposure, templateExposure, subtractedExposure, psfMatchingKernel, preConvKernel=None, xcen=None, ycen=None, svar=None, tvar=None)
 

Static Public Member Functions

def computeCorrectedDiffimPsf (kappa, psf, svar=0.04, tvar=0.04)
 

Public Attributes

 statsControl
 

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 52 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__``

Definition at line 86 of file imageDecorrelation.py.

Member Function Documentation

◆ computeCorrectedDiffimPsf()

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeCorrectedDiffimPsf (   kappa,
  psf,
  svar = 0.04,
  tvar = 0.04 
)
static
Compute the (decorrelated) difference image's new PSF.
new_psf = psf(k) * sqrt((svar + tvar) / (svar + tvar * kappa_ft(k)**2))

Parameters
----------
kappa : `numpy.ndarray`
    A matching kernel array derived from Alard & Lupton PSF matching
psf : `numpy.ndarray`
    The uncorrected psf array of the science image (and also of the diffim)
svar : `float`, optional
    Average variance of science image used for PSF matching
tvar : `float`, optional
    Average variance of template image used for PSF matching

Returns
-------
pcf : `numpy.ndarray`
    a 2-d numpy.array containing the new PSF

Definition at line 293 of file imageDecorrelation.py.

◆ computeVarianceMean()

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

Definition at line 103 of file imageDecorrelation.py.

◆ run()

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.run (   self,
  exposure,
  templateExposure,
  subtractedExposure,
  psfMatchingKernel,
  preConvKernel = None,
  xcen = None,
  ycen = None,
  svar = None,
  tvar = None 
)
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
----------
exposure : `lsst.afw.image.Exposure`
    The science afwImage.Exposure used for PSF matching
templateExposure : `lsst.afw.image.Exposure`
    The template exposure used for PSF matching
subtractedExposure :
    the subtracted exposure produced by
    `ip_diffim.ImagePsfMatchTask.subtractExposures()`
psfMatchingKernel :
    An (optionally spatially-varying) PSF matching kernel produced
    by `ip_diffim.ImagePsfMatchTask.subtractExposures()`
preConvKernel :
    if not None, then the `exposure` was pre-convolved with this kernel
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.

Returns
-------
result : `Struct`
    a `lsst.pipe.base.Struct` containing:

    - ``correctedExposure`` : the decorrelated diffim
    - ``correctionKernel`` : the decorrelation correction kernel (which may be ignored)

Notes
-----
The `subtractedExposure` is NOT updated

The returned `correctedExposure` has an updated PSF as well.

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.

Still TBD (ticket DM-6580): understand whether the convolution is correctly modifying
the variance plane of the new subtractedExposure.

Definition at line 112 of file imageDecorrelation.py.

Member Data Documentation

◆ ConfigClass

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

Definition at line 83 of file imageDecorrelation.py.

◆ statsControl

lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.statsControl

Definition at line 98 of file imageDecorrelation.py.


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