lsst.ip.diffim  21.0.0-6-g00874e7+793c1c31cc
Public Member Functions | Static Public Member Functions | Public Attributes | Static Public Attributes | List of all members
lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelMapper Class Reference
Inheritance diagram for lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelMapper:
lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask lsst.ip.diffim.imageMapReduce.ImageMapper

Public Member Functions

def __init__ (self, *args, **kwargs)
 
def run (self, subExposure, expandedSubExposure, fullBBox, template, science, alTaskResult=None, psfMatchingKernel=None, preConvKernel=None, **kwargs)
 
def computeVarianceMean (self, exposure)
 
def run (self, exposure, templateExposure, subtractedExposure, psfMatchingKernel, preConvKernel=None, xcen=None, ycen=None, svar=None, tvar=None)
 
def computeCommonShape (self, *shapes)
 
def computeCorrection (self, kappa, svar, tvar, preConvArr=None)
 
def computeCorrectedDiffimPsf (self, corrft, psfOld)
 
def computeCorrectedImage (self, corrft, imgOld)
 
def run (self, subExposure, expandedSubExposure, fullBBox, **kwargs)
 

Static Public Member Functions

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

Public Attributes

 statsControl
 
 freqSpaceShape
 

Static Public Attributes

 ConfigClass = DecorrelateALKernelConfig
 

Detailed Description

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 465 of file imageDecorrelation.py.

Constructor & Destructor Documentation

◆ __init__()

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelMapper.__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 from lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.

Definition at line 476 of file imageDecorrelation.py.

Member Function Documentation

◆ computeCommonShape()

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeCommonShape (   self,
shapes 
)
inherited
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 263 of file imageDecorrelation.py.

◆ computeCorrectedDiffimPsf()

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeCorrectedDiffimPsf (   self,
  corrft,
  psfOld 
)
inherited
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 401 of file imageDecorrelation.py.

◆ computeCorrectedImage()

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.computeCorrectedImage (   self,
  corrft,
  imgOld 
)
inherited
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 432 of file imageDecorrelation.py.

◆ computeCorrection()

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

◆ computeVarianceMean()

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

Definition at line 104 of file imageDecorrelation.py.

◆ padCenterOriginArray()

def 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 296 of file imageDecorrelation.py.

◆ run() [1/3]

def lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.run (   self,
  exposure,
  templateExposure,
  subtractedExposure,
  psfMatchingKernel,
  preConvKernel = None,
  xcen = None,
  ycen = None,
  svar = None,
  tvar = None 
)
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
----------
exposure : `lsst.afw.image.Exposure`
    The original science exposure (before `preConvKernel` applied) used for PSF matching.
templateExposure : `lsst.afw.image.Exposure`
    The original template exposure (before matched to the science exposure
    by `psfMatchingKernel`) warped into the science exposure dimensions. Always the PSF of the
    `templateExposure` should be matched to the PSF of `exposure`, see notes below.
subtractedExposure :
    the subtracted exposure produced by
    `ip_diffim.ImagePsfMatchTask.subtractExposures()`. The `subtractedExposure` must
    inherit its PSF from `exposure`, see notes below.
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 : `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.

In this task, it is _always_ the `templateExposure` that was matched to the `exposure`
by `psfMatchingKernel`. Swap arguments accordingly if actually the science exposure was matched
to a co-added template. In this case, tvar > svar typically occurs.

The `templateExposure` and `exposure` image dimensions must be the same.

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.

◆ run() [2/3]

def lsst.ip.diffim.imageMapReduce.ImageMapper.run (   self,
  subExposure,
  expandedSubExposure,
  fullBBox,
**  kwargs 
)
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.

◆ run() [3/3]

def 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.

Definition at line 479 of file imageDecorrelation.py.

Member Data Documentation

◆ ConfigClass

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

Definition at line 473 of file imageDecorrelation.py.

◆ freqSpaceShape

lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.freqSpaceShape
inherited

Definition at line 292 of file imageDecorrelation.py.

◆ statsControl

lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelTask.statsControl
inherited

Definition at line 99 of file imageDecorrelation.py.


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