DecorrelateALKernelTask¶
-
class
lsst.ip.diffim.
DecorrelateALKernelTask
(*args, **kwargs)¶ Bases:
lsst.pipe.base.Task
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.
Methods Summary
computeCommonShape
(*shapes)Calculate the common shape for FFT operations. computeCorrectedDiffimPsf
(corrft, psfOld)Compute the (decorrelated) difference image’s new PSF. computeCorrectedImage
(corrft, imgOld)Compute the decorrelated difference image. computeCorrection
(kappa, svar, tvar[, …])Compute the Lupton decorrelation post-convolution kernel for decorrelating an image difference, based on the PSF-matching kernel. computeVarianceMean
(exposure)emptyMetadata
()Empty (clear) the metadata for this Task and all sub-Tasks. getAllSchemaCatalogs
()Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict. getFullMetadata
()Get metadata for all tasks. getFullName
()Get the task name as a hierarchical name including parent task names. getName
()Get the name of the task. getSchemaCatalogs
()Get the schemas generated by this task. getTaskDict
()Get a dictionary of all tasks as a shallow copy. makeField
(doc)Make a lsst.pex.config.ConfigurableField
for this task.makeSubtask
(name, **keyArgs)Create a subtask as a new instance as the name
attribute of this task.padCenterOriginArray
(A, newShape[, useInverse])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. run
(exposure, templateExposure, …[, …])Perform decorrelation of an image difference exposure. timer
(name[, logLevel])Context manager to log performance data for an arbitrary block of code. Methods Documentation
-
computeCommonShape
(*shapes)¶ Calculate the common shape for FFT operations. Set
self.freqSpaceShape
internally.Parameters: Returns: - None.
Notes
For each dimension, gets the smallest even number greater than or equal to
N1+N2-1
whereN1
andN2
are the two largest values. In case of only one shape given, rounds up to even each dimension value.
-
computeCorrectedDiffimPsf
(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 beself.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.
- corrft :
-
computeCorrectedImage
(corrft, imgOld)¶ Compute the decorrelated difference image.
Parameters: - corrft :
numpy.ndarray
The frequency space representation of the correction calculated by
computeCorrection
. Shape must beself.freqSpaceShape
.- imgOld :
numpy.ndarray
The difference image to be corrected.
Returns: - imgNew :
numpy.ndarray
The corrected image, same size as the input.
- corrft :
-
computeCorrection
(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
offloat
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.- kappa :
-
computeVarianceMean
(exposure)¶
-
emptyMetadata
()¶ Empty (clear) the metadata for this Task and all sub-Tasks.
-
getAllSchemaCatalogs
()¶ Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.
Returns: - schemacatalogs :
dict
Keys are butler dataset type, values are a empty catalog (an instance of the appropriate
lsst.afw.table
Catalog type) for all tasks in the hierarchy, from the top-level task down through all subtasks.
Notes
This method may be called on any task in the hierarchy; it will return the same answer, regardless.
The default implementation should always suffice. If your subtask uses schemas the override
Task.getSchemaCatalogs
, not this method.- schemacatalogs :
-
getFullMetadata
()¶ Get metadata for all tasks.
Returns: - metadata :
lsst.daf.base.PropertySet
The
PropertySet
keys are the full task name. Values are metadata for the top-level task and all subtasks, sub-subtasks, etc.
Notes
The returned metadata includes timing information (if
@timer.timeMethod
is used) and any metadata set by the task. The name of each item consists of the full task name with.
replaced by:
, followed by.
and the name of the item, e.g.:topLevelTaskName:subtaskName:subsubtaskName.itemName
using
:
in the full task name disambiguates the rare situation that a task has a subtask and a metadata item with the same name.- metadata :
-
getFullName
()¶ Get the task name as a hierarchical name including parent task names.
Returns: - fullName :
str
The full name consists of the name of the parent task and each subtask separated by periods. For example:
- The full name of top-level task “top” is simply “top”.
- The full name of subtask “sub” of top-level task “top” is “top.sub”.
- The full name of subtask “sub2” of subtask “sub” of top-level task “top” is “top.sub.sub2”.
- fullName :
-
getSchemaCatalogs
()¶ Get the schemas generated by this task.
Returns: - schemaCatalogs :
dict
Keys are butler dataset type, values are an empty catalog (an instance of the appropriate
lsst.afw.table
Catalog type) for this task.
See also
Task.getAllSchemaCatalogs
Notes
Warning
Subclasses that use schemas must override this method. The default implementation returns an empty dict.
This method may be called at any time after the Task is constructed, which means that all task schemas should be computed at construction time, not when data is actually processed. This reflects the philosophy that the schema should not depend on the data.
Returning catalogs rather than just schemas allows us to save e.g. slots for SourceCatalog as well.
- schemaCatalogs :
-
getTaskDict
()¶ Get a dictionary of all tasks as a shallow copy.
Returns: - taskDict :
dict
Dictionary containing full task name: task object for the top-level task and all subtasks, sub-subtasks, etc.
- taskDict :
-
classmethod
makeField
(doc)¶ Make a
lsst.pex.config.ConfigurableField
for this task.Parameters: - doc :
str
Help text for the field.
Returns: - configurableField :
lsst.pex.config.ConfigurableField
A
ConfigurableField
for this task.
Examples
Provides a convenient way to specify this task is a subtask of another task.
Here is an example of use:
class OtherTaskConfig(lsst.pex.config.Config): aSubtask = ATaskClass.makeField("brief description of task")
- doc :
-
makeSubtask
(name, **keyArgs)¶ Create a subtask as a new instance as the
name
attribute of this task.Parameters: - name :
str
Brief name of the subtask.
- keyArgs
Extra keyword arguments used to construct the task. The following arguments are automatically provided and cannot be overridden:
- “config”.
- “parentTask”.
Notes
The subtask must be defined by
Task.config.name
, an instance ofConfigurableField
orRegistryField
.- name :
-
static
padCenterOriginArray
(A, newShape: tuple, useInverse=False)¶ 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
ofint
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.
- A :
-
run
(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 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 thetemplateExposure
should be matched to the PSF ofexposure
, see notes below.- subtractedExposure :
the subtracted exposure produced by
ip_diffim.ImagePsfMatchTask.subtractExposures()
. ThesubtractedExposure
must inherit its PSF fromexposure
, 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 returnedcorrectedExposure
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 theexposure
bypsfMatchingKernel
. Swap arguments accordingly if actually the science exposure was matched to a co-added template. In this case, tvar > svar typically occurs.The
templateExposure
andexposure
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.
- exposure :
-
timer
(name, logLevel=10000)¶ Context manager to log performance data for an arbitrary block of code.
Parameters: - name :
str
Name of code being timed; data will be logged using item name:
Start
andEnd
.- logLevel
A
lsst.log
level constant.
See also
timer.logInfo
Examples
Creating a timer context:
with self.timer("someCodeToTime"): pass # code to time
- name :
-