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

`calculateVariancePlane`(vplane1, vplane2, …)	Full propagation of the variance planes of the original exposures.
`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.
`computeDiffimCorrection`(kappa, svar, tvar)	Compute the Lupton decorrelation post-convolution kernel for decorrelating an image difference, based on the PSF-matching kernel.
`computeScoreCorrection`(kappa, svar, tvar, …)	Compute the correction kernel for a score image.
`computeVarianceMean`(exposure)
`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`estimateVariancePlane`(vplane1, vplane2, …)	Estimate the variance planes.
`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`(scienceExposure, templateExposure, …)	Perform decorrelation of an image difference or of a score difference exposure.
`timer`(name, logLevel)	Context manager to log performance data for an arbitrary block of code.

Methods Documentation

calculateVariancePlane(vplane1, vplane2, varMean1, varMean2, c1ft, c2ft)¶

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.

computeCommonShape(*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.

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 be `self.freqSpaceShape`. psfOld : `numpy.ndarray` The psf of the difference image to be corrected.
Returns:	correctedPsf : `lsst.meas.algorithms.KernelPsf` 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.

computeCorrectedImage(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.

computeDiffimCorrection(kappa, svar, tvar)¶

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.

computeScoreCorrection(kappa, svar, tvar, preConvArr)¶

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.

computeVarianceMean(exposure)¶

emptyMetadata() → None¶: Empty (clear) the metadata for this Task and all sub-Tasks.

static estimateVariancePlane(vplane1, vplane2, c1ft, c2ft)¶

Estimate the variance planes.

The estimation assumes that around each pixel the surrounding pixels’ sigmas within the convolution kernel are the same.

Parameters:	vplane1, vplane2 : `numpy.ndarray` of `float` Variance planes of the original (before pre-convolution or matching) exposures. 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 estimated variance plane of the difference/score image as a weighted sum of the input variances.

Notes

See DMTN-179 Section 5 about the variance plane calculations.

getAllSchemaCatalogs() → Dict[str, Any]¶

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.

getFullMetadata() → lsst.pipe.base._task_metadata.TaskMetadata¶

Get metadata for all tasks.

Returns:	metadata : `TaskMetadata` The 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.

getFullName() → str¶

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”.

getName() → str¶

Get the name of the task.

Returns:	taskName : `str` Name of the 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.

getTaskDict() → Dict[str, weakref.ReferenceType[Task]]¶

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.

classmethod makeField(doc: str) → lsst.pex.config.configurableField.ConfigurableField¶

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")

makeSubtask(name: str, **keyArgs) → None¶

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 of ConfigurableField or RegistryField.

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 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.

run(scienceExposure, templateExposure, subtractedExposure, psfMatchingKernel, preConvKernel=None, xcen=None, ycen=None, svar=None, tvar=None, templateMatched=True, preConvMode=False, **kwargs)¶

Perform decorrelation of an image difference or of a score difference exposure.

Corrects the difference or score image due to the convolution of the templateExposure with the A&L PSF matching kernel. See [DMTN-021, Equation 1](http://dmtn-021.lsst.io/#equation-1) and [DMTN-179](http://dmtn-179.lsst.io/) for details.

Parameters:

scienceExposure : lsst.afw.image.Exposure: The original science exposure (before pre-convolution, if preConvMode==True).
templateExposure : lsst.afw.image.Exposure: The original template exposure warped, but not psf-matched, to the science exposure.
subtractedExposure : lsst.afw.image.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 (the reflection of) this kernel. Must be normalized to sum to 1. Allowed only if templateMatched==True and preConvMode==True. Defaults to the PSF of the science exposure at the image center.
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.
preConvMode : bool, optional: If True, subtractedExposure is assumed to be a likelihood difference image and will be noise corrected as a likelihood image.
**kwargs: Additional keyword arguments propagated from DecorrelateALKernelSpatialTask.

Returns:

result : lsst.pipe.base.Struct

correctedExposure : the decorrelated diffim

Notes

If preConvMode==True, subtractedExposure is assumed to be a score image and the noise correction for likelihood images is applied. The resulting image is an optimal detection likelihood image when the templateExposure has noise. (See DMTN-179) If preConvKernel is not specified, the PSF of scienceExposure is assumed as pre-convolution kernel.

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 during image differencing. Otherwise the scienceExposure was matched (convolved) by psfMatchingKernel. In either case, note that the original template and science images are required, not the psf-matched version.

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.

timer(name: str, logLevel: int = 10) → Iterator[None]¶

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` and `End`. logLevel A `logging` level constant.

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DecorrelateALKernelTask¶