DecorrelateALKernelSpatialTask

class lsst.ip.diffim.DecorrelateALKernelSpatialTask(*args, **kwargs)

Bases: 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.

This task is a simple wrapper around @ref DecorrelateALKernelTask, which takes a spatiallyVarying parameter in its run method. If it is False, then it simply calls the run method of @ref DecorrelateALKernelTask. If it is True, then it uses the @ref ImageMapReduceTask framework to break the exposures into subExposures on a grid, and performs the run method of @ref DecorrelateALKernelTask on each subExposure. This enables it to account for spatially-varying PSFs and noise in the exposures when performing the decorrelation.

This task has no standalone example, however it is applied as a subtask of pipe.tasks.imageDifference.ImageDifferenceTask. There is also an example of its use in tests/testImageDecorrelation.py.

Methods Summary

computeVarianceMean(exposure)	Compute the mean of the variance plane of 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.
run(scienceExposure, 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

computeVarianceMean(exposure)

Compute the mean of the variance plane of exposure.

emptyMetadata() → None

Empty (clear) the metadata for this Task and all sub-Tasks.

getAllSchemaCatalogs() → Dict[str, Any]

Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.

Returns:

schemacatalogsdict

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() → TaskMetadata

Get metadata for all tasks.

Returns:

metadataTaskMetadata

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:

fullNamestr

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:

taskNamestr

Name of the task.

See also

getFullName

getSchemaCatalogs() → Dict[str, Any]

Get the schemas generated by this task.

Returns:

schemaCatalogsdict

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.

getTaskDict() → Dict[str, ReferenceType[Task]]

Get a dictionary of all tasks as a shallow copy.

Returns:

taskDictdict

Dictionary containing full task name: task object for the top-level task and all subtasks, sub-subtasks, etc.

classmethod makeField(doc: str) → ConfigurableField

Make a lsst.pex.config.ConfigurableField for this task.

Parameters:

docstr

Help text for the field.

Returns:

configurableFieldlsst.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: Any) → None

Create a subtask as a new instance as the name attribute of this task.

Parameters:

namestr

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.

run(scienceExposure, templateExposure, subtractedExposure, psfMatchingKernel, spatiallyVarying=True, preConvKernel=None, templateMatched=True, preConvMode=False)

Perform decorrelation of an image difference exposure.

Decorrelates the diffim due to the convolution of the templateExposure with the A&L psfMatchingKernel. If spatiallyVarying is True, it utilizes the spatially varying matching kernel via the imageMapReduce framework to perform spatially-varying decorrelation on a grid of subExposures.

Parameters:

scienceExposurelsst.afw.image.Exposure

the science Exposure used for PSF matching

templateExposurelsst.afw.image.Exposure

the template Exposure used for PSF matching

subtractedExposurelsst.afw.image.Exposure

the subtracted Exposure produced by ip_diffim.ImagePsfMatchTask.subtractExposures()

psfMatchingKernelan (optionally spatially-varying) PSF matching kernel produced

by ip_diffim.ImagePsfMatchTask.subtractExposures()

spatiallyVaryingbool

if True, perform the spatially-varying operation

preConvKernellsst.meas.algorithms.Psf

if not none, the scienceExposure has been pre-filtered with this kernel. (Currently this option is experimental.)

templateMatchedbool, optional

If True, the template exposure was matched (convolved) to the science exposure.

preConvModebool, optional

If True, subtractedExposure is assumed to be a likelihood difference image and will be noise corrected as a likelihood image.

Returns:

resultslsst.pipe.base.Struct

a structure containing: - correctedExposure : the decorrelated diffim

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

Context manager to log performance data for an arbitrary block of code.

Parameters:

namestr

Name of code being timed; data will be logged using item name: Start and End.

logLevel

A logging level constant.

See also

timer.logInfo

Examples

Creating a timer context:

with self.timer("someCodeToTime"):
    pass  # code to time