MakeKernelTask¶

class lsst.ip.diffim.MakeKernelTask(*args, **kwargs)¶

Bases: lsst.ip.diffim.PsfMatchTask

Construct a kernel for PSF matching two exposures.

Methods Summary

`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`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.
`getSelectSources`(exposure[, sigma, …])	Get sources to use for Psf-matching.
`getTaskDict`()	Get a dictionary of all tasks as a shallow copy.
`makeCandidateList`(templateExposure, …[, …])	Make a list of acceptable KernelCandidates.
`makeField`(doc)	Make a `lsst.pex.config.ConfigurableField` for this task.
`makeKernelBasisList`([targetFwhmPix, …])	Wrapper to set log messages for `lsst.ip.diffim.makeKernelBasisList`.
`makeSubtask`(name, **keyArgs)	Create a subtask as a new instance as the `name` attribute of this task.
`run`(template, science, kernelSources[, …])	Solve for the kernel and background model that best match two Exposures evaluated at the given source locations.
`selectKernelSources`(template, science[, …])	Select sources from a list of candidates, and extract footprints.
`timer`(name, logLevel)	Context manager to log performance data for an arbitrary block of code.

Methods Documentation

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

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

getFullName

getSelectSources(exposure, sigma=None, doSmooth=True, idFactory=None)¶

Get sources to use for Psf-matching.

This method runs detection and measurement on an exposure. The returned set of sources will be used as candidates for Psf-matching.

Parameters:	exposure : `lsst.afw.image.Exposure` Exposure on which to run detection/measurement sigma : `float`, optional PSF sigma, in pixels, used for smoothing the image for detection. If `None`, the PSF width will be used. doSmooth : `bool` Whether or not to smooth the Exposure with Psf before detection idFactory : `lsst.afw.table.IdFactory` Factory for the generation of Source ids
Returns:	selectSources : source catalog containing candidates for the Psf-matching

getTaskDict() → Dict[str, weakref.ReferenceType[lsst.pipe.base.task.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.

makeCandidateList(templateExposure, scienceExposure, kernelSize, candidateList=None, preconvolved=False)¶

Make a list of acceptable KernelCandidates.

Accept or generate a list of candidate sources for Psf-matching, and examine the Mask planes in both of the images for indications of bad pixels

Parameters:	templateExposure : `lsst.afw.image.Exposure` Exposure that will be convolved scienceExposure : `lsst.afw.image.Exposure` Exposure that will be matched-to kernelSize : `float` Dimensions of the Psf-matching Kernel, used to grow detection footprints candidateList : `list`, optional List of Sources to examine. Elements must be of type afw.table.Source or a type that wraps a Source and has a getSource() method, such as meas.algorithms.PsfCandidateF. preconvolved : `bool`, optional Was the science exposure already convolved with its PSF?
Returns:	candidateList : `list` of `dict` A list of dicts having a “source” and “footprint” field for the Sources deemed to be appropriate for Psf matching.
Raises:	RuntimeError If `candidateList` is empty or contains incompatible types.

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")

makeKernelBasisList(targetFwhmPix=None, referenceFwhmPix=None, basisDegGauss=None, basisSigmaGauss=None, metadata=None)¶

Wrapper to set log messages for lsst.ip.diffim.makeKernelBasisList.

Parameters:

targetFwhmPix : float, optional: Passed on to lsst.ip.diffim.generateAlardLuptonBasisList. Not used for delta function basis sets.
referenceFwhmPix : float, optional: Passed on to lsst.ip.diffim.generateAlardLuptonBasisList. Not used for delta function basis sets.
basisDegGauss : list of int, optional: Passed on to lsst.ip.diffim.generateAlardLuptonBasisList. Not used for delta function basis sets.
basisSigmaGauss : list of int, optional: Passed on to lsst.ip.diffim.generateAlardLuptonBasisList. Not used for delta function basis sets.
metadata : lsst.daf.base.PropertySet, optional: Passed on to lsst.ip.diffim.generateAlardLuptonBasisList. Not used for delta function basis sets.

Returns:

basisList: list of lsst.afw.math.kernel.FixedKernel: List of basis kernels.

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.

run(template, science, kernelSources, preconvolved=False)¶

Solve for the kernel and background model that best match two Exposures evaluated at the given source locations.

Parameters:

template : lsst.afw.image.Exposure: Exposure that will be convolved.
science : lsst.afw.image.Exposure: The exposure that will be matched.
kernelSources : list of dict: A list of dicts having a “source” and “footprint” field for the Sources deemed to be appropriate for Psf matching. Can be the output from selectKernelSources.
preconvolved : bool, optional: Was the science image convolved with its own PSF?

Returns:

results : lsst.pipe.base.Struct

psfMatchingKernel : lsst.afw.math.LinearCombinationKernel: Spatially varying Psf-matching kernel.
backgroundModel : lsst.afw.math.Function2D: Spatially varying background-matching function.

selectKernelSources(template, science, candidateList=None, preconvolved=False)¶

Select sources from a list of candidates, and extract footprints.

Parameters:

template : lsst.afw.image.Exposure: Exposure that will be convolved.
science : lsst.afw.image.Exposure: The exposure that will be matched.
candidateList : list, optional: List of Sources to examine. Elements must be of type afw.table.Source or a type that wraps a Source and has a getSource() method, such as meas.algorithms.PsfCandidateF.
preconvolved : bool, optional: Was the science image convolved with its own PSF?

Returns:

kernelSources : list of dict: A list of dicts having a “source” and “footprint” field for the Sources deemed to be appropriate for Psf matching.

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.

Navigation

MakeKernelTask¶