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.
`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.
`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.

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.

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¶