AlardLuptonSubtractTask¶

class lsst.ip.diffim.AlardLuptonSubtractTask(**kwargs)¶

Bases: PipelineTask

Compute the image difference of a science and template image using the Alard & Lupton (1998) algorithm.

Attributes Summary

canMultiprocess

Methods Summary

`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`finalize`(template, science, difference, kernel)	Decorrelate the difference image to undo the noise correlations caused by convolution.
`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.
`getResourceConfig`()	Return resource configuration for this 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`(template, science, sources)	PSF match, subtract, and decorrelate two images.
`runConvolveScience`(template, science, sources)	Convolve the science image with a PSF-matching kernel and subtract the template image.
`runConvolveTemplate`(template, science, sources)	Convolve the template image with a PSF-matching kernel and subtract from the science image.
`runQuantum`(butlerQC, inputRefs, outputRefs)	Method to do butler IO and or transforms to provide in memory objects for tasks run method
`timer`(name[, logLevel])	Context manager to log performance data for an arbitrary block of code.

Attributes Documentation

canMultiprocess: ClassVar[bool] = True¶

Methods Documentation

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

finalize(template, science, difference, kernel, templateMatched=True, preConvMode=False, preConvKernel=None, spatiallyVarying=False)¶

Decorrelate the difference image to undo the noise correlations caused by convolution.

Parameters:

templatelsst.afw.image.ExposureF: Template exposure, warped to match the science exposure.
sciencelsst.afw.image.ExposureF: Science exposure to subtract from the template.
differencelsst.afw.image.ExposureF: Result of subtracting template and science.
kernellsst.afw.math.Kernel: An (optionally spatially-varying) PSF matching kernel
templateMatchedbool, optional: Was the template PSF-matched to the science image?
preConvModebool, optional: Was the science image preconvolved with its own PSF before PSF matching the template?
preConvKernellsst.afw.detection.Psf, optional: If not None, then the science image was pre-convolved with (the reflection of) this kernel. Must be normalized to sum to 1.
spatiallyVaryingbool, optional: Compute the decorrelation kernel spatially varying across the image?

Returns:

correctedExposurelsst.afw.image.ExposureF: The decorrelated image difference.

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

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(template, science, sources)¶

PSF match, subtract, and decorrelate two images.

Parameters:

templatelsst.afw.image.ExposureF: Template exposure, warped to match the science exposure.
sciencelsst.afw.image.ExposureF: Science exposure to subtract from the template.
sourceslsst.afw.table.SourceCatalog: Identified sources on the science exposure. This catalog is used to select sources in order to perform the AL PSF matching on stamp images around them.

Returns:

resultslsst.pipe.base.Struct

differencelsst.afw.image.ExposureF: Result of subtracting template and science.
matchedTemplatelsst.afw.image.ExposureF: Warped and PSF-matched template exposure.
backgroundModellsst.afw.math.Chebyshev1Function2D: Background model that was fit while solving for the PSF-matching kernel
psfMatchingKernellsst.afw.math.Kernel: Kernel used to PSF-match the convolved image.

Raises:

RuntimeError: If an unsupported convolution mode is supplied.
lsst.pipe.base.NoWorkFound: Raised if fraction of good pixels, defined as not having NO_DATA set, is less then the configured requiredTemplateFraction

runConvolveScience(template, science, sources)¶

Convolve the science image with a PSF-matching kernel and subtract the template image.

Parameters:

templatelsst.afw.image.ExposureF: Template exposure, warped to match the science exposure.
sciencelsst.afw.image.ExposureF: Science exposure to subtract from the template.
sourceslsst.afw.table.SourceCatalog: Identified sources on the science exposure. This catalog is used to select sources in order to perform the AL PSF matching on stamp images around them.

Returns:

resultslsst.pipe.base.Struct

differencelsst.afw.image.ExposureF: Result of subtracting template and science.
matchedTemplatelsst.afw.image.ExposureF: Warped template exposure. Note that in this case, the template is not PSF-matched to the science image.
backgroundModellsst.afw.math.Chebyshev1Function2D: Background model that was fit while solving for the PSF-matching kernel
psfMatchingKernellsst.afw.math.Kernel: Kernel used to PSF-match the science image to the template.

runConvolveTemplate(template, science, sources)¶

Convolve the template image with a PSF-matching kernel and subtract from the science image.

Parameters:

templatelsst.afw.image.ExposureF: Template exposure, warped to match the science exposure.
sciencelsst.afw.image.ExposureF: Science exposure to subtract from the template.
sourceslsst.afw.table.SourceCatalog: Identified sources on the science exposure. This catalog is used to select sources in order to perform the AL PSF matching on stamp images around them.

Returns:

resultslsst.pipe.base.Struct

differencelsst.afw.image.ExposureF: Result of subtracting template and science.
matchedTemplatelsst.afw.image.ExposureF: Warped and PSF-matched template exposure.
backgroundModellsst.afw.math.Chebyshev1Function2D: Background model that was fit while solving for the PSF-matching kernel
psfMatchingKernellsst.afw.math.Kernel: Kernel used to PSF-match the template to the science image.

runQuantum(butlerQC: ButlerQuantumContext, inputRefs: InputQuantizedConnection, outputRefs: OutputQuantizedConnection) → None¶

Method to do butler IO and or transforms to provide in memory objects for tasks run method

Parameters:

butlerQCButlerQuantumContext: A butler which is specialized to operate in the context of a lsst.daf.butler.Quantum.
inputRefsInputQuantizedConnection: Datastructure whose attribute names are the names that identify connections defined in corresponding PipelineTaskConnections class. The values of these attributes are the lsst.daf.butler.DatasetRef objects associated with the defined input/prerequisite connections.
outputRefsOutputQuantizedConnection: Datastructure whose attribute names are the names that identify connections defined in corresponding PipelineTaskConnections class. The values of these attributes are the lsst.daf.butler.DatasetRef objects associated with the defined output connections.

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.

Navigation

AlardLuptonSubtractTask¶