AlardLuptonPreconvolveSubtractTask¶

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

Bases: AlardLuptonSubtractTask

Subtract a template from a science image, convolving the science image before computing the kernel, and also convolving the template before subtraction.

Attributes Summary

canMultiprocess

Methods Summary

`computeImageMetrics`(science, difference, stars)	Compute quality metrics (saved to the task metadata) on the difference image, at the locations of detected stars on the science image.
`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.
`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.
`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[, visitSummary])	Preconvolve the science image with its own PSF, convolve the template image with a PSF-matching kernel and subtract from the preconvolved science image.
`runConvolveScience`(template, science, ...)	Convolve the science image with a PSF-matching kernel and subtract the template image.
`runConvolveTemplate`(template, science, ...)	Convolve the template image with a PSF-matching kernel and subtract from the science image.
`runPreconvolve`(template, science, ...)	Convolve the science image with its own PSF, then convolve the template with a matching kernel and subtract to form the Score exposure.
`runQuantum`(butlerQC, inputRefs, outputRefs)	Do butler IO and transform to provide in memory objects for tasks `run` method.
`timer`(name[, logLevel])	Context manager to log performance data for an arbitrary block of code.
`updateMasks`(template, science)	Update the science and template mask planes before differencing.

Attributes Documentation

canMultiprocess: ClassVar[bool] = True¶

Methods Documentation

computeImageMetrics(science, difference, stars)¶

Compute quality metrics (saved to the task metadata) on the difference image, at the locations of detected stars on the science image. This restricts the metric to locations that should be well-subtracted.

Parameters:

sciencelsst.afw.image.ExposureF: Science exposure that was subtracted.
differencelsst.afw.image.ExposureF: Result of subtracting template and science.
starslsst.afw.table.SourceCatalog: Good calibration sources detected on science image; these footprints are what the metrics are computed on.

Notes

The task metadata does not include docstrings, so descriptions of the computed metrics are given here:

differenceFootprintRatioMean: Mean of the ratio of the absolute value of the difference image (with the mean absolute value of the sky regions on the difference image removed) to the science image, computed in the footprints of stars detected on the science image (the sums below are of the pixels in each star or sky footprint): \(\mathrm{mean}_{footprints}((\sum |difference| - \mathrm{mean}(\sum |difference_{sky}|)) / \sum science)\)
differenceFootprintRatioStdev: Standard Deviation across footprints of the above ratio.
differenceFootprintSkyRatioMean: Mean of the ratio of the absolute value of sky source regions on the difference image to the science image (the sum below is of the pixels in each sky source footprint): \(\mathrm{mean}_{footprints}(\sum |difference_{sky}| / \sum science_{sky})\)
differenceFootprintSkyRatioStdev: Standard Deivation across footprints of the above sky ratio.

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.

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: Get the full name of the task.

getTaskDict() → dict[str, weakref.ReferenceType[lsst.pipe.base.task.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, visitSummary=None)¶

Preconvolve the science image with its own PSF, convolve the template image with a PSF-matching kernel and subtract from the preconvolved science image.

Parameters:

templatelsst.afw.image.ExposureF: The template image, which has previously been warped to the science image. The template bbox will be padded by a few pixels compared to the science bbox.
sciencelsst.afw.image.ExposureF: The science exposure.
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.
visitSummarylsst.afw.table.ExposureCatalog, optional: Exposure catalog with complete external calibrations. Catalog uses the detector id for the catalog id, sorted on id for fast lookup.

Returns:

resultslsst.pipe.base.Struct

scoreExposurelsst.afw.image.ExposureF: Result of subtracting the convolved template and science images. Attached PSF is that of the original science image.
matchedTemplatelsst.afw.image.ExposureF: Warped and PSF-matched template exposure. Attached PSF is that of the original science image.
matchedSciencelsst.afw.image.ExposureF: The science exposure after convolving with its own PSF. Attached PSF is that of the original science image.
backgroundModellsst.afw.math.Function2D: Background model that was fit while solving for the PSF-matching kernel
psfMatchingKernellsst.afw.math.Kernel: Final kernel used to PSF-match the template to the science image.

runConvolveScience(template, science, selectSources)¶

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.
selectSourceslsst.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.Function2D: 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, selectSources)¶

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.
selectSourceslsst.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.Function2D: 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.

runPreconvolve(template, science, matchedScience, selectSources, preConvKernel)¶

Convolve the science image with its own PSF, then convolve the template with a matching kernel and subtract to form the Score exposure.

Parameters:

templatelsst.afw.image.ExposureF: Template exposure, warped to match the science exposure.
sciencelsst.afw.image.ExposureF: Science exposure to subtract from the template.
matchedSciencelsst.afw.image.ExposureF: The science exposure, convolved with the reflection of its own PSF.
selectSourceslsst.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.
preConvKernellsst.afw.math.Kernel: The reflection of the kernel that was used to preconvolve the science exposure. Must be normalized to sum to 1.

Returns:

resultslsst.pipe.base.Struct

scoreExposurelsst.afw.image.ExposureF: Result of subtracting the convolved template and science images. Attached PSF is that of the original science image.
matchedTemplatelsst.afw.image.ExposureF: Warped and PSF-matched template exposure. Attached PSF is that of the original science image.
matchedSciencelsst.afw.image.ExposureF: The science exposure after convolving with its own PSF. Attached PSF is that of the original science image.
backgroundModellsst.afw.math.Function2D: Background model that was fit while solving for the PSF-matching kernel
psfMatchingKernellsst.afw.math.Kernel: Final kernel used to PSF-match the template to the science image.

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

Do butler IO and transform to provide in memory objects for tasks run method.

Parameters:

butlerQCQuantumContext: 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.
logLevelint: A logging level constant.

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AlardLuptonPreconvolveSubtractTask¶