DetectAndMeasureScoreTask¶

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

Bases: DetectAndMeasureTask

Detect DIA sources using a score image, and measure the detections on the difference image.

Source detection is run on the supplied score, or maximum likelihood, image. Note that no additional convolution will be done in this case. Close positive and negative detections will optionally be merged into dipole diaSources. Sky sources, or forced detections in background regions, will optionally be added, and the configured measurement algorithm will be run on all detections.

Attributes Summary

canMultiprocess

Methods Summary

`addSkySources`(diaSources, mask, seed)	Add sources in empty regions of the difference image for measuring the background.
`calculateMetrics`(difference)	Add image QA metrics to the Task metadata.
`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.
`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.
`measureDiaSources`(diaSources, science, ...)	Use (matched) template and science image to constrain dipole fitting.
`measureForcedSources`(diaSources, science, wcs)	Perform forced measurement of the diaSources on the science image.
`processResults`(science, matchedTemplate, ...)	Measure and process the results of source detection.
`run`(science, matchedTemplate, difference, ...)	Detect and measure sources on a score image.
`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.

Attributes Documentation

canMultiprocess: ClassVar[bool] = True¶

Methods Documentation

addSkySources(diaSources, mask, seed)¶

Add sources in empty regions of the difference image for measuring the background.

Parameters:

diaSourceslsst.afw.table.SourceCatalog: The catalog of detected sources.
masklsst.afw.image.Mask: Mask plane for determining regions where Sky sources can be added.
seedint: Seed value to initialize the random number generator.

calculateMetrics(difference)¶

Add image QA metrics to the Task metadata.

Parameters:

differencelsst.afw.image.Exposure: The target image to calculate metrics for.

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

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.

measureDiaSources(diaSources, science, difference, matchedTemplate)¶

Use (matched) template and science image to constrain dipole fitting.

Parameters:

diaSourceslsst.afw.table.SourceCatalog: The catalog of detected sources.
sciencelsst.afw.image.ExposureF: Science exposure that the template was subtracted from.
differencelsst.afw.image.ExposureF: Result of subtracting template from the science image.
matchedTemplatelsst.afw.image.ExposureF: Warped and PSF-matched template that was used produce the difference image.

measureForcedSources(diaSources, science, wcs)¶

Perform forced measurement of the diaSources on the science image.

Parameters:

diaSourceslsst.afw.table.SourceCatalog: The catalog of detected sources.
sciencelsst.afw.image.ExposureF: Science exposure that the template was subtracted from.
wcslsst.afw.geom.SkyWcs: Coordinate system definition (wcs) for the exposure.

processResults(science, matchedTemplate, difference, sources, table, positiveFootprints=None, negativeFootprints=None)¶

Measure and process the results of source detection.

Parameters:

sourceslsst.afw.table.SourceCatalog: Detected sources on the difference exposure.
positiveFootprintslsst.afw.detection.FootprintSet, optional: Positive polarity footprints.
negativeFootprintslsst.afw.detection.FootprintSet, optional: Negative polarity footprints.
tablelsst.afw.table.SourceTable: Table object that will be used to create the SourceCatalog.
sciencelsst.afw.image.ExposureF: Science exposure that the template was subtracted from.
matchedTemplatelsst.afw.image.ExposureF: Warped and PSF-matched template that was used produce the difference image.
differencelsst.afw.image.ExposureF: Result of subtracting template from the science image.

Returns:

measurementResultslsst.pipe.base.Struct

subtractedMeasuredExposurelsst.afw.image.ExposureF: Subtracted exposure with detection mask applied.
diaSourceslsst.afw.table.SourceCatalog: The catalog of detected sources.

run(science, matchedTemplate, difference, scoreExposure, idFactory=None)¶

Detect and measure sources on a score image.

Parameters:

sciencelsst.afw.image.ExposureF: Science exposure that the template was subtracted from.
matchedTemplatelsst.afw.image.ExposureF: Warped and PSF-matched template that was used produce the difference image.
differencelsst.afw.image.ExposureF: Result of subtracting template from the science image.
scoreExposurelsst.afw.image.ExposureF: Score or maximum likelihood difference image
idFactorylsst.afw.table.IdFactory, optional: Generator object to assign ids to detected sources in the difference image.

Returns:

measurementResultslsst.pipe.base.Struct

subtractedMeasuredExposurelsst.afw.image.ExposureF: Subtracted exposure with detection mask applied.
diaSourceslsst.afw.table.SourceCatalog: The catalog of detected sources.

runQuantum(butlerQC: QuantumContext, inputRefs: InputQuantizedConnection, outputRefs: OutputQuantizedConnection)¶

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.

Navigation

DetectAndMeasureScoreTask¶