DetectAndMeasureTask¶

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

Bases: PipelineTask

Detect and measure sources on a difference image.

Attributes Summary

canMultiprocess

Methods Summary

`addSkySources`(diaSources, mask, seed)	Add sources in empty regions of the difference image for measuring the background.
`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.
`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.
`makeIdFactory`(expId, expBits)	Create IdFactory instance for unique 64 bit diaSource id-s.
`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.
`run`(science, template, difference, selectSources)	Detect and measure sources on a difference 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

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.

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:

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")

static makeIdFactory(expId, expBits)¶

Create IdFactory instance for unique 64 bit diaSource id-s.

Parameters:

expIdint: Exposure id.
expBits: `int`: Number of used bits in expId.

Returns:

idFactory: lsst.afw.table.IdFactory

Notes

The diasource id-s consists of the expId stored fixed in the highest value expBits of the 64-bit integer plus (bitwise or) a generated sequence number in the low value end of the integer.

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.

run(science, template, difference, selectSources, idFactory=None)¶

Detect and measure sources on a difference image.

Parameters:

sciencelsst.afw.image.ExposureF: Science exposure that the template was subtracted from.
templatelsst.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.
selectSourceslsst.afw.table.SourceCatalog, optional: Identified sources on the science exposure.
idFactorylsst.afw.table.IdFactory: Generator object to assign ids to detected sources in the difference image.

Returns:

resultslsst.pipe.base.Struct

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

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

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

DetectAndMeasureTask¶