MatchFakesTask¶

class lsst.pipe.tasks.matchFakes.MatchFakesTask(*args, **kwargs)¶

Bases: PipelineTask

Match a pre-existing catalog of fakes to a catalog of detections on a difference image.

This task is generally for injected sources that cannot be easily identified by their footprints such as in the case of detector sources post image differencing.

Deprecated since version v28.0: This task will be removed in v28.0 as it is replaced by source_injection tasks.

Attributes Summary

canMultiprocess

Methods Summary

`composeFakeCat`(fakeCats, skyMap)	Concatenate the fakeCats from tracts that may cover the exposure.
`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.
`getVisitMatchedFakeCat`(fakeCat, exposure)	Trim the fakeCat to select particular visit
`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`(fakeCats, skyMap, diffIm, ...)	Compose fakes into a single catalog and match fakes to detected diaSources within a difference image bound.
`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

composeFakeCat(fakeCats, skyMap)¶

Concatenate the fakeCats from tracts that may cover the exposure.

Parameters:

fakeCatslist of lsst.daf.butler.DeferredDatasetHandle: Set of fake cats to concatenate.
skyMaplsst.skymap.SkyMap: SkyMap defining the geometry of the tracts and patches.

Returns:

combinedFakeCatpandas.DataFrame: All fakes that cover the inner polygon of the tracts in this quantum.

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.

getVisitMatchedFakeCat(fakeCat, exposure)¶

Trim the fakeCat to select particular visit

Parameters:

fakeCatpandas.core.frame.DataFrame: The catalog of fake sources to add to the exposure
exposurelsst.afw.image.exposure.exposure.ExposureF: The exposure to add the fake sources to

Returns:

movingFakeCatpandas.DataFrame: All fakes that belong to the visit

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(fakeCats, skyMap, diffIm, associatedDiaSources)¶

Compose fakes into a single catalog and match fakes to detected diaSources within a difference image bound.

Parameters:

fakeCatspandas.DataFrame: List of catalog of fakes to match to detected diaSources.
skyMaplsst.skymap.SkyMap: SkyMap defining the tracts and patches the fakes are stored over.
diffImlsst.afw.image.Exposure: Difference image where associatedDiaSources were detected.
associatedDiaSourcespandas.DataFrame: Catalog of difference image sources detected in diffIm.

Returns:

resultlsst.pipe.base.Struct

Results struct with components.

matchedDiaSources : Fakes matched to input diaSources. Has length of fakeCat. (pandas.DataFrame)

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.

Navigation

MatchFakesTask¶