MatchVariableFakesTask

class lsst.pipe.tasks.matchFakes.MatchVariableFakesTask(*, config: Optional[PipelineTaskConfig] = None, log: Optional[Union[logging.Logger, LsstLogAdapter]] = None, initInputs: Optional[Dict[str, Any]] = None, **kwargs)

Bases: lsst.pipe.tasks.matchFakes.MatchFakesTask

Match injected fakes to their detected sources in the catalog and compute their expected brightness in a difference image assuming perfect subtraction.

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.

Attributes Summary

canMultiprocess

Methods Summary

composeFakeCat(fakeCats, skyMap)	Concatenate the fakeCats from tracts that may cover the exposure.
computeExpectedDiffMag(fakeCat, …)	Compute the magnitude expected in the difference image for this detector/visit.
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.
getResourceConfig()	Return resource configuration for this 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, ccdVisitFakeMagnitudes, …)	Match fakes to detected diaSources within a difference image bound.
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 = True

Methods Documentation

composeFakeCat(fakeCats, skyMap)

Concatenate the fakeCats from tracts that may cover the exposure.

Parameters:

fakeCats : list of lst.daf.butler.DeferredDatasetHandle

Set of fake cats to concatenate.

skyMap : lsst.skymap.SkyMap

SkyMap defining the geometry of the tracts and patches.

Returns:

combinedFakeCat : pandas.DataFrame

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

computeExpectedDiffMag(fakeCat, ccdVisitFakeMagnitudes, band)

Compute the magnitude expected in the difference image for this detector/visit. Modify fakeCat in place.

Negative magnitudes indicate that the source should be detected as a negative source.

Parameters:

fakeCat : pandas.DataFrame

Catalog of fake sources.

ccdVisitFakeMagnitudes : pandas.DataFrame

Magnitudes for variable sources in this specific ccdVisit.

band : str

Band that this ccdVisit was observed in.

emptyMetadata() → None

Empty (clear) the metadata for this Task and all sub-Tasks.

getFullMetadata() → lsst.pipe.base._task_metadata.TaskMetadata

Get metadata for all tasks.

Returns:

metadata : TaskMetadata

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:

fullName : str

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:

taskName : str

Name of the task.

See also

getFullName

getResourceConfig() → Optional[ResourceConfig]

Return resource configuration for this task.

Returns:

Object of type ResourceConfig or None if resource

configuration is not defined for this task.

getTaskDict() → Dict[str, weakref.ReferenceType[lsst.pipe.base.task.Task]]

Get a dictionary of all tasks as a shallow copy.

Returns:

taskDict : dict

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:

fakeCat : pandas.core.frame.DataFrame

The catalog of fake sources to add to the exposure

exposure : lsst.afw.image.exposure.exposure.ExposureF

The exposure to add the fake sources to

Returns:

movingFakeCat : pandas.DataFrame

All fakes that belong to the visit

classmethod makeField(doc: str) → lsst.pex.config.configurableField.ConfigurableField

Make a lsst.pex.config.ConfigurableField for this task.

Parameters:

doc : str

Help text for the field.

Returns:

configurableField : lsst.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) → None

Create a subtask as a new instance as the name attribute of this task.

Parameters:

name : str

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, ccdVisitFakeMagnitudes, skyMap, diffIm, associatedDiaSources, band)

Match fakes to detected diaSources within a difference image bound.

Parameters:

fakeCat : pandas.DataFrame

Catalog of fakes to match to detected diaSources.

diffIm : lsst.afw.image.Exposure

Difference image where associatedDiaSources were detected in.

associatedDiaSources : pandas.DataFrame

Catalog of difference image sources detected in diffIm.

Returns:

result : lsst.pipe.base.Struct

Results struct with components.

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

runQuantum(butlerQC, inputRefs, outputRefs)

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

Parameters:

butlerQC : ButlerQuantumContext

A butler which is specialized to operate in the context of a lsst.daf.butler.Quantum.

inputRefs : InputQuantizedConnection

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.

outputRefs : OutputQuantizedConnection

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:

name : str

Name of code being timed; data will be logged using item name: Start and End.

logLevel

A logging level constant.

See also

timer.logInfo

Examples

Creating a timer context:

with self.timer("someCodeToTime"):
    pass  # code to time