DetectCoaddSourcesTask

class lsst.pipe.tasks.multiBand.DetectCoaddSourcesTask(schema=None, **kwargs)

Bases: lsst.pipe.base.PipelineTask

Detect sources on a single filter coadd.

Coadding individual visits requires each exposure to be warped. This introduces covariance in the noise properties across pixels. Before detection, we correct the coadd variance by scaling the variance plane in the coadd to match the observed variance. This is an approximate approach – strictly, we should propagate the full covariance matrix – but it is simple and works well in practice.

After scaling the variance plane, we detect sources and generate footprints by delegating to the @ref SourceDetectionTask_ “detection” subtask.

DetectCoaddSourcesTask is meant to be run after assembling a coadded image in a given band. The purpose of the task is to update the background, detect all sources in a single band and generate a set of parent footprints. Subsequent tasks in the multi-band processing procedure will merge sources across bands and, eventually, perform forced photometry.

Parameters:

schema : lsst.afw.table.Schema, optional

Initial schema for the output catalog, modified-in place to include all fields set by this task. If None, the source minimal schema will be used.

**kwargs

Additional keyword arguments.

Attributes Summary

canMultiprocess

Methods Summary

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.
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(exposure, idFactory, expId)	Run detection on an exposure.
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

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.

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(exposure, idFactory, expId)

Run detection on an exposure.

First scale the variance plane to match the observed variance using ScaleVarianceTask. Then invoke the SourceDetectionTask_ “detection” subtask to detect sources.

Parameters:

exposure : lsst.afw.image.Exposure

Exposure on which to detect (may be backround-subtracted and scaled, depending on configuration).

idFactory : lsst.afw.table.IdFactory

IdFactory to set source identifiers.

expId : int

Exposure identifier (integer) for RNG seed.

Returns:

result : lsst.pipe.base.Struct

Results as a struct with attributes:

sources

Catalog of detections (lsst.afw.table.SourceCatalog).

backgrounds

List of backgrounds (list).

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