xlens.process_pipe.detect_multiband

Classes

`DetectCoaddSourcesConnections`
`DetectCoaddSourcesConfig`	Configuration parameters for the DetectCoaddSourcesTask
`DetectCoaddSourcesTask`	Detect sources on a single filter coadd.

Module Contents

class DetectCoaddSourcesConnections[source]

Bases: lsst.pipe.base.PipelineTaskConnections

detectionSchema[source]

exposure[source]

outputBackgrounds[source]

outputSources[source]

class DetectCoaddSourcesConfig[source]

Bases: lsst.pipe.base.PipelineTaskConfig

Configuration parameters for the DetectCoaddSourcesTask

detection[source]

idGenerator[source]

setDefaults()[source]

class DetectCoaddSourcesTask(schema=None, **kwargs)[source]

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.

_DefaultName = 'detectCoaddSources'[source]

ConfigClass[source]

schema = None[source]

detectionSchema[source]

runQuantum(butlerQC, inputRefs, outputRefs)[source]

run(exposure, idFactory, expId)[source]

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 – Results as a struct with attributes:

sources: Catalog of detections (lsst.afw.table.SourceCatalog).
backgrounds: List of backgrounds (list).

Return type:

lsst.pipe.base.Struct