lsst.meas.extensions.scarlet

lsst.meas.extensions.scarlet contains the pipeline task used to execute the scarlet deblending algorithm (Melchior et. al 2018).

Using lsst.meas.extensions.scarlet

Contributing

lsst.meas.extensions.scarlet is developed at https://github.com/lsst/meas_extensions_scarlet. You can find Jira issues for this module under the meas_extensions_scarlet component.

Python API reference

lsst.meas.extensions.scarlet Package

Functions

boundedDataToBox(nBands, boundedData)

Convert bounds from the data storage format to a scarlet.bbox.Box

dataToScarlet(blendData[, nBands, ...])

Convert the storage data model into a scarlet lite blend

deblend(mExposure, footprint, config, ...)

Deblend a parent footprint

deblend_lite(mExposure, modelPsf, footprint, ...)

Deblend a parent footprint

modelToHeavy(source, mExposure, blend[, ...])

Convert a scarlet model to a MultibandFootprint.

scarletLiteToData(blend, psfCenter, xy0)

Convert a scarlet lite blend into a persistable data object

scarletToData(blend, psfCenter, xy0)

Convert a scarlet blend into a persistable data object

updateBlendRecords(blendData, catalog, ...)

Create footprints and update band-dependent columns in the catalog

Classes

ComponentCube(model, center, bbox, model_bbox)

Dummy component for scarlet main sources.

DummyObservation(psfs, model_psf, bbox, dtype)

An observation that does not have any image data

ScarletBlendData(xy0, extent, sources, psfCenter)

Data for an entire blend.

ScarletComponentData(xy0, extent, center, model)

Data for a component expressed as a 3D data cube

ScarletDeblendConfig(*args, **kw)

MultibandDeblendConfig

ScarletDeblendTask(schema[, peakSchema])

Split blended sources into individual sources.

ScarletFactorizedComponentData(xy0, extent, ...)

Data for a factorized component

ScarletModelData(bands, psf[, blends])

A container that propagates scarlet models for an entire SourceCatalog

ScarletSourceData(components, ...)

Data for a scarlet source