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

Using lsst.meas.extensions.scarlet


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

Python API reference

lsst.meas.extensions.scarlet Package


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


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)


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