Update for scarlet lite changes

python/lsst/pipe/tasks/deblendCoaddSourcesPipeline.py

@@ -120,6 +120,13 @@ class DeblendCoaddSourcesMultiConnections(PipelineTaskConnections,
         multiple=True,
         dimensions=("tract", "patch", "band", "skymap")
     )
+    deconvolvedCoadds = cT.Input(
+        doc="Deconvolved coadds",
+        name="deconvolved_{inputCoaddName}_coadd",
+        storageClass="ExposureF",
+        multiple=True,
+        dimensions=("tract", "patch", "band", "skymap")
+    )
     outputSchema = cT.InitOutput(
         doc="Output of the schema used in deblending task",
         name="{outputCoaddName}Coadd_deblendedFlux_schema",
@@ -251,14 +258,26 @@ def runQuantum(self, butlerQC, inputRefs, outputRefs):
         inputRefs = reorderRefs(inputRefs, bandOrder, dataIdKey="band")
         inputs = butlerQC.get(inputRefs)
         inputs["idFactory"] = self.config.idGenerator.apply(butlerQC.quantum.dataId).make_table_id_factory()
-        inputs["filters"] = [dRef.dataId["band"] for dRef in inputRefs.coadds]
+
+        # Ensure that the coadd bands and deconvolved coadd bands match
+        bands = [dRef.dataId["band"] for dRef in inputRefs.coadds]
+        deconvBands = [dRef.dataId["band"] for dRef in inputRefs.deconvolvedCoadds]
+        if len(bands) != len(deconvBands):
+            raise RuntimeError("Number of coadd bands and deconvolved coadd bands do not match")
+
+        for band, deconvBand in zip(bands, deconvBands):
+            if band != deconvBand:
+                raise RuntimeError(f"Bands {band} and {deconvBand} do not match")
+
+        inputs["bands"] = [dRef.dataId["band"] for dRef in inputRefs.coadds]
         outputs = self.run(**inputs)
         butlerQC.put(outputs, outputRefs)
 
-    def run(self, coadds, filters, mergedDetections, idFactory):
+    def run(self, coadds, bands, mergedDetections, deconvolvedCoadds, idFactory):
         sources = self._makeSourceCatalog(mergedDetections, idFactory)
-        multiExposure = afwImage.MultibandExposure.fromExposures(filters, coadds)
-        catalog, modelData = self.multibandDeblend.run(multiExposure, sources)
+        multiExposure = afwImage.MultibandExposure.fromExposures(bands, coadds)
+        mDeconvolved = afwImage.MultibandExposure.fromExposures(bands, deconvolvedCoadds)
+        catalog, modelData = self.multibandDeblend.run(multiExposure, mDeconvolved, sources)
         retStruct = Struct(deblendedCatalog=catalog, scarletModelData=modelData)
         return retStruct
 

python/lsst/pipe/tasks/fit_coadd_multiband.py

@@ -356,7 +356,7 @@ def build_catexps(self, butlerQC, inputRefs, inputs) -> list[CatalogExposureInpu
                 catalog=catalog,
                 band=dataId['band'],
                 imageForRedistribution=exposure,
-                removeScarletData=True,
+                removeScarletData=False,
                 updateFluxColumns=False,
             )
             catexp_dict[dataId['band']] = CatalogExposureInputs(

schemas/Object.yaml

@@ -733,6 +733,7 @@ refFlags:
     - detect_isDeblendedModelSource
     - merge_peak_sky
     - deblend_nChild
+    - deblend_depth
     - deblend_nPeaks
     - deblend_failed
     - deblend_skipped

schemas/initial_stars_detector_standardized.yaml

@@ -173,6 +173,7 @@ flags:
    - deblend_hasStrayFlux
    - deblend_masked
    - deblend_nChild
+   - deblend_depth
    - deblend_parentTooBig
    - deblend_patchedTemplate
    - deblend_rampedTemplate
@@ -193,4 +194,3 @@ flag_rename_rules:
     - ['ext_shapeHSM_Hsm', 'hsm']
     - ['base_', '']
     - ['slot_', '']
-

tests/test_isPrimaryFlag.py

@@ -33,6 +33,7 @@
 from lsst.meas.algorithms import SourceDetectionTask, SkyObjectsTask, SetPrimaryFlagsTask
 import lsst.meas.extensions.scarlet as mes
 from lsst.meas.extensions.scarlet.scarletDeblendTask import ScarletDeblendTask
+from lsst.meas.extensions.scarlet.deconvolveExposureTask import DeconvolveExposureTask
 from lsst.meas.base import SingleFrameMeasurementTask
 from lsst.afw.table import SourceCatalog
 
@@ -214,6 +215,10 @@ def testIsScarletPrimaryFlag(self):
         skySourcesTask = SkyObjectsTask(name="skySources", config=skyConfig)
         schema.addField("merge_peak_sky", type="Flag")
 
+        # Initialize the deconvolution task
+        deconvolveConfig = DeconvolveExposureTask.ConfigClass()
+        deconvolveTask = DeconvolveExposureTask(config=deconvolveConfig)
+
         # Initialize the deblender task
         scarletConfig = ScarletDeblendTask.ConfigClass()
         scarletConfig.maxIter = 20
@@ -243,8 +248,16 @@ def testIsScarletPrimaryFlag(self):
             src = catalog.addNew()
             src.setFootprint(foot)
             src.set("merge_peak_sky", True)
+        # deconvolve the images
+        deconvolved = deconvolveTask.run(coadds["test"], catalog).deconvolved
+        mDeconvolved = afwImage.MultibandExposure.fromExposures(["test"], [deconvolved])
         # deblend
-        catalog, modelData = deblendTask.run(coadds, catalog)
+        # This is a hack because the variance is not calibrated properly
+        # (it is 3 orders of magnitude too high), which causes the deblender
+        # to improperly deblend most sources due to the sparsity constraint.
+        coadds.variance.array[:] = 2e-1
+        mDeconvolved.variance.array[:] = 2e-1
+        catalog, modelData = deblendTask.run(coadds, mDeconvolved, catalog)
         # Attach footprints to the catalog
         mes.io.updateCatalogFootprints(
             modelData=modelData,