22__all__ = [
"DetectCoaddSourcesConfig",
"DetectCoaddSourcesTask"]
24from lsst.pipe.base
import (Struct, PipelineTask, PipelineTaskConfig, PipelineTaskConnections)
25import lsst.pipe.base.connectionTypes
as cT
29 SingleFrameMeasurementTask,
31 CatalogCalculationTask,
32 SkyMapIdGeneratorConfig,
34from lsst.meas.extensions.scarlet.io
import updateCatalogFootprints
44from .mergeDetections
import MergeDetectionsConfig, MergeDetectionsTask
45from .mergeMeasurements
import MergeMeasurementsConfig, MergeMeasurementsTask
46from .multiBandUtils
import CullPeaksConfig
47from .deblendCoaddSourcesPipeline
import DeblendCoaddSourcesSingleConfig
48from .deblendCoaddSourcesPipeline
import DeblendCoaddSourcesSingleTask
49from .deblendCoaddSourcesPipeline
import DeblendCoaddSourcesMultiConfig
50from .deblendCoaddSourcesPipeline
import DeblendCoaddSourcesMultiTask
55* deepCoadd_det: detections from what used to be processCoadd (tract, patch, filter)
56* deepCoadd_mergeDet: merged detections (tract, patch)
57* deepCoadd_meas: measurements of merged detections (tract, patch, filter)
58* deepCoadd_ref: reference sources (tract, patch)
59All of these have associated *_schema catalogs that require no data ID and hold no records.
61In addition, we have a schema-only dataset, which saves the schema for the PeakRecords in
62the mergeDet, meas, and ref dataset Footprints:
63* deepCoadd_peak_schema
69 dimensions=(
"tract",
"patch",
"band",
"skymap"),
70 defaultTemplates={
"inputCoaddName":
"deep",
"outputCoaddName":
"deep"}):
71 detectionSchema = cT.InitOutput(
72 doc=
"Schema of the detection catalog",
73 name=
"{outputCoaddName}Coadd_det_schema",
74 storageClass=
"SourceCatalog",
77 doc=
"Exposure on which detections are to be performed",
78 name=
"{inputCoaddName}Coadd",
79 storageClass=
"ExposureF",
80 dimensions=(
"tract",
"patch",
"band",
"skymap")
82 outputBackgrounds = cT.Output(
83 doc=
"Output Backgrounds used in detection",
84 name=
"{outputCoaddName}Coadd_calexp_background",
85 storageClass=
"Background",
86 dimensions=(
"tract",
"patch",
"band",
"skymap")
88 outputSources = cT.Output(
89 doc=
"Detected sources catalog",
90 name=
"{outputCoaddName}Coadd_det",
91 storageClass=
"SourceCatalog",
92 dimensions=(
"tract",
"patch",
"band",
"skymap")
94 outputExposure = cT.Output(
95 doc=
"Exposure post detection",
96 name=
"{outputCoaddName}Coadd_calexp",
97 storageClass=
"ExposureF",
98 dimensions=(
"tract",
"patch",
"band",
"skymap")
102class DetectCoaddSourcesConfig(PipelineTaskConfig, pipelineConnections=DetectCoaddSourcesConnections):
103 """Configuration parameters for the DetectCoaddSourcesTask
106 doScaleVariance = Field(dtype=bool, default=
True, doc=
"Scale variance plane using empirical noise?")
107 scaleVariance = ConfigurableField(target=ScaleVarianceTask, doc=
"Variance rescaling")
108 detection = ConfigurableField(target=DynamicDetectionTask, doc=
"Source detection")
109 coaddName = Field(dtype=str, default=
"deep", doc=
"Name of coadd")
113 doc=
"Should be set to True if fake sources have been inserted into the input data.",
115 idGenerator = SkyMapIdGeneratorConfig.make_field()
117 def setDefaults(self):
118 super().setDefaults()
119 self.detection.thresholdType =
"pixel_stdev"
120 self.detection.isotropicGrow =
True
122 self.detection.reEstimateBackground =
False
123 self.detection.background.useApprox =
False
124 self.detection.background.binSize = 4096
125 self.detection.background.undersampleStyle =
'REDUCE_INTERP_ORDER'
126 self.detection.doTempWideBackground =
True
129 self.idGenerator.packer.n_bands =
None
132class DetectCoaddSourcesTask(PipelineTask):
133 """Detect sources on a single filter coadd.
135 Coadding individual visits requires each exposure to be warped. This
136 introduces covariance in the noise properties across pixels. Before
137 detection, we correct the coadd variance by scaling the variance plane in
138 the coadd to match the observed variance. This is an approximate
139 approach -- strictly, we should propagate the full covariance matrix --
140 but it is simple and works well in practice.
142 After scaling the variance plane, we detect sources and generate footprints
143 by delegating to the @ref SourceDetectionTask_ "detection" subtask.
145 DetectCoaddSourcesTask is meant to be run after assembling a coadded image
146 in a given band. The purpose of the task is to update the background,
147 detect all sources in a single band and generate a set of parent
148 footprints. Subsequent tasks in the multi-band processing procedure will
149 merge sources across bands and, eventually, perform forced photometry.
153 schema : `lsst.afw.table.Schema`, optional
154 Initial schema for the output catalog, modified-in place to include all
155 fields set by this task. If None, the source minimal schema will be used.
157 Additional keyword arguments.
160 _DefaultName =
"detectCoaddSources"
161 ConfigClass = DetectCoaddSourcesConfig
163 def __init__(self, schema=None, **kwargs):
166 super().__init__(**kwargs)
168 schema = afwTable.SourceTable.makeMinimalSchema()
170 self.makeSubtask(
"detection", schema=self.schema)
171 if self.config.doScaleVariance:
172 self.makeSubtask(
"scaleVariance")
174 self.detectionSchema = afwTable.SourceCatalog(self.schema)
176 def runQuantum(self, butlerQC, inputRefs, outputRefs):
177 inputs = butlerQC.get(inputRefs)
178 idGenerator = self.config.idGenerator.apply(butlerQC.quantum.dataId)
179 inputs[
"idFactory"] = idGenerator.make_table_id_factory()
180 inputs[
"expId"] = idGenerator.catalog_id
181 outputs = self.run(**inputs)
182 butlerQC.put(outputs, outputRefs)
184 def run(self, exposure, idFactory, expId):
185 """Run detection on an exposure.
187 First scale the variance plane to match the observed variance
188 using ``ScaleVarianceTask``. Then invoke the ``SourceDetectionTask_`` "detection" subtask to
193 exposure : `lsst.afw.image.Exposure`
194 Exposure on which to detect (may be backround-subtracted and scaled,
195 depending on configuration).
196 idFactory : `lsst.afw.table.IdFactory`
197 IdFactory to set source identifiers.
199 Exposure identifier (integer) for RNG seed.
203 result : `lsst.pipe.base.Struct`
204 Results as a struct with attributes:
207 Catalog of detections (`lsst.afw.table.SourceCatalog`).
209 List of backgrounds (`list`).
211 if self.config.doScaleVariance:
212 varScale = self.scaleVariance.run(exposure.maskedImage)
213 exposure.getMetadata().add(
"VARIANCE_SCALE", varScale)
214 backgrounds = afwMath.BackgroundList()
215 table = afwTable.SourceTable.make(self.schema, idFactory)
216 detections = self.detection.run(table, exposure, expId=expId)
217 sources = detections.sources
218 if hasattr(detections,
"background")
and detections.background:
219 for bg
in detections.background:
220 backgrounds.append(bg)
221 return Struct(outputSources=sources, outputBackgrounds=backgrounds, outputExposure=exposure)
224class MeasureMergedCoaddSourcesConnections(PipelineTaskConnections,
225 dimensions=(
"tract",
"patch",
"band",
"skymap"),
226 defaultTemplates={
"inputCoaddName":
"deep",
227 "outputCoaddName":
"deep",
228 "deblendedCatalog":
"deblendedFlux"}):
229 inputSchema = cT.InitInput(
230 doc=
"Input schema for measure merged task produced by a deblender or detection task",
231 name=
"{inputCoaddName}Coadd_deblendedFlux_schema",
232 storageClass=
"SourceCatalog"
234 outputSchema = cT.InitOutput(
235 doc=
"Output schema after all new fields are added by task",
236 name=
"{inputCoaddName}Coadd_meas_schema",
237 storageClass=
"SourceCatalog"
239 refCat = cT.PrerequisiteInput(
240 doc=
"Reference catalog used to match measured sources against known sources",
242 storageClass=
"SimpleCatalog",
243 dimensions=(
"skypix",),
248 doc=
"Input coadd image",
249 name=
"{inputCoaddName}Coadd_calexp",
250 storageClass=
"ExposureF",
251 dimensions=(
"tract",
"patch",
"band",
"skymap")
254 doc=
"SkyMap to use in processing",
255 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
256 storageClass=
"SkyMap",
257 dimensions=(
"skymap",),
259 visitCatalogs = cT.Input(
260 doc=
"Source catalogs for visits which overlap input tract, patch, band. Will be "
261 "further filtered in the task for the purpose of propagating flags from image calibration "
262 "and characterization to coadd objects. Only used in legacy PropagateVisitFlagsTask.",
264 dimensions=(
"instrument",
"visit",
"detector"),
265 storageClass=
"SourceCatalog",
268 sourceTableHandles = cT.Input(
269 doc=(
"Source tables that are derived from the ``CalibrateTask`` sources. "
270 "These tables contain astrometry and photometry flags, and optionally "
272 name=
"sourceTable_visit",
273 storageClass=
"DataFrame",
274 dimensions=(
"instrument",
"visit"),
278 finalizedSourceTableHandles = cT.Input(
279 doc=(
"Finalized source tables from ``FinalizeCalibrationTask``. These "
280 "tables contain PSF flags from the finalized PSF estimation."),
281 name=
"finalized_src_table",
282 storageClass=
"DataFrame",
283 dimensions=(
"instrument",
"visit"),
287 inputCatalog = cT.Input(
288 doc=(
"Name of the input catalog to use."
289 "If the single band deblender was used this should be 'deblendedFlux."
290 "If the multi-band deblender was used this should be 'deblendedModel, "
291 "or deblendedFlux if the multiband deblender was configured to output "
292 "deblended flux catalogs. If no deblending was performed this should "
294 name=
"{inputCoaddName}Coadd_{deblendedCatalog}",
295 storageClass=
"SourceCatalog",
296 dimensions=(
"tract",
"patch",
"band",
"skymap"),
298 scarletCatalog = cT.Input(
299 doc=
"Catalogs produced by multiband deblending",
300 name=
"{inputCoaddName}Coadd_deblendedCatalog",
301 storageClass=
"SourceCatalog",
302 dimensions=(
"tract",
"patch",
"skymap"),
304 scarletModels = cT.Input(
305 doc=
"Multiband scarlet models produced by the deblender",
306 name=
"{inputCoaddName}Coadd_scarletModelData",
307 storageClass=
"ScarletModelData",
308 dimensions=(
"tract",
"patch",
"skymap"),
310 outputSources = cT.Output(
311 doc=
"Source catalog containing all the measurement information generated in this task",
312 name=
"{outputCoaddName}Coadd_meas",
313 dimensions=(
"tract",
"patch",
"band",
"skymap"),
314 storageClass=
"SourceCatalog",
316 matchResult = cT.Output(
317 doc=
"Match catalog produced by configured matcher, optional on doMatchSources",
318 name=
"{outputCoaddName}Coadd_measMatch",
319 dimensions=(
"tract",
"patch",
"band",
"skymap"),
320 storageClass=
"Catalog",
322 denormMatches = cT.Output(
323 doc=
"Denormalized Match catalog produced by configured matcher, optional on "
324 "doWriteMatchesDenormalized",
325 name=
"{outputCoaddName}Coadd_measMatchFull",
326 dimensions=(
"tract",
"patch",
"band",
"skymap"),
327 storageClass=
"Catalog",
330 def __init__(self, *, config=None):
331 super().__init__(config=config)
332 if config.doPropagateFlags
is False:
333 self.inputs -= set((
"visitCatalogs",))
334 self.inputs -= set((
"sourceTableHandles",))
335 self.inputs -= set((
"finalizedSourceTableHandles",))
336 elif config.propagateFlags.target == PropagateSourceFlagsTask:
338 self.inputs -= set((
"visitCatalogs",))
340 if not config.propagateFlags.source_flags:
341 self.inputs -= set((
"sourceTableHandles",))
342 if not config.propagateFlags.finalized_source_flags:
343 self.inputs -= set((
"finalizedSourceTableHandles",))
346 self.inputs -= set((
"sourceTableHandles",))
347 self.inputs -= set((
"finalizedSourceTableHandles",))
349 if config.inputCatalog ==
"deblendedCatalog":
350 self.inputs -= set((
"inputCatalog",))
352 if not config.doAddFootprints:
353 self.inputs -= set((
"scarletModels",))
355 self.inputs -= set((
"deblendedCatalog"))
356 self.inputs -= set((
"scarletModels",))
358 if config.doMatchSources
is False:
359 self.outputs -= set((
"matchResult",))
361 if config.doWriteMatchesDenormalized
is False:
362 self.outputs -= set((
"denormMatches",))
365class MeasureMergedCoaddSourcesConfig(PipelineTaskConfig,
366 pipelineConnections=MeasureMergedCoaddSourcesConnections):
367 """Configuration parameters for the MeasureMergedCoaddSourcesTask
369 inputCatalog = ChoiceField(
371 default=
"deblendedCatalog",
373 "deblendedCatalog":
"Output catalog from ScarletDeblendTask",
374 "deblendedFlux":
"Output catalog from SourceDeblendTask",
375 "mergeDet":
"The merged detections before deblending."
377 doc=
"The name of the input catalog.",
379 doAddFootprints = Field(dtype=bool,
381 doc=
"Whether or not to add footprints to the input catalog from scarlet models. "
382 "This should be true whenever using the multi-band deblender, "
383 "otherwise this should be False.")
384 doConserveFlux = Field(dtype=bool, default=
True,
385 doc=
"Whether to use the deblender models as templates to re-distribute the flux "
386 "from the 'exposure' (True), or to perform measurements on the deblender "
388 doStripFootprints = Field(dtype=bool, default=
True,
389 doc=
"Whether to strip footprints from the output catalog before "
391 "This is usually done when using scarlet models to save disk space.")
392 measurement = ConfigurableField(target=SingleFrameMeasurementTask, doc=
"Source measurement")
393 setPrimaryFlags = ConfigurableField(target=SetPrimaryFlagsTask, doc=
"Set flags for primary tract/patch")
394 doPropagateFlags = Field(
395 dtype=bool, default=
True,
396 doc=
"Whether to match sources to CCD catalogs to propagate flags (to e.g. identify PSF stars)"
398 propagateFlags = ConfigurableField(target=PropagateSourceFlagsTask, doc=
"Propagate source flags to coadd")
399 doMatchSources = Field(dtype=bool, default=
True, doc=
"Match sources to reference catalog?")
400 match = ConfigurableField(target=DirectMatchTask, doc=
"Matching to reference catalog")
401 doWriteMatchesDenormalized = Field(
404 doc=(
"Write reference matches in denormalized format? "
405 "This format uses more disk space, but is more convenient to read."),
407 coaddName = Field(dtype=str, default=
"deep", doc=
"Name of coadd")
408 psfCache = Field(dtype=int, default=100, doc=
"Size of psfCache")
409 checkUnitsParseStrict = Field(
410 doc=
"Strictness of Astropy unit compatibility check, can be 'raise', 'warn' or 'silent'",
417 doc=
"Apply aperture corrections"
419 applyApCorr = ConfigurableField(
420 target=ApplyApCorrTask,
421 doc=
"Subtask to apply aperture corrections"
423 doRunCatalogCalculation = Field(
426 doc=
'Run catalogCalculation task'
428 catalogCalculation = ConfigurableField(
429 target=CatalogCalculationTask,
430 doc=
"Subtask to run catalogCalculation plugins on catalog"
436 doc=
"Should be set to True if fake sources have been inserted into the input data."
438 idGenerator = SkyMapIdGeneratorConfig.make_field()
442 return self.match.refObjLoader
444 def setDefaults(self):
445 super().setDefaults()
446 self.measurement.plugins.names |= [
'base_InputCount',
448 'base_LocalPhotoCalib',
450 self.measurement.plugins[
'base_PixelFlags'].masksFpAnywhere = [
'CLIPPED',
'SENSOR_EDGE',
451 'INEXACT_PSF',
'STREAK']
452 self.measurement.plugins[
'base_PixelFlags'].masksFpCenter = [
'CLIPPED',
'SENSOR_EDGE',
453 'INEXACT_PSF',
'STREAK']
456class MeasureMergedCoaddSourcesTask(PipelineTask):
457 """Deblend sources from main catalog in each coadd seperately and measure.
459 Use peaks and footprints from a master catalog to perform deblending and
460 measurement in each coadd.
462 Given a master input catalog of sources (peaks and footprints) or deblender
463 outputs(including a HeavyFootprint in each band), measure each source on
464 the coadd. Repeating this procedure with the same master catalog across
465 multiple coadds will generate a consistent set of child sources.
467 The deblender retains all peaks and deblends any missing peaks (dropouts in
468 that band) as PSFs. Source properties are measured and the @c is-primary
469 flag (indicating sources with no children) is set. Visit flags are
470 propagated to the coadd sources.
472 Optionally, we can match the coadd sources to an external reference
475 After MeasureMergedCoaddSourcesTask has been run on multiple coadds, we
476 have a set of per-band catalogs. The next stage in the multi-band
477 processing procedure will merge these measurements into a suitable catalog
478 for driving forced photometry.
482 schema : ``lsst.afw.table.Schema`, optional
483 The schema of the merged detection catalog used as input to this one.
484 peakSchema : ``lsst.afw.table.Schema`, optional
485 The schema of the PeakRecords in the Footprints in the merged detection catalog.
486 refObjLoader : `lsst.meas.algorithms.ReferenceObjectLoader`, optional
487 An instance of ReferenceObjectLoader that supplies an external reference
488 catalog. May be None if the loader can be constructed from the butler argument or all steps
489 requiring a reference catalog are disabled.
490 initInputs : `dict`, optional
491 Dictionary that can contain a key ``inputSchema`` containing the
492 input schema. If present will override the value of ``schema``.
494 Additional keyword arguments.
497 _DefaultName =
"measureCoaddSources"
498 ConfigClass = MeasureMergedCoaddSourcesConfig
500 def __init__(self, schema=None, peakSchema=None, refObjLoader=None, initInputs=None,
502 super().__init__(**kwargs)
503 self.deblended = self.config.inputCatalog.startswith(
"deblended")
504 self.inputCatalog =
"Coadd_" + self.config.inputCatalog
505 if initInputs
is not None:
506 schema = initInputs[
'inputSchema'].schema
508 raise ValueError(
"Schema must be defined.")
509 self.schemaMapper = afwTable.SchemaMapper(schema)
510 self.schemaMapper.addMinimalSchema(schema)
511 self.schema = self.schemaMapper.getOutputSchema()
512 afwTable.CoordKey.addErrorFields(self.schema)
514 self.makeSubtask(
"measurement", schema=self.schema, algMetadata=self.algMetadata)
515 self.makeSubtask(
"setPrimaryFlags", schema=self.schema)
516 if self.config.doMatchSources:
517 self.makeSubtask(
"match", refObjLoader=refObjLoader)
518 if self.config.doPropagateFlags:
519 self.makeSubtask(
"propagateFlags", schema=self.schema)
520 self.schema.checkUnits(parse_strict=self.config.checkUnitsParseStrict)
521 if self.config.doApCorr:
522 self.makeSubtask(
"applyApCorr", schema=self.schema)
523 if self.config.doRunCatalogCalculation:
524 self.makeSubtask(
"catalogCalculation", schema=self.schema)
526 self.outputSchema = afwTable.SourceCatalog(self.schema)
528 def runQuantum(self, butlerQC, inputRefs, outputRefs):
529 inputs = butlerQC.get(inputRefs)
531 refObjLoader = ReferenceObjectLoader([ref.datasetRef.dataId
for ref
in inputRefs.refCat],
532 inputs.pop(
'refCat'),
533 name=self.config.connections.refCat,
534 config=self.config.refObjLoader,
536 self.match.setRefObjLoader(refObjLoader)
540 inputs[
'exposure'].getPsf().setCacheCapacity(self.config.psfCache)
544 idGenerator = self.config.idGenerator.apply(butlerQC.quantum.dataId)
545 inputs[
'exposureId'] = idGenerator.catalog_id
548 table = afwTable.SourceTable.make(self.schema, idGenerator.make_table_id_factory())
549 sources = afwTable.SourceCatalog(table)
551 if "scarletCatalog" in inputs:
552 inputCatalog = inputs.pop(
"scarletCatalog")
553 catalogRef = inputRefs.scarletCatalog
555 inputCatalog = inputs.pop(
"inputCatalog")
556 catalogRef = inputRefs.inputCatalog
557 sources.extend(inputCatalog, self.schemaMapper)
560 if self.config.doAddFootprints:
561 modelData = inputs.pop(
'scarletModels')
562 if self.config.doConserveFlux:
563 imageForRedistribution = inputs[
'exposure']
565 imageForRedistribution =
None
566 updateCatalogFootprints(
569 band=inputRefs.exposure.dataId[
"band"],
570 imageForRedistribution=imageForRedistribution,
571 removeScarletData=
True,
572 updateFluxColumns=
True,
574 table = sources.getTable()
575 table.setMetadata(self.algMetadata)
576 inputs[
'sources'] = sources
578 skyMap = inputs.pop(
'skyMap')
579 tractNumber = catalogRef.dataId[
'tract']
580 tractInfo = skyMap[tractNumber]
581 patchInfo = tractInfo.getPatchInfo(catalogRef.dataId[
'patch'])
586 wcs=tractInfo.getWcs(),
587 bbox=patchInfo.getOuterBBox()
589 inputs[
'skyInfo'] = skyInfo
591 if self.config.doPropagateFlags:
592 if self.config.propagateFlags.target == PropagateSourceFlagsTask:
594 ccdInputs = inputs[
"exposure"].getInfo().getCoaddInputs().ccds
595 inputs[
"ccdInputs"] = ccdInputs
597 if "sourceTableHandles" in inputs:
598 sourceTableHandles = inputs.pop(
"sourceTableHandles")
599 sourceTableHandleDict = {handle.dataId[
"visit"]: handle
600 for handle
in sourceTableHandles}
601 inputs[
"sourceTableHandleDict"] = sourceTableHandleDict
602 if "finalizedSourceTableHandles" in inputs:
603 finalizedSourceTableHandles = inputs.pop(
"finalizedSourceTableHandles")
604 finalizedSourceTableHandleDict = {handle.dataId[
"visit"]: handle
605 for handle
in finalizedSourceTableHandles}
606 inputs[
"finalizedSourceTableHandleDict"] = finalizedSourceTableHandleDict
610 ccdInputs = inputs[
'exposure'].getInfo().getCoaddInputs().ccds
611 visitKey = ccdInputs.schema.find(
"visit").key
612 ccdKey = ccdInputs.schema.find(
"ccd").key
613 inputVisitIds = set()
615 for ccdRecord
in ccdInputs:
616 visit = ccdRecord.get(visitKey)
617 ccd = ccdRecord.get(ccdKey)
618 inputVisitIds.add((visit, ccd))
619 ccdRecordsWcs[(visit, ccd)] = ccdRecord.getWcs()
621 inputCatalogsToKeep = []
622 inputCatalogWcsUpdate = []
623 for i, dataRef
in enumerate(inputRefs.visitCatalogs):
624 key = (dataRef.dataId[
'visit'], dataRef.dataId[
'detector'])
625 if key
in inputVisitIds:
626 inputCatalogsToKeep.append(inputs[
'visitCatalogs'][i])
627 inputCatalogWcsUpdate.append(ccdRecordsWcs[key])
628 inputs[
'visitCatalogs'] = inputCatalogsToKeep
629 inputs[
'wcsUpdates'] = inputCatalogWcsUpdate
630 inputs[
'ccdInputs'] = ccdInputs
632 outputs = self.run(**inputs)
634 sources = outputs.outputSources
635 butlerQC.put(outputs, outputRefs)
637 def run(self, exposure, sources, skyInfo, exposureId, ccdInputs=None, visitCatalogs=None, wcsUpdates=None,
638 sourceTableHandleDict=None, finalizedSourceTableHandleDict=None):
639 """Run measurement algorithms on the input exposure, and optionally populate the
640 resulting catalog with extra information.
644 exposure : `lsst.afw.exposure.Exposure`
645 The input exposure on which measurements are to be performed.
646 sources : `lsst.afw.table.SourceCatalog`
647 A catalog built from the results of merged detections, or
649 skyInfo : `lsst.pipe.base.Struct`
650 A struct containing information about the position of the input exposure within
651 a `SkyMap`, the `SkyMap`, its `Wcs`, and its bounding box.
652 exposureId : `int` or `bytes`
653 Packed unique number or bytes unique to the input exposure.
654 ccdInputs : `lsst.afw.table.ExposureCatalog`, optional
655 Catalog containing information on the individual visits which went into making
657 visitCatalogs : `list` of `lsst.afw.table.SourceCatalogs`, optional
658 A list of source catalogs corresponding to measurements made on the individual
659 visits which went into the input exposure. If None and butler is `None` then
660 the task cannot propagate visit flags to the output catalog.
661 Deprecated, to be removed with PropagateVisitFlagsTask.
662 wcsUpdates : `list` of `lsst.afw.geom.SkyWcs`, optional
663 If visitCatalogs is not `None` this should be a list of wcs objects which correspond
664 to the input visits. Used to put all coordinates to common system. If `None` and
665 butler is `None` then the task cannot propagate visit flags to the output catalog.
666 Deprecated, to be removed with PropagateVisitFlagsTask.
667 sourceTableHandleDict : `dict` [`int`, `lsst.daf.butler.DeferredDatasetHandle`], optional
668 Dict for sourceTable_visit handles (key is visit) for propagating flags.
669 These tables are derived from the ``CalibrateTask`` sources, and contain
670 astrometry and photometry flags, and optionally PSF flags.
671 finalizedSourceTableHandleDict : `dict` [`int`, `lsst.daf.butler.DeferredDatasetHandle`], optional
672 Dict for finalized_src_table handles (key is visit) for propagating flags.
673 These tables are derived from ``FinalizeCalibrationTask`` and contain
674 PSF flags from the finalized PSF estimation.
678 results : `lsst.pipe.base.Struct`
679 Results of running measurement task. Will contain the catalog in the
680 sources attribute. Optionally will have results of matching to a
681 reference catalog in the matchResults attribute, and denormalized
682 matches in the denormMatches attribute.
684 self.measurement.run(sources, exposure, exposureId=exposureId)
686 if self.config.doApCorr:
687 self.applyApCorr.run(
689 apCorrMap=exposure.getInfo().getApCorrMap()
696 if not sources.isContiguous():
697 sources = sources.copy(deep=
True)
699 if self.config.doRunCatalogCalculation:
700 self.catalogCalculation.run(sources)
702 self.setPrimaryFlags.run(sources, skyMap=skyInfo.skyMap, tractInfo=skyInfo.tractInfo,
703 patchInfo=skyInfo.patchInfo)
704 if self.config.doPropagateFlags:
705 if self.config.propagateFlags.target == PropagateSourceFlagsTask:
707 self.propagateFlags.run(
710 sourceTableHandleDict,
711 finalizedSourceTableHandleDict
715 self.propagateFlags.run(
725 if self.config.doMatchSources:
726 matchResult = self.match.run(sources, exposure.getInfo().getFilter().bandLabel)
727 matches = afwTable.packMatches(matchResult.matches)
728 matches.table.setMetadata(matchResult.matchMeta)
729 results.matchResult = matches
730 if self.config.doWriteMatchesDenormalized:
731 if matchResult.matches:
732 denormMatches = denormalizeMatches(matchResult.matches, matchResult.matchMeta)
734 self.log.warning(
"No matches, so generating dummy denormalized matches file")
735 denormMatches = afwTable.BaseCatalog(afwTable.Schema())
737 denormMatches.getMetadata().add(
"COMMENT",
738 "This catalog is empty because no matches were found.")
739 results.denormMatches = denormMatches
740 results.denormMatches = denormMatches
742 results.outputSources = sources