Coverage for python/lsst/pipe/tasks/characterizeImage.py: 30%
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2# LSST Data Management System
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21#
22import numpy as np
24from lsstDebug import getDebugFrame
25import lsst.afw.table as afwTable
26import lsst.pex.config as pexConfig
27import lsst.pipe.base as pipeBase
28import lsst.daf.base as dafBase
29import lsst.pipe.base.connectionTypes as cT
30from lsst.afw.math import BackgroundList
31from lsst.afw.table import SourceTable, SourceCatalog
32from lsst.meas.algorithms import SubtractBackgroundTask, SourceDetectionTask, MeasureApCorrTask
33from lsst.meas.algorithms.installGaussianPsf import InstallGaussianPsfTask
34from lsst.meas.astrom import RefMatchTask, displayAstrometry
35from lsst.meas.algorithms import LoadIndexedReferenceObjectsTask
36from lsst.obs.base import ExposureIdInfo
37from lsst.meas.base import SingleFrameMeasurementTask, ApplyApCorrTask, CatalogCalculationTask
38from lsst.meas.deblender import SourceDeblendTask
39import lsst.meas.extensions.shapeHSM # noqa: F401 needed for default shape plugin
40from .measurePsf import MeasurePsfTask
41from .repair import RepairTask
42from .computeExposureSummaryStats import ComputeExposureSummaryStatsTask
43from lsst.pex.exceptions import LengthError
44from lsst.utils.timer import timeMethod
46__all__ = ["CharacterizeImageConfig", "CharacterizeImageTask"]
49class CharacterizeImageConnections(pipeBase.PipelineTaskConnections,
50 dimensions=("instrument", "visit", "detector")):
51 exposure = cT.Input(
52 doc="Input exposure data",
53 name="postISRCCD",
54 storageClass="Exposure",
55 dimensions=["instrument", "exposure", "detector"],
56 )
57 characterized = cT.Output(
58 doc="Output characterized data.",
59 name="icExp",
60 storageClass="ExposureF",
61 dimensions=["instrument", "visit", "detector"],
62 )
63 sourceCat = cT.Output(
64 doc="Output source catalog.",
65 name="icSrc",
66 storageClass="SourceCatalog",
67 dimensions=["instrument", "visit", "detector"],
68 )
69 backgroundModel = cT.Output(
70 doc="Output background model.",
71 name="icExpBackground",
72 storageClass="Background",
73 dimensions=["instrument", "visit", "detector"],
74 )
75 outputSchema = cT.InitOutput(
76 doc="Schema of the catalog produced by CharacterizeImage",
77 name="icSrc_schema",
78 storageClass="SourceCatalog",
79 )
81 def adjustQuantum(self, inputs, outputs, label, dataId):
82 # Docstring inherited from PipelineTaskConnections
83 try:
84 return super().adjustQuantum(inputs, outputs, label, dataId)
85 except pipeBase.ScalarError as err:
86 raise pipeBase.ScalarError(
87 "CharacterizeImageTask can at present only be run on visits that are associated with "
88 "exactly one exposure. Either this is not a valid exposure for this pipeline, or the "
89 "snap-combination step you probably want hasn't been configured to run between ISR and "
90 "this task (as of this writing, that would be because it hasn't been implemented yet)."
91 ) from err
94class CharacterizeImageConfig(pipeBase.PipelineTaskConfig,
95 pipelineConnections=CharacterizeImageConnections):
97 """!Config for CharacterizeImageTask"""
98 doMeasurePsf = pexConfig.Field(
99 dtype=bool,
100 default=True,
101 doc="Measure PSF? If False then for all subsequent operations use either existing PSF "
102 "model when present, or install simple PSF model when not (see installSimplePsf "
103 "config options)"
104 )
105 doWrite = pexConfig.Field(
106 dtype=bool,
107 default=True,
108 doc="Persist results?",
109 )
110 doWriteExposure = pexConfig.Field(
111 dtype=bool,
112 default=True,
113 doc="Write icExp and icExpBackground in addition to icSrc? Ignored if doWrite False.",
114 )
115 psfIterations = pexConfig.RangeField(
116 dtype=int,
117 default=2,
118 min=1,
119 doc="Number of iterations of detect sources, measure sources, "
120 "estimate PSF. If useSimplePsf is True then 2 should be plenty; "
121 "otherwise more may be wanted.",
122 )
123 background = pexConfig.ConfigurableField(
124 target=SubtractBackgroundTask,
125 doc="Configuration for initial background estimation",
126 )
127 detection = pexConfig.ConfigurableField(
128 target=SourceDetectionTask,
129 doc="Detect sources"
130 )
131 doDeblend = pexConfig.Field(
132 dtype=bool,
133 default=True,
134 doc="Run deblender input exposure"
135 )
136 deblend = pexConfig.ConfigurableField(
137 target=SourceDeblendTask,
138 doc="Split blended source into their components"
139 )
140 measurement = pexConfig.ConfigurableField(
141 target=SingleFrameMeasurementTask,
142 doc="Measure sources"
143 )
144 doApCorr = pexConfig.Field(
145 dtype=bool,
146 default=True,
147 doc="Run subtasks to measure and apply aperture corrections"
148 )
149 measureApCorr = pexConfig.ConfigurableField(
150 target=MeasureApCorrTask,
151 doc="Subtask to measure aperture corrections"
152 )
153 applyApCorr = pexConfig.ConfigurableField(
154 target=ApplyApCorrTask,
155 doc="Subtask to apply aperture corrections"
156 )
157 # If doApCorr is False, and the exposure does not have apcorrections already applied, the
158 # active plugins in catalogCalculation almost certainly should not contain the characterization plugin
159 catalogCalculation = pexConfig.ConfigurableField(
160 target=CatalogCalculationTask,
161 doc="Subtask to run catalogCalculation plugins on catalog"
162 )
163 doComputeSummaryStats = pexConfig.Field(
164 dtype=bool,
165 default=True,
166 doc="Run subtask to measure exposure summary statistics",
167 deprecated=("This subtask has been moved to CalibrateTask "
168 "with DM-30701.")
169 )
170 computeSummaryStats = pexConfig.ConfigurableField(
171 target=ComputeExposureSummaryStatsTask,
172 doc="Subtask to run computeSummaryStats on exposure",
173 deprecated=("This subtask has been moved to CalibrateTask "
174 "with DM-30701.")
175 )
176 useSimplePsf = pexConfig.Field(
177 dtype=bool,
178 default=True,
179 doc="Replace the existing PSF model with a simplified version that has the same sigma "
180 "at the start of each PSF determination iteration? Doing so makes PSF determination "
181 "converge more robustly and quickly.",
182 )
183 installSimplePsf = pexConfig.ConfigurableField(
184 target=InstallGaussianPsfTask,
185 doc="Install a simple PSF model",
186 )
187 refObjLoader = pexConfig.ConfigurableField(
188 target=LoadIndexedReferenceObjectsTask,
189 deprecated="This field does nothing. Will be removed after v24 (see DM-34768).",
190 doc="reference object loader",
191 )
192 ref_match = pexConfig.ConfigurableField(
193 target=RefMatchTask,
194 deprecated="This field was never usable. Will be removed after v24 (see DM-34768).",
195 doc="Task to load and match reference objects. Only used if measurePsf can use matches. "
196 "Warning: matching will only work well if the initial WCS is accurate enough "
197 "to give good matches (roughly: good to 3 arcsec across the CCD).",
198 )
199 measurePsf = pexConfig.ConfigurableField(
200 target=MeasurePsfTask,
201 doc="Measure PSF",
202 )
203 repair = pexConfig.ConfigurableField(
204 target=RepairTask,
205 doc="Remove cosmic rays",
206 )
207 requireCrForPsf = pexConfig.Field(
208 dtype=bool,
209 default=True,
210 doc="Require cosmic ray detection and masking to run successfully before measuring the PSF."
211 )
212 checkUnitsParseStrict = pexConfig.Field(
213 doc="Strictness of Astropy unit compatibility check, can be 'raise', 'warn' or 'silent'",
214 dtype=str,
215 default="raise",
216 )
218 def setDefaults(self):
219 super().setDefaults()
220 # just detect bright stars; includeThresholdMultipler=10 seems large,
221 # but these are the values we have been using
222 self.detection.thresholdValue = 5.0
223 self.detection.includeThresholdMultiplier = 10.0
224 self.detection.doTempLocalBackground = False
225 # do not deblend, as it makes a mess
226 self.doDeblend = False
227 # measure and apply aperture correction; note: measuring and applying aperture
228 # correction are disabled until the final measurement, after PSF is measured
229 self.doApCorr = True
230 # minimal set of measurements needed to determine PSF
231 self.measurement.plugins.names = [
232 "base_PixelFlags",
233 "base_SdssCentroid",
234 "ext_shapeHSM_HsmSourceMoments",
235 "base_GaussianFlux",
236 "base_PsfFlux",
237 "base_CircularApertureFlux",
238 ]
239 self.measurement.slots.shape = "ext_shapeHSM_HsmSourceMoments"
241 def validate(self):
242 if self.doApCorr and not self.measurePsf:
243 raise RuntimeError("Must measure PSF to measure aperture correction, "
244 "because flags determined by PSF measurement are used to identify "
245 "sources used to measure aperture correction")
247## \addtogroup LSST_task_documentation
248## \{
249## \page page_CharacterizeImageTask CharacterizeImageTask
250## \ref CharacterizeImageTask_ "CharacterizeImageTask"
251## \copybrief CharacterizeImageTask
252## \}
255class CharacterizeImageTask(pipeBase.PipelineTask, pipeBase.CmdLineTask):
256 r"""!
257 Measure bright sources and use this to estimate background and PSF of an exposure
259 @anchor CharacterizeImageTask_
261 @section pipe_tasks_characterizeImage_Contents Contents
263 - @ref pipe_tasks_characterizeImage_Purpose
264 - @ref pipe_tasks_characterizeImage_Initialize
265 - @ref pipe_tasks_characterizeImage_IO
266 - @ref pipe_tasks_characterizeImage_Config
267 - @ref pipe_tasks_characterizeImage_Debug
269 @section pipe_tasks_characterizeImage_Purpose Description
271 Given an exposure with defects repaired (masked and interpolated over, e.g. as output by IsrTask):
272 - detect and measure bright sources
273 - repair cosmic rays
274 - measure and subtract background
275 - measure PSF
277 @section pipe_tasks_characterizeImage_Initialize Task initialisation
279 @copydoc \_\_init\_\_
281 @section pipe_tasks_characterizeImage_IO Invoking the Task
283 If you want this task to unpersist inputs or persist outputs, then call
284 the `runDataRef` method (a thin wrapper around the `run` method).
286 If you already have the inputs unpersisted and do not want to persist the output
287 then it is more direct to call the `run` method:
289 @section pipe_tasks_characterizeImage_Config Configuration parameters
291 See @ref CharacterizeImageConfig
293 @section pipe_tasks_characterizeImage_Debug Debug variables
295 The command line task interface supports a flag
296 `--debug` to import `debug.py` from your `$PYTHONPATH`; see
297 <a href="https://pipelines.lsst.io/modules/lsstDebug/">the lsstDebug documentation</a>
298 for more about `debug.py`.
300 CharacterizeImageTask has a debug dictionary with the following keys:
301 <dl>
302 <dt>frame
303 <dd>int: if specified, the frame of first debug image displayed (defaults to 1)
304 <dt>repair_iter
305 <dd>bool; if True display image after each repair in the measure PSF loop
306 <dt>background_iter
307 <dd>bool; if True display image after each background subtraction in the measure PSF loop
308 <dt>measure_iter
309 <dd>bool; if True display image and sources at the end of each iteration of the measure PSF loop
310 See @ref lsst.meas.astrom.displayAstrometry for the meaning of the various symbols.
311 <dt>psf
312 <dd>bool; if True display image and sources after PSF is measured;
313 this will be identical to the final image displayed by measure_iter if measure_iter is true
314 <dt>repair
315 <dd>bool; if True display image and sources after final repair
316 <dt>measure
317 <dd>bool; if True display image and sources after final measurement
318 </dl>
320 For example, put something like:
321 @code{.py}
322 import lsstDebug
323 def DebugInfo(name):
324 di = lsstDebug.getInfo(name) # N.b. lsstDebug.Info(name) would call us recursively
325 if name == "lsst.pipe.tasks.characterizeImage":
326 di.display = dict(
327 repair = True,
328 )
330 return di
332 lsstDebug.Info = DebugInfo
333 @endcode
334 into your `debug.py` file and run `calibrateTask.py` with the `--debug` flag.
336 Some subtasks may have their own debug variables; see individual Task documentation.
337 """
339 # Example description used to live here, removed 2-20-2017 by MSSG
341 ConfigClass = CharacterizeImageConfig
342 _DefaultName = "characterizeImage"
343 RunnerClass = pipeBase.ButlerInitializedTaskRunner
345 def runQuantum(self, butlerQC, inputRefs, outputRefs):
346 inputs = butlerQC.get(inputRefs)
347 if 'exposureIdInfo' not in inputs.keys():
348 inputs['exposureIdInfo'] = ExposureIdInfo.fromDataId(butlerQC.quantum.dataId, "visit_detector")
349 outputs = self.run(**inputs)
350 butlerQC.put(outputs, outputRefs)
352 def __init__(self, butler=None, refObjLoader=None, schema=None, **kwargs):
353 """!Construct a CharacterizeImageTask
355 @param[in] butler A butler object is passed to the refObjLoader constructor in case
356 it is needed to load catalogs. May be None if a catalog-based star selector is
357 not used, if the reference object loader constructor does not require a butler,
358 or if a reference object loader is passed directly via the refObjLoader argument.
359 # TODO DM-34769: remove rebObjLoader kwarg here.
360 @param[in] refObjLoader An instance of LoadReferenceObjectsTasks that supplies an
361 external reference catalog to a catalog-based star selector. May be None if a
362 catalog star selector is not used or the loader can be constructed from the
363 butler argument.
364 @param[in,out] schema initial schema (an lsst.afw.table.SourceTable), or None
365 @param[in,out] kwargs other keyword arguments for lsst.pipe.base.CmdLineTask
366 """
367 super().__init__(**kwargs)
369 if schema is None:
370 schema = SourceTable.makeMinimalSchema()
371 self.schema = schema
372 self.makeSubtask("background")
373 self.makeSubtask("installSimplePsf")
374 self.makeSubtask("repair")
375 self.makeSubtask("measurePsf", schema=self.schema)
376 # TODO DM-34769: remove this `if` block
377 if self.config.doMeasurePsf and self.measurePsf.usesMatches:
378 if not refObjLoader:
379 self.makeSubtask('refObjLoader', butler=butler)
380 refObjLoader = self.refObjLoader
381 self.makeSubtask("ref_match", refObjLoader=refObjLoader)
382 self.algMetadata = dafBase.PropertyList()
383 self.makeSubtask('detection', schema=self.schema)
384 if self.config.doDeblend:
385 self.makeSubtask("deblend", schema=self.schema)
386 self.makeSubtask('measurement', schema=self.schema, algMetadata=self.algMetadata)
387 if self.config.doApCorr:
388 self.makeSubtask('measureApCorr', schema=self.schema)
389 self.makeSubtask('applyApCorr', schema=self.schema)
390 self.makeSubtask('catalogCalculation', schema=self.schema)
391 self._initialFrame = getDebugFrame(self._display, "frame") or 1
392 self._frame = self._initialFrame
393 self.schema.checkUnits(parse_strict=self.config.checkUnitsParseStrict)
394 self.outputSchema = afwTable.SourceCatalog(self.schema)
396 def getInitOutputDatasets(self):
397 outputCatSchema = afwTable.SourceCatalog(self.schema)
398 outputCatSchema.getTable().setMetadata(self.algMetadata)
399 return {'outputSchema': outputCatSchema}
401 @timeMethod
402 def runDataRef(self, dataRef, exposure=None, background=None, doUnpersist=True):
403 """!Characterize a science image and, if wanted, persist the results
405 This simply unpacks the exposure and passes it to the characterize method to do the work.
407 @param[in] dataRef: butler data reference for science exposure
408 @param[in,out] exposure exposure to characterize (an lsst.afw.image.ExposureF or similar).
409 If None then unpersist from "postISRCCD".
410 The following changes are made, depending on the config:
411 - set psf to the measured PSF
412 - set apCorrMap to the measured aperture correction
413 - subtract background
414 - interpolate over cosmic rays
415 - update detection and cosmic ray mask planes
416 @param[in,out] background initial model of background already subtracted from exposure
417 (an lsst.afw.math.BackgroundList). May be None if no background has been subtracted,
418 which is typical for image characterization.
419 A refined background model is output.
420 @param[in] doUnpersist if True the exposure is read from the repository
421 and the exposure and background arguments must be None;
422 if False the exposure must be provided.
423 True is intended for running as a command-line task, False for running as a subtask
425 @return same data as the characterize method
426 """
427 self._frame = self._initialFrame # reset debug display frame
428 self.log.info("Processing %s", dataRef.dataId)
430 if doUnpersist:
431 if exposure is not None or background is not None:
432 raise RuntimeError("doUnpersist true; exposure and background must be None")
433 exposure = dataRef.get("postISRCCD", immediate=True)
434 elif exposure is None:
435 raise RuntimeError("doUnpersist false; exposure must be provided")
437 exposureIdInfo = dataRef.get("expIdInfo")
439 charRes = self.run(
440 exposure=exposure,
441 exposureIdInfo=exposureIdInfo,
442 background=background,
443 )
445 if self.config.doWrite:
446 dataRef.put(charRes.sourceCat, "icSrc")
447 if self.config.doWriteExposure:
448 dataRef.put(charRes.exposure, "icExp")
449 dataRef.put(charRes.background, "icExpBackground")
451 return charRes
453 @timeMethod
454 def run(self, exposure, exposureIdInfo=None, background=None):
455 """!Characterize a science image
457 Peforms the following operations:
458 - Iterate the following config.psfIterations times, or once if config.doMeasurePsf false:
459 - detect and measure sources and estimate PSF (see detectMeasureAndEstimatePsf for details)
460 - interpolate over cosmic rays
461 - perform final measurement
463 @param[in,out] exposure exposure to characterize (an lsst.afw.image.ExposureF or similar).
464 The following changes are made:
465 - update or set psf
466 - set apCorrMap
467 - update detection and cosmic ray mask planes
468 - subtract background and interpolate over cosmic rays
469 @param[in] exposureIdInfo ID info for exposure (an lsst.obs.base.ExposureIdInfo).
470 If not provided, returned SourceCatalog IDs will not be globally unique.
471 @param[in,out] background initial model of background already subtracted from exposure
472 (an lsst.afw.math.BackgroundList). May be None if no background has been subtracted,
473 which is typical for image characterization.
475 @return pipe_base Struct containing these fields, all from the final iteration
476 of detectMeasureAndEstimatePsf:
477 - exposure: characterized exposure; image is repaired by interpolating over cosmic rays,
478 mask is updated accordingly, and the PSF model is set
479 - sourceCat: detected sources (an lsst.afw.table.SourceCatalog)
480 - background: model of background subtracted from exposure (an lsst.afw.math.BackgroundList)
481 - psfCellSet: spatial cells of PSF candidates (an lsst.afw.math.SpatialCellSet)
482 """
483 self._frame = self._initialFrame # reset debug display frame
485 if not self.config.doMeasurePsf and not exposure.hasPsf():
486 self.log.info("CharacterizeImageTask initialized with 'simple' PSF.")
487 self.installSimplePsf.run(exposure=exposure)
489 if exposureIdInfo is None:
490 exposureIdInfo = ExposureIdInfo()
492 # subtract an initial estimate of background level
493 background = self.background.run(exposure).background
495 psfIterations = self.config.psfIterations if self.config.doMeasurePsf else 1
496 for i in range(psfIterations):
497 dmeRes = self.detectMeasureAndEstimatePsf(
498 exposure=exposure,
499 exposureIdInfo=exposureIdInfo,
500 background=background,
501 )
503 psf = dmeRes.exposure.getPsf()
504 # Just need a rough estimate; average positions are fine
505 psfAvgPos = psf.getAveragePosition()
506 psfSigma = psf.computeShape(psfAvgPos).getDeterminantRadius()
507 psfDimensions = psf.computeImage(psfAvgPos).getDimensions()
508 medBackground = np.median(dmeRes.background.getImage().getArray())
509 self.log.info("iter %s; PSF sigma=%0.2f, dimensions=%s; median background=%0.2f",
510 i + 1, psfSigma, psfDimensions, medBackground)
511 if np.isnan(psfSigma):
512 raise RuntimeError("PSF sigma is NaN, cannot continue PSF determination.")
514 self.display("psf", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat)
516 # perform final repair with final PSF
517 self.repair.run(exposure=dmeRes.exposure)
518 self.display("repair", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat)
520 # perform final measurement with final PSF, including measuring and applying aperture correction,
521 # if wanted
522 self.measurement.run(measCat=dmeRes.sourceCat, exposure=dmeRes.exposure,
523 exposureId=exposureIdInfo.expId)
524 if self.config.doApCorr:
525 apCorrMap = self.measureApCorr.run(exposure=dmeRes.exposure, catalog=dmeRes.sourceCat).apCorrMap
526 dmeRes.exposure.getInfo().setApCorrMap(apCorrMap)
527 self.applyApCorr.run(catalog=dmeRes.sourceCat, apCorrMap=exposure.getInfo().getApCorrMap())
528 self.catalogCalculation.run(dmeRes.sourceCat)
530 self.display("measure", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat)
532 return pipeBase.Struct(
533 exposure=dmeRes.exposure,
534 sourceCat=dmeRes.sourceCat,
535 background=dmeRes.background,
536 psfCellSet=dmeRes.psfCellSet,
538 characterized=dmeRes.exposure,
539 backgroundModel=dmeRes.background
540 )
542 @timeMethod
543 def detectMeasureAndEstimatePsf(self, exposure, exposureIdInfo, background):
544 """!Perform one iteration of detect, measure and estimate PSF
546 Performs the following operations:
547 - if config.doMeasurePsf or not exposure.hasPsf():
548 - install a simple PSF model (replacing the existing one, if need be)
549 - interpolate over cosmic rays with keepCRs=True
550 - estimate background and subtract it from the exposure
551 - detect, deblend and measure sources, and subtract a refined background model;
552 - if config.doMeasurePsf:
553 - measure PSF
555 @param[in,out] exposure exposure to characterize (an lsst.afw.image.ExposureF or similar)
556 The following changes are made:
557 - update or set psf
558 - update detection and cosmic ray mask planes
559 - subtract background
560 @param[in] exposureIdInfo ID info for exposure (an lsst.obs_base.ExposureIdInfo)
561 @param[in,out] background initial model of background already subtracted from exposure
562 (an lsst.afw.math.BackgroundList).
564 @return pipe_base Struct containing these fields, all from the final iteration
565 of detect sources, measure sources and estimate PSF:
566 - exposure characterized exposure; image is repaired by interpolating over cosmic rays,
567 mask is updated accordingly, and the PSF model is set
568 - sourceCat detected sources (an lsst.afw.table.SourceCatalog)
569 - background model of background subtracted from exposure (an lsst.afw.math.BackgroundList)
570 - psfCellSet spatial cells of PSF candidates (an lsst.afw.math.SpatialCellSet)
571 """
572 # install a simple PSF model, if needed or wanted
573 if not exposure.hasPsf() or (self.config.doMeasurePsf and self.config.useSimplePsf):
574 self.log.info("PSF estimation initialized with 'simple' PSF")
575 self.installSimplePsf.run(exposure=exposure)
577 # run repair, but do not interpolate over cosmic rays (do that elsewhere, with the final PSF model)
578 if self.config.requireCrForPsf:
579 self.repair.run(exposure=exposure, keepCRs=True)
580 else:
581 try:
582 self.repair.run(exposure=exposure, keepCRs=True)
583 except LengthError:
584 self.log.warning("Skipping cosmic ray detection: Too many CR pixels (max %0.f)",
585 self.config.repair.cosmicray.nCrPixelMax)
587 self.display("repair_iter", exposure=exposure)
589 if background is None:
590 background = BackgroundList()
592 sourceIdFactory = exposureIdInfo.makeSourceIdFactory()
593 table = SourceTable.make(self.schema, sourceIdFactory)
594 table.setMetadata(self.algMetadata)
596 detRes = self.detection.run(table=table, exposure=exposure, doSmooth=True)
597 sourceCat = detRes.sources
598 if detRes.fpSets.background:
599 for bg in detRes.fpSets.background:
600 background.append(bg)
602 if self.config.doDeblend:
603 self.deblend.run(exposure=exposure, sources=sourceCat)
605 self.measurement.run(measCat=sourceCat, exposure=exposure, exposureId=exposureIdInfo.expId)
607 measPsfRes = pipeBase.Struct(cellSet=None)
608 if self.config.doMeasurePsf:
609 # TODO DM-34769: remove this `if` block, and the `matches` kwarg from measurePsf.run below.
610 if self.measurePsf.usesMatches:
611 matches = self.ref_match.loadAndMatch(exposure=exposure, sourceCat=sourceCat).matches
612 else:
613 matches = None
614 measPsfRes = self.measurePsf.run(exposure=exposure, sources=sourceCat, matches=matches,
615 expId=exposureIdInfo.expId)
616 self.display("measure_iter", exposure=exposure, sourceCat=sourceCat)
618 return pipeBase.Struct(
619 exposure=exposure,
620 sourceCat=sourceCat,
621 background=background,
622 psfCellSet=measPsfRes.cellSet,
623 )
625 def getSchemaCatalogs(self):
626 """Return a dict of empty catalogs for each catalog dataset produced by this task.
627 """
628 sourceCat = SourceCatalog(self.schema)
629 sourceCat.getTable().setMetadata(self.algMetadata)
630 return {"icSrc": sourceCat}
632 def display(self, itemName, exposure, sourceCat=None):
633 """Display exposure and sources on next frame, if display of itemName has been requested
635 @param[in] itemName name of item in debugInfo
636 @param[in] exposure exposure to display
637 @param[in] sourceCat source catalog to display
638 """
639 val = getDebugFrame(self._display, itemName)
640 if not val:
641 return
643 displayAstrometry(exposure=exposure, sourceCat=sourceCat, frame=self._frame, pause=False)
644 self._frame += 1