lsst.pipe.tasks g226e71a814+02e4f8badd
characterizeImage.py
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22import numpy as np
23
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
39from .measurePsf import MeasurePsfTask
40from .repair import RepairTask
41from .computeExposureSummaryStats import ComputeExposureSummaryStatsTask
42from lsst.pex.exceptions import LengthError
43from lsst.utils.timer import timeMethod
44
45__all__ = ["CharacterizeImageConfig", "CharacterizeImageTask"]
46
47
48class CharacterizeImageConnections(pipeBase.PipelineTaskConnections,
49 dimensions=("instrument", "visit", "detector")):
50 exposure = cT.Input(
51 doc="Input exposure data",
52 name="postISRCCD",
53 storageClass="Exposure",
54 dimensions=["instrument", "exposure", "detector"],
55 )
56 characterized = cT.Output(
57 doc="Output characterized data.",
58 name="icExp",
59 storageClass="ExposureF",
60 dimensions=["instrument", "visit", "detector"],
61 )
62 sourceCat = cT.Output(
63 doc="Output source catalog.",
64 name="icSrc",
65 storageClass="SourceCatalog",
66 dimensions=["instrument", "visit", "detector"],
67 )
68 backgroundModel = cT.Output(
69 doc="Output background model.",
70 name="icExpBackground",
71 storageClass="Background",
72 dimensions=["instrument", "visit", "detector"],
73 )
74 outputSchema = cT.InitOutput(
75 doc="Schema of the catalog produced by CharacterizeImage",
76 name="icSrc_schema",
77 storageClass="SourceCatalog",
78 )
79
80 def adjustQuantum(self, inputs, outputs, label, dataId):
81 # Docstring inherited from PipelineTaskConnections
82 try:
83 return super().adjustQuantum(inputs, outputs, label, dataId)
84 except pipeBase.ScalarError as err:
85 raise pipeBase.ScalarError(
86 "CharacterizeImageTask can at present only be run on visits that are associated with "
87 "exactly one exposure. Either this is not a valid exposure for this pipeline, or the "
88 "snap-combination step you probably want hasn't been configured to run between ISR and "
89 "this task (as of this writing, that would be because it hasn't been implemented yet)."
90 ) from err
91
92
93class CharacterizeImageConfig(pipeBase.PipelineTaskConfig,
94 pipelineConnections=CharacterizeImageConnections):
95
96 """!Config for CharacterizeImageTask"""
97 doMeasurePsf = pexConfig.Field(
98 dtype=bool,
99 default=True,
100 doc="Measure PSF? If False then for all subsequent operations use either existing PSF "
101 "model when present, or install simple PSF model when not (see installSimplePsf "
102 "config options)"
103 )
104 doWrite = pexConfig.Field(
105 dtype=bool,
106 default=True,
107 doc="Persist results?",
108 )
109 doWriteExposure = pexConfig.Field(
110 dtype=bool,
111 default=True,
112 doc="Write icExp and icExpBackground in addition to icSrc? Ignored if doWrite False.",
113 )
114 psfIterations = pexConfig.RangeField(
115 dtype=int,
116 default=2,
117 min=1,
118 doc="Number of iterations of detect sources, measure sources, "
119 "estimate PSF. If useSimplePsf is True then 2 should be plenty; "
120 "otherwise more may be wanted.",
121 )
122 background = pexConfig.ConfigurableField(
123 target=SubtractBackgroundTask,
124 doc="Configuration for initial background estimation",
125 )
126 detection = pexConfig.ConfigurableField(
127 target=SourceDetectionTask,
128 doc="Detect sources"
129 )
130 doDeblend = pexConfig.Field(
131 dtype=bool,
132 default=True,
133 doc="Run deblender input exposure"
134 )
135 deblend = pexConfig.ConfigurableField(
136 target=SourceDeblendTask,
137 doc="Split blended source into their components"
138 )
139 measurement = pexConfig.ConfigurableField(
140 target=SingleFrameMeasurementTask,
141 doc="Measure sources"
142 )
143 doApCorr = pexConfig.Field(
144 dtype=bool,
145 default=True,
146 doc="Run subtasks to measure and apply aperture corrections"
147 )
148 measureApCorr = pexConfig.ConfigurableField(
149 target=MeasureApCorrTask,
150 doc="Subtask to measure aperture corrections"
151 )
152 applyApCorr = pexConfig.ConfigurableField(
153 target=ApplyApCorrTask,
154 doc="Subtask to apply aperture corrections"
155 )
156 # If doApCorr is False, and the exposure does not have apcorrections already applied, the
157 # active plugins in catalogCalculation almost certainly should not contain the characterization plugin
158 catalogCalculation = pexConfig.ConfigurableField(
159 target=CatalogCalculationTask,
160 doc="Subtask to run catalogCalculation plugins on catalog"
161 )
162 doComputeSummaryStats = pexConfig.Field(
163 dtype=bool,
164 default=True,
165 doc="Run subtask to measure exposure summary statistics",
166 deprecated=("This subtask has been moved to CalibrateTask "
167 "with DM-30701.")
168 )
169 computeSummaryStats = pexConfig.ConfigurableField(
170 target=ComputeExposureSummaryStatsTask,
171 doc="Subtask to run computeSummaryStats on exposure",
172 deprecated=("This subtask has been moved to CalibrateTask "
173 "with DM-30701.")
174 )
175 useSimplePsf = pexConfig.Field(
176 dtype=bool,
177 default=True,
178 doc="Replace the existing PSF model with a simplified version that has the same sigma "
179 "at the start of each PSF determination iteration? Doing so makes PSF determination "
180 "converge more robustly and quickly.",
181 )
182 installSimplePsf = pexConfig.ConfigurableField(
183 target=InstallGaussianPsfTask,
184 doc="Install a simple PSF model",
185 )
186 refObjLoader = pexConfig.ConfigurableField(
187 target=LoadIndexedReferenceObjectsTask,
188 doc="reference object loader",
189 )
190 ref_match = pexConfig.ConfigurableField(
191 target=RefMatchTask,
192 doc="Task to load and match reference objects. Only used if measurePsf can use matches. "
193 "Warning: matching will only work well if the initial WCS is accurate enough "
194 "to give good matches (roughly: good to 3 arcsec across the CCD).",
195 )
196 measurePsf = pexConfig.ConfigurableField(
197 target=MeasurePsfTask,
198 doc="Measure PSF",
199 )
200 repair = pexConfig.ConfigurableField(
201 target=RepairTask,
202 doc="Remove cosmic rays",
203 )
204 requireCrForPsf = pexConfig.Field(
205 dtype=bool,
206 default=True,
207 doc="Require cosmic ray detection and masking to run successfully before measuring the PSF."
208 )
209 checkUnitsParseStrict = pexConfig.Field(
210 doc="Strictness of Astropy unit compatibility check, can be 'raise', 'warn' or 'silent'",
211 dtype=str,
212 default="raise",
213 )
214
215 def setDefaults(self):
216 super().setDefaults()
217 # just detect bright stars; includeThresholdMultipler=10 seems large,
218 # but these are the values we have been using
219 self.detectiondetection.thresholdValue = 5.0
220 self.detectiondetection.includeThresholdMultiplier = 10.0
221 self.detectiondetection.doTempLocalBackground = False
222 # do not deblend, as it makes a mess
223 self.doDeblenddoDeblend = False
224 # measure and apply aperture correction; note: measuring and applying aperture
225 # correction are disabled until the final measurement, after PSF is measured
226 self.doApCorrdoApCorr = True
227 # minimal set of measurements needed to determine PSF
228 self.measurementmeasurement.plugins.names = [
229 "base_PixelFlags",
230 "base_SdssCentroid",
231 "base_SdssShape",
232 "base_GaussianFlux",
233 "base_PsfFlux",
234 "base_CircularApertureFlux",
235 ]
236
237 def validate(self):
238 if self.doApCorrdoApCorr and not self.measurePsfmeasurePsf:
239 raise RuntimeError("Must measure PSF to measure aperture correction, "
240 "because flags determined by PSF measurement are used to identify "
241 "sources used to measure aperture correction")
242
243
249
250
251class CharacterizeImageTask(pipeBase.PipelineTask, pipeBase.CmdLineTask):
252 r"""!
253 Measure bright sources and use this to estimate background and PSF of an exposure
254
255 @anchor CharacterizeImageTask_
256
257 @section pipe_tasks_characterizeImage_Contents Contents
258
259 - @ref pipe_tasks_characterizeImage_Purpose
260 - @ref pipe_tasks_characterizeImage_Initialize
261 - @ref pipe_tasks_characterizeImage_IO
262 - @ref pipe_tasks_characterizeImage_Config
263 - @ref pipe_tasks_characterizeImage_Debug
264
265 @section pipe_tasks_characterizeImage_Purpose Description
266
267 Given an exposure with defects repaired (masked and interpolated over, e.g. as output by IsrTask):
268 - detect and measure bright sources
269 - repair cosmic rays
270 - measure and subtract background
271 - measure PSF
272
273 @section pipe_tasks_characterizeImage_Initialize Task initialisation
274
275 @copydoc \_\_init\_\_
276
277 @section pipe_tasks_characterizeImage_IO Invoking the Task
278
279 If you want this task to unpersist inputs or persist outputs, then call
280 the `runDataRef` method (a thin wrapper around the `run` method).
281
282 If you already have the inputs unpersisted and do not want to persist the output
283 then it is more direct to call the `run` method:
284
285 @section pipe_tasks_characterizeImage_Config Configuration parameters
286
287 See @ref CharacterizeImageConfig
288
289 @section pipe_tasks_characterizeImage_Debug Debug variables
290
291 The command line task interface supports a flag
292 `--debug` to import `debug.py` from your `$PYTHONPATH`; see
293 <a href="https://pipelines.lsst.io/modules/lsstDebug/">the lsstDebug documentation</a>
294 for more about `debug.py`.
295
296 CharacterizeImageTask has a debug dictionary with the following keys:
297 <dl>
298 <dt>frame
299 <dd>int: if specified, the frame of first debug image displayed (defaults to 1)
300 <dt>repair_iter
301 <dd>bool; if True display image after each repair in the measure PSF loop
302 <dt>background_iter
303 <dd>bool; if True display image after each background subtraction in the measure PSF loop
304 <dt>measure_iter
305 <dd>bool; if True display image and sources at the end of each iteration of the measure PSF loop
306 See @ref lsst.meas.astrom.displayAstrometry for the meaning of the various symbols.
307 <dt>psf
308 <dd>bool; if True display image and sources after PSF is measured;
309 this will be identical to the final image displayed by measure_iter if measure_iter is true
310 <dt>repair
311 <dd>bool; if True display image and sources after final repair
312 <dt>measure
313 <dd>bool; if True display image and sources after final measurement
314 </dl>
315
316 For example, put something like:
317 @code{.py}
318 import lsstDebug
319 def DebugInfo(name):
320 di = lsstDebug.getInfo(name) # N.b. lsstDebug.Info(name) would call us recursively
321 if name == "lsst.pipe.tasks.characterizeImage":
322 di.display = dict(
323 repair = True,
324 )
325
326 return di
327
328 lsstDebug.Info = DebugInfo
329 @endcode
330 into your `debug.py` file and run `calibrateTask.py` with the `--debug` flag.
331
332 Some subtasks may have their own debug variables; see individual Task documentation.
333 """
334
335 # Example description used to live here, removed 2-20-2017 by MSSG
336
337 ConfigClass = CharacterizeImageConfig
338 _DefaultName = "characterizeImage"
339 RunnerClass = pipeBase.ButlerInitializedTaskRunner
340
341 def runQuantum(self, butlerQC, inputRefs, outputRefs):
342 inputs = butlerQC.get(inputRefs)
343 if 'exposureIdInfo' not in inputs.keys():
344 inputs['exposureIdInfo'] = ExposureIdInfo.fromDataId(butlerQC.quantum.dataId, "visit_detector")
345 outputs = self.runrun(**inputs)
346 butlerQC.put(outputs, outputRefs)
347
348 def __init__(self, butler=None, refObjLoader=None, schema=None, **kwargs):
349 """!Construct a CharacterizeImageTask
350
351 @param[in] butler A butler object is passed to the refObjLoader constructor in case
352 it is needed to load catalogs. May be None if a catalog-based star selector is
353 not used, if the reference object loader constructor does not require a butler,
354 or if a reference object loader is passed directly via the refObjLoader argument.
355 @param[in] refObjLoader An instance of LoadReferenceObjectsTasks that supplies an
356 external reference catalog to a catalog-based star selector. May be None if a
357 catalog star selector is not used or the loader can be constructed from the
358 butler argument.
359 @param[in,out] schema initial schema (an lsst.afw.table.SourceTable), or None
360 @param[in,out] kwargs other keyword arguments for lsst.pipe.base.CmdLineTask
361 """
362 super().__init__(**kwargs)
363
364 if schema is None:
365 schema = SourceTable.makeMinimalSchema()
366 self.schemaschema = schema
367 self.makeSubtask("background")
368 self.makeSubtask("installSimplePsf")
369 self.makeSubtask("repair")
370 self.makeSubtask("measurePsf", schema=self.schemaschema)
371 if self.config.doMeasurePsf and self.measurePsf.usesMatches:
372 if not refObjLoader:
373 self.makeSubtask('refObjLoader', butler=butler)
374 refObjLoader = self.refObjLoader
375 self.makeSubtask("ref_match", refObjLoader=refObjLoader)
376 self.algMetadataalgMetadata = dafBase.PropertyList()
377 self.makeSubtask('detection', schema=self.schemaschema)
378 if self.config.doDeblend:
379 self.makeSubtask("deblend", schema=self.schemaschema)
380 self.makeSubtask('measurement', schema=self.schemaschema, algMetadata=self.algMetadataalgMetadata)
381 if self.config.doApCorr:
382 self.makeSubtask('measureApCorr', schema=self.schemaschema)
383 self.makeSubtask('applyApCorr', schema=self.schemaschema)
384 self.makeSubtask('catalogCalculation', schema=self.schemaschema)
385 self._initialFrame_initialFrame = getDebugFrame(self._display, "frame") or 1
386 self._frame_frame = self._initialFrame_initialFrame
387 self.schemaschema.checkUnits(parse_strict=self.config.checkUnitsParseStrict)
388 self.outputSchemaoutputSchema = afwTable.SourceCatalog(self.schemaschema)
389
391 outputCatSchema = afwTable.SourceCatalog(self.schemaschema)
392 outputCatSchema.getTable().setMetadata(self.algMetadataalgMetadata)
393 return {'outputSchema': outputCatSchema}
394
395 @timeMethod
396 def runDataRef(self, dataRef, exposure=None, background=None, doUnpersist=True):
397 """!Characterize a science image and, if wanted, persist the results
398
399 This simply unpacks the exposure and passes it to the characterize method to do the work.
400
401 @param[in] dataRef: butler data reference for science exposure
402 @param[in,out] exposure exposure to characterize (an lsst.afw.image.ExposureF or similar).
403 If None then unpersist from "postISRCCD".
404 The following changes are made, depending on the config:
405 - set psf to the measured PSF
406 - set apCorrMap to the measured aperture correction
407 - subtract background
408 - interpolate over cosmic rays
409 - update detection and cosmic ray mask planes
410 @param[in,out] background initial model of background already subtracted from exposure
411 (an lsst.afw.math.BackgroundList). May be None if no background has been subtracted,
412 which is typical for image characterization.
413 A refined background model is output.
414 @param[in] doUnpersist if True the exposure is read from the repository
415 and the exposure and background arguments must be None;
416 if False the exposure must be provided.
417 True is intended for running as a command-line task, False for running as a subtask
418
419 @return same data as the characterize method
420 """
421 self._frame_frame = self._initialFrame_initialFrame # reset debug display frame
422 self.log.info("Processing %s", dataRef.dataId)
423
424 if doUnpersist:
425 if exposure is not None or background is not None:
426 raise RuntimeError("doUnpersist true; exposure and background must be None")
427 exposure = dataRef.get("postISRCCD", immediate=True)
428 elif exposure is None:
429 raise RuntimeError("doUnpersist false; exposure must be provided")
430
431 exposureIdInfo = dataRef.get("expIdInfo")
432
433 charRes = self.runrun(
434 exposure=exposure,
435 exposureIdInfo=exposureIdInfo,
436 background=background,
437 )
438
439 if self.config.doWrite:
440 dataRef.put(charRes.sourceCat, "icSrc")
441 if self.config.doWriteExposure:
442 dataRef.put(charRes.exposure, "icExp")
443 dataRef.put(charRes.background, "icExpBackground")
444
445 return charRes
446
447 @timeMethod
448 def run(self, exposure, exposureIdInfo=None, background=None):
449 """!Characterize a science image
450
451 Peforms the following operations:
452 - Iterate the following config.psfIterations times, or once if config.doMeasurePsf false:
453 - detect and measure sources and estimate PSF (see detectMeasureAndEstimatePsf for details)
454 - interpolate over cosmic rays
455 - perform final measurement
456
457 @param[in,out] exposure exposure to characterize (an lsst.afw.image.ExposureF or similar).
458 The following changes are made:
459 - update or set psf
460 - set apCorrMap
461 - update detection and cosmic ray mask planes
462 - subtract background and interpolate over cosmic rays
463 @param[in] exposureIdInfo ID info for exposure (an lsst.obs.base.ExposureIdInfo).
464 If not provided, returned SourceCatalog IDs will not be globally unique.
465 @param[in,out] background initial model of background already subtracted from exposure
466 (an lsst.afw.math.BackgroundList). May be None if no background has been subtracted,
467 which is typical for image characterization.
468
469 @return pipe_base Struct containing these fields, all from the final iteration
470 of detectMeasureAndEstimatePsf:
471 - exposure: characterized exposure; image is repaired by interpolating over cosmic rays,
472 mask is updated accordingly, and the PSF model is set
473 - sourceCat: detected sources (an lsst.afw.table.SourceCatalog)
474 - background: model of background subtracted from exposure (an lsst.afw.math.BackgroundList)
475 - psfCellSet: spatial cells of PSF candidates (an lsst.afw.math.SpatialCellSet)
476 """
477 self._frame_frame = self._initialFrame_initialFrame # reset debug display frame
478
479 if not self.config.doMeasurePsf and not exposure.hasPsf():
480 self.log.info("CharacterizeImageTask initialized with 'simple' PSF.")
481 self.installSimplePsf.run(exposure=exposure)
482
483 if exposureIdInfo is None:
484 exposureIdInfo = ExposureIdInfo()
485
486 # subtract an initial estimate of background level
487 background = self.background.run(exposure).background
488
489 psfIterations = self.config.psfIterations if self.config.doMeasurePsf else 1
490 for i in range(psfIterations):
491 dmeRes = self.detectMeasureAndEstimatePsfdetectMeasureAndEstimatePsf(
492 exposure=exposure,
493 exposureIdInfo=exposureIdInfo,
494 background=background,
495 )
496
497 psf = dmeRes.exposure.getPsf()
498 # Just need a rough estimate; average positions are fine
499 psfAvgPos = psf.getAveragePosition()
500 psfSigma = psf.computeShape(psfAvgPos).getDeterminantRadius()
501 psfDimensions = psf.computeImage(psfAvgPos).getDimensions()
502 medBackground = np.median(dmeRes.background.getImage().getArray())
503 self.log.info("iter %s; PSF sigma=%0.2f, dimensions=%s; median background=%0.2f",
504 i + 1, psfSigma, psfDimensions, medBackground)
505
506 self.displaydisplay("psf", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat)
507
508 # perform final repair with final PSF
509 self.repair.run(exposure=dmeRes.exposure)
510 self.displaydisplay("repair", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat)
511
512 # perform final measurement with final PSF, including measuring and applying aperture correction,
513 # if wanted
514 self.measurement.run(measCat=dmeRes.sourceCat, exposure=dmeRes.exposure,
515 exposureId=exposureIdInfo.expId)
516 if self.config.doApCorr:
517 apCorrMap = self.measureApCorr.run(exposure=dmeRes.exposure, catalog=dmeRes.sourceCat).apCorrMap
518 dmeRes.exposure.getInfo().setApCorrMap(apCorrMap)
519 self.applyApCorr.run(catalog=dmeRes.sourceCat, apCorrMap=exposure.getInfo().getApCorrMap())
520 self.catalogCalculation.run(dmeRes.sourceCat)
521
522 self.displaydisplay("measure", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat)
523
524 return pipeBase.Struct(
525 exposure=dmeRes.exposure,
526 sourceCat=dmeRes.sourceCat,
527 background=dmeRes.background,
528 psfCellSet=dmeRes.psfCellSet,
529
530 characterized=dmeRes.exposure,
531 backgroundModel=dmeRes.background
532 )
533
534 @timeMethod
535 def detectMeasureAndEstimatePsf(self, exposure, exposureIdInfo, background):
536 """!Perform one iteration of detect, measure and estimate PSF
537
538 Performs the following operations:
539 - if config.doMeasurePsf or not exposure.hasPsf():
540 - install a simple PSF model (replacing the existing one, if need be)
541 - interpolate over cosmic rays with keepCRs=True
542 - estimate background and subtract it from the exposure
543 - detect, deblend and measure sources, and subtract a refined background model;
544 - if config.doMeasurePsf:
545 - measure PSF
546
547 @param[in,out] exposure exposure to characterize (an lsst.afw.image.ExposureF or similar)
548 The following changes are made:
549 - update or set psf
550 - update detection and cosmic ray mask planes
551 - subtract background
552 @param[in] exposureIdInfo ID info for exposure (an lsst.obs_base.ExposureIdInfo)
553 @param[in,out] background initial model of background already subtracted from exposure
554 (an lsst.afw.math.BackgroundList).
555
556 @return pipe_base Struct containing these fields, all from the final iteration
557 of detect sources, measure sources and estimate PSF:
558 - exposure characterized exposure; image is repaired by interpolating over cosmic rays,
559 mask is updated accordingly, and the PSF model is set
560 - sourceCat detected sources (an lsst.afw.table.SourceCatalog)
561 - background model of background subtracted from exposure (an lsst.afw.math.BackgroundList)
562 - psfCellSet spatial cells of PSF candidates (an lsst.afw.math.SpatialCellSet)
563 """
564 # install a simple PSF model, if needed or wanted
565 if not exposure.hasPsf() or (self.config.doMeasurePsf and self.config.useSimplePsf):
566 self.log.info("PSF estimation initialized with 'simple' PSF")
567 self.installSimplePsf.run(exposure=exposure)
568
569 # run repair, but do not interpolate over cosmic rays (do that elsewhere, with the final PSF model)
570 if self.config.requireCrForPsf:
571 self.repair.run(exposure=exposure, keepCRs=True)
572 else:
573 try:
574 self.repair.run(exposure=exposure, keepCRs=True)
575 except LengthError:
576 self.log.warning("Skipping cosmic ray detection: Too many CR pixels (max %0.f)",
577 self.config.repair.cosmicray.nCrPixelMax)
578
579 self.displaydisplay("repair_iter", exposure=exposure)
580
581 if background is None:
582 background = BackgroundList()
583
584 sourceIdFactory = exposureIdInfo.makeSourceIdFactory()
585 table = SourceTable.make(self.schemaschema, sourceIdFactory)
586 table.setMetadata(self.algMetadataalgMetadata)
587
588 detRes = self.detection.run(table=table, exposure=exposure, doSmooth=True)
589 sourceCat = detRes.sources
590 if detRes.fpSets.background:
591 for bg in detRes.fpSets.background:
592 background.append(bg)
593
594 if self.config.doDeblend:
595 self.deblend.run(exposure=exposure, sources=sourceCat)
596
597 self.measurement.run(measCat=sourceCat, exposure=exposure, exposureId=exposureIdInfo.expId)
598
599 measPsfRes = pipeBase.Struct(cellSet=None)
600 if self.config.doMeasurePsf:
601 if self.measurePsf.usesMatches:
602 matches = self.ref_match.loadAndMatch(exposure=exposure, sourceCat=sourceCat).matches
603 else:
604 matches = None
605 measPsfRes = self.measurePsf.run(exposure=exposure, sources=sourceCat, matches=matches,
606 expId=exposureIdInfo.expId)
607 self.displaydisplay("measure_iter", exposure=exposure, sourceCat=sourceCat)
608
609 return pipeBase.Struct(
610 exposure=exposure,
611 sourceCat=sourceCat,
612 background=background,
613 psfCellSet=measPsfRes.cellSet,
614 )
615
617 """Return a dict of empty catalogs for each catalog dataset produced by this task.
618 """
619 sourceCat = SourceCatalog(self.schemaschema)
620 sourceCat.getTable().setMetadata(self.algMetadataalgMetadata)
621 return {"icSrc": sourceCat}
622
623 def display(self, itemName, exposure, sourceCat=None):
624 """Display exposure and sources on next frame, if display of itemName has been requested
625
626 @param[in] itemName name of item in debugInfo
627 @param[in] exposure exposure to display
628 @param[in] sourceCat source catalog to display
629 """
630 val = getDebugFrame(self._display, itemName)
631 if not val:
632 return
633
634 displayAstrometry(exposure=exposure, sourceCat=sourceCat, frame=self._frame_frame, pause=False)
635 self._frame_frame += 1
def adjustQuantum(self, inputs, outputs, label, dataId)
Measure bright sources and use this to estimate background and PSF of an exposure.
def __init__(self, butler=None, refObjLoader=None, schema=None, **kwargs)
Construct a CharacterizeImageTask.
def runQuantum(self, butlerQC, inputRefs, outputRefs)
def runDataRef(self, dataRef, exposure=None, background=None, doUnpersist=True)
Characterize a science image and, if wanted, persist the results.
def run(self, exposure, exposureIdInfo=None, background=None)
Characterize a science image.
def detectMeasureAndEstimatePsf(self, exposure, exposureIdInfo, background)
Perform one iteration of detect, measure and estimate PSF.
def display(self, itemName, exposure, sourceCat=None)