Coverage for python/lsst/pipe/tasks/characterizeImage.py : 27%

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1#
2# LSST Data Management System
3# Copyright 2008-2015 AURA/LSST.
4#
5# This product includes software developed by the
6# LSST Project (http://www.lsst.org/).
7#
8# This program is free software: you can redistribute it and/or modify
9# it under the terms of the GNU General Public License as published by
10# the Free Software Foundation, either version 3 of the License, or
11# (at your option) any later version.
12#
13# This program is distributed in the hope that it will be useful,
14# but WITHOUT ANY WARRANTY; without even the implied warranty of
15# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
16# GNU General Public License for more details.
17#
18# You should have received a copy of the LSST License Statement and
19# the GNU General Public License along with this program. If not,
20# see <https://www.lsstcorp.org/LegalNotices/>.
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, IdFactory
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
42__all__ = ["CharacterizeImageConfig", "CharacterizeImageTask"]
45class CharacterizeImageConnections(pipeBase.PipelineTaskConnections,
46 dimensions=("instrument", "visit", "detector")):
47 exposure = cT.Input(
48 doc="Input exposure data",
49 name="postISRCCD",
50 storageClass="ExposureF",
51 dimensions=["instrument", "exposure", "detector"],
52 )
53 characterized = cT.Output(
54 doc="Output characterized data.",
55 name="icExp",
56 storageClass="ExposureF",
57 dimensions=["instrument", "visit", "detector"],
58 )
59 sourceCat = cT.Output(
60 doc="Output source catalog.",
61 name="icSrc",
62 storageClass="SourceCatalog",
63 dimensions=["instrument", "visit", "detector"],
64 )
65 backgroundModel = cT.Output(
66 doc="Output background model.",
67 name="icExpBackground",
68 storageClass="Background",
69 dimensions=["instrument", "visit", "detector"],
70 )
71 outputSchema = cT.InitOutput(
72 doc="Schema of the catalog produced by CharacterizeImage",
73 name="icSrc_schema",
74 storageClass="SourceCatalog",
75 )
77 def adjustQuantum(self, datasetRefMap: pipeBase.InputQuantizedConnection):
78 # Docstring inherited from PipelineTaskConnections
79 try:
80 return super().adjustQuantum(datasetRefMap)
81 except pipeBase.ScalarError as err:
82 raise pipeBase.ScalarError(
83 f"CharacterizeImageTask can at present only be run on visits that are associated with "
84 f"exactly one exposure. Either this is not a valid exposure for this pipeline, or the "
85 f"snap-combination step you probably want hasn't been configured to run between ISR and "
86 f"this task (as of this writing, that would be because it hasn't been implemented yet)."
87 ) from err
90class CharacterizeImageConfig(pipeBase.PipelineTaskConfig,
91 pipelineConnections=CharacterizeImageConnections):
93 """!Config for CharacterizeImageTask"""
94 doMeasurePsf = pexConfig.Field(
95 dtype=bool,
96 default=True,
97 doc="Measure PSF? If False then for all subsequent operations use either existing PSF "
98 "model when present, or install simple PSF model when not (see installSimplePsf "
99 "config options)"
100 )
101 doWrite = pexConfig.Field(
102 dtype=bool,
103 default=True,
104 doc="Persist results?",
105 )
106 doWriteExposure = pexConfig.Field(
107 dtype=bool,
108 default=True,
109 doc="Write icExp and icExpBackground in addition to icSrc? Ignored if doWrite False.",
110 )
111 psfIterations = pexConfig.RangeField(
112 dtype=int,
113 default=2,
114 min=1,
115 doc="Number of iterations of detect sources, measure sources, "
116 "estimate PSF. If useSimplePsf is True then 2 should be plenty; "
117 "otherwise more may be wanted.",
118 )
119 background = pexConfig.ConfigurableField(
120 target=SubtractBackgroundTask,
121 doc="Configuration for initial background estimation",
122 )
123 detection = pexConfig.ConfigurableField(
124 target=SourceDetectionTask,
125 doc="Detect sources"
126 )
127 doDeblend = pexConfig.Field(
128 dtype=bool,
129 default=True,
130 doc="Run deblender input exposure"
131 )
132 deblend = pexConfig.ConfigurableField(
133 target=SourceDeblendTask,
134 doc="Split blended source into their components"
135 )
136 measurement = pexConfig.ConfigurableField(
137 target=SingleFrameMeasurementTask,
138 doc="Measure sources"
139 )
140 doApCorr = pexConfig.Field(
141 dtype=bool,
142 default=True,
143 doc="Run subtasks to measure and apply aperture corrections"
144 )
145 measureApCorr = pexConfig.ConfigurableField(
146 target=MeasureApCorrTask,
147 doc="Subtask to measure aperture corrections"
148 )
149 applyApCorr = pexConfig.ConfigurableField(
150 target=ApplyApCorrTask,
151 doc="Subtask to apply aperture corrections"
152 )
153 # If doApCorr is False, and the exposure does not have apcorrections already applied, the
154 # active plugins in catalogCalculation almost certainly should not contain the characterization plugin
155 catalogCalculation = pexConfig.ConfigurableField(
156 target=CatalogCalculationTask,
157 doc="Subtask to run catalogCalculation plugins on catalog"
158 )
159 useSimplePsf = pexConfig.Field(
160 dtype=bool,
161 default=True,
162 doc="Replace the existing PSF model with a simplified version that has the same sigma "
163 "at the start of each PSF determination iteration? Doing so makes PSF determination "
164 "converge more robustly and quickly.",
165 )
166 installSimplePsf = pexConfig.ConfigurableField(
167 target=InstallGaussianPsfTask,
168 doc="Install a simple PSF model",
169 )
170 refObjLoader = pexConfig.ConfigurableField(
171 target=LoadIndexedReferenceObjectsTask,
172 doc="reference object loader",
173 )
174 ref_match = pexConfig.ConfigurableField(
175 target=RefMatchTask,
176 doc="Task to load and match reference objects. Only used if measurePsf can use matches. "
177 "Warning: matching will only work well if the initial WCS is accurate enough "
178 "to give good matches (roughly: good to 3 arcsec across the CCD).",
179 )
180 measurePsf = pexConfig.ConfigurableField(
181 target=MeasurePsfTask,
182 doc="Measure PSF",
183 )
184 repair = pexConfig.ConfigurableField(
185 target=RepairTask,
186 doc="Remove cosmic rays",
187 )
188 checkUnitsParseStrict = pexConfig.Field(
189 doc="Strictness of Astropy unit compatibility check, can be 'raise', 'warn' or 'silent'",
190 dtype=str,
191 default="raise",
192 )
194 def setDefaults(self):
195 super().setDefaults()
196 # just detect bright stars; includeThresholdMultipler=10 seems large,
197 # but these are the values we have been using
198 self.detection.thresholdValue = 5.0
199 self.detection.includeThresholdMultiplier = 10.0
200 self.detection.doTempLocalBackground = False
201 # do not deblend, as it makes a mess
202 self.doDeblend = False
203 # measure and apply aperture correction; note: measuring and applying aperture
204 # correction are disabled until the final measurement, after PSF is measured
205 self.doApCorr = True
206 # minimal set of measurements needed to determine PSF
207 self.measurement.plugins.names = [
208 "base_PixelFlags",
209 "base_SdssCentroid",
210 "base_SdssShape",
211 "base_GaussianFlux",
212 "base_PsfFlux",
213 "base_CircularApertureFlux",
214 ]
216 def validate(self):
217 if self.doApCorr and not self.measurePsf:
218 raise RuntimeError("Must measure PSF to measure aperture correction, "
219 "because flags determined by PSF measurement are used to identify "
220 "sources used to measure aperture correction")
222## \addtogroup LSST_task_documentation
223## \{
224## \page CharacterizeImageTask
225## \ref CharacterizeImageTask_ "CharacterizeImageTask"
226## \copybrief CharacterizeImageTask
227## \}
230class CharacterizeImageTask(pipeBase.PipelineTask, pipeBase.CmdLineTask):
231 r"""!Measure bright sources and use this to estimate background and PSF of an exposure
233 @anchor CharacterizeImageTask_
235 @section pipe_tasks_characterizeImage_Contents Contents
237 - @ref pipe_tasks_characterizeImage_Purpose
238 - @ref pipe_tasks_characterizeImage_Initialize
239 - @ref pipe_tasks_characterizeImage_IO
240 - @ref pipe_tasks_characterizeImage_Config
241 - @ref pipe_tasks_characterizeImage_Debug
244 @section pipe_tasks_characterizeImage_Purpose Description
246 Given an exposure with defects repaired (masked and interpolated over, e.g. as output by IsrTask):
247 - detect and measure bright sources
248 - repair cosmic rays
249 - measure and subtract background
250 - measure PSF
252 @section pipe_tasks_characterizeImage_Initialize Task initialisation
254 @copydoc \_\_init\_\_
256 @section pipe_tasks_characterizeImage_IO Invoking the Task
258 If you want this task to unpersist inputs or persist outputs, then call
259 the `runDataRef` method (a thin wrapper around the `run` method).
261 If you already have the inputs unpersisted and do not want to persist the output
262 then it is more direct to call the `run` method:
264 @section pipe_tasks_characterizeImage_Config Configuration parameters
266 See @ref CharacterizeImageConfig
268 @section pipe_tasks_characterizeImage_Debug Debug variables
270 The @link lsst.pipe.base.cmdLineTask.CmdLineTask command line task@endlink interface supports a flag
271 `--debug` to import `debug.py` from your `$PYTHONPATH`; see @ref baseDebug for more about `debug.py`.
273 CharacterizeImageTask has a debug dictionary with the following keys:
274 <dl>
275 <dt>frame
276 <dd>int: if specified, the frame of first debug image displayed (defaults to 1)
277 <dt>repair_iter
278 <dd>bool; if True display image after each repair in the measure PSF loop
279 <dt>background_iter
280 <dd>bool; if True display image after each background subtraction in the measure PSF loop
281 <dt>measure_iter
282 <dd>bool; if True display image and sources at the end of each iteration of the measure PSF loop
283 See @ref lsst.meas.astrom.displayAstrometry for the meaning of the various symbols.
284 <dt>psf
285 <dd>bool; if True display image and sources after PSF is measured;
286 this will be identical to the final image displayed by measure_iter if measure_iter is true
287 <dt>repair
288 <dd>bool; if True display image and sources after final repair
289 <dt>measure
290 <dd>bool; if True display image and sources after final measurement
291 </dl>
293 For example, put something like:
294 @code{.py}
295 import lsstDebug
296 def DebugInfo(name):
297 di = lsstDebug.getInfo(name) # N.b. lsstDebug.Info(name) would call us recursively
298 if name == "lsst.pipe.tasks.characterizeImage":
299 di.display = dict(
300 repair = True,
301 )
303 return di
305 lsstDebug.Info = DebugInfo
306 @endcode
307 into your `debug.py` file and run `calibrateTask.py` with the `--debug` flag.
309 Some subtasks may have their own debug variables; see individual Task documentation.
310 """
312 # Example description used to live here, removed 2-20-2017 by MSSG
314 ConfigClass = CharacterizeImageConfig
315 _DefaultName = "characterizeImage"
316 RunnerClass = pipeBase.ButlerInitializedTaskRunner
318 def runQuantum(self, butlerQC, inputRefs, outputRefs):
319 inputs = butlerQC.get(inputRefs)
320 if 'exposureIdInfo' not in inputs.keys():
321 exposureIdInfo = ExposureIdInfo()
322 exposureIdInfo.expId, exposureIdInfo.expBits = butlerQC.quantum.dataId.pack("visit_detector",
323 returnMaxBits=True)
324 inputs['exposureIdInfo'] = exposureIdInfo
325 outputs = self.run(**inputs)
326 butlerQC.put(outputs, outputRefs)
328 def __init__(self, butler=None, refObjLoader=None, schema=None, **kwargs):
329 """!Construct a CharacterizeImageTask
331 @param[in] butler A butler object is passed to the refObjLoader constructor in case
332 it is needed to load catalogs. May be None if a catalog-based star selector is
333 not used, if the reference object loader constructor does not require a butler,
334 or if a reference object loader is passed directly via the refObjLoader argument.
335 @param[in] refObjLoader An instance of LoadReferenceObjectsTasks that supplies an
336 external reference catalog to a catalog-based star selector. May be None if a
337 catalog star selector is not used or the loader can be constructed from the
338 butler argument.
339 @param[in,out] schema initial schema (an lsst.afw.table.SourceTable), or None
340 @param[in,out] kwargs other keyword arguments for lsst.pipe.base.CmdLineTask
341 """
342 super().__init__(**kwargs)
344 if schema is None:
345 schema = SourceTable.makeMinimalSchema()
346 self.schema = schema
347 self.makeSubtask("background")
348 self.makeSubtask("installSimplePsf")
349 self.makeSubtask("repair")
350 self.makeSubtask("measurePsf", schema=self.schema)
351 if self.config.doMeasurePsf and self.measurePsf.usesMatches:
352 if not refObjLoader:
353 self.makeSubtask('refObjLoader', butler=butler)
354 refObjLoader = self.refObjLoader
355 self.makeSubtask("ref_match", refObjLoader=refObjLoader)
356 self.algMetadata = dafBase.PropertyList()
357 self.makeSubtask('detection', schema=self.schema)
358 if self.config.doDeblend:
359 self.makeSubtask("deblend", schema=self.schema)
360 self.makeSubtask('measurement', schema=self.schema, algMetadata=self.algMetadata)
361 if self.config.doApCorr:
362 self.makeSubtask('measureApCorr', schema=self.schema)
363 self.makeSubtask('applyApCorr', schema=self.schema)
364 self.makeSubtask('catalogCalculation', schema=self.schema)
365 self._initialFrame = getDebugFrame(self._display, "frame") or 1
366 self._frame = self._initialFrame
367 self.schema.checkUnits(parse_strict=self.config.checkUnitsParseStrict)
368 self.outputSchema = afwTable.SourceCatalog(self.schema)
370 def getInitOutputDatasets(self):
371 outputCatSchema = afwTable.SourceCatalog(self.schema)
372 outputCatSchema.getTable().setMetadata(self.algMetadata)
373 return {'outputSchema': outputCatSchema}
375 @pipeBase.timeMethod
376 def runDataRef(self, dataRef, exposure=None, background=None, doUnpersist=True):
377 """!Characterize a science image and, if wanted, persist the results
379 This simply unpacks the exposure and passes it to the characterize method to do the work.
381 @param[in] dataRef: butler data reference for science exposure
382 @param[in,out] exposure exposure to characterize (an lsst.afw.image.ExposureF or similar).
383 If None then unpersist from "postISRCCD".
384 The following changes are made, depending on the config:
385 - set psf to the measured PSF
386 - set apCorrMap to the measured aperture correction
387 - subtract background
388 - interpolate over cosmic rays
389 - update detection and cosmic ray mask planes
390 @param[in,out] background initial model of background already subtracted from exposure
391 (an lsst.afw.math.BackgroundList). May be None if no background has been subtracted,
392 which is typical for image characterization.
393 A refined background model is output.
394 @param[in] doUnpersist if True the exposure is read from the repository
395 and the exposure and background arguments must be None;
396 if False the exposure must be provided.
397 True is intended for running as a command-line task, False for running as a subtask
399 @return same data as the characterize method
400 """
401 self._frame = self._initialFrame # reset debug display frame
402 self.log.info("Processing %s" % (dataRef.dataId))
404 if doUnpersist:
405 if exposure is not None or background is not None:
406 raise RuntimeError("doUnpersist true; exposure and background must be None")
407 exposure = dataRef.get("postISRCCD", immediate=True)
408 elif exposure is None:
409 raise RuntimeError("doUnpersist false; exposure must be provided")
411 exposureIdInfo = dataRef.get("expIdInfo")
413 charRes = self.run(
414 exposure=exposure,
415 exposureIdInfo=exposureIdInfo,
416 background=background,
417 )
419 if self.config.doWrite:
420 dataRef.put(charRes.sourceCat, "icSrc")
421 if self.config.doWriteExposure:
422 dataRef.put(charRes.exposure, "icExp")
423 dataRef.put(charRes.background, "icExpBackground")
425 return charRes
427 @pipeBase.timeMethod
428 def run(self, exposure, exposureIdInfo=None, background=None):
429 """!Characterize a science image
431 Peforms the following operations:
432 - Iterate the following config.psfIterations times, or once if config.doMeasurePsf false:
433 - detect and measure sources and estimate PSF (see detectMeasureAndEstimatePsf for details)
434 - interpolate over cosmic rays
435 - perform final measurement
437 @param[in,out] exposure exposure to characterize (an lsst.afw.image.ExposureF or similar).
438 The following changes are made:
439 - update or set psf
440 - set apCorrMap
441 - update detection and cosmic ray mask planes
442 - subtract background and interpolate over cosmic rays
443 @param[in] exposureIdInfo ID info for exposure (an lsst.obs.base.ExposureIdInfo).
444 If not provided, returned SourceCatalog IDs will not be globally unique.
445 @param[in,out] background initial model of background already subtracted from exposure
446 (an lsst.afw.math.BackgroundList). May be None if no background has been subtracted,
447 which is typical for image characterization.
449 @return pipe_base Struct containing these fields, all from the final iteration
450 of detectMeasureAndEstimatePsf:
451 - exposure: characterized exposure; image is repaired by interpolating over cosmic rays,
452 mask is updated accordingly, and the PSF model is set
453 - sourceCat: detected sources (an lsst.afw.table.SourceCatalog)
454 - background: model of background subtracted from exposure (an lsst.afw.math.BackgroundList)
455 - psfCellSet: spatial cells of PSF candidates (an lsst.afw.math.SpatialCellSet)
456 """
457 self._frame = self._initialFrame # reset debug display frame
459 if not self.config.doMeasurePsf and not exposure.hasPsf():
460 self.log.warn("Source catalog detected and measured with placeholder or default PSF")
461 self.installSimplePsf.run(exposure=exposure)
463 if exposureIdInfo is None:
464 exposureIdInfo = ExposureIdInfo()
466 # subtract an initial estimate of background level
467 background = self.background.run(exposure).background
469 psfIterations = self.config.psfIterations if self.config.doMeasurePsf else 1
470 for i in range(psfIterations):
471 dmeRes = self.detectMeasureAndEstimatePsf(
472 exposure=exposure,
473 exposureIdInfo=exposureIdInfo,
474 background=background,
475 )
477 psf = dmeRes.exposure.getPsf()
478 psfSigma = psf.computeShape().getDeterminantRadius()
479 psfDimensions = psf.computeImage().getDimensions()
480 medBackground = np.median(dmeRes.background.getImage().getArray())
481 self.log.info("iter %s; PSF sigma=%0.2f, dimensions=%s; median background=%0.2f" %
482 (i + 1, psfSigma, psfDimensions, medBackground))
484 self.display("psf", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat)
486 # perform final repair with final PSF
487 self.repair.run(exposure=dmeRes.exposure)
488 self.display("repair", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat)
490 # perform final measurement with final PSF, including measuring and applying aperture correction,
491 # if wanted
492 self.measurement.run(measCat=dmeRes.sourceCat, exposure=dmeRes.exposure,
493 exposureId=exposureIdInfo.expId)
494 if self.config.doApCorr:
495 apCorrMap = self.measureApCorr.run(exposure=dmeRes.exposure, catalog=dmeRes.sourceCat).apCorrMap
496 dmeRes.exposure.getInfo().setApCorrMap(apCorrMap)
497 self.applyApCorr.run(catalog=dmeRes.sourceCat, apCorrMap=exposure.getInfo().getApCorrMap())
498 self.catalogCalculation.run(dmeRes.sourceCat)
500 self.display("measure", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat)
502 return pipeBase.Struct(
503 exposure=dmeRes.exposure,
504 sourceCat=dmeRes.sourceCat,
505 background=dmeRes.background,
506 psfCellSet=dmeRes.psfCellSet,
508 characterized=dmeRes.exposure,
509 backgroundModel=dmeRes.background
510 )
512 @pipeBase.timeMethod
513 def detectMeasureAndEstimatePsf(self, exposure, exposureIdInfo, background):
514 """!Perform one iteration of detect, measure and estimate PSF
516 Performs the following operations:
517 - if config.doMeasurePsf or not exposure.hasPsf():
518 - install a simple PSF model (replacing the existing one, if need be)
519 - interpolate over cosmic rays with keepCRs=True
520 - estimate background and subtract it from the exposure
521 - detect, deblend and measure sources, and subtract a refined background model;
522 - if config.doMeasurePsf:
523 - measure PSF
525 @param[in,out] exposure exposure to characterize (an lsst.afw.image.ExposureF or similar)
526 The following changes are made:
527 - update or set psf
528 - update detection and cosmic ray mask planes
529 - subtract background
530 @param[in] exposureIdInfo ID info for exposure (an lsst.obs_base.ExposureIdInfo)
531 @param[in,out] background initial model of background already subtracted from exposure
532 (an lsst.afw.math.BackgroundList).
534 @return pipe_base Struct containing these fields, all from the final iteration
535 of detect sources, measure sources and estimate PSF:
536 - exposure characterized exposure; image is repaired by interpolating over cosmic rays,
537 mask is updated accordingly, and the PSF model is set
538 - sourceCat detected sources (an lsst.afw.table.SourceCatalog)
539 - background model of background subtracted from exposure (an lsst.afw.math.BackgroundList)
540 - psfCellSet spatial cells of PSF candidates (an lsst.afw.math.SpatialCellSet)
541 """
542 # install a simple PSF model, if needed or wanted
543 if not exposure.hasPsf() or (self.config.doMeasurePsf and self.config.useSimplePsf):
544 self.log.warn("Source catalog detected and measured with placeholder or default PSF")
545 self.installSimplePsf.run(exposure=exposure)
547 # run repair, but do not interpolate over cosmic rays (do that elsewhere, with the final PSF model)
548 self.repair.run(exposure=exposure, keepCRs=True)
549 self.display("repair_iter", exposure=exposure)
551 if background is None:
552 background = BackgroundList()
554 sourceIdFactory = IdFactory.makeSource(exposureIdInfo.expId, exposureIdInfo.unusedBits)
555 table = SourceTable.make(self.schema, sourceIdFactory)
556 table.setMetadata(self.algMetadata)
558 detRes = self.detection.run(table=table, exposure=exposure, doSmooth=True)
559 sourceCat = detRes.sources
560 if detRes.fpSets.background:
561 for bg in detRes.fpSets.background:
562 background.append(bg)
564 if self.config.doDeblend:
565 self.deblend.run(exposure=exposure, sources=sourceCat)
567 self.measurement.run(measCat=sourceCat, exposure=exposure, exposureId=exposureIdInfo.expId)
569 measPsfRes = pipeBase.Struct(cellSet=None)
570 if self.config.doMeasurePsf:
571 if self.measurePsf.usesMatches:
572 matches = self.ref_match.loadAndMatch(exposure=exposure, sourceCat=sourceCat).matches
573 else:
574 matches = None
575 measPsfRes = self.measurePsf.run(exposure=exposure, sources=sourceCat, matches=matches,
576 expId=exposureIdInfo.expId)
577 self.display("measure_iter", exposure=exposure, sourceCat=sourceCat)
579 return pipeBase.Struct(
580 exposure=exposure,
581 sourceCat=sourceCat,
582 background=background,
583 psfCellSet=measPsfRes.cellSet,
584 )
586 def getSchemaCatalogs(self):
587 """Return a dict of empty catalogs for each catalog dataset produced by this task.
588 """
589 sourceCat = SourceCatalog(self.schema)
590 sourceCat.getTable().setMetadata(self.algMetadata)
591 return {"icSrc": sourceCat}
593 def display(self, itemName, exposure, sourceCat=None):
594 """Display exposure and sources on next frame, if display of itemName has been requested
596 @param[in] itemName name of item in debugInfo
597 @param[in] exposure exposure to display
598 @param[in] sourceCat source catalog to display
599 """
600 val = getDebugFrame(self._display, itemName)
601 if not val:
602 return
604 displayAstrometry(exposure=exposure, sourceCat=sourceCat, frame=self._frame, pause=False)
605 self._frame += 1