lsst.pipe.tasks g4a6547c0d5+de68eba77a
Loading...
Searching...
No Matches
characterizeImage.py
Go to the documentation of this file.
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
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
43
44__all__ = ["CharacterizeImageConfig", "CharacterizeImageTask"]
45
46
47class CharacterizeImageConnections(pipeBase.PipelineTaskConnections,
48 dimensions=("instrument", "visit", "detector")):
49 exposure = cT.Input(
50 doc="Input exposure data",
51 name="postISRCCD",
52 storageClass="Exposure",
53 dimensions=["instrument", "exposure", "detector"],
54 )
55 characterized = cT.Output(
56 doc="Output characterized data.",
57 name="icExp",
58 storageClass="ExposureF",
59 dimensions=["instrument", "visit", "detector"],
60 )
61 sourceCat = cT.Output(
62 doc="Output source catalog.",
63 name="icSrc",
64 storageClass="SourceCatalog",
65 dimensions=["instrument", "visit", "detector"],
66 )
67 backgroundModel = cT.Output(
68 doc="Output background model.",
69 name="icExpBackground",
70 storageClass="Background",
71 dimensions=["instrument", "visit", "detector"],
72 )
73 outputSchema = cT.InitOutput(
74 doc="Schema of the catalog produced by CharacterizeImage",
75 name="icSrc_schema",
76 storageClass="SourceCatalog",
77 )
78
79 def adjustQuantum(self, inputs, outputs, label, dataId):
80 # Docstring inherited from PipelineTaskConnections
81 try:
82 return super().adjustQuantum(inputs, outputs, label, dataId)
83 except pipeBase.ScalarError as err:
84 raise pipeBase.ScalarError(
85 "CharacterizeImageTask can at present only be run on visits that are associated with "
86 "exactly one exposure. Either this is not a valid exposure for this pipeline, or the "
87 "snap-combination step you probably want hasn't been configured to run between ISR and "
88 "this task (as of this writing, that would be because it hasn't been implemented yet)."
89 ) from err
90
91
92class CharacterizeImageConfig(pipeBase.PipelineTaskConfig,
93 pipelineConnections=CharacterizeImageConnections):
94
95 """!Config for CharacterizeImageTask"""
96 doMeasurePsf = pexConfig.Field(
97 dtype=bool,
98 default=True,
99 doc="Measure PSF? If False then for all subsequent operations use either existing PSF "
100 "model when present, or install simple PSF model when not (see installSimplePsf "
101 "config options)"
102 )
103 doWrite = pexConfig.Field(
104 dtype=bool,
105 default=True,
106 doc="Persist results?",
107 )
108 doWriteExposure = pexConfig.Field(
109 dtype=bool,
110 default=True,
111 doc="Write icExp and icExpBackground in addition to icSrc? Ignored if doWrite False.",
112 )
113 psfIterations = pexConfig.RangeField(
114 dtype=int,
115 default=2,
116 min=1,
117 doc="Number of iterations of detect sources, measure sources, "
118 "estimate PSF. If useSimplePsf is True then 2 should be plenty; "
119 "otherwise more may be wanted.",
120 )
121 background = pexConfig.ConfigurableField(
122 target=SubtractBackgroundTask,
123 doc="Configuration for initial background estimation",
124 )
125 detection = pexConfig.ConfigurableField(
126 target=SourceDetectionTask,
127 doc="Detect sources"
128 )
129 doDeblend = pexConfig.Field(
130 dtype=bool,
131 default=True,
132 doc="Run deblender input exposure"
133 )
134 deblend = pexConfig.ConfigurableField(
135 target=SourceDeblendTask,
136 doc="Split blended source into their components"
137 )
138 measurement = pexConfig.ConfigurableField(
139 target=SingleFrameMeasurementTask,
140 doc="Measure sources"
141 )
142 doApCorr = pexConfig.Field(
143 dtype=bool,
144 default=True,
145 doc="Run subtasks to measure and apply aperture corrections"
146 )
147 measureApCorr = pexConfig.ConfigurableField(
148 target=MeasureApCorrTask,
149 doc="Subtask to measure aperture corrections"
150 )
151 applyApCorr = pexConfig.ConfigurableField(
152 target=ApplyApCorrTask,
153 doc="Subtask to apply aperture corrections"
154 )
155 # If doApCorr is False, and the exposure does not have apcorrections already applied, the
156 # active plugins in catalogCalculation almost certainly should not contain the characterization plugin
157 catalogCalculation = pexConfig.ConfigurableField(
158 target=CatalogCalculationTask,
159 doc="Subtask to run catalogCalculation plugins on catalog"
160 )
161 doComputeSummaryStats = pexConfig.Field(
162 dtype=bool,
163 default=True,
164 doc="Run subtask to measure exposure summary statistics",
165 deprecated=("This subtask has been moved to CalibrateTask "
166 "with DM-30701.")
167 )
168 computeSummaryStats = pexConfig.ConfigurableField(
169 target=ComputeExposureSummaryStatsTask,
170 doc="Subtask to run computeSummaryStats on exposure",
171 deprecated=("This subtask has been moved to CalibrateTask "
172 "with DM-30701.")
173 )
174 useSimplePsf = pexConfig.Field(
175 dtype=bool,
176 default=True,
177 doc="Replace the existing PSF model with a simplified version that has the same sigma "
178 "at the start of each PSF determination iteration? Doing so makes PSF determination "
179 "converge more robustly and quickly.",
180 )
181 installSimplePsf = pexConfig.ConfigurableField(
182 target=InstallGaussianPsfTask,
183 doc="Install a simple PSF model",
184 )
185 refObjLoader = pexConfig.ConfigurableField(
186 target=LoadIndexedReferenceObjectsTask,
187 doc="reference object loader",
188 )
189 ref_match = pexConfig.ConfigurableField(
190 target=RefMatchTask,
191 doc="Task to load and match reference objects. Only used if measurePsf can use matches. "
192 "Warning: matching will only work well if the initial WCS is accurate enough "
193 "to give good matches (roughly: good to 3 arcsec across the CCD).",
194 )
195 measurePsf = pexConfig.ConfigurableField(
196 target=MeasurePsfTask,
197 doc="Measure PSF",
198 )
199 repair = pexConfig.ConfigurableField(
200 target=RepairTask,
201 doc="Remove cosmic rays",
202 )
203 requireCrForPsf = pexConfig.Field(
204 dtype=bool,
205 default=True,
206 doc="Require cosmic ray detection and masking to run successfully before measuring the PSF."
207 )
208 checkUnitsParseStrict = pexConfig.Field(
209 doc="Strictness of Astropy unit compatibility check, can be 'raise', 'warn' or 'silent'",
210 dtype=str,
211 default="raise",
212 )
213
214 def setDefaults(self):
215 super().setDefaults()
216 # just detect bright stars; includeThresholdMultipler=10 seems large,
217 # but these are the values we have been using
218 self.detection.thresholdValue = 5.0
219 self.detection.includeThresholdMultiplier = 10.0
220 self.detection.doTempLocalBackground = False
221 # do not deblend, as it makes a mess
222 self.doDeblend = False
223 # measure and apply aperture correction; note: measuring and applying aperture
224 # correction are disabled until the final measurement, after PSF is measured
225 self.doApCorr = True
226 # minimal set of measurements needed to determine PSF
227 self.measurement.plugins.names = [
228 "base_PixelFlags",
229 "base_SdssCentroid",
230 "base_SdssShape",
231 "base_GaussianFlux",
232 "base_PsfFlux",
233 "base_CircularApertureFlux",
234 ]
235
236 def validate(self):
237 if self.doApCorr and not self.measurePsf:
238 raise RuntimeError("Must measure PSF to measure aperture correction, "
239 "because flags determined by PSF measurement are used to identify "
240 "sources used to measure aperture correction")
241
242
248
249
250class CharacterizeImageTask(pipeBase.PipelineTask, pipeBase.CmdLineTask):
251 r"""!Measure bright sources and use this to estimate background and PSF of an exposure
252
253 @anchor CharacterizeImageTask_
254
255 @section pipe_tasks_characterizeImage_Contents Contents
256
257 - @ref pipe_tasks_characterizeImage_Purpose
258 - @ref pipe_tasks_characterizeImage_Initialize
259 - @ref pipe_tasks_characterizeImage_IO
260 - @ref pipe_tasks_characterizeImage_Config
261 - @ref pipe_tasks_characterizeImage_Debug
262
263
264 @section pipe_tasks_characterizeImage_Purpose Description
265
266 Given an exposure with defects repaired (masked and interpolated over, e.g. as output by IsrTask):
267 - detect and measure bright sources
268 - repair cosmic rays
269 - measure and subtract background
270 - measure PSF
271
272 @section pipe_tasks_characterizeImage_Initialize Task initialisation
273
274 @copydoc \_\_init\_\_
275
276 @section pipe_tasks_characterizeImage_IO Invoking the Task
277
278 If you want this task to unpersist inputs or persist outputs, then call
279 the `runDataRef` method (a thin wrapper around the `run` method).
280
281 If you already have the inputs unpersisted and do not want to persist the output
282 then it is more direct to call the `run` method:
283
284 @section pipe_tasks_characterizeImage_Config Configuration parameters
285
286 See @ref CharacterizeImageConfig
287
288 @section pipe_tasks_characterizeImage_Debug Debug variables
289
290 The @link lsst.pipe.base.cmdLineTask.CmdLineTask command line task@endlink interface supports a flag
291 `--debug` to import `debug.py` from your `$PYTHONPATH`; see @ref baseDebug for more about `debug.py`.
292
293 CharacterizeImageTask has a debug dictionary with the following keys:
294 <dl>
295 <dt>frame
296 <dd>int: if specified, the frame of first debug image displayed (defaults to 1)
297 <dt>repair_iter
298 <dd>bool; if True display image after each repair in the measure PSF loop
299 <dt>background_iter
300 <dd>bool; if True display image after each background subtraction in the measure PSF loop
301 <dt>measure_iter
302 <dd>bool; if True display image and sources at the end of each iteration of the measure PSF loop
303 See @ref lsst.meas.astrom.displayAstrometry for the meaning of the various symbols.
304 <dt>psf
305 <dd>bool; if True display image and sources after PSF is measured;
306 this will be identical to the final image displayed by measure_iter if measure_iter is true
307 <dt>repair
308 <dd>bool; if True display image and sources after final repair
309 <dt>measure
310 <dd>bool; if True display image and sources after final measurement
311 </dl>
312
313 For example, put something like:
314 @code{.py}
315 import lsstDebug
316 def DebugInfo(name):
317 di = lsstDebug.getInfo(name) # N.b. lsstDebug.Info(name) would call us recursively
318 if name == "lsst.pipe.tasks.characterizeImage":
319 di.display = dict(
320 repair = True,
321 )
322
323 return di
324
325 lsstDebug.Info = DebugInfo
326 @endcode
327 into your `debug.py` file and run `calibrateTask.py` with the `--debug` flag.
328
329 Some subtasks may have their own debug variables; see individual Task documentation.
330 """
331
332 # Example description used to live here, removed 2-20-2017 by MSSG
333
334 ConfigClass = CharacterizeImageConfig
335 _DefaultName = "characterizeImage"
336 RunnerClass = pipeBase.ButlerInitializedTaskRunner
337
338 def runQuantum(self, butlerQC, inputRefs, outputRefs):
339 inputs = butlerQC.get(inputRefs)
340 if 'exposureIdInfo' not in inputs.keys():
341 inputs['exposureIdInfo'] = ExposureIdInfo.fromDataId(butlerQC.quantum.dataId, "visit_detector")
342 outputs = self.run(**inputs)
343 butlerQC.put(outputs, outputRefs)
344
345 def __init__(self, butler=None, refObjLoader=None, schema=None, **kwargs):
346 """!Construct a CharacterizeImageTask
347
348 @param[in] butler A butler object is passed to the refObjLoader constructor in case
349 it is needed to load catalogs. May be None if a catalog-based star selector is
350 not used, if the reference object loader constructor does not require a butler,
351 or if a reference object loader is passed directly via the refObjLoader argument.
352 @param[in] refObjLoader An instance of LoadReferenceObjectsTasks that supplies an
353 external reference catalog to a catalog-based star selector. May be None if a
354 catalog star selector is not used or the loader can be constructed from the
355 butler argument.
356 @param[in,out] schema initial schema (an lsst.afw.table.SourceTable), or None
357 @param[in,out] kwargs other keyword arguments for lsst.pipe.base.CmdLineTask
358 """
359 super().__init__(**kwargs)
360
361 if schema is None:
362 schema = SourceTable.makeMinimalSchema()
363 self.schema = schema
364 self.makeSubtask("background")
365 self.makeSubtask("installSimplePsf")
366 self.makeSubtask("repair")
367 self.makeSubtask("measurePsf", schema=self.schema)
368 if self.config.doMeasurePsf and self.measurePsf.usesMatches:
369 if not refObjLoader:
370 self.makeSubtask('refObjLoader', butler=butler)
371 refObjLoader = self.refObjLoader
372 self.makeSubtask("ref_match", refObjLoader=refObjLoader)
373 self.algMetadata = dafBase.PropertyList()
374 self.makeSubtask('detection', schema=self.schema)
375 if self.config.doDeblend:
376 self.makeSubtask("deblend", schema=self.schema)
377 self.makeSubtask('measurement', schema=self.schema, algMetadata=self.algMetadata)
378 if self.config.doApCorr:
379 self.makeSubtask('measureApCorr', schema=self.schema)
380 self.makeSubtask('applyApCorr', schema=self.schema)
381 self.makeSubtask('catalogCalculation', schema=self.schema)
382 self._initialFrame = getDebugFrame(self._display, "frame") or 1
383 self._frame = self._initialFrame
384 self.schema.checkUnits(parse_strict=self.config.checkUnitsParseStrict)
385 self.outputSchema = afwTable.SourceCatalog(self.schema)
386
388 outputCatSchema = afwTable.SourceCatalog(self.schema)
389 outputCatSchema.getTable().setMetadata(self.algMetadata)
390 return {'outputSchema': outputCatSchema}
391
392 @pipeBase.timeMethod
393 def runDataRef(self, dataRef, exposure=None, background=None, doUnpersist=True):
394 """!Characterize a science image and, if wanted, persist the results
395
396 This simply unpacks the exposure and passes it to the characterize method to do the work.
397
398 @param[in] dataRef: butler data reference for science exposure
399 @param[in,out] exposure exposure to characterize (an lsst.afw.image.ExposureF or similar).
400 If None then unpersist from "postISRCCD".
401 The following changes are made, depending on the config:
402 - set psf to the measured PSF
403 - set apCorrMap to the measured aperture correction
404 - subtract background
405 - interpolate over cosmic rays
406 - update detection and cosmic ray mask planes
407 @param[in,out] background initial model of background already subtracted from exposure
408 (an lsst.afw.math.BackgroundList). May be None if no background has been subtracted,
409 which is typical for image characterization.
410 A refined background model is output.
411 @param[in] doUnpersist if True the exposure is read from the repository
412 and the exposure and background arguments must be None;
413 if False the exposure must be provided.
414 True is intended for running as a command-line task, False for running as a subtask
415
416 @return same data as the characterize method
417 """
418 self._frame = self._initialFrame # reset debug display frame
419 self.log.info("Processing %s", dataRef.dataId)
420
421 if doUnpersist:
422 if exposure is not None or background is not None:
423 raise RuntimeError("doUnpersist true; exposure and background must be None")
424 exposure = dataRef.get("postISRCCD", immediate=True)
425 elif exposure is None:
426 raise RuntimeError("doUnpersist false; exposure must be provided")
427
428 exposureIdInfo = dataRef.get("expIdInfo")
429
430 charRes = self.run(
431 exposure=exposure,
432 exposureIdInfo=exposureIdInfo,
433 background=background,
434 )
435
436 if self.config.doWrite:
437 dataRef.put(charRes.sourceCat, "icSrc")
438 if self.config.doWriteExposure:
439 dataRef.put(charRes.exposure, "icExp")
440 dataRef.put(charRes.background, "icExpBackground")
441
442 return charRes
443
444 @pipeBase.timeMethod
445 def run(self, exposure, exposureIdInfo=None, background=None):
446 """!Characterize a science image
447
448 Peforms the following operations:
449 - Iterate the following config.psfIterations times, or once if config.doMeasurePsf false:
450 - detect and measure sources and estimate PSF (see detectMeasureAndEstimatePsf for details)
451 - interpolate over cosmic rays
452 - perform final measurement
453
454 @param[in,out] exposure exposure to characterize (an lsst.afw.image.ExposureF or similar).
455 The following changes are made:
456 - update or set psf
457 - set apCorrMap
458 - update detection and cosmic ray mask planes
459 - subtract background and interpolate over cosmic rays
460 @param[in] exposureIdInfo ID info for exposure (an lsst.obs.base.ExposureIdInfo).
461 If not provided, returned SourceCatalog IDs will not be globally unique.
462 @param[in,out] background initial model of background already subtracted from exposure
463 (an lsst.afw.math.BackgroundList). May be None if no background has been subtracted,
464 which is typical for image characterization.
465
466 @return pipe_base Struct containing these fields, all from the final iteration
467 of detectMeasureAndEstimatePsf:
468 - exposure: characterized exposure; image is repaired by interpolating over cosmic rays,
469 mask is updated accordingly, and the PSF model is set
470 - sourceCat: detected sources (an lsst.afw.table.SourceCatalog)
471 - background: model of background subtracted from exposure (an lsst.afw.math.BackgroundList)
472 - psfCellSet: spatial cells of PSF candidates (an lsst.afw.math.SpatialCellSet)
473 """
474 self._frame = self._initialFrame # reset debug display frame
475
476 if not self.config.doMeasurePsf and not exposure.hasPsf():
477 self.log.info("CharacterizeImageTask initialized with 'simple' PSF.")
478 self.installSimplePsf.run(exposure=exposure)
479
480 if exposureIdInfo is None:
481 exposureIdInfo = ExposureIdInfo()
482
483 # subtract an initial estimate of background level
484 background = self.background.run(exposure).background
485
486 psfIterations = self.config.psfIterations if self.config.doMeasurePsf else 1
487 for i in range(psfIterations):
488 dmeRes = self.detectMeasureAndEstimatePsf(
489 exposure=exposure,
490 exposureIdInfo=exposureIdInfo,
491 background=background,
492 )
493
494 psf = dmeRes.exposure.getPsf()
495 psfSigma = psf.computeShape().getDeterminantRadius()
496 psfDimensions = psf.computeImage().getDimensions()
497 medBackground = np.median(dmeRes.background.getImage().getArray())
498 self.log.info("iter %s; PSF sigma=%0.2f, dimensions=%s; median background=%0.2f",
499 i + 1, psfSigma, psfDimensions, medBackground)
500
501 self.display("psf", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat)
502
503 # perform final repair with final PSF
504 self.repair.run(exposure=dmeRes.exposure)
505 self.display("repair", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat)
506
507 # perform final measurement with final PSF, including measuring and applying aperture correction,
508 # if wanted
509 self.measurement.run(measCat=dmeRes.sourceCat, exposure=dmeRes.exposure,
510 exposureId=exposureIdInfo.expId)
511 if self.config.doApCorr:
512 apCorrMap = self.measureApCorr.run(exposure=dmeRes.exposure, catalog=dmeRes.sourceCat).apCorrMap
513 dmeRes.exposure.getInfo().setApCorrMap(apCorrMap)
514 self.applyApCorr.run(catalog=dmeRes.sourceCat, apCorrMap=exposure.getInfo().getApCorrMap())
515 self.catalogCalculation.run(dmeRes.sourceCat)
516
517 self.display("measure", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat)
518
519 return pipeBase.Struct(
520 exposure=dmeRes.exposure,
521 sourceCat=dmeRes.sourceCat,
522 background=dmeRes.background,
523 psfCellSet=dmeRes.psfCellSet,
524
525 characterized=dmeRes.exposure,
526 backgroundModel=dmeRes.background
527 )
528
529 @pipeBase.timeMethod
530 def detectMeasureAndEstimatePsf(self, exposure, exposureIdInfo, background):
531 """!Perform one iteration of detect, measure and estimate PSF
532
533 Performs the following operations:
534 - if config.doMeasurePsf or not exposure.hasPsf():
535 - install a simple PSF model (replacing the existing one, if need be)
536 - interpolate over cosmic rays with keepCRs=True
537 - estimate background and subtract it from the exposure
538 - detect, deblend and measure sources, and subtract a refined background model;
539 - if config.doMeasurePsf:
540 - measure PSF
541
542 @param[in,out] exposure exposure to characterize (an lsst.afw.image.ExposureF or similar)
543 The following changes are made:
544 - update or set psf
545 - update detection and cosmic ray mask planes
546 - subtract background
547 @param[in] exposureIdInfo ID info for exposure (an lsst.obs_base.ExposureIdInfo)
548 @param[in,out] background initial model of background already subtracted from exposure
549 (an lsst.afw.math.BackgroundList).
550
551 @return pipe_base Struct containing these fields, all from the final iteration
552 of detect sources, measure sources and estimate PSF:
553 - exposure characterized exposure; image is repaired by interpolating over cosmic rays,
554 mask is updated accordingly, and the PSF model is set
555 - sourceCat detected sources (an lsst.afw.table.SourceCatalog)
556 - background model of background subtracted from exposure (an lsst.afw.math.BackgroundList)
557 - psfCellSet spatial cells of PSF candidates (an lsst.afw.math.SpatialCellSet)
558 """
559 # install a simple PSF model, if needed or wanted
560 if not exposure.hasPsf() or (self.config.doMeasurePsf and self.config.useSimplePsf):
561 self.log.info("PSF estimation initialized with 'simple' PSF")
562 self.installSimplePsf.run(exposure=exposure)
563
564 # run repair, but do not interpolate over cosmic rays (do that elsewhere, with the final PSF model)
565 if self.config.requireCrForPsf:
566 self.repair.run(exposure=exposure, keepCRs=True)
567 else:
568 try:
569 self.repair.run(exposure=exposure, keepCRs=True)
570 except LengthError:
571 self.log.warning("Skipping cosmic ray detection: Too many CR pixels (max %0.f)",
572 self.config.repair.cosmicray.nCrPixelMax)
573
574 self.display("repair_iter", exposure=exposure)
575
576 if background is None:
577 background = BackgroundList()
578
579 sourceIdFactory = exposureIdInfo.makeSourceIdFactory()
580 table = SourceTable.make(self.schema, sourceIdFactory)
581 table.setMetadata(self.algMetadata)
582
583 detRes = self.detection.run(table=table, exposure=exposure, doSmooth=True)
584 sourceCat = detRes.sources
585 if detRes.fpSets.background:
586 for bg in detRes.fpSets.background:
587 background.append(bg)
588
589 if self.config.doDeblend:
590 self.deblend.run(exposure=exposure, sources=sourceCat)
591
592 self.measurement.run(measCat=sourceCat, exposure=exposure, exposureId=exposureIdInfo.expId)
593
594 measPsfRes = pipeBase.Struct(cellSet=None)
595 if self.config.doMeasurePsf:
596 if self.measurePsf.usesMatches:
597 matches = self.ref_match.loadAndMatch(exposure=exposure, sourceCat=sourceCat).matches
598 else:
599 matches = None
600 measPsfRes = self.measurePsf.run(exposure=exposure, sources=sourceCat, matches=matches,
601 expId=exposureIdInfo.expId)
602 self.display("measure_iter", exposure=exposure, sourceCat=sourceCat)
603
604 return pipeBase.Struct(
605 exposure=exposure,
606 sourceCat=sourceCat,
607 background=background,
608 psfCellSet=measPsfRes.cellSet,
609 )
610
612 """Return a dict of empty catalogs for each catalog dataset produced by this task.
613 """
614 sourceCat = SourceCatalog(self.schema)
615 sourceCat.getTable().setMetadata(self.algMetadata)
616 return {"icSrc": sourceCat}
617
618 def display(self, itemName, exposure, sourceCat=None):
619 """Display exposure and sources on next frame, if display of itemName has been requested
620
621 @param[in] itemName name of item in debugInfo
622 @param[in] exposure exposure to display
623 @param[in] sourceCat source catalog to display
624 """
625 val = getDebugFrame(self._display, itemName)
626 if not val:
627 return
628
629 displayAstrometry(exposure=exposure, sourceCat=sourceCat, frame=self._frame, pause=False)
630 self._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)