Coverage for python/lsst/pipe/tasks/calibrateImage.py: 25%
249 statements
« prev ^ index » next coverage.py v7.4.4, created at 2024-03-16 09:33 +0000
« prev ^ index » next coverage.py v7.4.4, created at 2024-03-16 09:33 +0000
1# This file is part of pipe_tasks.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <https://www.gnu.org/licenses/>.
22import collections.abc
24import numpy as np
26import lsst.afw.table as afwTable
27import lsst.afw.image as afwImage
28import lsst.meas.algorithms
29import lsst.meas.algorithms.installGaussianPsf
30import lsst.meas.algorithms.measureApCorr
31import lsst.meas.algorithms.setPrimaryFlags
32import lsst.meas.base
33import lsst.meas.astrom
34import lsst.meas.deblender
35import lsst.meas.extensions.shapeHSM
36import lsst.pex.config as pexConfig
37import lsst.pipe.base as pipeBase
38from lsst.pipe.base import connectionTypes
39from lsst.utils.timer import timeMethod
41from . import measurePsf, repair, photoCal, computeExposureSummaryStats, snapCombine
44class CalibrateImageConnections(pipeBase.PipelineTaskConnections,
45 dimensions=("instrument", "visit", "detector")):
47 astrometry_ref_cat = connectionTypes.PrerequisiteInput(
48 doc="Reference catalog to use for astrometric calibration.",
49 name="gaia_dr3_20230707",
50 storageClass="SimpleCatalog",
51 dimensions=("skypix",),
52 deferLoad=True,
53 multiple=True,
54 )
55 photometry_ref_cat = connectionTypes.PrerequisiteInput(
56 doc="Reference catalog to use for photometric calibration.",
57 name="ps1_pv3_3pi_20170110",
58 storageClass="SimpleCatalog",
59 dimensions=("skypix",),
60 deferLoad=True,
61 multiple=True
62 )
64 exposures = connectionTypes.Input(
65 doc="Exposure (or two snaps) to be calibrated, and detected and measured on.",
66 name="postISRCCD",
67 storageClass="Exposure",
68 multiple=True, # to handle 1 exposure or 2 snaps
69 dimensions=["instrument", "exposure", "detector"],
70 )
72 # outputs
73 initial_stars_schema = connectionTypes.InitOutput(
74 doc="Schema of the output initial stars catalog.",
75 name="initial_stars_schema",
76 storageClass="SourceCatalog",
77 )
79 # TODO: We want some kind of flag on Exposures/Catalogs to make it obvious
80 # which components had failed to be computed/persisted
81 output_exposure = connectionTypes.Output(
82 doc="Photometrically calibrated exposure with fitted calibrations and summary statistics.",
83 name="initial_pvi",
84 storageClass="ExposureF",
85 dimensions=("instrument", "visit", "detector"),
86 )
87 stars = connectionTypes.Output(
88 doc="Catalog of unresolved sources detected on the calibrated exposure.",
89 name="initial_stars_detector",
90 storageClass="ArrowAstropy",
91 dimensions=["instrument", "visit", "detector"],
92 )
93 stars_footprints = connectionTypes.Output(
94 doc="Catalog of unresolved sources detected on the calibrated exposure; "
95 "includes source footprints.",
96 name="initial_stars_footprints_detector",
97 storageClass="SourceCatalog",
98 dimensions=["instrument", "visit", "detector"],
99 )
100 applied_photo_calib = connectionTypes.Output(
101 doc="Photometric calibration that was applied to exposure.",
102 name="initial_photoCalib_detector",
103 storageClass="PhotoCalib",
104 dimensions=("instrument", "visit", "detector"),
105 )
106 background = connectionTypes.Output(
107 doc="Background models estimated during calibration task.",
108 name="initial_pvi_background",
109 storageClass="Background",
110 dimensions=("instrument", "visit", "detector"),
111 )
113 # Optional outputs
114 psf_stars_footprints = connectionTypes.Output(
115 doc="Catalog of bright unresolved sources detected on the exposure used for PSF determination; "
116 "includes source footprints.",
117 name="initial_psf_stars_footprints_detector",
118 storageClass="SourceCatalog",
119 dimensions=["instrument", "visit", "detector"],
120 )
121 psf_stars = connectionTypes.Output(
122 doc="Catalog of bright unresolved sources detected on the exposure used for PSF determination.",
123 name="initial_psf_stars_detector",
124 storageClass="ArrowAstropy",
125 dimensions=["instrument", "visit", "detector"],
126 )
127 astrometry_matches = connectionTypes.Output(
128 doc="Source to reference catalog matches from the astrometry solver.",
129 name="initial_astrometry_match_detector",
130 storageClass="Catalog",
131 dimensions=("instrument", "visit", "detector"),
132 )
133 photometry_matches = connectionTypes.Output(
134 doc="Source to reference catalog matches from the photometry solver.",
135 name="initial_photometry_match_detector",
136 storageClass="Catalog",
137 dimensions=("instrument", "visit", "detector"),
138 )
140 def __init__(self, *, config=None):
141 super().__init__(config=config)
142 if not config.optional_outputs:
143 del self.psf_stars
144 del self.psf_stars_footprints
145 del self.astrometry_matches
146 del self.photometry_matches
149class CalibrateImageConfig(pipeBase.PipelineTaskConfig, pipelineConnections=CalibrateImageConnections):
150 optional_outputs = pexConfig.ListField(
151 doc="Which optional outputs to save (as their connection name)?",
152 dtype=str,
153 # TODO: note somewhere to disable this for benchmarking, but should
154 # we always have it on for production runs?
155 default=["psf_stars", "psf_stars_footprints", "astrometry_matches", "photometry_matches"],
156 optional=True
157 )
159 # To generate catalog ids consistently across subtasks.
160 id_generator = lsst.meas.base.DetectorVisitIdGeneratorConfig.make_field()
162 snap_combine = pexConfig.ConfigurableField(
163 target=snapCombine.SnapCombineTask,
164 doc="Task to combine two snaps to make one exposure.",
165 )
167 # subtasks used during psf characterization
168 install_simple_psf = pexConfig.ConfigurableField(
169 target=lsst.meas.algorithms.installGaussianPsf.InstallGaussianPsfTask,
170 doc="Task to install a simple PSF model into the input exposure to use "
171 "when detecting bright sources for PSF estimation.",
172 )
173 psf_repair = pexConfig.ConfigurableField(
174 target=repair.RepairTask,
175 doc="Task to repair cosmic rays on the exposure before PSF determination.",
176 )
177 psf_subtract_background = pexConfig.ConfigurableField(
178 target=lsst.meas.algorithms.SubtractBackgroundTask,
179 doc="Task to perform intial background subtraction, before first detection pass.",
180 )
181 psf_detection = pexConfig.ConfigurableField(
182 target=lsst.meas.algorithms.SourceDetectionTask,
183 doc="Task to detect sources for PSF determination."
184 )
185 psf_source_measurement = pexConfig.ConfigurableField(
186 target=lsst.meas.base.SingleFrameMeasurementTask,
187 doc="Task to measure sources to be used for psf estimation."
188 )
189 psf_measure_psf = pexConfig.ConfigurableField(
190 target=measurePsf.MeasurePsfTask,
191 doc="Task to measure the psf on bright sources."
192 )
194 # TODO DM-39203: we can remove aperture correction from this task once we are
195 # using the shape-based star/galaxy code.
196 measure_aperture_correction = pexConfig.ConfigurableField(
197 target=lsst.meas.algorithms.measureApCorr.MeasureApCorrTask,
198 doc="Task to compute the aperture correction from the bright stars."
199 )
201 # subtasks used during star measurement
202 star_detection = pexConfig.ConfigurableField(
203 target=lsst.meas.algorithms.SourceDetectionTask,
204 doc="Task to detect stars to return in the output catalog."
205 )
206 star_sky_sources = pexConfig.ConfigurableField(
207 target=lsst.meas.algorithms.SkyObjectsTask,
208 doc="Task to generate sky sources ('empty' regions where there are no detections).",
209 )
210 star_deblend = pexConfig.ConfigurableField(
211 target=lsst.meas.deblender.SourceDeblendTask,
212 doc="Split blended sources into their components."
213 )
214 star_measurement = pexConfig.ConfigurableField(
215 target=lsst.meas.base.SingleFrameMeasurementTask,
216 doc="Task to measure stars to return in the output catalog."
217 )
218 star_apply_aperture_correction = pexConfig.ConfigurableField(
219 target=lsst.meas.base.ApplyApCorrTask,
220 doc="Task to apply aperture corrections to the selected stars."
221 )
222 star_catalog_calculation = pexConfig.ConfigurableField(
223 target=lsst.meas.base.CatalogCalculationTask,
224 doc="Task to compute extendedness values on the star catalog, "
225 "for the star selector to remove extended sources."
226 )
227 star_set_primary_flags = pexConfig.ConfigurableField(
228 target=lsst.meas.algorithms.setPrimaryFlags.SetPrimaryFlagsTask,
229 doc="Task to add isPrimary to the catalog."
230 )
231 star_selector = lsst.meas.algorithms.sourceSelectorRegistry.makeField(
232 default="science",
233 doc="Task to select isolated stars to use for calibration."
234 )
236 # final calibrations and statistics
237 astrometry = pexConfig.ConfigurableField(
238 target=lsst.meas.astrom.AstrometryTask,
239 doc="Task to perform astrometric calibration to fit a WCS.",
240 )
241 astrometry_ref_loader = pexConfig.ConfigField(
242 dtype=lsst.meas.algorithms.LoadReferenceObjectsConfig,
243 doc="Configuration of reference object loader for astrometric fit.",
244 )
245 photometry = pexConfig.ConfigurableField(
246 target=photoCal.PhotoCalTask,
247 doc="Task to perform photometric calibration to fit a PhotoCalib.",
248 )
249 photometry_ref_loader = pexConfig.ConfigField(
250 dtype=lsst.meas.algorithms.LoadReferenceObjectsConfig,
251 doc="Configuration of reference object loader for photometric fit.",
252 )
254 compute_summary_stats = pexConfig.ConfigurableField(
255 target=computeExposureSummaryStats.ComputeExposureSummaryStatsTask,
256 doc="Task to to compute summary statistics on the calibrated exposure."
257 )
259 def setDefaults(self):
260 super().setDefaults()
262 # Use a very broad PSF here, to throughly reject CRs.
263 # TODO investigation: a large initial psf guess may make stars look
264 # like CRs for very good seeing images.
265 self.install_simple_psf.fwhm = 4
267 # S/N>=50 sources for PSF determination, but detection to S/N=5.
268 self.psf_detection.thresholdValue = 5.0
269 self.psf_detection.includeThresholdMultiplier = 10.0
270 # TODO investigation: Probably want False here, but that may require
271 # tweaking the background spatial scale, to make it small enough to
272 # prevent extra peaks in the wings of bright objects.
273 self.psf_detection.doTempLocalBackground = False
274 # NOTE: we do want reEstimateBackground=True in psf_detection, so that
275 # each measurement step is done with the best background available.
277 # Minimal measurement plugins for PSF determination.
278 # TODO DM-39203: We can drop GaussianFlux and PsfFlux, if we use
279 # shapeHSM/moments for star/galaxy separation.
280 # TODO DM-39203: we can remove aperture correction from this task once
281 # we are using the shape-based star/galaxy code.
282 self.psf_source_measurement.plugins = ["base_PixelFlags",
283 "base_SdssCentroid",
284 "ext_shapeHSM_HsmSourceMoments",
285 "base_CircularApertureFlux",
286 "base_GaussianFlux",
287 "base_PsfFlux",
288 "base_ClassificationSizeExtendedness",
289 ]
290 self.psf_source_measurement.slots.shape = "ext_shapeHSM_HsmSourceMoments"
291 # Only measure apertures we need for PSF measurement.
292 self.psf_source_measurement.plugins["base_CircularApertureFlux"].radii = [12.0]
293 # TODO DM-40843: Remove this line once this is the psfex default.
294 self.psf_measure_psf.psfDeterminer["psfex"].photometricFluxField = \
295 "base_CircularApertureFlux_12_0_instFlux"
297 # No extendeness information available: we need the aperture
298 # corrections to determine that.
299 self.measure_aperture_correction.sourceSelector["science"].doUnresolved = False
300 self.measure_aperture_correction.sourceSelector["science"].flags.good = ["calib_psf_used"]
301 self.measure_aperture_correction.sourceSelector["science"].flags.bad = []
303 # Detection for good S/N for astrometry/photometry and other
304 # downstream tasks; detection mask to S/N>=5, but S/N>=10 peaks.
305 self.star_detection.thresholdValue = 5.0
306 self.star_detection.includeThresholdMultiplier = 2.0
307 self.star_measurement.plugins = ["base_PixelFlags",
308 "base_SdssCentroid",
309 "ext_shapeHSM_HsmSourceMoments",
310 'ext_shapeHSM_HsmPsfMoments',
311 "base_GaussianFlux",
312 "base_PsfFlux",
313 "base_CircularApertureFlux",
314 "base_ClassificationSizeExtendedness",
315 ]
316 self.star_measurement.slots.psfShape = "ext_shapeHSM_HsmPsfMoments"
317 self.star_measurement.slots.shape = "ext_shapeHSM_HsmSourceMoments"
318 # Only measure the apertures we need for star selection.
319 self.star_measurement.plugins["base_CircularApertureFlux"].radii = [12.0]
321 # Select isolated stars with reliable measurements and no bad flags.
322 self.star_selector["science"].doFlags = True
323 self.star_selector["science"].doUnresolved = True
324 self.star_selector["science"].doSignalToNoise = True
325 self.star_selector["science"].doIsolated = True
326 self.star_selector["science"].signalToNoise.minimum = 10.0
327 # Keep sky sources in the output catalog, even though they aren't
328 # wanted for calibration.
329 self.star_selector["science"].doSkySources = True
331 # Use the affine WCS fitter (assumes we have a good camera geometry).
332 self.astrometry.wcsFitter.retarget(lsst.meas.astrom.FitAffineWcsTask)
333 # phot_g_mean is the primary Gaia band for all input bands.
334 self.astrometry_ref_loader.anyFilterMapsToThis = "phot_g_mean"
336 # Only reject sky sources; we already selected good stars.
337 self.astrometry.sourceSelector["science"].doFlags = True
338 self.astrometry.sourceSelector["science"].flags.bad = ["sky_source"]
339 self.photometry.match.sourceSelection.doFlags = True
340 self.photometry.match.sourceSelection.flags.bad = ["sky_source"]
342 # All sources should be good for PSF summary statistics.
343 # TODO: These should both be changed to calib_psf_used with DM-41640.
344 self.compute_summary_stats.starSelection = "calib_photometry_used"
345 self.compute_summary_stats.starSelector.flags.good = ["calib_photometry_used"]
348class CalibrateImageTask(pipeBase.PipelineTask):
349 """Compute the PSF, aperture corrections, astrometric and photometric
350 calibrations, and summary statistics for a single science exposure, and
351 produce a catalog of brighter stars that were used to calibrate it.
353 Parameters
354 ----------
355 initial_stars_schema : `lsst.afw.table.Schema`
356 Schema of the initial_stars output catalog.
357 """
358 _DefaultName = "calibrateImage"
359 ConfigClass = CalibrateImageConfig
361 def __init__(self, initial_stars_schema=None, **kwargs):
362 super().__init__(**kwargs)
364 self.makeSubtask("snap_combine")
366 # PSF determination subtasks
367 self.makeSubtask("install_simple_psf")
368 self.makeSubtask("psf_repair")
369 self.makeSubtask("psf_subtract_background")
370 self.psf_schema = afwTable.SourceTable.makeMinimalSchema()
371 self.makeSubtask("psf_detection", schema=self.psf_schema)
372 self.makeSubtask("psf_source_measurement", schema=self.psf_schema)
373 self.makeSubtask("psf_measure_psf", schema=self.psf_schema)
375 self.makeSubtask("measure_aperture_correction", schema=self.psf_schema)
377 # star measurement subtasks
378 if initial_stars_schema is None:
379 initial_stars_schema = afwTable.SourceTable.makeMinimalSchema()
381 # These fields let us track which sources were used for psf and
382 # aperture correction calculations.
383 self.psf_fields = ("calib_psf_candidate", "calib_psf_used", "calib_psf_reserved",
384 # TODO DM-39203: these can be removed once apcorr is gone.
385 "apcorr_slot_CalibFlux_used", "apcorr_base_GaussianFlux_used",
386 "apcorr_base_PsfFlux_used")
387 for field in self.psf_fields:
388 item = self.psf_schema.find(field)
389 initial_stars_schema.addField(item.getField())
391 afwTable.CoordKey.addErrorFields(initial_stars_schema)
392 self.makeSubtask("star_detection", schema=initial_stars_schema)
393 self.makeSubtask("star_sky_sources", schema=initial_stars_schema)
394 self.makeSubtask("star_deblend", schema=initial_stars_schema)
395 self.makeSubtask("star_measurement", schema=initial_stars_schema)
396 self.makeSubtask("star_apply_aperture_correction", schema=initial_stars_schema)
397 self.makeSubtask("star_catalog_calculation", schema=initial_stars_schema)
398 self.makeSubtask("star_set_primary_flags", schema=initial_stars_schema, isSingleFrame=True)
399 self.makeSubtask("star_selector")
401 self.makeSubtask("astrometry", schema=initial_stars_schema)
402 self.makeSubtask("photometry", schema=initial_stars_schema)
404 self.makeSubtask("compute_summary_stats")
406 # For the butler to persist it.
407 self.initial_stars_schema = afwTable.SourceCatalog(initial_stars_schema)
409 def runQuantum(self, butlerQC, inputRefs, outputRefs):
410 inputs = butlerQC.get(inputRefs)
411 id_generator = self.config.id_generator.apply(butlerQC.quantum.dataId)
413 astrometry_loader = lsst.meas.algorithms.ReferenceObjectLoader(
414 dataIds=[ref.datasetRef.dataId for ref in inputRefs.astrometry_ref_cat],
415 refCats=inputs.pop("astrometry_ref_cat"),
416 name=self.config.connections.astrometry_ref_cat,
417 config=self.config.astrometry_ref_loader, log=self.log)
418 self.astrometry.setRefObjLoader(astrometry_loader)
420 photometry_loader = lsst.meas.algorithms.ReferenceObjectLoader(
421 dataIds=[ref.datasetRef.dataId for ref in inputRefs.photometry_ref_cat],
422 refCats=inputs.pop("photometry_ref_cat"),
423 name=self.config.connections.photometry_ref_cat,
424 config=self.config.photometry_ref_loader, log=self.log)
425 self.photometry.match.setRefObjLoader(photometry_loader)
427 outputs = self.run(id_generator=id_generator, **inputs)
429 butlerQC.put(outputs, outputRefs)
431 @timeMethod
432 def run(self, *, exposures, id_generator=None):
433 """Find stars and perform psf measurement, then do a deeper detection
434 and measurement and calibrate astrometry and photometry from that.
436 Parameters
437 ----------
438 exposures : `lsst.afw.image.Exposure` or `list` [`lsst.afw.image.Exposure`]
439 Post-ISR exposure(s), with an initial WCS, VisitInfo, and Filter.
440 Modified in-place during processing if only one is passed.
441 If two exposures are passed, treat them as snaps and combine
442 before doing further processing.
443 id_generator : `lsst.meas.base.IdGenerator`, optional
444 Object that generates source IDs and provides random seeds.
446 Returns
447 -------
448 result : `lsst.pipe.base.Struct`
449 Results as a struct with attributes:
451 ``output_exposure``
452 Calibrated exposure, with pixels in nJy units.
453 (`lsst.afw.image.Exposure`)
454 ``stars``
455 Stars that were used to calibrate the exposure, with
456 calibrated fluxes and magnitudes.
457 (`astropy.table.Table`)
458 ``stars_footprints``
459 Footprints of stars that were used to calibrate the exposure.
460 (`lsst.afw.table.SourceCatalog`)
461 ``psf_stars``
462 Stars that were used to determine the image PSF.
463 (`astropy.table.Table`)
464 ``psf_stars_footprints``
465 Footprints of stars that were used to determine the image PSF.
466 (`lsst.afw.table.SourceCatalog`)
467 ``background``
468 Background that was fit to the exposure when detecting
469 ``stars``. (`lsst.afw.math.BackgroundList`)
470 ``applied_photo_calib``
471 Photometric calibration that was fit to the star catalog and
472 applied to the exposure. (`lsst.afw.image.PhotoCalib`)
473 ``astrometry_matches``
474 Reference catalog stars matches used in the astrometric fit.
475 (`list` [`lsst.afw.table.ReferenceMatch`] or `lsst.afw.table.BaseCatalog`)
476 ``photometry_matches``
477 Reference catalog stars matches used in the photometric fit.
478 (`list` [`lsst.afw.table.ReferenceMatch`] or `lsst.afw.table.BaseCatalog`)
479 """
480 if id_generator is None:
481 id_generator = lsst.meas.base.IdGenerator()
483 exposure = self._handle_snaps(exposures)
485 # TODO remove on DM-43083: work around the fact that we don't want
486 # to run streak detection in this task in production.
487 exposure.mask.addMaskPlane("STREAK")
489 psf_stars, background, candidates = self._compute_psf(exposure)
491 self._measure_aperture_correction(exposure, psf_stars)
493 stars = self._find_stars(exposure, background, id_generator)
494 self._match_psf_stars(psf_stars, stars)
496 astrometry_matches, astrometry_meta = self._fit_astrometry(exposure, stars)
497 stars, photometry_matches, photometry_meta, photo_calib = self._fit_photometry(exposure, stars)
499 self._summarize(exposure, stars, background)
501 if self.config.optional_outputs:
502 astrometry_matches = lsst.meas.astrom.denormalizeMatches(astrometry_matches, astrometry_meta)
503 photometry_matches = lsst.meas.astrom.denormalizeMatches(photometry_matches, photometry_meta)
505 return pipeBase.Struct(output_exposure=exposure,
506 stars_footprints=stars,
507 stars=stars.asAstropy(),
508 psf_stars_footprints=psf_stars,
509 psf_stars=psf_stars.asAstropy(),
510 background=background,
511 applied_photo_calib=photo_calib,
512 astrometry_matches=astrometry_matches,
513 photometry_matches=photometry_matches)
515 def _handle_snaps(self, exposure):
516 """Combine two snaps into one exposure, or return a single exposure.
518 Parameters
519 ----------
520 exposure : `lsst.afw.image.Exposure` or `list` [`lsst.afw.image.Exposure]`
521 One or two exposures to combine as snaps.
523 Returns
524 -------
525 exposure : `lsst.afw.image.Exposure`
526 A single exposure to continue processing.
528 Raises
529 ------
530 RuntimeError
531 Raised if input does not contain either 1 or 2 exposures.
532 """
533 if isinstance(exposure, lsst.afw.image.Exposure):
534 return exposure
536 if isinstance(exposure, collections.abc.Sequence):
537 match len(exposure):
538 case 1:
539 return exposure[0]
540 case 2:
541 return self.snap_combine.run(exposure[0], exposure[1]).exposure
542 case n:
543 raise RuntimeError(f"Can only process 1 or 2 snaps, not {n}.")
545 def _compute_psf(self, exposure, guess_psf=True):
546 """Find bright sources detected on an exposure and fit a PSF model to
547 them, repairing likely cosmic rays before detection.
549 Repair, detect, measure, and compute PSF twice, to ensure the PSF
550 model does not include contributions from cosmic rays.
552 Parameters
553 ----------
554 exposure : `lsst.afw.image.Exposure`
555 Exposure to detect and measure bright stars on.
557 Returns
558 -------
559 sources : `lsst.afw.table.SourceCatalog`
560 Catalog of detected bright sources.
561 background : `lsst.afw.math.BackgroundList`
562 Background that was fit to the exposure during detection.
563 cell_set : `lsst.afw.math.SpatialCellSet`
564 PSF candidates returned by the psf determiner.
565 """
566 def log_psf(msg):
567 """Log the parameters of the psf and background, with a prepended
568 message.
569 """
570 position = exposure.psf.getAveragePosition()
571 sigma = exposure.psf.computeShape(position).getDeterminantRadius()
572 dimensions = exposure.psf.computeImage(position).getDimensions()
573 median_background = np.median(background.getImage().array)
574 self.log.info("%s sigma=%0.4f, dimensions=%s; median background=%0.2f",
575 msg, sigma, dimensions, median_background)
577 self.log.info("First pass detection with Guassian PSF FWHM=%s pixels",
578 self.config.install_simple_psf.fwhm)
579 self.install_simple_psf.run(exposure=exposure)
581 background = self.psf_subtract_background.run(exposure=exposure).background
582 log_psf("Initial PSF:")
583 self.psf_repair.run(exposure=exposure, keepCRs=True)
585 table = afwTable.SourceTable.make(self.psf_schema)
586 # Re-estimate the background during this detection step, so that
587 # measurement uses the most accurate background-subtraction.
588 detections = self.psf_detection.run(table=table, exposure=exposure, background=background)
589 self.psf_source_measurement.run(detections.sources, exposure)
590 psf_result = self.psf_measure_psf.run(exposure=exposure, sources=detections.sources)
591 # Replace the initial PSF with something simpler for the second
592 # repair/detect/measure/measure_psf step: this can help it converge.
593 self.install_simple_psf.run(exposure=exposure)
595 log_psf("Rerunning with simple PSF:")
596 # TODO investigation: Should we only re-run repair here, to use the
597 # new PSF? Maybe we *do* need to re-run measurement with PsfFlux, to
598 # use the fitted PSF?
599 # TODO investigation: do we need a separate measurement task here
600 # for the post-psf_measure_psf step, since we only want to do PsfFlux
601 # and GaussianFlux *after* we have a PSF? Maybe that's not relevant
602 # once DM-39203 is merged?
603 self.psf_repair.run(exposure=exposure, keepCRs=True)
604 # Re-estimate the background during this detection step, so that
605 # measurement uses the most accurate background-subtraction.
606 detections = self.psf_detection.run(table=table, exposure=exposure, background=background)
607 self.psf_source_measurement.run(detections.sources, exposure)
608 psf_result = self.psf_measure_psf.run(exposure=exposure, sources=detections.sources)
610 log_psf("Final PSF:")
612 # Final repair with final PSF, removing cosmic rays this time.
613 self.psf_repair.run(exposure=exposure)
614 # Final measurement with the CRs removed.
615 self.psf_source_measurement.run(detections.sources, exposure)
617 # PSF is set on exposure; only return candidates for optional saving.
618 return detections.sources, background, psf_result.cellSet
620 def _measure_aperture_correction(self, exposure, bright_sources):
621 """Measure and set the ApCorrMap on the Exposure, using
622 previously-measured bright sources.
624 Parameters
625 ----------
626 exposure : `lsst.afw.image.Exposure`
627 Exposure to set the ApCorrMap on.
628 bright_sources : `lsst.afw.table.SourceCatalog`
629 Catalog of detected bright sources; modified to include columns
630 necessary for point source determination for the aperture correction
631 calculation.
632 """
633 result = self.measure_aperture_correction.run(exposure, bright_sources)
634 exposure.setApCorrMap(result.apCorrMap)
636 def _find_stars(self, exposure, background, id_generator):
637 """Detect stars on an exposure that has a PSF model, and measure their
638 PSF, circular aperture, compensated gaussian fluxes.
640 Parameters
641 ----------
642 exposure : `lsst.afw.image.Exposure`
643 Exposure to set the ApCorrMap on.
644 background : `lsst.afw.math.BackgroundList`
645 Background that was fit to the exposure during detection;
646 modified in-place during subsequent detection.
647 id_generator : `lsst.meas.base.IdGenerator`
648 Object that generates source IDs and provides random seeds.
650 Returns
651 -------
652 stars : `SourceCatalog`
653 Sources that are very likely to be stars, with a limited set of
654 measurements performed on them.
655 """
656 table = afwTable.SourceTable.make(self.initial_stars_schema.schema)
657 # Re-estimate the background during this detection step, so that
658 # measurement uses the most accurate background-subtraction.
659 detections = self.star_detection.run(table=table, exposure=exposure, background=background)
660 sources = detections.sources
661 self.star_sky_sources.run(exposure.mask, id_generator.catalog_id, sources)
663 # TODO investigation: Could this deblender throw away blends of non-PSF sources?
664 self.star_deblend.run(exposure=exposure, sources=sources)
665 # The deblender may not produce a contiguous catalog; ensure
666 # contiguity for subsequent tasks.
667 if not sources.isContiguous():
668 sources = sources.copy(deep=True)
670 # Measure everything, and use those results to select only stars.
671 self.star_measurement.run(sources, exposure)
672 self.star_apply_aperture_correction.run(sources, exposure.info.getApCorrMap())
673 self.star_catalog_calculation.run(sources)
674 self.star_set_primary_flags.run(sources)
676 result = self.star_selector.run(sources)
677 # The star selector may not produce a contiguous catalog.
678 if not result.sourceCat.isContiguous():
679 return result.sourceCat.copy(deep=True)
680 else:
681 return result.sourceCat
683 def _match_psf_stars(self, psf_stars, stars):
684 """Match calibration stars to psf stars, to identify which were psf
685 candidates, and which were used or reserved during psf measurement.
687 Parameters
688 ----------
689 psf_stars : `lsst.afw.table.SourceCatalog`
690 PSF candidate stars that were sent to the psf determiner. Used to
691 populate psf-related flag fields.
692 stars : `lsst.afw.table.SourceCatalog`
693 Stars that will be used for calibration; psf-related fields will
694 be updated in-place.
696 Notes
697 -----
698 This code was adapted from CalibrateTask.copyIcSourceFields().
699 """
700 control = afwTable.MatchControl()
701 # Return all matched objects, to separate blends.
702 control.findOnlyClosest = False
703 matches = afwTable.matchXy(psf_stars, stars, 3.0, control)
704 deblend_key = stars.schema["deblend_nChild"].asKey()
705 matches = [m for m in matches if m[1].get(deblend_key) == 0]
707 # Because we had to allow multiple matches to handle parents, we now
708 # need to prune to the best (closest) matches.
709 # Closest matches is a dict of psf_stars source ID to Match record
710 # (psf_stars source, sourceCat source, distance in pixels).
711 best = {}
712 for match_psf, match_stars, d in matches:
713 match = best.get(match_psf.getId())
714 if match is None or d <= match[2]:
715 best[match_psf.getId()] = (match_psf, match_stars, d)
716 matches = list(best.values())
717 # We'll use this to construct index arrays into each catalog.
718 ids = np.array([(match_psf.getId(), match_stars.getId()) for match_psf, match_stars, d in matches]).T
720 # Check that no stars sources are listed twice; we already know
721 # that each match has a unique psf_stars id, due to using as the key
722 # in best above.
723 n_matches = len(matches)
724 n_unique = len(set(m[1].getId() for m in matches))
725 if n_unique != n_matches:
726 self.log.warning("%d psf_stars matched only %d stars; ",
727 n_matches, n_unique)
728 if n_matches == 0:
729 msg = (f"0 psf_stars out of {len(psf_stars)} matched {len(stars)} calib stars."
730 " Downstream processes probably won't have useful stars in this case."
731 " Is `star_source_selector` too strict?")
732 # TODO DM-39842: Turn this into an AlgorithmicError.
733 raise RuntimeError(msg)
735 # The indices of the IDs, so we can update the flag fields as arrays.
736 idx_psf_stars = np.searchsorted(psf_stars["id"], ids[0])
737 idx_stars = np.searchsorted(stars["id"], ids[1])
738 for field in self.psf_fields:
739 result = np.zeros(len(stars), dtype=bool)
740 result[idx_stars] = psf_stars[field][idx_psf_stars]
741 stars[field] = result
743 def _fit_astrometry(self, exposure, stars):
744 """Fit an astrometric model to the data and return the reference
745 matches used in the fit, and the fitted WCS.
747 Parameters
748 ----------
749 exposure : `lsst.afw.image.Exposure`
750 Exposure that is being fit, to get PSF and other metadata from.
751 Modified to add the fitted skyWcs.
752 stars : `SourceCatalog`
753 Good stars selected for use in calibration, with RA/Dec coordinates
754 computed from the pixel positions and fitted WCS.
756 Returns
757 -------
758 matches : `list` [`lsst.afw.table.ReferenceMatch`]
759 Reference/stars matches used in the fit.
760 """
761 result = self.astrometry.run(stars, exposure)
762 return result.matches, result.matchMeta
764 def _fit_photometry(self, exposure, stars):
765 """Fit a photometric model to the data and return the reference
766 matches used in the fit, and the fitted PhotoCalib.
768 Parameters
769 ----------
770 exposure : `lsst.afw.image.Exposure`
771 Exposure that is being fit, to get PSF and other metadata from.
772 Modified to be in nanojanksy units, with an assigned photoCalib
773 identically 1.
774 stars : `lsst.afw.table.SourceCatalog`
775 Good stars selected for use in calibration.
777 Returns
778 -------
779 calibrated_stars : `lsst.afw.table.SourceCatalog`
780 Star catalog with flux/magnitude columns computed from the fitted
781 photoCalib.
782 matches : `list` [`lsst.afw.table.ReferenceMatch`]
783 Reference/stars matches used in the fit.
784 photoCalib : `lsst.afw.image.PhotoCalib`
785 Photometric calibration that was fit to the star catalog.
786 """
787 result = self.photometry.run(exposure, stars)
788 calibrated_stars = result.photoCalib.calibrateCatalog(stars)
789 exposure.maskedImage = result.photoCalib.calibrateImage(exposure.maskedImage)
790 identity = afwImage.PhotoCalib(1.0,
791 result.photoCalib.getCalibrationErr(),
792 bbox=exposure.getBBox())
793 exposure.setPhotoCalib(identity)
795 return calibrated_stars, result.matches, result.matchMeta, result.photoCalib
797 def _summarize(self, exposure, stars, background):
798 """Compute summary statistics on the exposure and update in-place the
799 calibrations attached to it.
801 Parameters
802 ----------
803 exposure : `lsst.afw.image.Exposure`
804 Exposure that was calibrated, to get PSF and other metadata from.
805 Modified to contain the computed summary statistics.
806 stars : `SourceCatalog`
807 Good stars selected used in calibration.
808 background : `lsst.afw.math.BackgroundList`
809 Background that was fit to the exposure during detection of the
810 above stars.
811 """
812 # TODO investigation: because this takes the photoCalib from the
813 # exposure, photometric summary values may be "incorrect" (i.e. they
814 # will reflect the ==1 nJy calibration on the exposure, not the
815 # applied calibration). This needs to be checked.
816 summary = self.compute_summary_stats.run(exposure, stars, background)
817 exposure.info.setSummaryStats(summary)