Coverage for python/lsst/pipe/tasks/calibrateImage.py: 24%
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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 DM-38732: We want some kind of flag on Exposures/Catalogs to make
80 # it obvious which components had failed to be computed/persisted.
81 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 ]
289 self.psf_source_measurement.slots.shape = "ext_shapeHSM_HsmSourceMoments"
290 # Only measure apertures we need for PSF measurement.
291 self.psf_source_measurement.plugins["base_CircularApertureFlux"].radii = [12.0]
292 # TODO DM-40843: Remove this line once this is the psfex default.
293 self.psf_measure_psf.psfDeterminer["psfex"].photometricFluxField = \
294 "base_CircularApertureFlux_12_0_instFlux"
296 # No extendeness information available: we need the aperture
297 # corrections to determine that.
298 self.measure_aperture_correction.sourceSelector["science"].doUnresolved = False
299 self.measure_aperture_correction.sourceSelector["science"].flags.good = ["calib_psf_used"]
300 self.measure_aperture_correction.sourceSelector["science"].flags.bad = []
302 # Detection for good S/N for astrometry/photometry and other
303 # downstream tasks; detection mask to S/N>=5, but S/N>=10 peaks.
304 self.star_detection.thresholdValue = 5.0
305 self.star_detection.includeThresholdMultiplier = 2.0
306 self.star_measurement.plugins = ["base_PixelFlags",
307 "base_SdssCentroid",
308 "ext_shapeHSM_HsmSourceMoments",
309 'ext_shapeHSM_HsmPsfMoments',
310 "base_GaussianFlux",
311 "base_PsfFlux",
312 "base_CircularApertureFlux",
313 "base_ClassificationSizeExtendedness",
314 ]
315 self.star_measurement.slots.psfShape = "ext_shapeHSM_HsmPsfMoments"
316 self.star_measurement.slots.shape = "ext_shapeHSM_HsmSourceMoments"
317 # Only measure the apertures we need for star selection.
318 self.star_measurement.plugins["base_CircularApertureFlux"].radii = [12.0]
320 # Select isolated stars with reliable measurements and no bad flags.
321 self.star_selector["science"].doFlags = True
322 self.star_selector["science"].doUnresolved = True
323 self.star_selector["science"].doSignalToNoise = True
324 self.star_selector["science"].doIsolated = True
325 self.star_selector["science"].signalToNoise.minimum = 10.0
326 # Keep sky sources in the output catalog, even though they aren't
327 # wanted for calibration.
328 self.star_selector["science"].doSkySources = True
330 # Use the affine WCS fitter (assumes we have a good camera geometry).
331 self.astrometry.wcsFitter.retarget(lsst.meas.astrom.FitAffineWcsTask)
332 # phot_g_mean is the primary Gaia band for all input bands.
333 self.astrometry_ref_loader.anyFilterMapsToThis = "phot_g_mean"
335 # Only reject sky sources; we already selected good stars.
336 self.astrometry.sourceSelector["science"].doFlags = True
337 self.astrometry.sourceSelector["science"].flags.bad = ["sky_source"]
338 self.photometry.match.sourceSelection.doFlags = True
339 self.photometry.match.sourceSelection.flags.bad = ["sky_source"]
341 # All sources should be good for PSF summary statistics.
342 # TODO: These should both be changed to calib_psf_used with DM-41640.
343 self.compute_summary_stats.starSelection = "calib_photometry_used"
344 self.compute_summary_stats.starSelector.flags.good = ["calib_photometry_used"]
347class CalibrateImageTask(pipeBase.PipelineTask):
348 """Compute the PSF, aperture corrections, astrometric and photometric
349 calibrations, and summary statistics for a single science exposure, and
350 produce a catalog of brighter stars that were used to calibrate it.
352 Parameters
353 ----------
354 initial_stars_schema : `lsst.afw.table.Schema`
355 Schema of the initial_stars output catalog.
356 """
357 _DefaultName = "calibrateImage"
358 ConfigClass = CalibrateImageConfig
360 def __init__(self, initial_stars_schema=None, **kwargs):
361 super().__init__(**kwargs)
363 self.makeSubtask("snap_combine")
365 # PSF determination subtasks
366 self.makeSubtask("install_simple_psf")
367 self.makeSubtask("psf_repair")
368 self.makeSubtask("psf_subtract_background")
369 self.psf_schema = afwTable.SourceTable.makeMinimalSchema()
370 self.makeSubtask("psf_detection", schema=self.psf_schema)
371 self.makeSubtask("psf_source_measurement", schema=self.psf_schema)
372 self.makeSubtask("psf_measure_psf", schema=self.psf_schema)
374 self.makeSubtask("measure_aperture_correction", schema=self.psf_schema)
376 # star measurement subtasks
377 if initial_stars_schema is None:
378 initial_stars_schema = afwTable.SourceTable.makeMinimalSchema()
380 # These fields let us track which sources were used for psf and
381 # aperture correction calculations.
382 self.psf_fields = ("calib_psf_candidate", "calib_psf_used", "calib_psf_reserved",
383 # TODO DM-39203: these can be removed once apcorr is gone.
384 "apcorr_slot_CalibFlux_used", "apcorr_base_GaussianFlux_used",
385 "apcorr_base_PsfFlux_used")
386 for field in self.psf_fields:
387 item = self.psf_schema.find(field)
388 initial_stars_schema.addField(item.getField())
390 afwTable.CoordKey.addErrorFields(initial_stars_schema)
391 self.makeSubtask("star_detection", schema=initial_stars_schema)
392 self.makeSubtask("star_sky_sources", schema=initial_stars_schema)
393 self.makeSubtask("star_deblend", schema=initial_stars_schema)
394 self.makeSubtask("star_measurement", schema=initial_stars_schema)
395 self.makeSubtask("star_apply_aperture_correction", schema=initial_stars_schema)
396 self.makeSubtask("star_catalog_calculation", schema=initial_stars_schema)
397 self.makeSubtask("star_set_primary_flags", schema=initial_stars_schema, isSingleFrame=True)
398 self.makeSubtask("star_selector")
400 self.makeSubtask("astrometry", schema=initial_stars_schema)
401 self.makeSubtask("photometry", schema=initial_stars_schema)
403 self.makeSubtask("compute_summary_stats")
405 # For the butler to persist it.
406 self.initial_stars_schema = afwTable.SourceCatalog(initial_stars_schema)
408 def runQuantum(self, butlerQC, inputRefs, outputRefs):
409 inputs = butlerQC.get(inputRefs)
410 exposures = inputs.pop("exposures")
412 id_generator = self.config.id_generator.apply(butlerQC.quantum.dataId)
414 astrometry_loader = lsst.meas.algorithms.ReferenceObjectLoader(
415 dataIds=[ref.datasetRef.dataId for ref in inputRefs.astrometry_ref_cat],
416 refCats=inputs.pop("astrometry_ref_cat"),
417 name=self.config.connections.astrometry_ref_cat,
418 config=self.config.astrometry_ref_loader, log=self.log)
419 self.astrometry.setRefObjLoader(astrometry_loader)
421 photometry_loader = lsst.meas.algorithms.ReferenceObjectLoader(
422 dataIds=[ref.datasetRef.dataId for ref in inputRefs.photometry_ref_cat],
423 refCats=inputs.pop("photometry_ref_cat"),
424 name=self.config.connections.photometry_ref_cat,
425 config=self.config.photometry_ref_loader, log=self.log)
426 self.photometry.match.setRefObjLoader(photometry_loader)
428 # This should not happen with a properly configured execution context.
429 assert not inputs, "runQuantum got more inputs than expected"
431 # Specify the fields that `annotate` needs below, to ensure they
432 # exist, even as None.
433 result = pipeBase.Struct(exposure=None,
434 stars_footprints=None,
435 psf_stars_footprints=None,
436 )
437 try:
438 self.run(exposures=exposures, result=result, id_generator=id_generator)
439 except pipeBase.AlgorithmError as e:
440 error = pipeBase.AnnotatedPartialOutputsError.annotate(
441 e,
442 self,
443 result.exposure,
444 result.psf_stars_footprints,
445 result.stars_footprints,
446 log=self.log
447 )
448 butlerQC.put(result, outputRefs)
449 raise error from e
451 butlerQC.put(result, outputRefs)
453 @timeMethod
454 def run(self, *, exposures, id_generator=None, result=None):
455 """Find stars and perform psf measurement, then do a deeper detection
456 and measurement and calibrate astrometry and photometry from that.
458 Parameters
459 ----------
460 exposures : `lsst.afw.image.Exposure` or `list` [`lsst.afw.image.Exposure`]
461 Post-ISR exposure(s), with an initial WCS, VisitInfo, and Filter.
462 Modified in-place during processing if only one is passed.
463 If two exposures are passed, treat them as snaps and combine
464 before doing further processing.
465 id_generator : `lsst.meas.base.IdGenerator`, optional
466 Object that generates source IDs and provides random seeds.
467 result : `lsst.pipe.base.Struct`, optional
468 Result struct that is modified to allow saving of partial outputs
469 for some failure conditions. If the task completes successfully,
470 this is also returned.
472 Returns
473 -------
474 result : `lsst.pipe.base.Struct`
475 Results as a struct with attributes:
477 ``exposure``
478 Calibrated exposure, with pixels in nJy units.
479 (`lsst.afw.image.Exposure`)
480 ``stars``
481 Stars that were used to calibrate the exposure, with
482 calibrated fluxes and magnitudes.
483 (`astropy.table.Table`)
484 ``stars_footprints``
485 Footprints of stars that were used to calibrate the exposure.
486 (`lsst.afw.table.SourceCatalog`)
487 ``psf_stars``
488 Stars that were used to determine the image PSF.
489 (`astropy.table.Table`)
490 ``psf_stars_footprints``
491 Footprints of stars that were used to determine the image PSF.
492 (`lsst.afw.table.SourceCatalog`)
493 ``background``
494 Background that was fit to the exposure when detecting
495 ``stars``. (`lsst.afw.math.BackgroundList`)
496 ``applied_photo_calib``
497 Photometric calibration that was fit to the star catalog and
498 applied to the exposure. (`lsst.afw.image.PhotoCalib`)
499 ``astrometry_matches``
500 Reference catalog stars matches used in the astrometric fit.
501 (`list` [`lsst.afw.table.ReferenceMatch`] or `lsst.afw.table.BaseCatalog`)
502 ``photometry_matches``
503 Reference catalog stars matches used in the photometric fit.
504 (`list` [`lsst.afw.table.ReferenceMatch`] or `lsst.afw.table.BaseCatalog`)
505 """
506 if result is None:
507 result = pipeBase.Struct()
508 if id_generator is None:
509 id_generator = lsst.meas.base.IdGenerator()
511 result.exposure = self._handle_snaps(exposures)
513 # TODO remove on DM-43083: work around the fact that we don't want
514 # to run streak detection in this task in production.
515 result.exposure.mask.addMaskPlane("STREAK")
517 result.psf_stars_footprints, result.background, candidates = self._compute_psf(result.exposure,
518 id_generator)
519 result.psf_stars = result.psf_stars_footprints.asAstropy()
521 self._measure_aperture_correction(result.exposure, result.psf_stars)
523 result.stars_footprints = self._find_stars(result.exposure, result.background, id_generator)
524 self._match_psf_stars(result.psf_stars_footprints, result.stars_footprints)
525 result.stars = result.stars_footprints.asAstropy()
527 astrometry_matches, astrometry_meta = self._fit_astrometry(result.exposure, result.stars_footprints)
528 if self.config.optional_outputs:
529 result.astrometry_matches = lsst.meas.astrom.denormalizeMatches(astrometry_matches,
530 astrometry_meta)
532 result.stars_footprints, photometry_matches, \
533 photometry_meta, result.applied_photo_calib = self._fit_photometry(result.exposure,
534 result.stars_footprints)
535 # fit_photometry returns a new catalog, so we need a new astropy table view.
536 result.stars = result.stars_footprints.asAstropy()
537 if self.config.optional_outputs:
538 result.photometry_matches = lsst.meas.astrom.denormalizeMatches(photometry_matches,
539 photometry_meta)
541 self._summarize(result.exposure, result.stars_footprints, result.background)
543 return result
545 def _handle_snaps(self, exposure):
546 """Combine two snaps into one exposure, or return a single exposure.
548 Parameters
549 ----------
550 exposure : `lsst.afw.image.Exposure` or `list` [`lsst.afw.image.Exposure]`
551 One or two exposures to combine as snaps.
553 Returns
554 -------
555 exposure : `lsst.afw.image.Exposure`
556 A single exposure to continue processing.
558 Raises
559 ------
560 RuntimeError
561 Raised if input does not contain either 1 or 2 exposures.
562 """
563 if isinstance(exposure, lsst.afw.image.Exposure):
564 return exposure
566 if isinstance(exposure, collections.abc.Sequence):
567 match len(exposure):
568 case 1:
569 return exposure[0]
570 case 2:
571 return self.snap_combine.run(exposure[0], exposure[1]).exposure
572 case n:
573 raise RuntimeError(f"Can only process 1 or 2 snaps, not {n}.")
575 def _compute_psf(self, exposure, id_generator):
576 """Find bright sources detected on an exposure and fit a PSF model to
577 them, repairing likely cosmic rays before detection.
579 Repair, detect, measure, and compute PSF twice, to ensure the PSF
580 model does not include contributions from cosmic rays.
582 Parameters
583 ----------
584 exposure : `lsst.afw.image.Exposure`
585 Exposure to detect and measure bright stars on.
586 id_generator : `lsst.meas.base.IdGenerator`, optional
587 Object that generates source IDs and provides random seeds.
589 Returns
590 -------
591 sources : `lsst.afw.table.SourceCatalog`
592 Catalog of detected bright sources.
593 background : `lsst.afw.math.BackgroundList`
594 Background that was fit to the exposure during detection.
595 cell_set : `lsst.afw.math.SpatialCellSet`
596 PSF candidates returned by the psf determiner.
597 """
598 def log_psf(msg):
599 """Log the parameters of the psf and background, with a prepended
600 message.
601 """
602 position = exposure.psf.getAveragePosition()
603 sigma = exposure.psf.computeShape(position).getDeterminantRadius()
604 dimensions = exposure.psf.computeImage(position).getDimensions()
605 median_background = np.median(background.getImage().array)
606 self.log.info("%s sigma=%0.4f, dimensions=%s; median background=%0.2f",
607 msg, sigma, dimensions, median_background)
609 self.log.info("First pass detection with Guassian PSF FWHM=%s pixels",
610 self.config.install_simple_psf.fwhm)
611 self.install_simple_psf.run(exposure=exposure)
613 background = self.psf_subtract_background.run(exposure=exposure).background
614 log_psf("Initial PSF:")
615 self.psf_repair.run(exposure=exposure, keepCRs=True)
617 table = afwTable.SourceTable.make(self.psf_schema, id_generator.make_table_id_factory())
618 # Re-estimate the background during this detection step, so that
619 # measurement uses the most accurate background-subtraction.
620 detections = self.psf_detection.run(table=table, exposure=exposure, background=background)
621 self.psf_source_measurement.run(detections.sources, exposure)
622 psf_result = self.psf_measure_psf.run(exposure=exposure, sources=detections.sources)
623 # Replace the initial PSF with something simpler for the second
624 # repair/detect/measure/measure_psf step: this can help it converge.
625 self.install_simple_psf.run(exposure=exposure)
627 log_psf("Rerunning with simple PSF:")
628 # TODO investigation: Should we only re-run repair here, to use the
629 # new PSF? Maybe we *do* need to re-run measurement with PsfFlux, to
630 # use the fitted PSF?
631 # TODO investigation: do we need a separate measurement task here
632 # for the post-psf_measure_psf step, since we only want to do PsfFlux
633 # and GaussianFlux *after* we have a PSF? Maybe that's not relevant
634 # once DM-39203 is merged?
635 self.psf_repair.run(exposure=exposure, keepCRs=True)
636 # Re-estimate the background during this detection step, so that
637 # measurement uses the most accurate background-subtraction.
638 detections = self.psf_detection.run(table=table, exposure=exposure, background=background)
639 self.psf_source_measurement.run(detections.sources, exposure)
640 psf_result = self.psf_measure_psf.run(exposure=exposure, sources=detections.sources)
642 log_psf("Final PSF:")
644 # Final repair with final PSF, removing cosmic rays this time.
645 self.psf_repair.run(exposure=exposure)
646 # Final measurement with the CRs removed.
647 self.psf_source_measurement.run(detections.sources, exposure)
649 # PSF is set on exposure; only return candidates for optional saving.
650 return detections.sources, background, psf_result.cellSet
652 def _measure_aperture_correction(self, exposure, bright_sources):
653 """Measure and set the ApCorrMap on the Exposure, using
654 previously-measured bright sources.
656 Parameters
657 ----------
658 exposure : `lsst.afw.image.Exposure`
659 Exposure to set the ApCorrMap on.
660 bright_sources : `lsst.afw.table.SourceCatalog`
661 Catalog of detected bright sources; modified to include columns
662 necessary for point source determination for the aperture correction
663 calculation.
664 """
665 result = self.measure_aperture_correction.run(exposure, bright_sources)
666 exposure.setApCorrMap(result.apCorrMap)
668 def _find_stars(self, exposure, background, id_generator):
669 """Detect stars on an exposure that has a PSF model, and measure their
670 PSF, circular aperture, compensated gaussian fluxes.
672 Parameters
673 ----------
674 exposure : `lsst.afw.image.Exposure`
675 Exposure to set the ApCorrMap on.
676 background : `lsst.afw.math.BackgroundList`
677 Background that was fit to the exposure during detection;
678 modified in-place during subsequent detection.
679 id_generator : `lsst.meas.base.IdGenerator`
680 Object that generates source IDs and provides random seeds.
682 Returns
683 -------
684 stars : `SourceCatalog`
685 Sources that are very likely to be stars, with a limited set of
686 measurements performed on them.
687 """
688 table = afwTable.SourceTable.make(self.initial_stars_schema.schema,
689 id_generator.make_table_id_factory())
690 # Re-estimate the background during this detection step, so that
691 # measurement uses the most accurate background-subtraction.
692 detections = self.star_detection.run(table=table, exposure=exposure, background=background)
693 sources = detections.sources
694 self.star_sky_sources.run(exposure.mask, id_generator.catalog_id, sources)
696 # TODO investigation: Could this deblender throw away blends of non-PSF sources?
697 self.star_deblend.run(exposure=exposure, sources=sources)
698 # The deblender may not produce a contiguous catalog; ensure
699 # contiguity for subsequent tasks.
700 if not sources.isContiguous():
701 sources = sources.copy(deep=True)
703 # Measure everything, and use those results to select only stars.
704 self.star_measurement.run(sources, exposure)
705 self.star_apply_aperture_correction.run(sources, exposure.info.getApCorrMap())
706 self.star_catalog_calculation.run(sources)
707 self.star_set_primary_flags.run(sources)
709 result = self.star_selector.run(sources)
710 # The star selector may not produce a contiguous catalog.
711 if not result.sourceCat.isContiguous():
712 return result.sourceCat.copy(deep=True)
713 else:
714 return result.sourceCat
716 def _match_psf_stars(self, psf_stars, stars):
717 """Match calibration stars to psf stars, to identify which were psf
718 candidates, and which were used or reserved during psf measurement.
720 Parameters
721 ----------
722 psf_stars : `lsst.afw.table.SourceCatalog`
723 PSF candidate stars that were sent to the psf determiner. Used to
724 populate psf-related flag fields.
725 stars : `lsst.afw.table.SourceCatalog`
726 Stars that will be used for calibration; psf-related fields will
727 be updated in-place.
729 Notes
730 -----
731 This code was adapted from CalibrateTask.copyIcSourceFields().
732 """
733 control = afwTable.MatchControl()
734 # Return all matched objects, to separate blends.
735 control.findOnlyClosest = False
736 matches = afwTable.matchXy(psf_stars, stars, 3.0, control)
737 deblend_key = stars.schema["deblend_nChild"].asKey()
738 matches = [m for m in matches if m[1].get(deblend_key) == 0]
740 # Because we had to allow multiple matches to handle parents, we now
741 # need to prune to the best (closest) matches.
742 # Closest matches is a dict of psf_stars source ID to Match record
743 # (psf_stars source, sourceCat source, distance in pixels).
744 best = {}
745 for match_psf, match_stars, d in matches:
746 match = best.get(match_psf.getId())
747 if match is None or d <= match[2]:
748 best[match_psf.getId()] = (match_psf, match_stars, d)
749 matches = list(best.values())
750 # We'll use this to construct index arrays into each catalog.
751 ids = np.array([(match_psf.getId(), match_stars.getId()) for match_psf, match_stars, d in matches]).T
753 # Check that no stars sources are listed twice; we already know
754 # that each match has a unique psf_stars id, due to using as the key
755 # in best above.
756 n_matches = len(matches)
757 n_unique = len(set(m[1].getId() for m in matches))
758 if n_unique != n_matches:
759 self.log.warning("%d psf_stars matched only %d stars; ",
760 n_matches, n_unique)
761 if n_matches == 0:
762 msg = (f"0 psf_stars out of {len(psf_stars)} matched {len(stars)} calib stars."
763 " Downstream processes probably won't have useful stars in this case."
764 " Is `star_source_selector` too strict?")
765 # TODO DM-39842: Turn this into an AlgorithmicError.
766 raise RuntimeError(msg)
768 # The indices of the IDs, so we can update the flag fields as arrays.
769 idx_psf_stars = np.searchsorted(psf_stars["id"], ids[0])
770 idx_stars = np.searchsorted(stars["id"], ids[1])
771 for field in self.psf_fields:
772 result = np.zeros(len(stars), dtype=bool)
773 result[idx_stars] = psf_stars[field][idx_psf_stars]
774 stars[field] = result
776 def _fit_astrometry(self, exposure, stars):
777 """Fit an astrometric model to the data and return the reference
778 matches used in the fit, and the fitted WCS.
780 Parameters
781 ----------
782 exposure : `lsst.afw.image.Exposure`
783 Exposure that is being fit, to get PSF and other metadata from.
784 Modified to add the fitted skyWcs.
785 stars : `SourceCatalog`
786 Good stars selected for use in calibration, with RA/Dec coordinates
787 computed from the pixel positions and fitted WCS.
789 Returns
790 -------
791 matches : `list` [`lsst.afw.table.ReferenceMatch`]
792 Reference/stars matches used in the fit.
793 """
794 result = self.astrometry.run(stars, exposure)
795 return result.matches, result.matchMeta
797 def _fit_photometry(self, exposure, stars):
798 """Fit a photometric model to the data and return the reference
799 matches used in the fit, and the fitted PhotoCalib.
801 Parameters
802 ----------
803 exposure : `lsst.afw.image.Exposure`
804 Exposure that is being fit, to get PSF and other metadata from.
805 Modified to be in nanojanksy units, with an assigned photoCalib
806 identically 1.
807 stars : `lsst.afw.table.SourceCatalog`
808 Good stars selected for use in calibration.
810 Returns
811 -------
812 calibrated_stars : `lsst.afw.table.SourceCatalog`
813 Star catalog with flux/magnitude columns computed from the fitted
814 photoCalib.
815 matches : `list` [`lsst.afw.table.ReferenceMatch`]
816 Reference/stars matches used in the fit.
817 photoCalib : `lsst.afw.image.PhotoCalib`
818 Photometric calibration that was fit to the star catalog.
819 """
820 result = self.photometry.run(exposure, stars)
821 calibrated_stars = result.photoCalib.calibrateCatalog(stars)
822 exposure.maskedImage = result.photoCalib.calibrateImage(exposure.maskedImage)
823 identity = afwImage.PhotoCalib(1.0,
824 result.photoCalib.getCalibrationErr(),
825 bbox=exposure.getBBox())
826 exposure.setPhotoCalib(identity)
828 return calibrated_stars, result.matches, result.matchMeta, result.photoCalib
830 def _summarize(self, exposure, stars, background):
831 """Compute summary statistics on the exposure and update in-place the
832 calibrations attached to it.
834 Parameters
835 ----------
836 exposure : `lsst.afw.image.Exposure`
837 Exposure that was calibrated, to get PSF and other metadata from.
838 Modified to contain the computed summary statistics.
839 stars : `SourceCatalog`
840 Good stars selected used in calibration.
841 background : `lsst.afw.math.BackgroundList`
842 Background that was fit to the exposure during detection of the
843 above stars.
844 """
845 # TODO investigation: because this takes the photoCalib from the
846 # exposure, photometric summary values may be "incorrect" (i.e. they
847 # will reflect the ==1 nJy calibration on the exposure, not the
848 # applied calibration). This needs to be checked.
849 summary = self.compute_summary_stats.run(exposure, stars, background)
850 exposure.info.setSummaryStats(summary)