Coverage for python/lsst/pipe/tasks/calibrateImage.py: 26%
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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, maskStreaks, 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_mask_streaks = pexConfig.ConfigurableField(
211 target=maskStreaks.MaskStreaksTask,
212 doc="Task for masking streaks. Adds a STREAK mask plane to an exposure.",
213 )
214 star_deblend = pexConfig.ConfigurableField(
215 target=lsst.meas.deblender.SourceDeblendTask,
216 doc="Split blended sources into their components."
217 )
218 star_measurement = pexConfig.ConfigurableField(
219 target=lsst.meas.base.SingleFrameMeasurementTask,
220 doc="Task to measure stars to return in the output catalog."
221 )
222 star_apply_aperture_correction = pexConfig.ConfigurableField(
223 target=lsst.meas.base.ApplyApCorrTask,
224 doc="Task to apply aperture corrections to the selected stars."
225 )
226 star_catalog_calculation = pexConfig.ConfigurableField(
227 target=lsst.meas.base.CatalogCalculationTask,
228 doc="Task to compute extendedness values on the star catalog, "
229 "for the star selector to remove extended sources."
230 )
231 star_set_primary_flags = pexConfig.ConfigurableField(
232 target=lsst.meas.algorithms.setPrimaryFlags.SetPrimaryFlagsTask,
233 doc="Task to add isPrimary to the catalog."
234 )
235 star_selector = lsst.meas.algorithms.sourceSelectorRegistry.makeField(
236 default="science",
237 doc="Task to select isolated stars to use for calibration."
238 )
240 # final calibrations and statistics
241 astrometry = pexConfig.ConfigurableField(
242 target=lsst.meas.astrom.AstrometryTask,
243 doc="Task to perform astrometric calibration to fit a WCS.",
244 )
245 astrometry_ref_loader = pexConfig.ConfigField(
246 dtype=lsst.meas.algorithms.LoadReferenceObjectsConfig,
247 doc="Configuration of reference object loader for astrometric fit.",
248 )
249 photometry = pexConfig.ConfigurableField(
250 target=photoCal.PhotoCalTask,
251 doc="Task to perform photometric calibration to fit a PhotoCalib.",
252 )
253 photometry_ref_loader = pexConfig.ConfigField(
254 dtype=lsst.meas.algorithms.LoadReferenceObjectsConfig,
255 doc="Configuration of reference object loader for photometric fit.",
256 )
258 compute_summary_stats = pexConfig.ConfigurableField(
259 target=computeExposureSummaryStats.ComputeExposureSummaryStatsTask,
260 doc="Task to to compute summary statistics on the calibrated exposure."
261 )
263 def setDefaults(self):
264 super().setDefaults()
266 # Use a very broad PSF here, to throughly reject CRs.
267 # TODO investigation: a large initial psf guess may make stars look
268 # like CRs for very good seeing images.
269 self.install_simple_psf.fwhm = 4
271 # S/N>=50 sources for PSF determination, but detection to S/N=5.
272 self.psf_detection.thresholdValue = 5.0
273 self.psf_detection.includeThresholdMultiplier = 10.0
274 # TODO investigation: Probably want False here, but that may require
275 # tweaking the background spatial scale, to make it small enough to
276 # prevent extra peaks in the wings of bright objects.
277 self.psf_detection.doTempLocalBackground = False
278 # NOTE: we do want reEstimateBackground=True in psf_detection, so that
279 # each measurement step is done with the best background available.
281 # Minimal measurement plugins for PSF determination.
282 # TODO DM-39203: We can drop GaussianFlux and PsfFlux, if we use
283 # shapeHSM/moments for star/galaxy separation.
284 # TODO DM-39203: we can remove aperture correction from this task once
285 # we are using the shape-based star/galaxy code.
286 self.psf_source_measurement.plugins = ["base_PixelFlags",
287 "base_SdssCentroid",
288 "ext_shapeHSM_HsmSourceMoments",
289 "base_CircularApertureFlux",
290 "base_GaussianFlux",
291 "base_PsfFlux",
292 "base_ClassificationSizeExtendedness",
293 ]
294 self.psf_source_measurement.slots.shape = "ext_shapeHSM_HsmSourceMoments"
295 # Only measure apertures we need for PSF measurement.
296 self.psf_source_measurement.plugins["base_CircularApertureFlux"].radii = [12.0]
297 # TODO DM-40843: Remove this line once this is the psfex default.
298 self.psf_measure_psf.psfDeterminer["psfex"].photometricFluxField = \
299 "base_CircularApertureFlux_12_0_instFlux"
301 # No extendeness information available: we need the aperture
302 # corrections to determine that.
303 self.measure_aperture_correction.sourceSelector["science"].doUnresolved = False
304 self.measure_aperture_correction.sourceSelector["science"].flags.good = ["calib_psf_used"]
305 self.measure_aperture_correction.sourceSelector["science"].flags.bad = []
307 # Detection for good S/N for astrometry/photometry and other
308 # downstream tasks; detection mask to S/N>=5, but S/N>=10 peaks.
309 self.star_detection.thresholdValue = 5.0
310 self.star_detection.includeThresholdMultiplier = 2.0
311 self.star_measurement.plugins = ["base_PixelFlags",
312 "base_SdssCentroid",
313 "ext_shapeHSM_HsmSourceMoments",
314 'ext_shapeHSM_HsmPsfMoments',
315 "base_GaussianFlux",
316 "base_PsfFlux",
317 "base_CircularApertureFlux",
318 "base_ClassificationSizeExtendedness",
319 ]
320 self.star_measurement.slots.psfShape = "ext_shapeHSM_HsmPsfMoments"
321 self.star_measurement.slots.shape = "ext_shapeHSM_HsmSourceMoments"
322 # Only measure the apertures we need for star selection.
323 self.star_measurement.plugins["base_CircularApertureFlux"].radii = [12.0]
325 # Keep track of which footprints contain streaks
326 self.star_measurement.plugins['base_PixelFlags'].masksFpAnywhere = ['STREAK']
327 self.star_measurement.plugins['base_PixelFlags'].masksFpCenter = ['STREAK']
329 # Select isolated stars with reliable measurements and no bad flags.
330 self.star_selector["science"].doFlags = True
331 self.star_selector["science"].doUnresolved = True
332 self.star_selector["science"].doSignalToNoise = True
333 self.star_selector["science"].doIsolated = True
334 self.star_selector["science"].signalToNoise.minimum = 10.0
335 # Keep sky sources in the output catalog, even though they aren't
336 # wanted for calibration.
337 self.star_selector["science"].doSkySources = True
339 # Use the affine WCS fitter (assumes we have a good camera geometry).
340 self.astrometry.wcsFitter.retarget(lsst.meas.astrom.FitAffineWcsTask)
341 # phot_g_mean is the primary Gaia band for all input bands.
342 self.astrometry_ref_loader.anyFilterMapsToThis = "phot_g_mean"
344 # Only reject sky sources; we already selected good stars.
345 self.astrometry.sourceSelector["science"].doFlags = True
346 self.astrometry.sourceSelector["science"].flags.bad = ["sky_source"]
347 self.photometry.match.sourceSelection.doFlags = True
348 self.photometry.match.sourceSelection.flags.bad = ["sky_source"]
350 # All sources should be good for PSF summary statistics.
351 # TODO: These should both be changed to calib_psf_used with DM-41640.
352 self.compute_summary_stats.starSelection = "calib_photometry_used"
353 self.compute_summary_stats.starSelector.flags.good = ["calib_photometry_used"]
356class CalibrateImageTask(pipeBase.PipelineTask):
357 """Compute the PSF, aperture corrections, astrometric and photometric
358 calibrations, and summary statistics for a single science exposure, and
359 produce a catalog of brighter stars that were used to calibrate it.
361 Parameters
362 ----------
363 initial_stars_schema : `lsst.afw.table.Schema`
364 Schema of the initial_stars output catalog.
365 """
366 _DefaultName = "calibrateImage"
367 ConfigClass = CalibrateImageConfig
369 def __init__(self, initial_stars_schema=None, **kwargs):
370 super().__init__(**kwargs)
372 self.makeSubtask("snap_combine")
374 # PSF determination subtasks
375 self.makeSubtask("install_simple_psf")
376 self.makeSubtask("psf_repair")
377 self.makeSubtask("psf_subtract_background")
378 self.psf_schema = afwTable.SourceTable.makeMinimalSchema()
379 self.makeSubtask("psf_detection", schema=self.psf_schema)
380 self.makeSubtask("psf_source_measurement", schema=self.psf_schema)
381 self.makeSubtask("psf_measure_psf", schema=self.psf_schema)
383 self.makeSubtask("measure_aperture_correction", schema=self.psf_schema)
385 # star measurement subtasks
386 if initial_stars_schema is None:
387 initial_stars_schema = afwTable.SourceTable.makeMinimalSchema()
389 # These fields let us track which sources were used for psf and
390 # aperture correction calculations.
391 self.psf_fields = ("calib_psf_candidate", "calib_psf_used", "calib_psf_reserved",
392 # TODO DM-39203: these can be removed once apcorr is gone.
393 "apcorr_slot_CalibFlux_used", "apcorr_base_GaussianFlux_used",
394 "apcorr_base_PsfFlux_used")
395 for field in self.psf_fields:
396 item = self.psf_schema.find(field)
397 initial_stars_schema.addField(item.getField())
399 afwTable.CoordKey.addErrorFields(initial_stars_schema)
400 self.makeSubtask("star_detection", schema=initial_stars_schema)
401 self.makeSubtask("star_sky_sources", schema=initial_stars_schema)
402 self.makeSubtask("star_mask_streaks")
403 self.makeSubtask("star_deblend", schema=initial_stars_schema)
404 self.makeSubtask("star_measurement", schema=initial_stars_schema)
405 self.makeSubtask("star_apply_aperture_correction", schema=initial_stars_schema)
406 self.makeSubtask("star_catalog_calculation", schema=initial_stars_schema)
407 self.makeSubtask("star_set_primary_flags", schema=initial_stars_schema, isSingleFrame=True)
408 self.makeSubtask("star_selector")
410 self.makeSubtask("astrometry", schema=initial_stars_schema)
411 self.makeSubtask("photometry", schema=initial_stars_schema)
413 self.makeSubtask("compute_summary_stats")
415 # For the butler to persist it.
416 self.initial_stars_schema = afwTable.SourceCatalog(initial_stars_schema)
418 def runQuantum(self, butlerQC, inputRefs, outputRefs):
419 inputs = butlerQC.get(inputRefs)
420 id_generator = self.config.id_generator.apply(butlerQC.quantum.dataId)
422 astrometry_loader = lsst.meas.algorithms.ReferenceObjectLoader(
423 dataIds=[ref.datasetRef.dataId for ref in inputRefs.astrometry_ref_cat],
424 refCats=inputs.pop("astrometry_ref_cat"),
425 name=self.config.connections.astrometry_ref_cat,
426 config=self.config.astrometry_ref_loader, log=self.log)
427 self.astrometry.setRefObjLoader(astrometry_loader)
429 photometry_loader = lsst.meas.algorithms.ReferenceObjectLoader(
430 dataIds=[ref.datasetRef.dataId for ref in inputRefs.photometry_ref_cat],
431 refCats=inputs.pop("photometry_ref_cat"),
432 name=self.config.connections.photometry_ref_cat,
433 config=self.config.photometry_ref_loader, log=self.log)
434 self.photometry.match.setRefObjLoader(photometry_loader)
436 outputs = self.run(id_generator=id_generator, **inputs)
438 butlerQC.put(outputs, outputRefs)
440 @timeMethod
441 def run(self, *, exposures, id_generator=None):
442 """Find stars and perform psf measurement, then do a deeper detection
443 and measurement and calibrate astrometry and photometry from that.
445 Parameters
446 ----------
447 exposures : `lsst.afw.image.Exposure` or `list` [`lsst.afw.image.Exposure`]
448 Post-ISR exposure(s), with an initial WCS, VisitInfo, and Filter.
449 Modified in-place during processing if only one is passed.
450 If two exposures are passed, treat them as snaps and combine
451 before doing further processing.
452 id_generator : `lsst.meas.base.IdGenerator`, optional
453 Object that generates source IDs and provides random seeds.
455 Returns
456 -------
457 result : `lsst.pipe.base.Struct`
458 Results as a struct with attributes:
460 ``output_exposure``
461 Calibrated exposure, with pixels in nJy units.
462 (`lsst.afw.image.Exposure`)
463 ``stars``
464 Stars that were used to calibrate the exposure, with
465 calibrated fluxes and magnitudes.
466 (`astropy.table.Table`)
467 ``stars_footprints``
468 Footprints of stars that were used to calibrate the exposure.
469 (`lsst.afw.table.SourceCatalog`)
470 ``psf_stars``
471 Stars that were used to determine the image PSF.
472 (`astropy.table.Table`)
473 ``psf_stars_footprints``
474 Footprints of stars that were used to determine the image PSF.
475 (`lsst.afw.table.SourceCatalog`)
476 ``background``
477 Background that was fit to the exposure when detecting
478 ``stars``. (`lsst.afw.math.BackgroundList`)
479 ``applied_photo_calib``
480 Photometric calibration that was fit to the star catalog and
481 applied to the exposure. (`lsst.afw.image.PhotoCalib`)
482 ``astrometry_matches``
483 Reference catalog stars matches used in the astrometric fit.
484 (`list` [`lsst.afw.table.ReferenceMatch`] or `lsst.afw.table.BaseCatalog`)
485 ``photometry_matches``
486 Reference catalog stars matches used in the photometric fit.
487 (`list` [`lsst.afw.table.ReferenceMatch`] or `lsst.afw.table.BaseCatalog`)
488 """
489 if id_generator is None:
490 id_generator = lsst.meas.base.IdGenerator()
492 exposure = self._handle_snaps(exposures)
494 psf_stars, background, candidates = self._compute_psf(exposure)
496 self._measure_aperture_correction(exposure, psf_stars)
498 stars = self._find_stars(exposure, background, id_generator)
499 self._match_psf_stars(psf_stars, stars)
501 astrometry_matches, astrometry_meta = self._fit_astrometry(exposure, stars)
502 stars, photometry_matches, photometry_meta, photo_calib = self._fit_photometry(exposure, stars)
504 self._summarize(exposure, stars, background)
506 if self.config.optional_outputs:
507 astrometry_matches = lsst.meas.astrom.denormalizeMatches(astrometry_matches, astrometry_meta)
508 photometry_matches = lsst.meas.astrom.denormalizeMatches(photometry_matches, photometry_meta)
510 return pipeBase.Struct(output_exposure=exposure,
511 stars_footprints=stars,
512 stars=stars.asAstropy(),
513 psf_stars_footprints=psf_stars,
514 psf_stars=psf_stars.asAstropy(),
515 background=background,
516 applied_photo_calib=photo_calib,
517 astrometry_matches=astrometry_matches,
518 photometry_matches=photometry_matches)
520 def _handle_snaps(self, exposure):
521 """Combine two snaps into one exposure, or return a single exposure.
523 Parameters
524 ----------
525 exposure : `lsst.afw.image.Exposure` or `list` [`lsst.afw.image.Exposure]`
526 One or two exposures to combine as snaps.
528 Returns
529 -------
530 exposure : `lsst.afw.image.Exposure`
531 A single exposure to continue processing.
533 Raises
534 ------
535 RuntimeError
536 Raised if input does not contain either 1 or 2 exposures.
537 """
538 if isinstance(exposure, lsst.afw.image.Exposure):
539 return exposure
541 if isinstance(exposure, collections.abc.Sequence):
542 match len(exposure):
543 case 1:
544 return exposure[0]
545 case 2:
546 return self.snap_combine.run(exposure[0], exposure[1]).exposure
547 case n:
548 raise RuntimeError(f"Can only process 1 or 2 snaps, not {n}.")
550 def _compute_psf(self, exposure, guess_psf=True):
551 """Find bright sources detected on an exposure and fit a PSF model to
552 them, repairing likely cosmic rays before detection.
554 Repair, detect, measure, and compute PSF twice, to ensure the PSF
555 model does not include contributions from cosmic rays.
557 Parameters
558 ----------
559 exposure : `lsst.afw.image.Exposure`
560 Exposure to detect and measure bright stars on.
562 Returns
563 -------
564 sources : `lsst.afw.table.SourceCatalog`
565 Catalog of detected bright sources.
566 background : `lsst.afw.math.BackgroundList`
567 Background that was fit to the exposure during detection.
568 cell_set : `lsst.afw.math.SpatialCellSet`
569 PSF candidates returned by the psf determiner.
570 """
571 def log_psf(msg):
572 """Log the parameters of the psf and background, with a prepended
573 message.
574 """
575 position = exposure.psf.getAveragePosition()
576 sigma = exposure.psf.computeShape(position).getDeterminantRadius()
577 dimensions = exposure.psf.computeImage(position).getDimensions()
578 median_background = np.median(background.getImage().array)
579 self.log.info("%s sigma=%0.4f, dimensions=%s; median background=%0.2f",
580 msg, sigma, dimensions, median_background)
582 self.log.info("First pass detection with Guassian PSF FWHM=%s pixels",
583 self.config.install_simple_psf.fwhm)
584 self.install_simple_psf.run(exposure=exposure)
586 background = self.psf_subtract_background.run(exposure=exposure).background
587 log_psf("Initial PSF:")
588 self.psf_repair.run(exposure=exposure, keepCRs=True)
590 table = afwTable.SourceTable.make(self.psf_schema)
591 # Re-estimate the background during this detection step, so that
592 # measurement uses the most accurate background-subtraction.
593 detections = self.psf_detection.run(table=table, exposure=exposure, background=background)
594 self.psf_source_measurement.run(detections.sources, exposure)
595 psf_result = self.psf_measure_psf.run(exposure=exposure, sources=detections.sources)
596 # Replace the initial PSF with something simpler for the second
597 # repair/detect/measure/measure_psf step: this can help it converge.
598 self.install_simple_psf.run(exposure=exposure)
600 log_psf("Rerunning with simple PSF:")
601 # TODO investigation: Should we only re-run repair here, to use the
602 # new PSF? Maybe we *do* need to re-run measurement with PsfFlux, to
603 # use the fitted PSF?
604 # TODO investigation: do we need a separate measurement task here
605 # for the post-psf_measure_psf step, since we only want to do PsfFlux
606 # and GaussianFlux *after* we have a PSF? Maybe that's not relevant
607 # once DM-39203 is merged?
608 self.psf_repair.run(exposure=exposure, keepCRs=True)
609 # Re-estimate the background during this detection step, so that
610 # measurement uses the most accurate background-subtraction.
611 detections = self.psf_detection.run(table=table, exposure=exposure, background=background)
612 self.psf_source_measurement.run(detections.sources, exposure)
613 psf_result = self.psf_measure_psf.run(exposure=exposure, sources=detections.sources)
615 log_psf("Final PSF:")
617 # Final repair with final PSF, removing cosmic rays this time.
618 self.psf_repair.run(exposure=exposure)
619 # Final measurement with the CRs removed.
620 self.psf_source_measurement.run(detections.sources, exposure)
622 # PSF is set on exposure; only return candidates for optional saving.
623 return detections.sources, background, psf_result.cellSet
625 def _measure_aperture_correction(self, exposure, bright_sources):
626 """Measure and set the ApCorrMap on the Exposure, using
627 previously-measured bright sources.
629 Parameters
630 ----------
631 exposure : `lsst.afw.image.Exposure`
632 Exposure to set the ApCorrMap on.
633 bright_sources : `lsst.afw.table.SourceCatalog`
634 Catalog of detected bright sources; modified to include columns
635 necessary for point source determination for the aperture correction
636 calculation.
637 """
638 result = self.measure_aperture_correction.run(exposure, bright_sources)
639 exposure.setApCorrMap(result.apCorrMap)
641 def _find_stars(self, exposure, background, id_generator):
642 """Detect stars on an exposure that has a PSF model, and measure their
643 PSF, circular aperture, compensated gaussian fluxes.
645 Parameters
646 ----------
647 exposure : `lsst.afw.image.Exposure`
648 Exposure to set the ApCorrMap on.
649 background : `lsst.afw.math.BackgroundList`
650 Background that was fit to the exposure during detection;
651 modified in-place during subsequent detection.
652 id_generator : `lsst.meas.base.IdGenerator`
653 Object that generates source IDs and provides random seeds.
655 Returns
656 -------
657 stars : `SourceCatalog`
658 Sources that are very likely to be stars, with a limited set of
659 measurements performed on them.
660 """
661 table = afwTable.SourceTable.make(self.initial_stars_schema.schema)
662 # Re-estimate the background during this detection step, so that
663 # measurement uses the most accurate background-subtraction.
664 detections = self.star_detection.run(table=table, exposure=exposure, background=background)
665 sources = detections.sources
666 self.star_sky_sources.run(exposure.mask, id_generator.catalog_id, sources)
668 # Mask streaks
669 self.star_mask_streaks.run(exposure)
671 # TODO investigation: Could this deblender throw away blends of non-PSF sources?
672 self.star_deblend.run(exposure=exposure, sources=sources)
673 # The deblender may not produce a contiguous catalog; ensure
674 # contiguity for subsequent tasks.
675 if not sources.isContiguous():
676 sources = sources.copy(deep=True)
678 # Measure everything, and use those results to select only stars.
679 self.star_measurement.run(sources, exposure)
680 self.star_apply_aperture_correction.run(sources, exposure.info.getApCorrMap())
681 self.star_catalog_calculation.run(sources)
682 self.star_set_primary_flags.run(sources)
684 result = self.star_selector.run(sources)
685 # The star selector may not produce a contiguous catalog.
686 if not result.sourceCat.isContiguous():
687 return result.sourceCat.copy(deep=True)
688 else:
689 return result.sourceCat
691 def _match_psf_stars(self, psf_stars, stars):
692 """Match calibration stars to psf stars, to identify which were psf
693 candidates, and which were used or reserved during psf measurement.
695 Parameters
696 ----------
697 psf_stars : `lsst.afw.table.SourceCatalog`
698 PSF candidate stars that were sent to the psf determiner. Used to
699 populate psf-related flag fields.
700 stars : `lsst.afw.table.SourceCatalog`
701 Stars that will be used for calibration; psf-related fields will
702 be updated in-place.
704 Notes
705 -----
706 This code was adapted from CalibrateTask.copyIcSourceFields().
707 """
708 control = afwTable.MatchControl()
709 # Return all matched objects, to separate blends.
710 control.findOnlyClosest = False
711 matches = afwTable.matchXy(psf_stars, stars, 3.0, control)
712 deblend_key = stars.schema["deblend_nChild"].asKey()
713 matches = [m for m in matches if m[1].get(deblend_key) == 0]
715 # Because we had to allow multiple matches to handle parents, we now
716 # need to prune to the best (closest) matches.
717 # Closest matches is a dict of psf_stars source ID to Match record
718 # (psf_stars source, sourceCat source, distance in pixels).
719 best = {}
720 for match_psf, match_stars, d in matches:
721 match = best.get(match_psf.getId())
722 if match is None or d <= match[2]:
723 best[match_psf.getId()] = (match_psf, match_stars, d)
724 matches = list(best.values())
725 # We'll use this to construct index arrays into each catalog.
726 ids = np.array([(match_psf.getId(), match_stars.getId()) for match_psf, match_stars, d in matches]).T
728 # Check that no stars sources are listed twice; we already know
729 # that each match has a unique psf_stars id, due to using as the key
730 # in best above.
731 n_matches = len(matches)
732 n_unique = len(set(m[1].getId() for m in matches))
733 if n_unique != n_matches:
734 self.log.warning("%d psf_stars matched only %d stars; ",
735 n_matches, n_unique)
737 # The indices of the IDs, so we can update the flag fields as arrays.
738 idx_psf_stars = np.searchsorted(psf_stars["id"], ids[0])
739 idx_stars = np.searchsorted(stars["id"], ids[1])
740 for field in self.psf_fields:
741 result = np.zeros(len(stars), dtype=bool)
742 result[idx_stars] = psf_stars[field][idx_psf_stars]
743 stars[field] = result
745 def _fit_astrometry(self, exposure, stars):
746 """Fit an astrometric model to the data and return the reference
747 matches used in the fit, and the fitted WCS.
749 Parameters
750 ----------
751 exposure : `lsst.afw.image.Exposure`
752 Exposure that is being fit, to get PSF and other metadata from.
753 Modified to add the fitted skyWcs.
754 stars : `SourceCatalog`
755 Good stars selected for use in calibration, with RA/Dec coordinates
756 computed from the pixel positions and fitted WCS.
758 Returns
759 -------
760 matches : `list` [`lsst.afw.table.ReferenceMatch`]
761 Reference/stars matches used in the fit.
762 """
763 result = self.astrometry.run(stars, exposure)
764 return result.matches, result.matchMeta
766 def _fit_photometry(self, exposure, stars):
767 """Fit a photometric model to the data and return the reference
768 matches used in the fit, and the fitted PhotoCalib.
770 Parameters
771 ----------
772 exposure : `lsst.afw.image.Exposure`
773 Exposure that is being fit, to get PSF and other metadata from.
774 Modified to be in nanojanksy units, with an assigned photoCalib
775 identically 1.
776 stars : `lsst.afw.table.SourceCatalog`
777 Good stars selected for use in calibration.
779 Returns
780 -------
781 calibrated_stars : `lsst.afw.table.SourceCatalog`
782 Star catalog with flux/magnitude columns computed from the fitted
783 photoCalib.
784 matches : `list` [`lsst.afw.table.ReferenceMatch`]
785 Reference/stars matches used in the fit.
786 photoCalib : `lsst.afw.image.PhotoCalib`
787 Photometric calibration that was fit to the star catalog.
788 """
789 result = self.photometry.run(exposure, stars)
790 calibrated_stars = result.photoCalib.calibrateCatalog(stars)
791 exposure.maskedImage = result.photoCalib.calibrateImage(exposure.maskedImage)
792 identity = afwImage.PhotoCalib(1.0,
793 result.photoCalib.getCalibrationErr(),
794 bbox=exposure.getBBox())
795 exposure.setPhotoCalib(identity)
797 return calibrated_stars, result.matches, result.matchMeta, result.photoCalib
799 def _summarize(self, exposure, stars, background):
800 """Compute summary statistics on the exposure and update in-place the
801 calibrations attached to it.
803 Parameters
804 ----------
805 exposure : `lsst.afw.image.Exposure`
806 Exposure that was calibrated, to get PSF and other metadata from.
807 Modified to contain the computed summary statistics.
808 stars : `SourceCatalog`
809 Good stars selected used in calibration.
810 background : `lsst.afw.math.BackgroundList`
811 Background that was fit to the exposure during detection of the
812 above stars.
813 """
814 # TODO investigation: because this takes the photoCalib from the
815 # exposure, photometric summary values may be "incorrect" (i.e. they
816 # will reflect the ==1 nJy calibration on the exposure, not the
817 # applied calibration). This needs to be checked.
818 summary = self.compute_summary_stats.run(exposure, stars, background)
819 exposure.info.setSummaryStats(summary)