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1# This file is part of jointcal.
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
23import os
25import astropy.time
26import numpy as np
27import astropy.units as u
29import lsst.geom
30import lsst.utils
31import lsst.pex.config as pexConfig
32import lsst.pipe.base as pipeBase
33from lsst.afw.image import fluxErrFromABMagErr
34import lsst.pex.exceptions as pexExceptions
35import lsst.afw.table
36import lsst.log
37import lsst.meas.algorithms
38from lsst.pipe.tasks.colorterms import ColortermLibrary
39from lsst.verify import Job, Measurement
41from lsst.meas.algorithms import LoadIndexedReferenceObjectsTask, ReferenceSourceSelectorTask
42from lsst.meas.algorithms.sourceSelector import sourceSelectorRegistry
44from .dataIds import PerTractCcdDataIdContainer
46import lsst.jointcal
47from lsst.jointcal import MinimizeResult
49__all__ = ["JointcalConfig", "JointcalRunner", "JointcalTask"]
51Photometry = collections.namedtuple('Photometry', ('fit', 'model'))
52Astrometry = collections.namedtuple('Astrometry', ('fit', 'model', 'sky_to_tan_projection'))
55# TODO: move this to MeasurementSet in lsst.verify per DM-12655.
56def add_measurement(job, name, value):
57 meas = Measurement(job.metrics[name], value)
58 job.measurements.insert(meas)
61class JointcalRunner(pipeBase.ButlerInitializedTaskRunner):
62 """Subclass of TaskRunner for jointcalTask
64 jointcalTask.runDataRef() takes a number of arguments, one of which is a list of dataRefs
65 extracted from the command line (whereas most CmdLineTasks' runDataRef methods take
66 single dataRef, are are called repeatedly). This class transforms the processed
67 arguments generated by the ArgumentParser into the arguments expected by
68 Jointcal.runDataRef().
70 See pipeBase.TaskRunner for more information.
71 """
73 @staticmethod
74 def getTargetList(parsedCmd, **kwargs):
75 """
76 Return a list of tuples per tract, each containing (dataRefs, kwargs).
78 Jointcal operates on lists of dataRefs simultaneously.
79 """
80 kwargs['profile_jointcal'] = parsedCmd.profile_jointcal
81 kwargs['butler'] = parsedCmd.butler
83 # organize data IDs by tract
84 refListDict = {}
85 for ref in parsedCmd.id.refList:
86 refListDict.setdefault(ref.dataId["tract"], []).append(ref)
87 # we call runDataRef() once with each tract
88 result = [(refListDict[tract], kwargs) for tract in sorted(refListDict.keys())]
89 return result
91 def __call__(self, args):
92 """
93 Parameters
94 ----------
95 args
96 Arguments for Task.runDataRef()
98 Returns
99 -------
100 pipe.base.Struct
101 if self.doReturnResults is False:
103 - ``exitStatus``: 0 if the task completed successfully, 1 otherwise.
105 if self.doReturnResults is True:
107 - ``result``: the result of calling jointcal.runDataRef()
108 - ``exitStatus``: 0 if the task completed successfully, 1 otherwise.
109 """
110 exitStatus = 0 # exit status for shell
112 # NOTE: cannot call self.makeTask because that assumes args[0] is a single dataRef.
113 dataRefList, kwargs = args
114 butler = kwargs.pop('butler')
115 task = self.TaskClass(config=self.config, log=self.log, butler=butler)
116 result = None
117 try:
118 result = task.runDataRef(dataRefList, **kwargs)
119 exitStatus = result.exitStatus
120 job_path = butler.get('verify_job_filename')
121 result.job.write(job_path[0])
122 except Exception as e: # catch everything, sort it out later.
123 if self.doRaise:
124 raise e
125 else:
126 exitStatus = 1
127 eName = type(e).__name__
128 tract = dataRefList[0].dataId['tract']
129 task.log.fatal("Failed processing tract %s, %s: %s", tract, eName, e)
131 # Put the butler back into kwargs for the other Tasks.
132 kwargs['butler'] = butler
133 if self.doReturnResults:
134 return pipeBase.Struct(result=result, exitStatus=exitStatus)
135 else:
136 return pipeBase.Struct(exitStatus=exitStatus)
139class JointcalConfig(pexConfig.Config):
140 """Configuration for JointcalTask"""
142 doAstrometry = pexConfig.Field(
143 doc="Fit astrometry and write the fitted result.",
144 dtype=bool,
145 default=True
146 )
147 doPhotometry = pexConfig.Field(
148 doc="Fit photometry and write the fitted result.",
149 dtype=bool,
150 default=True
151 )
152 coaddName = pexConfig.Field(
153 doc="Type of coadd, typically deep or goodSeeing",
154 dtype=str,
155 default="deep"
156 )
157 positionErrorPedestal = pexConfig.Field(
158 doc="Systematic term to apply to the measured position error (pixels)",
159 dtype=float,
160 default=0.02,
161 )
162 photometryErrorPedestal = pexConfig.Field(
163 doc="Systematic term to apply to the measured error on flux or magnitude as a "
164 "fraction of source flux or magnitude delta (e.g. 0.05 is 5% of flux or +50 millimag).",
165 dtype=float,
166 default=0.0,
167 )
168 # TODO: DM-6885 matchCut should be an geom.Angle
169 matchCut = pexConfig.Field(
170 doc="Matching radius between fitted and reference stars (arcseconds)",
171 dtype=float,
172 default=3.0,
173 )
174 minMeasurements = pexConfig.Field(
175 doc="Minimum number of associated measured stars for a fitted star to be included in the fit",
176 dtype=int,
177 default=2,
178 )
179 minMeasuredStarsPerCcd = pexConfig.Field(
180 doc="Minimum number of measuredStars per ccdImage before printing warnings",
181 dtype=int,
182 default=100,
183 )
184 minRefStarsPerCcd = pexConfig.Field(
185 doc="Minimum number of measuredStars per ccdImage before printing warnings",
186 dtype=int,
187 default=30,
188 )
189 allowLineSearch = pexConfig.Field(
190 doc="Allow a line search during minimization, if it is reasonable for the model"
191 " (models with a significant non-linear component, e.g. constrainedPhotometry).",
192 dtype=bool,
193 default=False
194 )
195 astrometrySimpleOrder = pexConfig.Field(
196 doc="Polynomial order for fitting the simple astrometry model.",
197 dtype=int,
198 default=3,
199 )
200 astrometryChipOrder = pexConfig.Field(
201 doc="Order of the per-chip transform for the constrained astrometry model.",
202 dtype=int,
203 default=1,
204 )
205 astrometryVisitOrder = pexConfig.Field(
206 doc="Order of the per-visit transform for the constrained astrometry model.",
207 dtype=int,
208 default=5,
209 )
210 useInputWcs = pexConfig.Field(
211 doc="Use the input calexp WCSs to initialize a SimpleAstrometryModel.",
212 dtype=bool,
213 default=True,
214 )
215 astrometryModel = pexConfig.ChoiceField(
216 doc="Type of model to fit to astrometry",
217 dtype=str,
218 default="constrained",
219 allowed={"simple": "One polynomial per ccd",
220 "constrained": "One polynomial per ccd, and one polynomial per visit"}
221 )
222 photometryModel = pexConfig.ChoiceField(
223 doc="Type of model to fit to photometry",
224 dtype=str,
225 default="constrainedMagnitude",
226 allowed={"simpleFlux": "One constant zeropoint per ccd and visit, fitting in flux space.",
227 "constrainedFlux": "Constrained zeropoint per ccd, and one polynomial per visit,"
228 " fitting in flux space.",
229 "simpleMagnitude": "One constant zeropoint per ccd and visit,"
230 " fitting in magnitude space.",
231 "constrainedMagnitude": "Constrained zeropoint per ccd, and one polynomial per visit,"
232 " fitting in magnitude space.",
233 }
234 )
235 applyColorTerms = pexConfig.Field(
236 doc="Apply photometric color terms to reference stars?"
237 "Requires that colorterms be set to a ColortermLibrary",
238 dtype=bool,
239 default=False
240 )
241 colorterms = pexConfig.ConfigField(
242 doc="Library of photometric reference catalog name to color term dict.",
243 dtype=ColortermLibrary,
244 )
245 photometryVisitOrder = pexConfig.Field(
246 doc="Order of the per-visit polynomial transform for the constrained photometry model.",
247 dtype=int,
248 default=7,
249 )
250 photometryDoRankUpdate = pexConfig.Field(
251 doc=("Do the rank update step during minimization. "
252 "Skipping this can help deal with models that are too non-linear."),
253 dtype=bool,
254 default=True,
255 )
256 astrometryDoRankUpdate = pexConfig.Field(
257 doc=("Do the rank update step during minimization (should not change the astrometry fit). "
258 "Skipping this can help deal with models that are too non-linear."),
259 dtype=bool,
260 default=True,
261 )
262 outlierRejectSigma = pexConfig.Field(
263 doc="How many sigma to reject outliers at during minimization.",
264 dtype=float,
265 default=5.0,
266 )
267 maxPhotometrySteps = pexConfig.Field(
268 doc="Maximum number of minimize iterations to take when fitting photometry.",
269 dtype=int,
270 default=20,
271 )
272 maxAstrometrySteps = pexConfig.Field(
273 doc="Maximum number of minimize iterations to take when fitting photometry.",
274 dtype=int,
275 default=20,
276 )
277 astrometryRefObjLoader = pexConfig.ConfigurableField(
278 target=LoadIndexedReferenceObjectsTask,
279 doc="Reference object loader for astrometric fit",
280 )
281 photometryRefObjLoader = pexConfig.ConfigurableField(
282 target=LoadIndexedReferenceObjectsTask,
283 doc="Reference object loader for photometric fit",
284 )
285 sourceSelector = sourceSelectorRegistry.makeField(
286 doc="How to select sources for cross-matching",
287 default="astrometry"
288 )
289 astrometryReferenceSelector = pexConfig.ConfigurableField(
290 target=ReferenceSourceSelectorTask,
291 doc="How to down-select the loaded astrometry reference catalog.",
292 )
293 photometryReferenceSelector = pexConfig.ConfigurableField(
294 target=ReferenceSourceSelectorTask,
295 doc="How to down-select the loaded photometry reference catalog.",
296 )
297 astrometryReferenceErr = pexConfig.Field(
298 doc=("Uncertainty on reference catalog coordinates [mas] to use in place of the `coord_*Err` fields. "
299 "If None, then raise an exception if the reference catalog is missing coordinate errors. "
300 "If specified, overrides any existing `coord_*Err` values."),
301 dtype=float,
302 default=None,
303 optional=True
304 )
305 writeInitMatrix = pexConfig.Field(
306 dtype=bool,
307 doc=("Write the pre/post-initialization Hessian and gradient to text files, for debugging. "
308 "The output files will be of the form 'astrometry_preinit-mat.txt', in the current directory. "
309 "Note that these files are the dense versions of the matrix, and so may be very large."),
310 default=False
311 )
312 writeChi2FilesInitialFinal = pexConfig.Field(
313 dtype=bool,
314 doc="Write .csv files containing the contributions to chi2 for the initialization and final fit.",
315 default=False
316 )
317 writeChi2FilesOuterLoop = pexConfig.Field(
318 dtype=bool,
319 doc="Write .csv files containing the contributions to chi2 for the outer fit loop.",
320 default=False
321 )
322 writeInitialModel = pexConfig.Field(
323 dtype=bool,
324 doc=("Write the pre-initialization model to text files, for debugging."
325 " Output is written to `initial[Astro|Photo]metryModel.txt` in the current working directory."),
326 default=False
327 )
328 debugOutputPath = pexConfig.Field(
329 dtype=str,
330 default=".",
331 doc=("Path to write debug output files to. Used by "
332 "`writeInitialModel`, `writeChi2Files*`, `writeInitMatrix`.")
333 )
334 sourceFluxType = pexConfig.Field(
335 dtype=str,
336 doc="Source flux field to use in source selection and to get fluxes from the catalog.",
337 default='Calib'
338 )
340 def validate(self):
341 super().validate()
342 if self.doPhotometry and self.applyColorTerms and len(self.colorterms.data) == 0:
343 msg = "applyColorTerms=True requires the `colorterms` field be set to a ColortermLibrary."
344 raise pexConfig.FieldValidationError(JointcalConfig.colorterms, self, msg)
345 if self.doAstrometry and not self.doPhotometry and self.applyColorTerms:
346 msg = ("Only doing astrometry, but Colorterms are not applied for astrometry;"
347 "applyColorTerms=True will be ignored.")
348 lsst.log.warn(msg)
350 def setDefaults(self):
351 # Use science source selector which can filter on extendedness, SNR, and whether blended
352 self.sourceSelector.name = 'science'
353 # Use only stars because aperture fluxes of galaxies are biased and depend on seeing
354 self.sourceSelector['science'].doUnresolved = True
355 # with dependable signal to noise ratio.
356 self.sourceSelector['science'].doSignalToNoise = True
357 # Min SNR must be > 0 because jointcal cannot handle negative fluxes,
358 # and S/N > 10 to use sources that are not too faint, and thus better measured.
359 self.sourceSelector['science'].signalToNoise.minimum = 10.
360 # Base SNR on CalibFlux because that is the flux jointcal that fits and must be positive
361 fluxField = f"slot_{self.sourceFluxType}Flux_instFlux"
362 self.sourceSelector['science'].signalToNoise.fluxField = fluxField
363 self.sourceSelector['science'].signalToNoise.errField = fluxField + "Err"
364 # Do not trust blended sources' aperture fluxes which also depend on seeing
365 self.sourceSelector['science'].doIsolated = True
366 # Do not trust either flux or centroid measurements with flags,
367 # chosen from the usual QA flags for stars)
368 self.sourceSelector['science'].doFlags = True
369 badFlags = ['base_PixelFlags_flag_edge', 'base_PixelFlags_flag_saturated',
370 'base_PixelFlags_flag_interpolatedCenter', 'base_SdssCentroid_flag',
371 'base_PsfFlux_flag', 'base_PixelFlags_flag_suspectCenter']
372 self.sourceSelector['science'].flags.bad = badFlags
374 # Default to Gaia-DR2 (with proper motions) for astrometry and
375 # PS1-DR1 for photometry, with a reasonable initial filterMap.
376 self.astrometryRefObjLoader.ref_dataset_name = "gaia_dr2_20200414"
377 self.astrometryRefObjLoader.requireProperMotion = True
378 self.astrometryRefObjLoader.filterMap = {'u': 'phot_g_mean',
379 'g': 'phot_g_mean',
380 'r': 'phot_g_mean',
381 'i': 'phot_g_mean',
382 'z': 'phot_g_mean',
383 'y': 'phot_g_mean'}
384 self.photometryRefObjLoader.ref_dataset_name = "ps1_pv3_3pi_20170110"
387def writeModel(model, filename, log):
388 """Write model to outfile."""
389 with open(filename, "w") as file:
390 file.write(repr(model))
391 log.info("Wrote %s to file: %s", model, filename)
394class JointcalTask(pipeBase.CmdLineTask):
395 """Jointly astrometrically and photometrically calibrate a group of images."""
397 ConfigClass = JointcalConfig
398 RunnerClass = JointcalRunner
399 _DefaultName = "jointcal"
401 def __init__(self, butler=None, profile_jointcal=False, **kwargs):
402 """
403 Instantiate a JointcalTask.
405 Parameters
406 ----------
407 butler : `lsst.daf.persistence.Butler`
408 The butler is passed to the refObjLoader constructor in case it is
409 needed. Ignored if the refObjLoader argument provides a loader directly.
410 Used to initialize the astrometry and photometry refObjLoaders.
411 profile_jointcal : `bool`
412 Set to True to profile different stages of this jointcal run.
413 """
414 pipeBase.CmdLineTask.__init__(self, **kwargs)
415 self.profile_jointcal = profile_jointcal
416 self.makeSubtask("sourceSelector")
417 if self.config.doAstrometry:
418 self.makeSubtask('astrometryRefObjLoader', butler=butler)
419 self.makeSubtask("astrometryReferenceSelector")
420 else:
421 self.astrometryRefObjLoader = None
422 if self.config.doPhotometry:
423 self.makeSubtask('photometryRefObjLoader', butler=butler)
424 self.makeSubtask("photometryReferenceSelector")
425 else:
426 self.photometryRefObjLoader = None
428 # To hold various computed metrics for use by tests
429 self.job = Job.load_metrics_package(subset='jointcal')
431 # We don't currently need to persist the metadata.
432 # If we do in the future, we will have to add appropriate dataset templates
433 # to each obs package (the metadata template should look like `jointcal_wcs`).
434 def _getMetadataName(self):
435 return None
437 @classmethod
438 def _makeArgumentParser(cls):
439 """Create an argument parser"""
440 parser = pipeBase.ArgumentParser(name=cls._DefaultName)
441 parser.add_argument("--profile_jointcal", default=False, action="store_true",
442 help="Profile steps of jointcal separately.")
443 parser.add_id_argument("--id", "calexp", help="data ID, e.g. --id visit=6789 ccd=0..9",
444 ContainerClass=PerTractCcdDataIdContainer)
445 return parser
447 def _build_ccdImage(self, dataRef, associations, jointcalControl):
448 """
449 Extract the necessary things from this dataRef to add a new ccdImage.
451 Parameters
452 ----------
453 dataRef : `lsst.daf.persistence.ButlerDataRef`
454 DataRef to extract info from.
455 associations : `lsst.jointcal.Associations`
456 Object to add the info to, to construct a new CcdImage
457 jointcalControl : `jointcal.JointcalControl`
458 Control object for associations management
460 Returns
461 ------
462 namedtuple
463 ``wcs``
464 The TAN WCS of this image, read from the calexp
465 (`lsst.afw.geom.SkyWcs`).
466 ``key``
467 A key to identify this dataRef by its visit and ccd ids
468 (`namedtuple`).
469 ``filter``
470 This calexp's filter (`str`).
471 """
472 if "visit" in dataRef.dataId.keys():
473 visit = dataRef.dataId["visit"]
474 else:
475 visit = dataRef.getButler().queryMetadata("calexp", ("visit"), dataRef.dataId)[0]
477 src = dataRef.get("src", flags=lsst.afw.table.SOURCE_IO_NO_FOOTPRINTS, immediate=True)
479 visitInfo = dataRef.get('calexp_visitInfo')
480 detector = dataRef.get('calexp_detector')
481 ccdId = detector.getId()
482 photoCalib = dataRef.get('calexp_photoCalib')
483 tanWcs = dataRef.get('calexp_wcs')
484 bbox = dataRef.get('calexp_bbox')
485 filt = dataRef.get('calexp_filter')
486 filterName = filt.getName()
488 goodSrc = self.sourceSelector.run(src)
490 if len(goodSrc.sourceCat) == 0:
491 self.log.warn("No sources selected in visit %s ccd %s", visit, ccdId)
492 else:
493 self.log.info("%d sources selected in visit %d ccd %d", len(goodSrc.sourceCat), visit, ccdId)
494 associations.createCcdImage(goodSrc.sourceCat,
495 tanWcs,
496 visitInfo,
497 bbox,
498 filterName,
499 photoCalib,
500 detector,
501 visit,
502 ccdId,
503 jointcalControl)
505 Result = collections.namedtuple('Result_from_build_CcdImage', ('wcs', 'key', 'filter'))
506 Key = collections.namedtuple('Key', ('visit', 'ccd'))
507 return Result(tanWcs, Key(visit, ccdId), filterName)
509 def _getDebugPath(self, filename):
510 """Constructs a path to filename using the configured debug path.
511 """
512 return os.path.join(self.config.debugOutputPath, filename)
514 @pipeBase.timeMethod
515 def runDataRef(self, dataRefs, profile_jointcal=False):
516 """
517 Jointly calibrate the astrometry and photometry across a set of images.
519 Parameters
520 ----------
521 dataRefs : `list` of `lsst.daf.persistence.ButlerDataRef`
522 List of data references to the exposures to be fit.
523 profile_jointcal : `bool`
524 Profile the individual steps of jointcal.
526 Returns
527 -------
528 result : `lsst.pipe.base.Struct`
529 Struct of metadata from the fit, containing:
531 ``dataRefs``
532 The provided data references that were fit (with updated WCSs)
533 ``oldWcsList``
534 The original WCS from each dataRef
535 ``metrics``
536 Dictionary of internally-computed metrics for testing/validation.
537 """
538 if len(dataRefs) == 0:
539 raise ValueError('Need a non-empty list of data references!')
541 exitStatus = 0 # exit status for shell
543 sourceFluxField = "slot_%sFlux" % (self.config.sourceFluxType,)
544 jointcalControl = lsst.jointcal.JointcalControl(sourceFluxField)
545 associations = lsst.jointcal.Associations()
547 visit_ccd_to_dataRef = {}
548 oldWcsList = []
549 filters = []
550 load_cat_prof_file = 'jointcal_build_ccdImage.prof' if profile_jointcal else ''
551 with pipeBase.cmdLineTask.profile(load_cat_prof_file):
552 # We need the bounding-box of the focal plane for photometry visit models.
553 # NOTE: we only need to read it once, because its the same for all exposures of a camera.
554 camera = dataRefs[0].get('camera', immediate=True)
555 self.focalPlaneBBox = camera.getFpBBox()
556 for ref in dataRefs:
557 result = self._build_ccdImage(ref, associations, jointcalControl)
558 oldWcsList.append(result.wcs)
559 visit_ccd_to_dataRef[result.key] = ref
560 filters.append(result.filter)
561 filters = collections.Counter(filters)
563 associations.computeCommonTangentPoint()
565 boundingCircle = associations.computeBoundingCircle()
566 center = lsst.geom.SpherePoint(boundingCircle.getCenter())
567 radius = lsst.geom.Angle(boundingCircle.getOpeningAngle().asRadians(), lsst.geom.radians)
569 self.log.info(f"Data has center={center} with radius={radius.asDegrees()} degrees.")
571 # Determine a default filter associated with the catalog. See DM-9093
572 defaultFilter = filters.most_common(1)[0][0]
573 self.log.debug("Using %s band for reference flux", defaultFilter)
575 # TODO: need a better way to get the tract.
576 tract = dataRefs[0].dataId['tract']
578 if self.config.doAstrometry:
579 astrometry = self._do_load_refcat_and_fit(associations, defaultFilter, center, radius,
580 name="astrometry",
581 refObjLoader=self.astrometryRefObjLoader,
582 referenceSelector=self.astrometryReferenceSelector,
583 fit_function=self._fit_astrometry,
584 profile_jointcal=profile_jointcal,
585 tract=tract)
586 self._write_astrometry_results(associations, astrometry.model, visit_ccd_to_dataRef)
587 else:
588 astrometry = Astrometry(None, None, None)
590 if self.config.doPhotometry:
591 photometry = self._do_load_refcat_and_fit(associations, defaultFilter, center, radius,
592 name="photometry",
593 refObjLoader=self.photometryRefObjLoader,
594 referenceSelector=self.photometryReferenceSelector,
595 fit_function=self._fit_photometry,
596 profile_jointcal=profile_jointcal,
597 tract=tract,
598 filters=filters,
599 reject_bad_fluxes=True)
600 self._write_photometry_results(associations, photometry.model, visit_ccd_to_dataRef)
601 else:
602 photometry = Photometry(None, None)
604 return pipeBase.Struct(dataRefs=dataRefs,
605 oldWcsList=oldWcsList,
606 job=self.job,
607 astrometryRefObjLoader=self.astrometryRefObjLoader,
608 photometryRefObjLoader=self.photometryRefObjLoader,
609 defaultFilter=defaultFilter,
610 exitStatus=exitStatus)
612 def _get_refcat_coordinate_error_override(self, refCat, name):
613 """Check whether we should override the refcat coordinate errors, and
614 return the overridden error if necessary.
616 Parameters
617 ----------
618 refCat : `lsst.afw.table.SimpleCatalog`
619 The reference catalog to check for a ``coord_raErr`` field.
620 name : `str`
621 Whether we are doing "astrometry" or "photometry".
623 Returns
624 -------
625 refCoordErr : `float`
626 The refcat coordinate error to use, or NaN if we are not overriding
627 those fields.
629 Raises
630 ------
631 lsst.pex.config.FieldValidationError
632 Raised if the refcat does not contain coordinate errors and
633 ``config.astrometryReferenceErr`` is not set.
634 """
635 # This value doesn't matter for photometry, so just set something to
636 # keep old refcats from causing problems.
637 if name.lower() == "photometry":
638 if 'coord_raErr' not in refCat.schema:
639 return 100
640 else:
641 return float('nan')
643 if self.config.astrometryReferenceErr is None and 'coord_raErr' not in refCat.schema:
644 msg = ("Reference catalog does not contain coordinate errors, "
645 "and config.astrometryReferenceErr not supplied.")
646 raise pexConfig.FieldValidationError(JointcalConfig.astrometryReferenceErr,
647 self.config,
648 msg)
650 if self.config.astrometryReferenceErr is not None and 'coord_raErr' in refCat.schema:
651 self.log.warn("Overriding reference catalog coordinate errors with %f/coordinate [mas]",
652 self.config.astrometryReferenceErr)
654 if self.config.astrometryReferenceErr is None:
655 return float('nan')
656 else:
657 return self.config.astrometryReferenceErr
659 def _compute_proper_motion_epoch(self, ccdImageList):
660 """Return the proper motion correction epoch of the provided images.
662 Parameters
663 ----------
664 ccdImageList : `list` [`lsst.jointcal.CcdImage`]
665 The images to compute the appropriate epoch for.
667 Returns
668 -------
669 epoch : `astropy.time.Time`
670 The date to use for proper motion corrections.
671 """
672 mjds = [ccdImage.getMjd() for ccdImage in ccdImageList]
673 return astropy.time.Time(np.mean(mjds), format='mjd', scale="tai")
675 def _do_load_refcat_and_fit(self, associations, defaultFilter, center, radius,
676 filters=[],
677 tract="", profile_jointcal=False, match_cut=3.0,
678 reject_bad_fluxes=False, *,
679 name="", refObjLoader=None, referenceSelector=None,
680 fit_function=None):
681 """Load reference catalog, perform the fit, and return the result.
683 Parameters
684 ----------
685 associations : `lsst.jointcal.Associations`
686 The star/reference star associations to fit.
687 defaultFilter : `str`
688 filter to load from reference catalog.
689 center : `lsst.geom.SpherePoint`
690 ICRS center of field to load from reference catalog.
691 radius : `lsst.geom.Angle`
692 On-sky radius to load from reference catalog.
693 name : `str`
694 Name of thing being fit: "astrometry" or "photometry".
695 refObjLoader : `lsst.meas.algorithms.LoadReferenceObjectsTask`
696 Reference object loader to use to load a reference catalog.
697 referenceSelector : `lsst.meas.algorithms.ReferenceSourceSelectorTask`
698 Selector to use to pick objects from the loaded reference catalog.
699 fit_function : callable
700 Function to call to perform fit (takes Associations object).
701 filters : `list` [`str`], optional
702 List of filters to load from the reference catalog.
703 tract : `str`, optional
704 Name of tract currently being fit.
705 profile_jointcal : `bool`, optional
706 Separately profile the fitting step.
707 match_cut : `float`, optional
708 Radius in arcseconds to find cross-catalog matches to during
709 associations.associateCatalogs.
710 reject_bad_fluxes : `bool`, optional
711 Reject refCat sources with NaN/inf flux or NaN/0 fluxErr.
713 Returns
714 -------
715 result : `Photometry` or `Astrometry`
716 Result of `fit_function()`
717 """
718 self.log.info("====== Now processing %s...", name)
719 # TODO: this should not print "trying to invert a singular transformation:"
720 # if it does that, something's not right about the WCS...
721 associations.associateCatalogs(match_cut)
722 add_measurement(self.job, 'jointcal.associated_%s_fittedStars' % name,
723 associations.fittedStarListSize())
725 applyColorterms = False if name.lower() == "astrometry" else self.config.applyColorTerms
726 epoch = self._compute_proper_motion_epoch(associations.getCcdImageList())
727 refCat, fluxField = self._load_reference_catalog(refObjLoader, referenceSelector,
728 center, radius, defaultFilter,
729 applyColorterms=applyColorterms,
730 epoch=epoch)
731 refCoordErr = self._get_refcat_coordinate_error_override(refCat, name)
733 associations.collectRefStars(refCat,
734 self.config.matchCut*lsst.geom.arcseconds,
735 fluxField,
736 refCoordinateErr=refCoordErr,
737 rejectBadFluxes=reject_bad_fluxes)
738 add_measurement(self.job, 'jointcal.collected_%s_refStars' % name,
739 associations.refStarListSize())
741 associations.prepareFittedStars(self.config.minMeasurements)
743 self._check_star_lists(associations, name)
744 add_measurement(self.job, 'jointcal.selected_%s_refStars' % name,
745 associations.nFittedStarsWithAssociatedRefStar())
746 add_measurement(self.job, 'jointcal.selected_%s_fittedStars' % name,
747 associations.fittedStarListSize())
748 add_measurement(self.job, 'jointcal.selected_%s_ccdImages' % name,
749 associations.nCcdImagesValidForFit())
751 load_cat_prof_file = 'jointcal_fit_%s.prof'%name if profile_jointcal else ''
752 dataName = "{}_{}".format(tract, defaultFilter)
753 with pipeBase.cmdLineTask.profile(load_cat_prof_file):
754 result = fit_function(associations, dataName)
755 # TODO DM-12446: turn this into a "butler save" somehow.
756 # Save reference and measurement chi2 contributions for this data
757 if self.config.writeChi2FilesInitialFinal:
758 baseName = self._getDebugPath(f"{name}_final_chi2-{dataName}")
759 result.fit.saveChi2Contributions(baseName+"{type}")
760 self.log.info("Wrote chi2 contributions files: %s", baseName)
762 return result
764 def _load_reference_catalog(self, refObjLoader, referenceSelector, center, radius, filterName,
765 applyColorterms=False, epoch=None):
766 """Load the necessary reference catalog sources, convert fluxes to
767 correct units, and apply color term corrections if requested.
769 Parameters
770 ----------
771 refObjLoader : `lsst.meas.algorithms.LoadReferenceObjectsTask`
772 The reference catalog loader to use to get the data.
773 referenceSelector : `lsst.meas.algorithms.ReferenceSourceSelectorTask`
774 Source selector to apply to loaded reference catalog.
775 center : `lsst.geom.SpherePoint`
776 The center around which to load sources.
777 radius : `lsst.geom.Angle`
778 The radius around ``center`` to load sources in.
779 filterName : `str`
780 The name of the camera filter to load fluxes for.
781 applyColorterms : `bool`
782 Apply colorterm corrections to the refcat for ``filterName``?
783 epoch : `astropy.time.Time`, optional
784 Epoch to which to correct refcat proper motion and parallax,
785 or `None` to not apply such corrections.
787 Returns
788 -------
789 refCat : `lsst.afw.table.SimpleCatalog`
790 The loaded reference catalog.
791 fluxField : `str`
792 The name of the reference catalog flux field appropriate for ``filterName``.
793 """
794 skyCircle = refObjLoader.loadSkyCircle(center,
795 radius,
796 filterName,
797 epoch=epoch)
799 selected = referenceSelector.run(skyCircle.refCat)
800 # Need memory contiguity to get reference filters as a vector.
801 if not selected.sourceCat.isContiguous():
802 refCat = selected.sourceCat.copy(deep=True)
803 else:
804 refCat = selected.sourceCat
806 if applyColorterms:
807 refCatName = refObjLoader.ref_dataset_name
808 self.log.info("Applying color terms for filterName=%r reference catalog=%s",
809 filterName, refCatName)
810 colorterm = self.config.colorterms.getColorterm(
811 filterName=filterName, photoCatName=refCatName, doRaise=True)
813 refMag, refMagErr = colorterm.getCorrectedMagnitudes(refCat, filterName)
814 refCat[skyCircle.fluxField] = u.Magnitude(refMag, u.ABmag).to_value(u.nJy)
815 # TODO: I didn't want to use this, but I'll deal with it in DM-16903
816 refCat[skyCircle.fluxField+'Err'] = fluxErrFromABMagErr(refMagErr, refMag) * 1e9
818 return refCat, skyCircle.fluxField
820 def _check_star_lists(self, associations, name):
821 # TODO: these should be len(blah), but we need this properly wrapped first.
822 if associations.nCcdImagesValidForFit() == 0:
823 raise RuntimeError('No images in the ccdImageList!')
824 if associations.fittedStarListSize() == 0:
825 raise RuntimeError('No stars in the {} fittedStarList!'.format(name))
826 if associations.refStarListSize() == 0:
827 raise RuntimeError('No stars in the {} reference star list!'.format(name))
829 def _logChi2AndValidate(self, associations, fit, model, chi2Label, writeChi2Name=None):
830 """Compute chi2, log it, validate the model, and return chi2.
832 Parameters
833 ----------
834 associations : `lsst.jointcal.Associations`
835 The star/reference star associations to fit.
836 fit : `lsst.jointcal.FitterBase`
837 The fitter to use for minimization.
838 model : `lsst.jointcal.Model`
839 The model being fit.
840 chi2Label : `str`
841 Label to describe the chi2 (e.g. "Initialized", "Final").
842 writeChi2Name : `str`, optional
843 Filename prefix to write the chi2 contributions to.
844 Do not supply an extension: an appropriate one will be added.
846 Returns
847 -------
848 chi2: `lsst.jointcal.Chi2Accumulator`
849 The chi2 object for the current fitter and model.
851 Raises
852 ------
853 FloatingPointError
854 Raised if chi2 is infinite or NaN.
855 ValueError
856 Raised if the model is not valid.
857 """
858 if writeChi2Name is not None:
859 fullpath = self._getDebugPath(writeChi2Name)
860 fit.saveChi2Contributions(fullpath+"{type}")
861 self.log.info("Wrote chi2 contributions files: %s", fullpath)
863 chi2 = fit.computeChi2()
864 self.log.info("%s %s", chi2Label, chi2)
865 self._check_stars(associations)
866 if not np.isfinite(chi2.chi2):
867 raise FloatingPointError(f'{chi2Label} chi2 is invalid: {chi2}')
868 if not model.validate(associations.getCcdImageList(), chi2.ndof):
869 raise ValueError("Model is not valid: check log messages for warnings.")
870 return chi2
872 def _fit_photometry(self, associations, dataName=None):
873 """
874 Fit the photometric data.
876 Parameters
877 ----------
878 associations : `lsst.jointcal.Associations`
879 The star/reference star associations to fit.
880 dataName : `str`
881 Name of the data being processed (e.g. "1234_HSC-Y"), for
882 identifying debugging files.
884 Returns
885 -------
886 fit_result : `namedtuple`
887 fit : `lsst.jointcal.PhotometryFit`
888 The photometric fitter used to perform the fit.
889 model : `lsst.jointcal.PhotometryModel`
890 The photometric model that was fit.
891 """
892 self.log.info("=== Starting photometric fitting...")
894 # TODO: should use pex.config.RegistryField here (see DM-9195)
895 if self.config.photometryModel == "constrainedFlux":
896 model = lsst.jointcal.ConstrainedFluxModel(associations.getCcdImageList(),
897 self.focalPlaneBBox,
898 visitOrder=self.config.photometryVisitOrder,
899 errorPedestal=self.config.photometryErrorPedestal)
900 # potentially nonlinear problem, so we may need a line search to converge.
901 doLineSearch = self.config.allowLineSearch
902 elif self.config.photometryModel == "constrainedMagnitude":
903 model = lsst.jointcal.ConstrainedMagnitudeModel(associations.getCcdImageList(),
904 self.focalPlaneBBox,
905 visitOrder=self.config.photometryVisitOrder,
906 errorPedestal=self.config.photometryErrorPedestal)
907 # potentially nonlinear problem, so we may need a line search to converge.
908 doLineSearch = self.config.allowLineSearch
909 elif self.config.photometryModel == "simpleFlux":
910 model = lsst.jointcal.SimpleFluxModel(associations.getCcdImageList(),
911 errorPedestal=self.config.photometryErrorPedestal)
912 doLineSearch = False # purely linear in model parameters, so no line search needed
913 elif self.config.photometryModel == "simpleMagnitude":
914 model = lsst.jointcal.SimpleMagnitudeModel(associations.getCcdImageList(),
915 errorPedestal=self.config.photometryErrorPedestal)
916 doLineSearch = False # purely linear in model parameters, so no line search needed
918 fit = lsst.jointcal.PhotometryFit(associations, model)
919 # TODO DM-12446: turn this into a "butler save" somehow.
920 # Save reference and measurement chi2 contributions for this data
921 if self.config.writeChi2FilesInitialFinal:
922 baseName = f"photometry_initial_chi2-{dataName}"
923 else:
924 baseName = None
925 if self.config.writeInitialModel:
926 fullpath = self._getDebugPath("initialPhotometryModel.txt")
927 writeModel(model, fullpath, self.log)
928 self._logChi2AndValidate(associations, fit, model, "Initialized", writeChi2Name=baseName)
930 def getChi2Name(whatToFit):
931 if self.config.writeChi2FilesOuterLoop:
932 return f"photometry_init-%s_chi2-{dataName}" % whatToFit
933 else:
934 return None
936 # The constrained model needs the visit transform fit first; the chip
937 # transform is initialized from the singleFrame PhotoCalib, so it's close.
938 dumpMatrixFile = self._getDebugPath("photometry_preinit") if self.config.writeInitMatrix else ""
939 if self.config.photometryModel.startswith("constrained"):
940 # no line search: should be purely (or nearly) linear,
941 # and we want a large step size to initialize with.
942 fit.minimize("ModelVisit", dumpMatrixFile=dumpMatrixFile)
943 self._logChi2AndValidate(associations, fit, model, "Initialize ModelVisit",
944 writeChi2Name=getChi2Name("ModelVisit"))
945 dumpMatrixFile = "" # so we don't redo the output on the next step
947 fit.minimize("Model", doLineSearch=doLineSearch, dumpMatrixFile=dumpMatrixFile)
948 self._logChi2AndValidate(associations, fit, model, "Initialize Model",
949 writeChi2Name=getChi2Name("Model"))
951 fit.minimize("Fluxes") # no line search: always purely linear.
952 self._logChi2AndValidate(associations, fit, model, "Initialize Fluxes",
953 writeChi2Name=getChi2Name("Fluxes"))
955 fit.minimize("Model Fluxes", doLineSearch=doLineSearch)
956 self._logChi2AndValidate(associations, fit, model, "Initialize ModelFluxes",
957 writeChi2Name=getChi2Name("ModelFluxes"))
959 model.freezeErrorTransform()
960 self.log.debug("Photometry error scales are frozen.")
962 chi2 = self._iterate_fit(associations,
963 fit,
964 self.config.maxPhotometrySteps,
965 "photometry",
966 "Model Fluxes",
967 doRankUpdate=self.config.photometryDoRankUpdate,
968 doLineSearch=doLineSearch,
969 dataName=dataName)
971 add_measurement(self.job, 'jointcal.photometry_final_chi2', chi2.chi2)
972 add_measurement(self.job, 'jointcal.photometry_final_ndof', chi2.ndof)
973 return Photometry(fit, model)
975 def _fit_astrometry(self, associations, dataName=None):
976 """
977 Fit the astrometric data.
979 Parameters
980 ----------
981 associations : `lsst.jointcal.Associations`
982 The star/reference star associations to fit.
983 dataName : `str`
984 Name of the data being processed (e.g. "1234_HSC-Y"), for
985 identifying debugging files.
987 Returns
988 -------
989 fit_result : `namedtuple`
990 fit : `lsst.jointcal.AstrometryFit`
991 The astrometric fitter used to perform the fit.
992 model : `lsst.jointcal.AstrometryModel`
993 The astrometric model that was fit.
994 sky_to_tan_projection : `lsst.jointcal.ProjectionHandler`
995 The model for the sky to tangent plane projection that was used in the fit.
996 """
998 self.log.info("=== Starting astrometric fitting...")
1000 associations.deprojectFittedStars()
1002 # NOTE: need to return sky_to_tan_projection so that it doesn't get garbage collected.
1003 # TODO: could we package sky_to_tan_projection and model together so we don't have to manage
1004 # them so carefully?
1005 sky_to_tan_projection = lsst.jointcal.OneTPPerVisitHandler(associations.getCcdImageList())
1007 if self.config.astrometryModel == "constrained":
1008 model = lsst.jointcal.ConstrainedAstrometryModel(associations.getCcdImageList(),
1009 sky_to_tan_projection,
1010 chipOrder=self.config.astrometryChipOrder,
1011 visitOrder=self.config.astrometryVisitOrder)
1012 elif self.config.astrometryModel == "simple":
1013 model = lsst.jointcal.SimpleAstrometryModel(associations.getCcdImageList(),
1014 sky_to_tan_projection,
1015 self.config.useInputWcs,
1016 nNotFit=0,
1017 order=self.config.astrometrySimpleOrder)
1019 fit = lsst.jointcal.AstrometryFit(associations, model, self.config.positionErrorPedestal)
1020 # TODO DM-12446: turn this into a "butler save" somehow.
1021 # Save reference and measurement chi2 contributions for this data
1022 if self.config.writeChi2FilesInitialFinal:
1023 baseName = f"astrometry_initial_chi2-{dataName}"
1024 else:
1025 baseName = None
1026 if self.config.writeInitialModel:
1027 fullpath = self._getDebugPath("initialAstrometryModel.txt")
1028 writeModel(model, fullpath, self.log)
1029 self._logChi2AndValidate(associations, fit, model, "Initial", writeChi2Name=baseName)
1031 def getChi2Name(whatToFit):
1032 if self.config.writeChi2FilesOuterLoop:
1033 return f"astrometry_init-%s_chi2-{dataName}" % whatToFit
1034 else:
1035 return None
1037 dumpMatrixFile = self._getDebugPath("astrometry_preinit") if self.config.writeInitMatrix else ""
1038 # The constrained model needs the visit transform fit first; the chip
1039 # transform is initialized from the detector's cameraGeom, so it's close.
1040 if self.config.astrometryModel == "constrained":
1041 fit.minimize("DistortionsVisit", dumpMatrixFile=dumpMatrixFile)
1042 self._logChi2AndValidate(associations, fit, model, "Initialize DistortionsVisit",
1043 writeChi2Name=getChi2Name("DistortionsVisit"))
1044 dumpMatrixFile = "" # so we don't redo the output on the next step
1046 fit.minimize("Distortions", dumpMatrixFile=dumpMatrixFile)
1047 self._logChi2AndValidate(associations, fit, model, "Initialize Distortions",
1048 writeChi2Name=getChi2Name("Distortions"))
1050 fit.minimize("Positions")
1051 self._logChi2AndValidate(associations, fit, model, "Initialize Positions",
1052 writeChi2Name=getChi2Name("Positions"))
1054 fit.minimize("Distortions Positions")
1055 self._logChi2AndValidate(associations, fit, model, "Initialize DistortionsPositions",
1056 writeChi2Name=getChi2Name("DistortionsPositions"))
1058 chi2 = self._iterate_fit(associations,
1059 fit,
1060 self.config.maxAstrometrySteps,
1061 "astrometry",
1062 "Distortions Positions",
1063 doRankUpdate=self.config.astrometryDoRankUpdate,
1064 dataName=dataName)
1066 add_measurement(self.job, 'jointcal.astrometry_final_chi2', chi2.chi2)
1067 add_measurement(self.job, 'jointcal.astrometry_final_ndof', chi2.ndof)
1069 return Astrometry(fit, model, sky_to_tan_projection)
1071 def _check_stars(self, associations):
1072 """Count measured and reference stars per ccd and warn/log them."""
1073 for ccdImage in associations.getCcdImageList():
1074 nMeasuredStars, nRefStars = ccdImage.countStars()
1075 self.log.debug("ccdImage %s has %s measured and %s reference stars",
1076 ccdImage.getName(), nMeasuredStars, nRefStars)
1077 if nMeasuredStars < self.config.minMeasuredStarsPerCcd:
1078 self.log.warn("ccdImage %s has only %s measuredStars (desired %s)",
1079 ccdImage.getName(), nMeasuredStars, self.config.minMeasuredStarsPerCcd)
1080 if nRefStars < self.config.minRefStarsPerCcd:
1081 self.log.warn("ccdImage %s has only %s RefStars (desired %s)",
1082 ccdImage.getName(), nRefStars, self.config.minRefStarsPerCcd)
1084 def _iterate_fit(self, associations, fitter, max_steps, name, whatToFit,
1085 dataName="",
1086 doRankUpdate=True,
1087 doLineSearch=False):
1088 """Run fitter.minimize up to max_steps times, returning the final chi2.
1090 Parameters
1091 ----------
1092 associations : `lsst.jointcal.Associations`
1093 The star/reference star associations to fit.
1094 fitter : `lsst.jointcal.FitterBase`
1095 The fitter to use for minimization.
1096 max_steps : `int`
1097 Maximum number of steps to run outlier rejection before declaring
1098 convergence failure.
1099 name : {'photometry' or 'astrometry'}
1100 What type of data are we fitting (for logs and debugging files).
1101 whatToFit : `str`
1102 Passed to ``fitter.minimize()`` to define the parameters to fit.
1103 dataName : `str`, optional
1104 Descriptive name for this dataset (e.g. tract and filter),
1105 for debugging.
1106 doRankUpdate : `bool`, optional
1107 Do an Eigen rank update during minimization, or recompute the full
1108 matrix and gradient?
1109 doLineSearch : `bool`, optional
1110 Do a line search for the optimum step during minimization?
1112 Returns
1113 -------
1114 chi2: `lsst.jointcal.Chi2Statistic`
1115 The final chi2 after the fit converges, or is forced to end.
1117 Raises
1118 ------
1119 FloatingPointError
1120 Raised if the fitter fails with a non-finite value.
1121 RuntimeError
1122 Raised if the fitter fails for some other reason;
1123 log messages will provide further details.
1124 """
1125 dumpMatrixFile = self._getDebugPath(f"{name}_postinit") if self.config.writeInitMatrix else ""
1126 for i in range(max_steps):
1127 if self.config.writeChi2FilesOuterLoop:
1128 writeChi2Name = f"{name}_iterate_{i}_chi2-{dataName}"
1129 else:
1130 writeChi2Name = None
1131 result = fitter.minimize(whatToFit,
1132 self.config.outlierRejectSigma,
1133 doRankUpdate=doRankUpdate,
1134 doLineSearch=doLineSearch,
1135 dumpMatrixFile=dumpMatrixFile)
1136 dumpMatrixFile = "" # clear it so we don't write the matrix again.
1137 chi2 = self._logChi2AndValidate(associations, fitter, fitter.getModel(),
1138 f"Fit iteration {i}", writeChi2Name=writeChi2Name)
1140 if result == MinimizeResult.Converged:
1141 if doRankUpdate:
1142 self.log.debug("fit has converged - no more outliers - redo minimization "
1143 "one more time in case we have lost accuracy in rank update.")
1144 # Redo minimization one more time in case we have lost accuracy in rank update
1145 result = fitter.minimize(whatToFit, self.config.outlierRejectSigma)
1146 chi2 = self._logChi2AndValidate(associations, fitter, fitter.getModel(), "Fit completed")
1148 # log a message for a large final chi2, TODO: DM-15247 for something better
1149 if chi2.chi2/chi2.ndof >= 4.0:
1150 self.log.error("Potentially bad fit: High chi-squared/ndof.")
1152 break
1153 elif result == MinimizeResult.Chi2Increased:
1154 self.log.warn("still some outliers but chi2 increases - retry")
1155 elif result == MinimizeResult.NonFinite:
1156 filename = self._getDebugPath("{}_failure-nonfinite_chi2-{}.csv".format(name, dataName))
1157 # TODO DM-12446: turn this into a "butler save" somehow.
1158 fitter.saveChi2Contributions(filename+"{type}")
1159 msg = "Nonfinite value in chi2 minimization, cannot complete fit. Dumped star tables to: {}"
1160 raise FloatingPointError(msg.format(filename))
1161 elif result == MinimizeResult.Failed:
1162 raise RuntimeError("Chi2 minimization failure, cannot complete fit.")
1163 else:
1164 raise RuntimeError("Unxepected return code from minimize().")
1165 else:
1166 self.log.error("%s failed to converge after %d steps"%(name, max_steps))
1168 return chi2
1170 def _write_astrometry_results(self, associations, model, visit_ccd_to_dataRef):
1171 """
1172 Write the fitted astrometric results to a new 'jointcal_wcs' dataRef.
1174 Parameters
1175 ----------
1176 associations : `lsst.jointcal.Associations`
1177 The star/reference star associations to fit.
1178 model : `lsst.jointcal.AstrometryModel`
1179 The astrometric model that was fit.
1180 visit_ccd_to_dataRef : `dict` of Key: `lsst.daf.persistence.ButlerDataRef`
1181 Dict of ccdImage identifiers to dataRefs that were fit.
1182 """
1184 ccdImageList = associations.getCcdImageList()
1185 for ccdImage in ccdImageList:
1186 # TODO: there must be a better way to identify this ccdImage than a visit,ccd pair?
1187 ccd = ccdImage.ccdId
1188 visit = ccdImage.visit
1189 dataRef = visit_ccd_to_dataRef[(visit, ccd)]
1190 self.log.info("Updating WCS for visit: %d, ccd: %d", visit, ccd)
1191 skyWcs = model.makeSkyWcs(ccdImage)
1192 try:
1193 dataRef.put(skyWcs, 'jointcal_wcs')
1194 except pexExceptions.Exception as e:
1195 self.log.fatal('Failed to write updated Wcs: %s', str(e))
1196 raise e
1198 def _write_photometry_results(self, associations, model, visit_ccd_to_dataRef):
1199 """
1200 Write the fitted photometric results to a new 'jointcal_photoCalib' dataRef.
1202 Parameters
1203 ----------
1204 associations : `lsst.jointcal.Associations`
1205 The star/reference star associations to fit.
1206 model : `lsst.jointcal.PhotometryModel`
1207 The photoometric model that was fit.
1208 visit_ccd_to_dataRef : `dict` of Key: `lsst.daf.persistence.ButlerDataRef`
1209 Dict of ccdImage identifiers to dataRefs that were fit.
1210 """
1212 ccdImageList = associations.getCcdImageList()
1213 for ccdImage in ccdImageList:
1214 # TODO: there must be a better way to identify this ccdImage than a visit,ccd pair?
1215 ccd = ccdImage.ccdId
1216 visit = ccdImage.visit
1217 dataRef = visit_ccd_to_dataRef[(visit, ccd)]
1218 self.log.info("Updating PhotoCalib for visit: %d, ccd: %d", visit, ccd)
1219 photoCalib = model.toPhotoCalib(ccdImage)
1220 try:
1221 dataRef.put(photoCalib, 'jointcal_photoCalib')
1222 except pexExceptions.Exception as e:
1223 self.log.fatal('Failed to write updated PhotoCalib: %s', str(e))
1224 raise e