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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 dataclasses
23import collections
24import os
26import astropy.time
27import numpy as np
28import astropy.units as u
30import lsst.geom
31import lsst.utils
32import lsst.pex.config as pexConfig
33import lsst.pipe.base as pipeBase
34from lsst.afw.image import fluxErrFromABMagErr
35import lsst.pex.exceptions as pexExceptions
36import lsst.afw.cameraGeom
37import lsst.afw.table
38import lsst.log
39import lsst.meas.algorithms
40from lsst.pipe.tasks.colorterms import ColortermLibrary
41from lsst.verify import Job, Measurement
43from lsst.meas.algorithms import LoadIndexedReferenceObjectsTask, ReferenceSourceSelectorTask
44from lsst.meas.algorithms.sourceSelector import sourceSelectorRegistry
46from .dataIds import PerTractCcdDataIdContainer
48import lsst.jointcal
49from lsst.jointcal import MinimizeResult
51__all__ = ["JointcalConfig", "JointcalRunner", "JointcalTask"]
53Photometry = collections.namedtuple('Photometry', ('fit', 'model'))
54Astrometry = collections.namedtuple('Astrometry', ('fit', 'model', 'sky_to_tan_projection'))
57# TODO: move this to MeasurementSet in lsst.verify per DM-12655.
58def add_measurement(job, name, value):
59 meas = Measurement(job.metrics[name], value)
60 job.measurements.insert(meas)
63class JointcalRunner(pipeBase.ButlerInitializedTaskRunner):
64 """Subclass of TaskRunner for jointcalTask
66 jointcalTask.runDataRef() takes a number of arguments, one of which is a list of dataRefs
67 extracted from the command line (whereas most CmdLineTasks' runDataRef methods take
68 single dataRef, are are called repeatedly). This class transforms the processed
69 arguments generated by the ArgumentParser into the arguments expected by
70 Jointcal.runDataRef().
72 See pipeBase.TaskRunner for more information.
73 """
75 @staticmethod
76 def getTargetList(parsedCmd, **kwargs):
77 """
78 Return a list of tuples per tract, each containing (dataRefs, kwargs).
80 Jointcal operates on lists of dataRefs simultaneously.
81 """
82 kwargs['profile_jointcal'] = parsedCmd.profile_jointcal
83 kwargs['butler'] = parsedCmd.butler
85 # organize data IDs by tract
86 refListDict = {}
87 for ref in parsedCmd.id.refList:
88 refListDict.setdefault(ref.dataId["tract"], []).append(ref)
89 # we call runDataRef() once with each tract
90 result = [(refListDict[tract], kwargs) for tract in sorted(refListDict.keys())]
91 return result
93 def __call__(self, args):
94 """
95 Parameters
96 ----------
97 args
98 Arguments for Task.runDataRef()
100 Returns
101 -------
102 pipe.base.Struct
103 if self.doReturnResults is False:
105 - ``exitStatus``: 0 if the task completed successfully, 1 otherwise.
107 if self.doReturnResults is True:
109 - ``result``: the result of calling jointcal.runDataRef()
110 - ``exitStatus``: 0 if the task completed successfully, 1 otherwise.
111 """
112 exitStatus = 0 # exit status for shell
114 # NOTE: cannot call self.makeTask because that assumes args[0] is a single dataRef.
115 dataRefList, kwargs = args
116 butler = kwargs.pop('butler')
117 task = self.TaskClass(config=self.config, log=self.log, butler=butler)
118 result = None
119 try:
120 result = task.runDataRef(dataRefList, **kwargs)
121 exitStatus = result.exitStatus
122 job_path = butler.get('verify_job_filename')
123 result.job.write(job_path[0])
124 except Exception as e: # catch everything, sort it out later.
125 if self.doRaise:
126 raise e
127 else:
128 exitStatus = 1
129 eName = type(e).__name__
130 tract = dataRefList[0].dataId['tract']
131 task.log.fatal("Failed processing tract %s, %s: %s", tract, eName, e)
133 # Put the butler back into kwargs for the other Tasks.
134 kwargs['butler'] = butler
135 if self.doReturnResults:
136 return pipeBase.Struct(result=result, exitStatus=exitStatus)
137 else:
138 return pipeBase.Struct(exitStatus=exitStatus)
141class JointcalConfig(pexConfig.Config):
142 """Configuration for JointcalTask"""
144 doAstrometry = pexConfig.Field(
145 doc="Fit astrometry and write the fitted result.",
146 dtype=bool,
147 default=True
148 )
149 doPhotometry = pexConfig.Field(
150 doc="Fit photometry and write the fitted result.",
151 dtype=bool,
152 default=True
153 )
154 coaddName = pexConfig.Field(
155 doc="Type of coadd, typically deep or goodSeeing",
156 dtype=str,
157 default="deep"
158 )
159 positionErrorPedestal = pexConfig.Field(
160 doc="Systematic term to apply to the measured position error (pixels)",
161 dtype=float,
162 default=0.02,
163 )
164 photometryErrorPedestal = pexConfig.Field(
165 doc="Systematic term to apply to the measured error on flux or magnitude as a "
166 "fraction of source flux or magnitude delta (e.g. 0.05 is 5% of flux or +50 millimag).",
167 dtype=float,
168 default=0.0,
169 )
170 # TODO: DM-6885 matchCut should be an geom.Angle
171 matchCut = pexConfig.Field(
172 doc="Matching radius between fitted and reference stars (arcseconds)",
173 dtype=float,
174 default=3.0,
175 )
176 minMeasurements = pexConfig.Field(
177 doc="Minimum number of associated measured stars for a fitted star to be included in the fit",
178 dtype=int,
179 default=2,
180 )
181 minMeasuredStarsPerCcd = pexConfig.Field(
182 doc="Minimum number of measuredStars per ccdImage before printing warnings",
183 dtype=int,
184 default=100,
185 )
186 minRefStarsPerCcd = pexConfig.Field(
187 doc="Minimum number of measuredStars per ccdImage before printing warnings",
188 dtype=int,
189 default=30,
190 )
191 allowLineSearch = pexConfig.Field(
192 doc="Allow a line search during minimization, if it is reasonable for the model"
193 " (models with a significant non-linear component, e.g. constrainedPhotometry).",
194 dtype=bool,
195 default=False
196 )
197 astrometrySimpleOrder = pexConfig.Field(
198 doc="Polynomial order for fitting the simple astrometry model.",
199 dtype=int,
200 default=3,
201 )
202 astrometryChipOrder = pexConfig.Field(
203 doc="Order of the per-chip transform for the constrained astrometry model.",
204 dtype=int,
205 default=1,
206 )
207 astrometryVisitOrder = pexConfig.Field(
208 doc="Order of the per-visit transform for the constrained astrometry model.",
209 dtype=int,
210 default=5,
211 )
212 useInputWcs = pexConfig.Field(
213 doc="Use the input calexp WCSs to initialize a SimpleAstrometryModel.",
214 dtype=bool,
215 default=True,
216 )
217 astrometryModel = pexConfig.ChoiceField(
218 doc="Type of model to fit to astrometry",
219 dtype=str,
220 default="constrained",
221 allowed={"simple": "One polynomial per ccd",
222 "constrained": "One polynomial per ccd, and one polynomial per visit"}
223 )
224 photometryModel = pexConfig.ChoiceField(
225 doc="Type of model to fit to photometry",
226 dtype=str,
227 default="constrainedMagnitude",
228 allowed={"simpleFlux": "One constant zeropoint per ccd and visit, fitting in flux space.",
229 "constrainedFlux": "Constrained zeropoint per ccd, and one polynomial per visit,"
230 " fitting in flux space.",
231 "simpleMagnitude": "One constant zeropoint per ccd and visit,"
232 " fitting in magnitude space.",
233 "constrainedMagnitude": "Constrained zeropoint per ccd, and one polynomial per visit,"
234 " fitting in magnitude space.",
235 }
236 )
237 applyColorTerms = pexConfig.Field(
238 doc="Apply photometric color terms to reference stars?"
239 "Requires that colorterms be set to a ColortermLibrary",
240 dtype=bool,
241 default=False
242 )
243 colorterms = pexConfig.ConfigField(
244 doc="Library of photometric reference catalog name to color term dict.",
245 dtype=ColortermLibrary,
246 )
247 photometryVisitOrder = pexConfig.Field(
248 doc="Order of the per-visit polynomial transform for the constrained photometry model.",
249 dtype=int,
250 default=7,
251 )
252 photometryDoRankUpdate = pexConfig.Field(
253 doc=("Do the rank update step during minimization. "
254 "Skipping this can help deal with models that are too non-linear."),
255 dtype=bool,
256 default=True,
257 )
258 astrometryDoRankUpdate = pexConfig.Field(
259 doc=("Do the rank update step during minimization (should not change the astrometry fit). "
260 "Skipping this can help deal with models that are too non-linear."),
261 dtype=bool,
262 default=True,
263 )
264 outlierRejectSigma = pexConfig.Field(
265 doc="How many sigma to reject outliers at during minimization.",
266 dtype=float,
267 default=5.0,
268 )
269 maxPhotometrySteps = pexConfig.Field(
270 doc="Maximum number of minimize iterations to take when fitting photometry.",
271 dtype=int,
272 default=20,
273 )
274 maxAstrometrySteps = pexConfig.Field(
275 doc="Maximum number of minimize iterations to take when fitting photometry.",
276 dtype=int,
277 default=20,
278 )
279 astrometryRefObjLoader = pexConfig.ConfigurableField(
280 target=LoadIndexedReferenceObjectsTask,
281 doc="Reference object loader for astrometric fit",
282 )
283 photometryRefObjLoader = pexConfig.ConfigurableField(
284 target=LoadIndexedReferenceObjectsTask,
285 doc="Reference object loader for photometric fit",
286 )
287 sourceSelector = sourceSelectorRegistry.makeField(
288 doc="How to select sources for cross-matching",
289 default="astrometry"
290 )
291 astrometryReferenceSelector = pexConfig.ConfigurableField(
292 target=ReferenceSourceSelectorTask,
293 doc="How to down-select the loaded astrometry reference catalog.",
294 )
295 photometryReferenceSelector = pexConfig.ConfigurableField(
296 target=ReferenceSourceSelectorTask,
297 doc="How to down-select the loaded photometry reference catalog.",
298 )
299 astrometryReferenceErr = pexConfig.Field(
300 doc=("Uncertainty on reference catalog coordinates [mas] to use in place of the `coord_*Err` fields. "
301 "If None, then raise an exception if the reference catalog is missing coordinate errors. "
302 "If specified, overrides any existing `coord_*Err` values."),
303 dtype=float,
304 default=None,
305 optional=True
306 )
307 writeInitMatrix = pexConfig.Field(
308 dtype=bool,
309 doc=("Write the pre/post-initialization Hessian and gradient to text files, for debugging. "
310 "The output files will be of the form 'astrometry_preinit-mat.txt', in the current directory. "
311 "Note that these files are the dense versions of the matrix, and so may be very large."),
312 default=False
313 )
314 writeChi2FilesInitialFinal = pexConfig.Field(
315 dtype=bool,
316 doc="Write .csv files containing the contributions to chi2 for the initialization and final fit.",
317 default=False
318 )
319 writeChi2FilesOuterLoop = pexConfig.Field(
320 dtype=bool,
321 doc="Write .csv files containing the contributions to chi2 for the outer fit loop.",
322 default=False
323 )
324 writeInitialModel = pexConfig.Field(
325 dtype=bool,
326 doc=("Write the pre-initialization model to text files, for debugging."
327 " Output is written to `initial[Astro|Photo]metryModel.txt` in the current working directory."),
328 default=False
329 )
330 debugOutputPath = pexConfig.Field(
331 dtype=str,
332 default=".",
333 doc=("Path to write debug output files to. Used by "
334 "`writeInitialModel`, `writeChi2Files*`, `writeInitMatrix`.")
335 )
336 sourceFluxType = pexConfig.Field(
337 dtype=str,
338 doc="Source flux field to use in source selection and to get fluxes from the catalog.",
339 default='Calib'
340 )
342 def validate(self):
343 super().validate()
344 if self.doPhotometry and self.applyColorTerms and len(self.colorterms.data) == 0:
345 msg = "applyColorTerms=True requires the `colorterms` field be set to a ColortermLibrary."
346 raise pexConfig.FieldValidationError(JointcalConfig.colorterms, self, msg)
347 if self.doAstrometry and not self.doPhotometry and self.applyColorTerms:
348 msg = ("Only doing astrometry, but Colorterms are not applied for astrometry;"
349 "applyColorTerms=True will be ignored.")
350 lsst.log.warn(msg)
352 def setDefaults(self):
353 # Use science source selector which can filter on extendedness, SNR, and whether blended
354 self.sourceSelector.name = 'science'
355 # Use only stars because aperture fluxes of galaxies are biased and depend on seeing
356 self.sourceSelector['science'].doUnresolved = True
357 # with dependable signal to noise ratio.
358 self.sourceSelector['science'].doSignalToNoise = True
359 # Min SNR must be > 0 because jointcal cannot handle negative fluxes,
360 # and S/N > 10 to use sources that are not too faint, and thus better measured.
361 self.sourceSelector['science'].signalToNoise.minimum = 10.
362 # Base SNR on CalibFlux because that is the flux jointcal that fits and must be positive
363 fluxField = f"slot_{self.sourceFluxType}Flux_instFlux"
364 self.sourceSelector['science'].signalToNoise.fluxField = fluxField
365 self.sourceSelector['science'].signalToNoise.errField = fluxField + "Err"
366 # Do not trust blended sources' aperture fluxes which also depend on seeing
367 self.sourceSelector['science'].doIsolated = True
368 # Do not trust either flux or centroid measurements with flags,
369 # chosen from the usual QA flags for stars)
370 self.sourceSelector['science'].doFlags = True
371 badFlags = ['base_PixelFlags_flag_edge', 'base_PixelFlags_flag_saturated',
372 'base_PixelFlags_flag_interpolatedCenter', 'base_SdssCentroid_flag',
373 'base_PsfFlux_flag', 'base_PixelFlags_flag_suspectCenter']
374 self.sourceSelector['science'].flags.bad = badFlags
376 # Default to Gaia-DR2 (with proper motions) for astrometry and
377 # PS1-DR1 for photometry, with a reasonable initial filterMap.
378 self.astrometryRefObjLoader.ref_dataset_name = "gaia_dr2_20200414"
379 self.astrometryRefObjLoader.requireProperMotion = True
380 self.astrometryRefObjLoader.anyFilterMapsToThis = 'phot_g_mean'
381 self.photometryRefObjLoader.ref_dataset_name = "ps1_pv3_3pi_20170110"
384def writeModel(model, filename, log):
385 """Write model to outfile."""
386 with open(filename, "w") as file:
387 file.write(repr(model))
388 log.info("Wrote %s to file: %s", model, filename)
391@dataclasses.dataclass
392class JointcalInputData:
393 """The input data jointcal needs for each detector/visit."""
394 visit: int
395 """The visit identifier of this exposure."""
396 catalog: lsst.afw.table.SourceCatalog
397 """The catalog derived from this exposure."""
398 visitInfo: lsst.afw.image.VisitInfo
399 """The VisitInfo of this exposure."""
400 detector: lsst.afw.cameraGeom.Detector
401 """The detector of this exposure."""
402 photoCalib: lsst.afw.image.PhotoCalib
403 """The photometric calibration of this exposure."""
404 wcs: lsst.afw.geom.skyWcs
405 """The WCS of this exposure."""
406 bbox: lsst.geom.Box2I
407 """The bounding box of this exposure."""
408 filter: lsst.afw.image.FilterLabel
409 """The filter of this exposure."""
412class JointcalTask(pipeBase.PipelineTask, pipeBase.CmdLineTask):
413 """Astrometricly and photometricly calibrate across multiple visits of the
414 same field.
416 Parameters
417 ----------
418 butler : `lsst.daf.persistence.Butler`
419 The butler is passed to the refObjLoader constructor in case it is
420 needed. Ignored if the refObjLoader argument provides a loader directly.
421 Used to initialize the astrometry and photometry refObjLoaders.
422 profile_jointcal : `bool`
423 Set to True to profile different stages of this jointcal run.
424 """
426 ConfigClass = JointcalConfig
427 RunnerClass = JointcalRunner
428 _DefaultName = "jointcal"
430 def __init__(self, butler=None, profile_jointcal=False, **kwargs):
431 super().__init__(**kwargs)
432 self.profile_jointcal = profile_jointcal
433 self.makeSubtask("sourceSelector")
434 if self.config.doAstrometry:
435 self.makeSubtask('astrometryRefObjLoader', butler=butler)
436 self.makeSubtask("astrometryReferenceSelector")
437 else:
438 self.astrometryRefObjLoader = None
439 if self.config.doPhotometry:
440 self.makeSubtask('photometryRefObjLoader', butler=butler)
441 self.makeSubtask("photometryReferenceSelector")
442 else:
443 self.photometryRefObjLoader = None
445 # To hold various computed metrics for use by tests
446 self.job = Job.load_metrics_package(subset='jointcal')
448 # We don't currently need to persist the metadata.
449 # If we do in the future, we will have to add appropriate dataset templates
450 # to each obs package (the metadata template should look like `jointcal_wcs`).
451 def _getMetadataName(self):
452 return None
454 @classmethod
455 def _makeArgumentParser(cls):
456 """Create an argument parser"""
457 parser = pipeBase.ArgumentParser(name=cls._DefaultName)
458 parser.add_argument("--profile_jointcal", default=False, action="store_true",
459 help="Profile steps of jointcal separately.")
460 parser.add_id_argument("--id", "calexp", help="data ID, e.g. --id visit=6789 ccd=0..9",
461 ContainerClass=PerTractCcdDataIdContainer)
462 return parser
464 def _build_ccdImage(self, data, associations, jointcalControl):
465 """
466 Extract the necessary things from this dataRef to add a new ccdImage.
468 Parameters
469 ----------
470 data : `JointcalInputData`
471 The loaded input data.
472 associations : `lsst.jointcal.Associations`
473 Object to add the info to, to construct a new CcdImage
474 jointcalControl : `jointcal.JointcalControl`
475 Control object for associations management
477 Returns
478 ------
479 namedtuple
480 ``wcs``
481 The TAN WCS of this image, read from the calexp
482 (`lsst.afw.geom.SkyWcs`).
483 ``key``
484 A key to identify this dataRef by its visit and ccd ids
485 (`namedtuple`).
486 ``band``
487 This calexp's filter band (`str`) (used to e.g. load refcats)
488 """
489 goodSrc = self.sourceSelector.run(data.catalog)
491 if len(goodSrc.sourceCat) == 0:
492 self.log.warn("No sources selected in visit %s ccd %s", data.visit, data.detector.getId())
493 else:
494 self.log.info("%d sources selected in visit %d ccd %d", len(goodSrc.sourceCat),
495 data.visit,
496 data.detector.getId())
497 associations.createCcdImage(goodSrc.sourceCat,
498 data.wcs,
499 data.visitInfo,
500 data.bbox,
501 data.filter.physicalLabel,
502 data.photoCalib,
503 data.detector,
504 data.visit,
505 data.detector.getId(),
506 jointcalControl)
508 Result = collections.namedtuple('Result_from_build_CcdImage', ('wcs', 'key', 'band'))
509 Key = collections.namedtuple('Key', ('visit', 'ccd'))
510 return Result(data.wcs, Key(data.visit, data.detector.getId()), data.filter.bandLabel)
512 def _readDataId(self, butler, dataId):
513 """Read all of the data for one dataId from the butler. (gen2 version)"""
514 # Not all instruments have `visit` in their dataIds.
515 if "visit" in dataId.keys():
516 visit = dataId["visit"]
517 else:
518 visit = butler.getButler().queryMetadata("calexp", ("visit"), butler.dataId)[0]
520 catalog = butler.get('src',
521 flags=lsst.afw.table.SOURCE_IO_NO_FOOTPRINTS,
522 dataId=dataId)
523 return JointcalInputData(visit=visit,
524 catalog=catalog,
525 visitInfo=butler.get('calexp_visitInfo', dataId=dataId),
526 detector=butler.get('calexp_detector', dataId=dataId),
527 photoCalib=butler.get('calexp_photoCalib', dataId=dataId),
528 wcs=butler.get('calexp_wcs', dataId=dataId),
529 bbox=butler.get('calexp_bbox', dataId=dataId),
530 filter=butler.get('calexp_filterLabel', dataId=dataId))
532 def loadData(self, dataRefs, associations, jointcalControl, profile_jointcal=False):
533 """Read the data that jointcal needs to run. (Gen2 version)"""
534 visit_ccd_to_dataRef = {}
535 oldWcsList = []
536 bands = []
537 load_cat_prof_file = 'jointcal_loadData.prof' if profile_jointcal else ''
538 with pipeBase.cmdLineTask.profile(load_cat_prof_file):
539 # Need the bounding-box of the focal plane (the same for all visits) for photometry visit models
540 camera = dataRefs[0].get('camera', immediate=True)
541 self.focalPlaneBBox = camera.getFpBBox()
542 for dataRef in dataRefs:
543 data = self._readDataId(dataRef.getButler(), dataRef.dataId)
544 result = self._build_ccdImage(data, associations, jointcalControl)
545 oldWcsList.append(result.wcs)
546 visit_ccd_to_dataRef[result.key] = dataRef
547 bands.append(result.band)
548 bands = collections.Counter(bands)
550 return oldWcsList, bands, visit_ccd_to_dataRef
552 def _getDebugPath(self, filename):
553 """Constructs a path to filename using the configured debug path.
554 """
555 return os.path.join(self.config.debugOutputPath, filename)
557 def _prep_sky(self, associations, bands):
558 """Prepare on-sky and other data that must be computed after data has
559 been read.
560 """
561 associations.computeCommonTangentPoint()
563 boundingCircle = associations.computeBoundingCircle()
564 center = lsst.geom.SpherePoint(boundingCircle.getCenter())
565 radius = lsst.geom.Angle(boundingCircle.getOpeningAngle().asRadians(), lsst.geom.radians)
567 self.log.info(f"Data has center={center} with radius={radius.asDegrees()} degrees.")
569 # Determine a default filter band associated with the catalog. See DM-9093
570 defaultBand = bands.most_common(1)[0][0]
571 self.log.debug("Using '%s' filter band for reference flux", defaultBand)
573 return boundingCircle, center, radius, defaultBand
575 @pipeBase.timeMethod
576 def runDataRef(self, dataRefs, profile_jointcal=False):
577 """
578 Jointly calibrate the astrometry and photometry across a set of images.
580 NOTE: this is for gen2 middleware only.
582 Parameters
583 ----------
584 dataRefs : `list` of `lsst.daf.persistence.ButlerDataRef`
585 List of data references to the exposures to be fit.
586 profile_jointcal : `bool`
587 Profile the individual steps of jointcal.
589 Returns
590 -------
591 result : `lsst.pipe.base.Struct`
592 Struct of metadata from the fit, containing:
594 ``dataRefs``
595 The provided data references that were fit (with updated WCSs)
596 ``oldWcsList``
597 The original WCS from each dataRef
598 ``metrics``
599 Dictionary of internally-computed metrics for testing/validation.
600 """
601 if len(dataRefs) == 0:
602 raise ValueError('Need a non-empty list of data references!')
604 exitStatus = 0 # exit status for shell
606 sourceFluxField = "slot_%sFlux" % (self.config.sourceFluxType,)
607 jointcalControl = lsst.jointcal.JointcalControl(sourceFluxField)
608 associations = lsst.jointcal.Associations()
610 oldWcsList, bands, visit_ccd_to_dataRef = self.loadData(dataRefs,
611 associations,
612 jointcalControl,
613 profile_jointcal=profile_jointcal)
615 boundingCircle, center, radius, defaultBand = self._prep_sky(associations, bands)
616 epoch = self._compute_proper_motion_epoch(associations.getCcdImageList())
618 tract = dataRefs[0].dataId['tract']
620 if self.config.doAstrometry:
621 astrometry = self._do_load_refcat_and_fit(associations, defaultBand, center, radius,
622 name="astrometry",
623 refObjLoader=self.astrometryRefObjLoader,
624 referenceSelector=self.astrometryReferenceSelector,
625 fit_function=self._fit_astrometry,
626 profile_jointcal=profile_jointcal,
627 tract=tract,
628 epoch=epoch)
629 self._write_astrometry_results(associations, astrometry.model, visit_ccd_to_dataRef)
630 else:
631 astrometry = Astrometry(None, None, None)
633 if self.config.doPhotometry:
634 photometry = self._do_load_refcat_and_fit(associations, defaultBand, center, radius,
635 name="photometry",
636 refObjLoader=self.photometryRefObjLoader,
637 referenceSelector=self.photometryReferenceSelector,
638 fit_function=self._fit_photometry,
639 profile_jointcal=profile_jointcal,
640 tract=tract,
641 epoch=epoch,
642 reject_bad_fluxes=True)
643 self._write_photometry_results(associations, photometry.model, visit_ccd_to_dataRef)
644 else:
645 photometry = Photometry(None, None)
647 return pipeBase.Struct(dataRefs=dataRefs,
648 oldWcsList=oldWcsList,
649 job=self.job,
650 astrometryRefObjLoader=self.astrometryRefObjLoader,
651 photometryRefObjLoader=self.photometryRefObjLoader,
652 defaultBand=defaultBand,
653 epoch=epoch,
654 exitStatus=exitStatus)
656 def _get_refcat_coordinate_error_override(self, refCat, name):
657 """Check whether we should override the refcat coordinate errors, and
658 return the overridden error if necessary.
660 Parameters
661 ----------
662 refCat : `lsst.afw.table.SimpleCatalog`
663 The reference catalog to check for a ``coord_raErr`` field.
664 name : `str`
665 Whether we are doing "astrometry" or "photometry".
667 Returns
668 -------
669 refCoordErr : `float`
670 The refcat coordinate error to use, or NaN if we are not overriding
671 those fields.
673 Raises
674 ------
675 lsst.pex.config.FieldValidationError
676 Raised if the refcat does not contain coordinate errors and
677 ``config.astrometryReferenceErr`` is not set.
678 """
679 # This value doesn't matter for photometry, so just set something to
680 # keep old refcats from causing problems.
681 if name.lower() == "photometry":
682 if 'coord_raErr' not in refCat.schema:
683 return 100
684 else:
685 return float('nan')
687 if self.config.astrometryReferenceErr is None and 'coord_raErr' not in refCat.schema:
688 msg = ("Reference catalog does not contain coordinate errors, "
689 "and config.astrometryReferenceErr not supplied.")
690 raise pexConfig.FieldValidationError(JointcalConfig.astrometryReferenceErr,
691 self.config,
692 msg)
694 if self.config.astrometryReferenceErr is not None and 'coord_raErr' in refCat.schema:
695 self.log.warn("Overriding reference catalog coordinate errors with %f/coordinate [mas]",
696 self.config.astrometryReferenceErr)
698 if self.config.astrometryReferenceErr is None:
699 return float('nan')
700 else:
701 return self.config.astrometryReferenceErr
703 def _compute_proper_motion_epoch(self, ccdImageList):
704 """Return the proper motion correction epoch of the provided images.
706 Parameters
707 ----------
708 ccdImageList : `list` [`lsst.jointcal.CcdImage`]
709 The images to compute the appropriate epoch for.
711 Returns
712 -------
713 epoch : `astropy.time.Time`
714 The date to use for proper motion corrections.
715 """
716 mjds = [ccdImage.getMjd() for ccdImage in ccdImageList]
717 return astropy.time.Time(np.mean(mjds), format='mjd', scale="tai")
719 def _do_load_refcat_and_fit(self, associations, defaultBand, center, radius,
720 tract="", profile_jointcal=False, match_cut=3.0,
721 reject_bad_fluxes=False, *,
722 name="", refObjLoader=None, referenceSelector=None,
723 fit_function=None, epoch=None):
724 """Load reference catalog, perform the fit, and return the result.
726 Parameters
727 ----------
728 associations : `lsst.jointcal.Associations`
729 The star/reference star associations to fit.
730 defaultBand : `str`
731 filter to load from reference catalog.
732 center : `lsst.geom.SpherePoint`
733 ICRS center of field to load from reference catalog.
734 radius : `lsst.geom.Angle`
735 On-sky radius to load from reference catalog.
736 name : `str`
737 Name of thing being fit: "astrometry" or "photometry".
738 refObjLoader : `lsst.meas.algorithms.LoadReferenceObjectsTask`
739 Reference object loader to use to load a reference catalog.
740 referenceSelector : `lsst.meas.algorithms.ReferenceSourceSelectorTask`
741 Selector to use to pick objects from the loaded reference catalog.
742 fit_function : callable
743 Function to call to perform fit (takes Associations object).
744 tract : `str`, optional
745 Name of tract currently being fit.
746 profile_jointcal : `bool`, optional
747 Separately profile the fitting step.
748 match_cut : `float`, optional
749 Radius in arcseconds to find cross-catalog matches to during
750 associations.associateCatalogs.
751 reject_bad_fluxes : `bool`, optional
752 Reject refCat sources with NaN/inf flux or NaN/0 fluxErr.
753 epoch : `astropy.time.Time`, optional
754 Epoch to which to correct refcat proper motion and parallax,
755 or `None` to not apply such corrections.
757 Returns
758 -------
759 result : `Photometry` or `Astrometry`
760 Result of `fit_function()`
761 """
762 self.log.info("====== Now processing %s...", name)
763 # TODO: this should not print "trying to invert a singular transformation:"
764 # if it does that, something's not right about the WCS...
765 associations.associateCatalogs(match_cut)
766 add_measurement(self.job, 'jointcal.associated_%s_fittedStars' % name,
767 associations.fittedStarListSize())
769 applyColorterms = False if name.lower() == "astrometry" else self.config.applyColorTerms
770 refCat, fluxField = self._load_reference_catalog(refObjLoader, referenceSelector,
771 center, radius, defaultBand,
772 applyColorterms=applyColorterms,
773 epoch=epoch)
774 refCoordErr = self._get_refcat_coordinate_error_override(refCat, name)
776 associations.collectRefStars(refCat,
777 self.config.matchCut*lsst.geom.arcseconds,
778 fluxField,
779 refCoordinateErr=refCoordErr,
780 rejectBadFluxes=reject_bad_fluxes)
781 add_measurement(self.job, 'jointcal.collected_%s_refStars' % name,
782 associations.refStarListSize())
784 associations.prepareFittedStars(self.config.minMeasurements)
786 self._check_star_lists(associations, name)
787 add_measurement(self.job, 'jointcal.selected_%s_refStars' % name,
788 associations.nFittedStarsWithAssociatedRefStar())
789 add_measurement(self.job, 'jointcal.selected_%s_fittedStars' % name,
790 associations.fittedStarListSize())
791 add_measurement(self.job, 'jointcal.selected_%s_ccdImages' % name,
792 associations.nCcdImagesValidForFit())
794 load_cat_prof_file = 'jointcal_fit_%s.prof'%name if profile_jointcal else ''
795 dataName = "{}_{}".format(tract, defaultBand)
796 with pipeBase.cmdLineTask.profile(load_cat_prof_file):
797 result = fit_function(associations, dataName)
798 # TODO DM-12446: turn this into a "butler save" somehow.
799 # Save reference and measurement chi2 contributions for this data
800 if self.config.writeChi2FilesInitialFinal:
801 baseName = self._getDebugPath(f"{name}_final_chi2-{dataName}")
802 result.fit.saveChi2Contributions(baseName+"{type}")
803 self.log.info("Wrote chi2 contributions files: %s", baseName)
805 return result
807 def _load_reference_catalog(self, refObjLoader, referenceSelector, center, radius, filterName,
808 applyColorterms=False, epoch=None):
809 """Load the necessary reference catalog sources, convert fluxes to
810 correct units, and apply color term corrections if requested.
812 Parameters
813 ----------
814 refObjLoader : `lsst.meas.algorithms.LoadReferenceObjectsTask`
815 The reference catalog loader to use to get the data.
816 referenceSelector : `lsst.meas.algorithms.ReferenceSourceSelectorTask`
817 Source selector to apply to loaded reference catalog.
818 center : `lsst.geom.SpherePoint`
819 The center around which to load sources.
820 radius : `lsst.geom.Angle`
821 The radius around ``center`` to load sources in.
822 filterName : `str`
823 The name of the camera filter to load fluxes for.
824 applyColorterms : `bool`
825 Apply colorterm corrections to the refcat for ``filterName``?
826 epoch : `astropy.time.Time`, optional
827 Epoch to which to correct refcat proper motion and parallax,
828 or `None` to not apply such corrections.
830 Returns
831 -------
832 refCat : `lsst.afw.table.SimpleCatalog`
833 The loaded reference catalog.
834 fluxField : `str`
835 The name of the reference catalog flux field appropriate for ``filterName``.
836 """
837 skyCircle = refObjLoader.loadSkyCircle(center,
838 radius,
839 filterName,
840 epoch=epoch)
842 selected = referenceSelector.run(skyCircle.refCat)
843 # Need memory contiguity to get reference filters as a vector.
844 if not selected.sourceCat.isContiguous():
845 refCat = selected.sourceCat.copy(deep=True)
846 else:
847 refCat = selected.sourceCat
849 if applyColorterms:
850 refCatName = refObjLoader.ref_dataset_name
851 self.log.info("Applying color terms for filterName=%r reference catalog=%s",
852 filterName, refCatName)
853 colorterm = self.config.colorterms.getColorterm(
854 filterName=filterName, photoCatName=refCatName, doRaise=True)
856 refMag, refMagErr = colorterm.getCorrectedMagnitudes(refCat, filterName)
857 refCat[skyCircle.fluxField] = u.Magnitude(refMag, u.ABmag).to_value(u.nJy)
858 # TODO: I didn't want to use this, but I'll deal with it in DM-16903
859 refCat[skyCircle.fluxField+'Err'] = fluxErrFromABMagErr(refMagErr, refMag) * 1e9
861 return refCat, skyCircle.fluxField
863 def _check_star_lists(self, associations, name):
864 # TODO: these should be len(blah), but we need this properly wrapped first.
865 if associations.nCcdImagesValidForFit() == 0:
866 raise RuntimeError('No images in the ccdImageList!')
867 if associations.fittedStarListSize() == 0:
868 raise RuntimeError('No stars in the {} fittedStarList!'.format(name))
869 if associations.refStarListSize() == 0:
870 raise RuntimeError('No stars in the {} reference star list!'.format(name))
872 def _logChi2AndValidate(self, associations, fit, model, chi2Label, writeChi2Name=None):
873 """Compute chi2, log it, validate the model, and return chi2.
875 Parameters
876 ----------
877 associations : `lsst.jointcal.Associations`
878 The star/reference star associations to fit.
879 fit : `lsst.jointcal.FitterBase`
880 The fitter to use for minimization.
881 model : `lsst.jointcal.Model`
882 The model being fit.
883 chi2Label : `str`
884 Label to describe the chi2 (e.g. "Initialized", "Final").
885 writeChi2Name : `str`, optional
886 Filename prefix to write the chi2 contributions to.
887 Do not supply an extension: an appropriate one will be added.
889 Returns
890 -------
891 chi2: `lsst.jointcal.Chi2Accumulator`
892 The chi2 object for the current fitter and model.
894 Raises
895 ------
896 FloatingPointError
897 Raised if chi2 is infinite or NaN.
898 ValueError
899 Raised if the model is not valid.
900 """
901 if writeChi2Name is not None:
902 fullpath = self._getDebugPath(writeChi2Name)
903 fit.saveChi2Contributions(fullpath+"{type}")
904 self.log.info("Wrote chi2 contributions files: %s", fullpath)
906 chi2 = fit.computeChi2()
907 self.log.info("%s %s", chi2Label, chi2)
908 self._check_stars(associations)
909 if not np.isfinite(chi2.chi2):
910 raise FloatingPointError(f'{chi2Label} chi2 is invalid: {chi2}')
911 if not model.validate(associations.getCcdImageList(), chi2.ndof):
912 raise ValueError("Model is not valid: check log messages for warnings.")
913 return chi2
915 def _fit_photometry(self, associations, dataName=None):
916 """
917 Fit the photometric data.
919 Parameters
920 ----------
921 associations : `lsst.jointcal.Associations`
922 The star/reference star associations to fit.
923 dataName : `str`
924 Name of the data being processed (e.g. "1234_HSC-Y"), for
925 identifying debugging files.
927 Returns
928 -------
929 fit_result : `namedtuple`
930 fit : `lsst.jointcal.PhotometryFit`
931 The photometric fitter used to perform the fit.
932 model : `lsst.jointcal.PhotometryModel`
933 The photometric model that was fit.
934 """
935 self.log.info("=== Starting photometric fitting...")
937 # TODO: should use pex.config.RegistryField here (see DM-9195)
938 if self.config.photometryModel == "constrainedFlux":
939 model = lsst.jointcal.ConstrainedFluxModel(associations.getCcdImageList(),
940 self.focalPlaneBBox,
941 visitOrder=self.config.photometryVisitOrder,
942 errorPedestal=self.config.photometryErrorPedestal)
943 # potentially nonlinear problem, so we may need a line search to converge.
944 doLineSearch = self.config.allowLineSearch
945 elif self.config.photometryModel == "constrainedMagnitude":
946 model = lsst.jointcal.ConstrainedMagnitudeModel(associations.getCcdImageList(),
947 self.focalPlaneBBox,
948 visitOrder=self.config.photometryVisitOrder,
949 errorPedestal=self.config.photometryErrorPedestal)
950 # potentially nonlinear problem, so we may need a line search to converge.
951 doLineSearch = self.config.allowLineSearch
952 elif self.config.photometryModel == "simpleFlux":
953 model = lsst.jointcal.SimpleFluxModel(associations.getCcdImageList(),
954 errorPedestal=self.config.photometryErrorPedestal)
955 doLineSearch = False # purely linear in model parameters, so no line search needed
956 elif self.config.photometryModel == "simpleMagnitude":
957 model = lsst.jointcal.SimpleMagnitudeModel(associations.getCcdImageList(),
958 errorPedestal=self.config.photometryErrorPedestal)
959 doLineSearch = False # purely linear in model parameters, so no line search needed
961 fit = lsst.jointcal.PhotometryFit(associations, model)
962 # TODO DM-12446: turn this into a "butler save" somehow.
963 # Save reference and measurement chi2 contributions for this data
964 if self.config.writeChi2FilesInitialFinal:
965 baseName = f"photometry_initial_chi2-{dataName}"
966 else:
967 baseName = None
968 if self.config.writeInitialModel:
969 fullpath = self._getDebugPath("initialPhotometryModel.txt")
970 writeModel(model, fullpath, self.log)
971 self._logChi2AndValidate(associations, fit, model, "Initialized", writeChi2Name=baseName)
973 def getChi2Name(whatToFit):
974 if self.config.writeChi2FilesOuterLoop:
975 return f"photometry_init-%s_chi2-{dataName}" % whatToFit
976 else:
977 return None
979 # The constrained model needs the visit transform fit first; the chip
980 # transform is initialized from the singleFrame PhotoCalib, so it's close.
981 dumpMatrixFile = self._getDebugPath("photometry_preinit") if self.config.writeInitMatrix else ""
982 if self.config.photometryModel.startswith("constrained"):
983 # no line search: should be purely (or nearly) linear,
984 # and we want a large step size to initialize with.
985 fit.minimize("ModelVisit", dumpMatrixFile=dumpMatrixFile)
986 self._logChi2AndValidate(associations, fit, model, "Initialize ModelVisit",
987 writeChi2Name=getChi2Name("ModelVisit"))
988 dumpMatrixFile = "" # so we don't redo the output on the next step
990 fit.minimize("Model", doLineSearch=doLineSearch, dumpMatrixFile=dumpMatrixFile)
991 self._logChi2AndValidate(associations, fit, model, "Initialize Model",
992 writeChi2Name=getChi2Name("Model"))
994 fit.minimize("Fluxes") # no line search: always purely linear.
995 self._logChi2AndValidate(associations, fit, model, "Initialize Fluxes",
996 writeChi2Name=getChi2Name("Fluxes"))
998 fit.minimize("Model Fluxes", doLineSearch=doLineSearch)
999 self._logChi2AndValidate(associations, fit, model, "Initialize ModelFluxes",
1000 writeChi2Name=getChi2Name("ModelFluxes"))
1002 model.freezeErrorTransform()
1003 self.log.debug("Photometry error scales are frozen.")
1005 chi2 = self._iterate_fit(associations,
1006 fit,
1007 self.config.maxPhotometrySteps,
1008 "photometry",
1009 "Model Fluxes",
1010 doRankUpdate=self.config.photometryDoRankUpdate,
1011 doLineSearch=doLineSearch,
1012 dataName=dataName)
1014 add_measurement(self.job, 'jointcal.photometry_final_chi2', chi2.chi2)
1015 add_measurement(self.job, 'jointcal.photometry_final_ndof', chi2.ndof)
1016 return Photometry(fit, model)
1018 def _fit_astrometry(self, associations, dataName=None):
1019 """
1020 Fit the astrometric data.
1022 Parameters
1023 ----------
1024 associations : `lsst.jointcal.Associations`
1025 The star/reference star associations to fit.
1026 dataName : `str`
1027 Name of the data being processed (e.g. "1234_HSC-Y"), for
1028 identifying debugging files.
1030 Returns
1031 -------
1032 fit_result : `namedtuple`
1033 fit : `lsst.jointcal.AstrometryFit`
1034 The astrometric fitter used to perform the fit.
1035 model : `lsst.jointcal.AstrometryModel`
1036 The astrometric model that was fit.
1037 sky_to_tan_projection : `lsst.jointcal.ProjectionHandler`
1038 The model for the sky to tangent plane projection that was used in the fit.
1039 """
1041 self.log.info("=== Starting astrometric fitting...")
1043 associations.deprojectFittedStars()
1045 # NOTE: need to return sky_to_tan_projection so that it doesn't get garbage collected.
1046 # TODO: could we package sky_to_tan_projection and model together so we don't have to manage
1047 # them so carefully?
1048 sky_to_tan_projection = lsst.jointcal.OneTPPerVisitHandler(associations.getCcdImageList())
1050 if self.config.astrometryModel == "constrained":
1051 model = lsst.jointcal.ConstrainedAstrometryModel(associations.getCcdImageList(),
1052 sky_to_tan_projection,
1053 chipOrder=self.config.astrometryChipOrder,
1054 visitOrder=self.config.astrometryVisitOrder)
1055 elif self.config.astrometryModel == "simple":
1056 model = lsst.jointcal.SimpleAstrometryModel(associations.getCcdImageList(),
1057 sky_to_tan_projection,
1058 self.config.useInputWcs,
1059 nNotFit=0,
1060 order=self.config.astrometrySimpleOrder)
1062 fit = lsst.jointcal.AstrometryFit(associations, model, self.config.positionErrorPedestal)
1063 # TODO DM-12446: turn this into a "butler save" somehow.
1064 # Save reference and measurement chi2 contributions for this data
1065 if self.config.writeChi2FilesInitialFinal:
1066 baseName = f"astrometry_initial_chi2-{dataName}"
1067 else:
1068 baseName = None
1069 if self.config.writeInitialModel:
1070 fullpath = self._getDebugPath("initialAstrometryModel.txt")
1071 writeModel(model, fullpath, self.log)
1072 self._logChi2AndValidate(associations, fit, model, "Initial", writeChi2Name=baseName)
1074 def getChi2Name(whatToFit):
1075 if self.config.writeChi2FilesOuterLoop:
1076 return f"astrometry_init-%s_chi2-{dataName}" % whatToFit
1077 else:
1078 return None
1080 dumpMatrixFile = self._getDebugPath("astrometry_preinit") if self.config.writeInitMatrix else ""
1081 # The constrained model needs the visit transform fit first; the chip
1082 # transform is initialized from the detector's cameraGeom, so it's close.
1083 if self.config.astrometryModel == "constrained":
1084 fit.minimize("DistortionsVisit", dumpMatrixFile=dumpMatrixFile)
1085 self._logChi2AndValidate(associations, fit, model, "Initialize DistortionsVisit",
1086 writeChi2Name=getChi2Name("DistortionsVisit"))
1087 dumpMatrixFile = "" # so we don't redo the output on the next step
1089 fit.minimize("Distortions", dumpMatrixFile=dumpMatrixFile)
1090 self._logChi2AndValidate(associations, fit, model, "Initialize Distortions",
1091 writeChi2Name=getChi2Name("Distortions"))
1093 fit.minimize("Positions")
1094 self._logChi2AndValidate(associations, fit, model, "Initialize Positions",
1095 writeChi2Name=getChi2Name("Positions"))
1097 fit.minimize("Distortions Positions")
1098 self._logChi2AndValidate(associations, fit, model, "Initialize DistortionsPositions",
1099 writeChi2Name=getChi2Name("DistortionsPositions"))
1101 chi2 = self._iterate_fit(associations,
1102 fit,
1103 self.config.maxAstrometrySteps,
1104 "astrometry",
1105 "Distortions Positions",
1106 doRankUpdate=self.config.astrometryDoRankUpdate,
1107 dataName=dataName)
1109 add_measurement(self.job, 'jointcal.astrometry_final_chi2', chi2.chi2)
1110 add_measurement(self.job, 'jointcal.astrometry_final_ndof', chi2.ndof)
1112 return Astrometry(fit, model, sky_to_tan_projection)
1114 def _check_stars(self, associations):
1115 """Count measured and reference stars per ccd and warn/log them."""
1116 for ccdImage in associations.getCcdImageList():
1117 nMeasuredStars, nRefStars = ccdImage.countStars()
1118 self.log.debug("ccdImage %s has %s measured and %s reference stars",
1119 ccdImage.getName(), nMeasuredStars, nRefStars)
1120 if nMeasuredStars < self.config.minMeasuredStarsPerCcd:
1121 self.log.warn("ccdImage %s has only %s measuredStars (desired %s)",
1122 ccdImage.getName(), nMeasuredStars, self.config.minMeasuredStarsPerCcd)
1123 if nRefStars < self.config.minRefStarsPerCcd:
1124 self.log.warn("ccdImage %s has only %s RefStars (desired %s)",
1125 ccdImage.getName(), nRefStars, self.config.minRefStarsPerCcd)
1127 def _iterate_fit(self, associations, fitter, max_steps, name, whatToFit,
1128 dataName="",
1129 doRankUpdate=True,
1130 doLineSearch=False):
1131 """Run fitter.minimize up to max_steps times, returning the final chi2.
1133 Parameters
1134 ----------
1135 associations : `lsst.jointcal.Associations`
1136 The star/reference star associations to fit.
1137 fitter : `lsst.jointcal.FitterBase`
1138 The fitter to use for minimization.
1139 max_steps : `int`
1140 Maximum number of steps to run outlier rejection before declaring
1141 convergence failure.
1142 name : {'photometry' or 'astrometry'}
1143 What type of data are we fitting (for logs and debugging files).
1144 whatToFit : `str`
1145 Passed to ``fitter.minimize()`` to define the parameters to fit.
1146 dataName : `str`, optional
1147 Descriptive name for this dataset (e.g. tract and filter),
1148 for debugging.
1149 doRankUpdate : `bool`, optional
1150 Do an Eigen rank update during minimization, or recompute the full
1151 matrix and gradient?
1152 doLineSearch : `bool`, optional
1153 Do a line search for the optimum step during minimization?
1155 Returns
1156 -------
1157 chi2: `lsst.jointcal.Chi2Statistic`
1158 The final chi2 after the fit converges, or is forced to end.
1160 Raises
1161 ------
1162 FloatingPointError
1163 Raised if the fitter fails with a non-finite value.
1164 RuntimeError
1165 Raised if the fitter fails for some other reason;
1166 log messages will provide further details.
1167 """
1168 dumpMatrixFile = self._getDebugPath(f"{name}_postinit") if self.config.writeInitMatrix else ""
1169 oldChi2 = lsst.jointcal.Chi2Statistic()
1170 oldChi2.chi2 = float("inf")
1171 for i in range(max_steps):
1172 if self.config.writeChi2FilesOuterLoop:
1173 writeChi2Name = f"{name}_iterate_{i}_chi2-{dataName}"
1174 else:
1175 writeChi2Name = None
1176 result = fitter.minimize(whatToFit,
1177 self.config.outlierRejectSigma,
1178 doRankUpdate=doRankUpdate,
1179 doLineSearch=doLineSearch,
1180 dumpMatrixFile=dumpMatrixFile)
1181 dumpMatrixFile = "" # clear it so we don't write the matrix again.
1182 chi2 = self._logChi2AndValidate(associations, fitter, fitter.getModel(),
1183 f"Fit iteration {i}", writeChi2Name=writeChi2Name)
1185 if result == MinimizeResult.Converged:
1186 if doRankUpdate:
1187 self.log.debug("fit has converged - no more outliers - redo minimization "
1188 "one more time in case we have lost accuracy in rank update.")
1189 # Redo minimization one more time in case we have lost accuracy in rank update
1190 result = fitter.minimize(whatToFit, self.config.outlierRejectSigma)
1191 chi2 = self._logChi2AndValidate(associations, fitter, fitter.getModel(), "Fit completed")
1193 # log a message for a large final chi2, TODO: DM-15247 for something better
1194 if chi2.chi2/chi2.ndof >= 4.0:
1195 self.log.error("Potentially bad fit: High chi-squared/ndof.")
1197 break
1198 elif result == MinimizeResult.Chi2Increased:
1199 self.log.warn("Still some outliers remaining but chi2 increased - retry")
1200 # Check whether the increase was large enough to cause trouble.
1201 chi2Ratio = chi2.chi2 / oldChi2.chi2
1202 if chi2Ratio > 1.5:
1203 self.log.warn('Significant chi2 increase by a factor of %.4g / %.4g = %.4g',
1204 chi2.chi2, oldChi2.chi2, chi2Ratio)
1205 # Based on a variety of HSC jointcal logs (see DM-25779), it
1206 # appears that chi2 increases more than a factor of ~2 always
1207 # result in the fit diverging rapidly and ending at chi2 > 1e10.
1208 # Using 10 as the "failure" threshold gives some room between
1209 # leaving a warning and bailing early.
1210 if chi2Ratio > 10:
1211 msg = ("Large chi2 increase between steps: fit likely cannot converge."
1212 " Try setting one or more of the `writeChi2*` config fields and looking"
1213 " at how individual star chi2-values evolve during the fit.")
1214 raise RuntimeError(msg)
1215 oldChi2 = chi2
1216 elif result == MinimizeResult.NonFinite:
1217 filename = self._getDebugPath("{}_failure-nonfinite_chi2-{}.csv".format(name, dataName))
1218 # TODO DM-12446: turn this into a "butler save" somehow.
1219 fitter.saveChi2Contributions(filename+"{type}")
1220 msg = "Nonfinite value in chi2 minimization, cannot complete fit. Dumped star tables to: {}"
1221 raise FloatingPointError(msg.format(filename))
1222 elif result == MinimizeResult.Failed:
1223 raise RuntimeError("Chi2 minimization failure, cannot complete fit.")
1224 else:
1225 raise RuntimeError("Unxepected return code from minimize().")
1226 else:
1227 self.log.error("%s failed to converge after %d steps"%(name, max_steps))
1229 return chi2
1231 def _write_astrometry_results(self, associations, model, visit_ccd_to_dataRef):
1232 """
1233 Write the fitted astrometric results to a new 'jointcal_wcs' dataRef.
1235 Parameters
1236 ----------
1237 associations : `lsst.jointcal.Associations`
1238 The star/reference star associations to fit.
1239 model : `lsst.jointcal.AstrometryModel`
1240 The astrometric model that was fit.
1241 visit_ccd_to_dataRef : `dict` of Key: `lsst.daf.persistence.ButlerDataRef`
1242 Dict of ccdImage identifiers to dataRefs that were fit.
1243 """
1245 ccdImageList = associations.getCcdImageList()
1246 for ccdImage in ccdImageList:
1247 # TODO: there must be a better way to identify this ccdImage than a visit,ccd pair?
1248 ccd = ccdImage.ccdId
1249 visit = ccdImage.visit
1250 dataRef = visit_ccd_to_dataRef[(visit, ccd)]
1251 self.log.info("Updating WCS for visit: %d, ccd: %d", visit, ccd)
1252 skyWcs = model.makeSkyWcs(ccdImage)
1253 try:
1254 dataRef.put(skyWcs, 'jointcal_wcs')
1255 except pexExceptions.Exception as e:
1256 self.log.fatal('Failed to write updated Wcs: %s', str(e))
1257 raise e
1259 def _write_photometry_results(self, associations, model, visit_ccd_to_dataRef):
1260 """
1261 Write the fitted photometric results to a new 'jointcal_photoCalib' dataRef.
1263 Parameters
1264 ----------
1265 associations : `lsst.jointcal.Associations`
1266 The star/reference star associations to fit.
1267 model : `lsst.jointcal.PhotometryModel`
1268 The photoometric model that was fit.
1269 visit_ccd_to_dataRef : `dict` of Key: `lsst.daf.persistence.ButlerDataRef`
1270 Dict of ccdImage identifiers to dataRefs that were fit.
1271 """
1273 ccdImageList = associations.getCcdImageList()
1274 for ccdImage in ccdImageList:
1275 # TODO: there must be a better way to identify this ccdImage than a visit,ccd pair?
1276 ccd = ccdImage.ccdId
1277 visit = ccdImage.visit
1278 dataRef = visit_ccd_to_dataRef[(visit, ccd)]
1279 self.log.info("Updating PhotoCalib for visit: %d, ccd: %d", visit, ccd)
1280 photoCalib = model.toPhotoCalib(ccdImage)
1281 try:
1282 dataRef.put(photoCalib, 'jointcal_photoCalib')
1283 except pexExceptions.Exception as e:
1284 self.log.fatal('Failed to write updated PhotoCalib: %s', str(e))
1285 raise e