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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
39from lsst.obs.base import Instrument
40from lsst.pipe.tasks.colorterms import ColortermLibrary
41from lsst.verify import Job, Measurement
43from lsst.meas.algorithms import (LoadIndexedReferenceObjectsTask, ReferenceSourceSelectorTask,
44 ReferenceObjectLoader)
45from lsst.meas.algorithms.sourceSelector import sourceSelectorRegistry
47from .dataIds import PerTractCcdDataIdContainer
49import lsst.jointcal
50from lsst.jointcal import MinimizeResult
52__all__ = ["JointcalConfig", "JointcalRunner", "JointcalTask"]
54Photometry = collections.namedtuple('Photometry', ('fit', 'model'))
55Astrometry = collections.namedtuple('Astrometry', ('fit', 'model', 'sky_to_tan_projection'))
58# TODO: move this to MeasurementSet in lsst.verify per DM-12655.
59def add_measurement(job, name, value):
60 meas = Measurement(job.metrics[name], value)
61 job.measurements.insert(meas)
64class JointcalRunner(pipeBase.ButlerInitializedTaskRunner):
65 """Subclass of TaskRunner for jointcalTask (gen2)
67 jointcalTask.runDataRef() takes a number of arguments, one of which is a list of dataRefs
68 extracted from the command line (whereas most CmdLineTasks' runDataRef methods take
69 single dataRef, are are called repeatedly). This class transforms the processed
70 arguments generated by the ArgumentParser into the arguments expected by
71 Jointcal.runDataRef().
73 See pipeBase.TaskRunner for more information.
74 """
76 @staticmethod
77 def getTargetList(parsedCmd, **kwargs):
78 """
79 Return a list of tuples per tract, each containing (dataRefs, kwargs).
81 Jointcal operates on lists of dataRefs simultaneously.
82 """
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)
141def lookupStaticCalibrations(datasetType, registry, quantumDataId, collections):
142 """Lookup function that asserts/hopes that a static calibration dataset
143 exists in a particular collection, since this task can't provide a single
144 date/time to use to search for one properly.
146 This is mostly useful for the ``camera`` dataset, in cases where the task's
147 quantum dimensions do *not* include something temporal, like ``exposure``
148 or ``visit``.
150 Parameters
151 ----------
152 datasetType : `lsst.daf.butler.DatasetType`
153 Type of dataset being searched for.
154 registry : `lsst.daf.butler.Registry`
155 Data repository registry to search.
156 quantumDataId : `lsst.daf.butler.DataCoordinate`
157 Data ID of the quantum this camera should match.
158 collections : `Iterable` [ `str` ]
159 Collections that should be searched - but this lookup function works
160 by ignoring this in favor of a more-or-less hard-coded value.
162 Returns
163 -------
164 refs : `Iterator` [ `lsst.daf.butler.DatasetRef` ]
165 Iterator over dataset references; should have only one element.
167 Notes
168 -----
169 This implementation duplicates one in fgcmcal, and is at least quite
170 similar to another in cp_pipe. This duplicate has the most documentation.
171 Fixing this is DM-29661.
172 """
173 instrument = Instrument.fromName(quantumDataId["instrument"], registry)
174 unboundedCollection = instrument.makeUnboundedCalibrationRunName()
175 return registry.queryDatasets(datasetType,
176 dataId=quantumDataId,
177 collections=[unboundedCollection],
178 findFirst=True)
181def lookupVisitRefCats(datasetType, registry, quantumDataId, collections):
182 """Lookup function that finds all refcats for all visits that overlap a
183 tract, rather than just the refcats that directly overlap the tract.
185 Parameters
186 ----------
187 datasetType : `lsst.daf.butler.DatasetType`
188 Type of dataset being searched for.
189 registry : `lsst.daf.butler.Registry`
190 Data repository registry to search.
191 quantumDataId : `lsst.daf.butler.DataCoordinate`
192 Data ID of the quantum; expected to be something we can use as a
193 constraint to query for overlapping visits.
194 collections : `Iterable` [ `str` ]
195 Collections to search.
197 Returns
198 -------
199 refs : `Iterator` [ `lsst.daf.butler.DatasetRef` ]
200 Iterator over refcat references.
201 """
202 refs = set()
203 # Use .expanded() on the query methods below because we need data IDs with
204 # regions, both in the outer loop over visits (queryDatasets will expand
205 # any data ID we give it, but doing it up-front in bulk is much more
206 # efficient) and in the data IDs of the DatasetRefs this function yields
207 # (because the RefCatLoader relies on them to do some of its own
208 # filtering).
209 for visit_data_id in set(registry.queryDataIds("visit", dataId=quantumDataId).expanded()):
210 refs.update(
211 registry.queryDatasets(
212 datasetType,
213 collections=collections,
214 dataId=visit_data_id,
215 findFirst=True,
216 ).expanded()
217 )
218 yield from refs
221class JointcalTaskConnections(pipeBase.PipelineTaskConnections,
222 dimensions=("skymap", "tract", "instrument", "physical_filter")):
223 """Middleware input/output connections for jointcal data."""
224 inputCamera = pipeBase.connectionTypes.PrerequisiteInput(
225 doc="The camera instrument that took these observations.",
226 name="camera",
227 storageClass="Camera",
228 dimensions=("instrument",),
229 isCalibration=True,
230 lookupFunction=lookupStaticCalibrations,
231 )
232 inputSourceTableVisit = pipeBase.connectionTypes.Input(
233 doc="Source table in parquet format, per visit",
234 name="sourceTable_visit",
235 storageClass="DataFrame",
236 dimensions=("instrument", "visit"),
237 deferLoad=True,
238 multiple=True,
239 )
240 inputVisitSummary = pipeBase.connectionTypes.Input(
241 doc=("Per-visit consolidated exposure metadata built from calexps. "
242 "These catalogs use detector id for the id and must be sorted for "
243 "fast lookups of a detector."),
244 name="visitSummary",
245 storageClass="ExposureCatalog",
246 dimensions=("instrument", "visit"),
247 deferLoad=True,
248 multiple=True,
249 )
250 astrometryRefCat = pipeBase.connectionTypes.PrerequisiteInput(
251 doc="The astrometry reference catalog to match to loaded input catalog sources.",
252 name="gaia_dr2_20200414",
253 storageClass="SimpleCatalog",
254 dimensions=("skypix",),
255 deferLoad=True,
256 multiple=True,
257 lookupFunction=lookupVisitRefCats,
258 )
259 photometryRefCat = pipeBase.connectionTypes.PrerequisiteInput(
260 doc="The photometry reference catalog to match to loaded input catalog sources.",
261 name="ps1_pv3_3pi_20170110",
262 storageClass="SimpleCatalog",
263 dimensions=("skypix",),
264 deferLoad=True,
265 multiple=True,
266 lookupFunction=lookupVisitRefCats,
267 )
269 outputWcs = pipeBase.connectionTypes.Output(
270 doc=("Per-tract, per-visit world coordinate systems derived from the fitted model."
271 " These catalogs only contain entries for detectors with an output, and use"
272 " the detector id for the catalog id, sorted on id for fast lookups of a detector."),
273 name="jointcalSkyWcsCatalog",
274 storageClass="ExposureCatalog",
275 dimensions=("instrument", "visit", "skymap", "tract"),
276 multiple=True
277 )
278 outputPhotoCalib = pipeBase.connectionTypes.Output(
279 doc=("Per-tract, per-visit photometric calibrations derived from the fitted model."
280 " These catalogs only contain entries for detectors with an output, and use"
281 " the detector id for the catalog id, sorted on id for fast lookups of a detector."),
282 name="jointcalPhotoCalibCatalog",
283 storageClass="ExposureCatalog",
284 dimensions=("instrument", "visit", "skymap", "tract"),
285 multiple=True
286 )
288 def __init__(self, *, config=None):
289 super().__init__(config=config)
290 # When we are only doing one of astrometry or photometry, we don't
291 # need the reference catalog or produce the outputs for the other.
292 # This informs the middleware of that when the QuantumGraph is
293 # generated, so we don't block on getting something we won't need or
294 # create an expectation that downstream tasks will be able to consume
295 # something we won't produce.
296 if not config.doAstrometry:
297 self.prerequisiteInputs.remove("astrometryRefCat")
298 self.outputs.remove("outputWcs")
299 if not config.doPhotometry:
300 self.prerequisiteInputs.remove("photometryRefCat")
301 self.outputs.remove("outputPhotoCalib")
304class JointcalConfig(pipeBase.PipelineTaskConfig,
305 pipelineConnections=JointcalTaskConnections):
306 """Configuration for JointcalTask"""
308 doAstrometry = pexConfig.Field(
309 doc="Fit astrometry and write the fitted result.",
310 dtype=bool,
311 default=True
312 )
313 doPhotometry = pexConfig.Field(
314 doc="Fit photometry and write the fitted result.",
315 dtype=bool,
316 default=True
317 )
318 coaddName = pexConfig.Field(
319 doc="Type of coadd, typically deep or goodSeeing",
320 dtype=str,
321 default="deep"
322 )
323 # TODO DM-29008: Change this to "ApFlux_12_0" before gen2 removal.
324 sourceFluxType = pexConfig.Field(
325 dtype=str,
326 doc="Source flux field to use in source selection and to get fluxes from the catalog.",
327 default='Calib'
328 )
329 positionErrorPedestal = pexConfig.Field(
330 doc="Systematic term to apply to the measured position error (pixels)",
331 dtype=float,
332 default=0.02,
333 )
334 photometryErrorPedestal = pexConfig.Field(
335 doc="Systematic term to apply to the measured error on flux or magnitude as a "
336 "fraction of source flux or magnitude delta (e.g. 0.05 is 5% of flux or +50 millimag).",
337 dtype=float,
338 default=0.0,
339 )
340 # TODO: DM-6885 matchCut should be an geom.Angle
341 matchCut = pexConfig.Field(
342 doc="Matching radius between fitted and reference stars (arcseconds)",
343 dtype=float,
344 default=3.0,
345 )
346 minMeasurements = pexConfig.Field(
347 doc="Minimum number of associated measured stars for a fitted star to be included in the fit",
348 dtype=int,
349 default=2,
350 )
351 minMeasuredStarsPerCcd = pexConfig.Field(
352 doc="Minimum number of measuredStars per ccdImage before printing warnings",
353 dtype=int,
354 default=100,
355 )
356 minRefStarsPerCcd = pexConfig.Field(
357 doc="Minimum number of measuredStars per ccdImage before printing warnings",
358 dtype=int,
359 default=30,
360 )
361 allowLineSearch = pexConfig.Field(
362 doc="Allow a line search during minimization, if it is reasonable for the model"
363 " (models with a significant non-linear component, e.g. constrainedPhotometry).",
364 dtype=bool,
365 default=False
366 )
367 astrometrySimpleOrder = pexConfig.Field(
368 doc="Polynomial order for fitting the simple astrometry model.",
369 dtype=int,
370 default=3,
371 )
372 astrometryChipOrder = pexConfig.Field(
373 doc="Order of the per-chip transform for the constrained astrometry model.",
374 dtype=int,
375 default=1,
376 )
377 astrometryVisitOrder = pexConfig.Field(
378 doc="Order of the per-visit transform for the constrained astrometry model.",
379 dtype=int,
380 default=5,
381 )
382 useInputWcs = pexConfig.Field(
383 doc="Use the input calexp WCSs to initialize a SimpleAstrometryModel.",
384 dtype=bool,
385 default=True,
386 )
387 astrometryModel = pexConfig.ChoiceField(
388 doc="Type of model to fit to astrometry",
389 dtype=str,
390 default="constrained",
391 allowed={"simple": "One polynomial per ccd",
392 "constrained": "One polynomial per ccd, and one polynomial per visit"}
393 )
394 photometryModel = pexConfig.ChoiceField(
395 doc="Type of model to fit to photometry",
396 dtype=str,
397 default="constrainedMagnitude",
398 allowed={"simpleFlux": "One constant zeropoint per ccd and visit, fitting in flux space.",
399 "constrainedFlux": "Constrained zeropoint per ccd, and one polynomial per visit,"
400 " fitting in flux space.",
401 "simpleMagnitude": "One constant zeropoint per ccd and visit,"
402 " fitting in magnitude space.",
403 "constrainedMagnitude": "Constrained zeropoint per ccd, and one polynomial per visit,"
404 " fitting in magnitude space.",
405 }
406 )
407 applyColorTerms = pexConfig.Field(
408 doc="Apply photometric color terms to reference stars?"
409 "Requires that colorterms be set to a ColortermLibrary",
410 dtype=bool,
411 default=False
412 )
413 colorterms = pexConfig.ConfigField(
414 doc="Library of photometric reference catalog name to color term dict.",
415 dtype=ColortermLibrary,
416 )
417 photometryVisitOrder = pexConfig.Field(
418 doc="Order of the per-visit polynomial transform for the constrained photometry model.",
419 dtype=int,
420 default=7,
421 )
422 photometryDoRankUpdate = pexConfig.Field(
423 doc=("Do the rank update step during minimization. "
424 "Skipping this can help deal with models that are too non-linear."),
425 dtype=bool,
426 default=True,
427 )
428 astrometryDoRankUpdate = pexConfig.Field(
429 doc=("Do the rank update step during minimization (should not change the astrometry fit). "
430 "Skipping this can help deal with models that are too non-linear."),
431 dtype=bool,
432 default=True,
433 )
434 outlierRejectSigma = pexConfig.Field(
435 doc="How many sigma to reject outliers at during minimization.",
436 dtype=float,
437 default=5.0,
438 )
439 maxPhotometrySteps = pexConfig.Field(
440 doc="Maximum number of minimize iterations to take when fitting photometry.",
441 dtype=int,
442 default=20,
443 )
444 maxAstrometrySteps = pexConfig.Field(
445 doc="Maximum number of minimize iterations to take when fitting photometry.",
446 dtype=int,
447 default=20,
448 )
449 astrometryRefObjLoader = pexConfig.ConfigurableField(
450 target=LoadIndexedReferenceObjectsTask,
451 doc="Reference object loader for astrometric fit",
452 )
453 photometryRefObjLoader = pexConfig.ConfigurableField(
454 target=LoadIndexedReferenceObjectsTask,
455 doc="Reference object loader for photometric fit",
456 )
457 sourceSelector = sourceSelectorRegistry.makeField(
458 doc="How to select sources for cross-matching",
459 default="astrometry"
460 )
461 astrometryReferenceSelector = pexConfig.ConfigurableField(
462 target=ReferenceSourceSelectorTask,
463 doc="How to down-select the loaded astrometry reference catalog.",
464 )
465 photometryReferenceSelector = pexConfig.ConfigurableField(
466 target=ReferenceSourceSelectorTask,
467 doc="How to down-select the loaded photometry reference catalog.",
468 )
469 astrometryReferenceErr = pexConfig.Field(
470 doc=("Uncertainty on reference catalog coordinates [mas] to use in place of the `coord_*Err` fields. "
471 "If None, then raise an exception if the reference catalog is missing coordinate errors. "
472 "If specified, overrides any existing `coord_*Err` values."),
473 dtype=float,
474 default=None,
475 optional=True
476 )
478 # configs for outputting debug information
479 writeInitMatrix = pexConfig.Field(
480 dtype=bool,
481 doc=("Write the pre/post-initialization Hessian and gradient to text files, for debugging. "
482 "The output files will be of the form 'astrometry_preinit-mat.txt', in the current directory. "
483 "Note that these files are the dense versions of the matrix, and so may be very large."),
484 default=False
485 )
486 writeChi2FilesInitialFinal = pexConfig.Field(
487 dtype=bool,
488 doc="Write .csv files containing the contributions to chi2 for the initialization and final fit.",
489 default=False
490 )
491 writeChi2FilesOuterLoop = pexConfig.Field(
492 dtype=bool,
493 doc="Write .csv files containing the contributions to chi2 for the outer fit loop.",
494 default=False
495 )
496 writeInitialModel = pexConfig.Field(
497 dtype=bool,
498 doc=("Write the pre-initialization model to text files, for debugging."
499 " Output is written to `initial[Astro|Photo]metryModel.txt` in the current working directory."),
500 default=False
501 )
502 debugOutputPath = pexConfig.Field(
503 dtype=str,
504 default=".",
505 doc=("Path to write debug output files to. Used by "
506 "`writeInitialModel`, `writeChi2Files*`, `writeInitMatrix`.")
507 )
508 detailedProfile = pexConfig.Field(
509 dtype=bool,
510 default=False,
511 doc="Output separate profiling information for different parts of jointcal, e.g. data read, fitting"
512 )
514 def validate(self):
515 super().validate()
516 if self.doPhotometry and self.applyColorTerms and len(self.colorterms.data) == 0:
517 msg = "applyColorTerms=True requires the `colorterms` field be set to a ColortermLibrary."
518 raise pexConfig.FieldValidationError(JointcalConfig.colorterms, self, msg)
519 if self.doAstrometry and not self.doPhotometry and self.applyColorTerms:
520 msg = ("Only doing astrometry, but Colorterms are not applied for astrometry;"
521 "applyColorTerms=True will be ignored.")
522 lsst.log.warn(msg)
524 def setDefaults(self):
525 # Use science source selector which can filter on extendedness, SNR, and whether blended
526 self.sourceSelector.name = 'science'
527 # Use only stars because aperture fluxes of galaxies are biased and depend on seeing
528 self.sourceSelector['science'].doUnresolved = True
529 # with dependable signal to noise ratio.
530 self.sourceSelector['science'].doSignalToNoise = True
531 # Min SNR must be > 0 because jointcal cannot handle negative fluxes,
532 # and S/N > 10 to use sources that are not too faint, and thus better measured.
533 self.sourceSelector['science'].signalToNoise.minimum = 10.
534 # Base SNR on CalibFlux because that is the flux jointcal that fits and must be positive
535 fluxField = f"slot_{self.sourceFluxType}Flux_instFlux"
536 self.sourceSelector['science'].signalToNoise.fluxField = fluxField
537 self.sourceSelector['science'].signalToNoise.errField = fluxField + "Err"
538 # Do not trust blended sources' aperture fluxes which also depend on seeing
539 self.sourceSelector['science'].doIsolated = True
540 # Do not trust either flux or centroid measurements with flags,
541 # chosen from the usual QA flags for stars)
542 self.sourceSelector['science'].doFlags = True
543 badFlags = ['base_PixelFlags_flag_edge', 'base_PixelFlags_flag_saturated',
544 'base_PixelFlags_flag_interpolatedCenter', 'base_SdssCentroid_flag',
545 'base_PsfFlux_flag', 'base_PixelFlags_flag_suspectCenter']
546 self.sourceSelector['science'].flags.bad = badFlags
548 # Default to Gaia-DR2 (with proper motions) for astrometry and
549 # PS1-DR1 for photometry, with a reasonable initial filterMap.
550 self.astrometryRefObjLoader.ref_dataset_name = "gaia_dr2_20200414"
551 self.astrometryRefObjLoader.requireProperMotion = True
552 self.astrometryRefObjLoader.anyFilterMapsToThis = 'phot_g_mean'
553 self.photometryRefObjLoader.ref_dataset_name = "ps1_pv3_3pi_20170110"
556def writeModel(model, filename, log):
557 """Write model to outfile."""
558 with open(filename, "w") as file:
559 file.write(repr(model))
560 log.info("Wrote %s to file: %s", model, filename)
563@dataclasses.dataclass
564class JointcalInputData:
565 """The input data jointcal needs for each detector/visit."""
566 visit: int
567 """The visit identifier of this exposure."""
568 catalog: lsst.afw.table.SourceCatalog
569 """The catalog derived from this exposure."""
570 visitInfo: lsst.afw.image.VisitInfo
571 """The VisitInfo of this exposure."""
572 detector: lsst.afw.cameraGeom.Detector
573 """The detector of this exposure."""
574 photoCalib: lsst.afw.image.PhotoCalib
575 """The photometric calibration of this exposure."""
576 wcs: lsst.afw.geom.skyWcs
577 """The WCS of this exposure."""
578 bbox: lsst.geom.Box2I
579 """The bounding box of this exposure."""
580 filter: lsst.afw.image.FilterLabel
581 """The filter of this exposure."""
584class JointcalTask(pipeBase.PipelineTask, pipeBase.CmdLineTask):
585 """Astrometricly and photometricly calibrate across multiple visits of the
586 same field.
588 Parameters
589 ----------
590 butler : `lsst.daf.persistence.Butler`
591 The butler is passed to the refObjLoader constructor in case it is
592 needed. Ignored if the refObjLoader argument provides a loader directly.
593 Used to initialize the astrometry and photometry refObjLoaders.
594 initInputs : `dict`, optional
595 Dictionary used to initialize PipelineTasks (empty for jointcal).
596 """
598 ConfigClass = JointcalConfig
599 RunnerClass = JointcalRunner
600 _DefaultName = "jointcal"
602 def __init__(self, butler=None, initInputs=None, **kwargs):
603 super().__init__(**kwargs)
604 self.makeSubtask("sourceSelector")
605 if self.config.doAstrometry:
606 if initInputs is None:
607 # gen3 middleware does refcat things internally (and will not have a butler here)
608 self.makeSubtask('astrometryRefObjLoader', butler=butler)
609 self.makeSubtask("astrometryReferenceSelector")
610 else:
611 self.astrometryRefObjLoader = None
612 if self.config.doPhotometry:
613 if initInputs is None:
614 # gen3 middleware does refcat things internally (and will not have a butler here)
615 self.makeSubtask('photometryRefObjLoader', butler=butler)
616 self.makeSubtask("photometryReferenceSelector")
617 else:
618 self.photometryRefObjLoader = None
620 # To hold various computed metrics for use by tests
621 self.job = Job.load_metrics_package(subset='jointcal')
623 def runQuantum(self, butlerQC, inputRefs, outputRefs):
624 # We override runQuantum to set up the refObjLoaders and write the
625 # outputs to the correct refs.
626 inputs = butlerQC.get(inputRefs)
627 # We want the tract number for writing debug files
628 tract = butlerQC.quantum.dataId['tract']
629 if self.config.doAstrometry:
630 self.astrometryRefObjLoader = ReferenceObjectLoader(
631 dataIds=[ref.datasetRef.dataId
632 for ref in inputRefs.astrometryRefCat],
633 refCats=inputs.pop('astrometryRefCat'),
634 config=self.config.astrometryRefObjLoader,
635 log=self.log)
636 if self.config.doPhotometry:
637 self.photometryRefObjLoader = ReferenceObjectLoader(
638 dataIds=[ref.datasetRef.dataId
639 for ref in inputRefs.photometryRefCat],
640 refCats=inputs.pop('photometryRefCat'),
641 config=self.config.photometryRefObjLoader,
642 log=self.log)
643 outputs = self.run(**inputs, tract=tract)
644 if self.config.doAstrometry:
645 self._put_output(butlerQC, outputs.outputWcs, outputRefs.outputWcs,
646 inputs['inputCamera'], "setWcs")
647 if self.config.doPhotometry:
648 self._put_output(butlerQC, outputs.outputPhotoCalib, outputRefs.outputPhotoCalib,
649 inputs['inputCamera'], "setPhotoCalib")
651 def _put_output(self, butlerQC, outputs, outputRefs, camera, setter):
652 """Persist the output datasets to their appropriate datarefs.
654 Parameters
655 ----------
656 butlerQC : `ButlerQuantumContext`
657 A butler which is specialized to operate in the context of a
658 `lsst.daf.butler.Quantum`; This is the input to `runQuantum`.
659 outputs : `dict` [`tuple`, `lsst.afw.geom.SkyWcs`] or
660 `dict` [`tuple, `lsst.afw.image.PhotoCalib`]
661 The fitted objects to persist.
662 outputRefs : `list` [`OutputQuantizedConnection`]
663 The DatasetRefs to persist the data to.
664 camera : `lsst.afw.cameraGeom.Camera`
665 The camera for this instrument, to get detector ids from.
666 setter : `str`
667 The method to call on the ExposureCatalog to set each output.
668 """
669 schema = lsst.afw.table.ExposureTable.makeMinimalSchema()
670 schema.addField('visit', type='I', doc='Visit number')
672 def new_catalog(visit, size):
673 """Return an catalog ready to be filled with appropriate output."""
674 catalog = lsst.afw.table.ExposureCatalog(schema)
675 catalog.resize(size)
676 catalog['visit'] = visit
677 metadata = lsst.daf.base.PropertyList()
678 metadata.add("COMMENT", "Catalog id is detector id, sorted.")
679 metadata.add("COMMENT", "Only detectors with data have entries.")
680 return catalog
682 # count how many detectors have output for each visit
683 detectors_per_visit = collections.defaultdict(int)
684 for key in outputs:
685 # key is (visit, detector_id), and we only need visit here
686 detectors_per_visit[key[0]] += 1
688 for ref in outputRefs:
689 visit = ref.dataId['visit']
690 catalog = new_catalog(visit, detectors_per_visit[visit])
691 # Iterate over every detector and skip the ones we don't have output for.
692 i = 0
693 for detector in camera:
694 detectorId = detector.getId()
695 key = (ref.dataId['visit'], detectorId)
696 if key not in outputs:
697 # skip detectors we don't have output for
698 self.log.debug("No %s output for detector %s in visit %s",
699 setter[3:], detectorId, visit)
700 continue
702 catalog[i].setId(detectorId)
703 getattr(catalog[i], setter)(outputs[key])
704 i += 1
706 catalog.sort() # ensure that the detectors are in sorted order, for fast lookups
707 butlerQC.put(catalog, ref)
708 self.log.info("Wrote %s detectors to %s", i, ref)
710 def run(self, inputSourceTableVisit, inputVisitSummary, inputCamera, tract=None):
711 # Docstring inherited.
713 # We take values out of the Parquet table, and put them in "flux_",
714 # and the config.sourceFluxType field is used during that extraction,
715 # so just use "flux" here.
716 sourceFluxField = "flux"
717 jointcalControl = lsst.jointcal.JointcalControl(sourceFluxField)
718 associations = lsst.jointcal.Associations()
719 self.focalPlaneBBox = inputCamera.getFpBBox()
720 oldWcsList, bands = self._load_data(inputSourceTableVisit,
721 inputVisitSummary,
722 associations,
723 jointcalControl,
724 inputCamera)
726 boundingCircle, center, radius, defaultFilter = self._prep_sky(associations, bands)
727 epoch = self._compute_proper_motion_epoch(associations.getCcdImageList())
729 if self.config.doAstrometry:
730 astrometry = self._do_load_refcat_and_fit(associations, defaultFilter, center, radius,
731 name="astrometry",
732 refObjLoader=self.astrometryRefObjLoader,
733 referenceSelector=self.astrometryReferenceSelector,
734 fit_function=self._fit_astrometry,
735 tract=tract,
736 epoch=epoch)
737 astrometry_output = self._make_output(associations.getCcdImageList(),
738 astrometry.model,
739 "makeSkyWcs")
740 else:
741 astrometry_output = None
743 if self.config.doPhotometry:
744 photometry = self._do_load_refcat_and_fit(associations, defaultFilter, center, radius,
745 name="photometry",
746 refObjLoader=self.photometryRefObjLoader,
747 referenceSelector=self.photometryReferenceSelector,
748 fit_function=self._fit_photometry,
749 tract=tract,
750 epoch=epoch,
751 reject_bad_fluxes=True)
752 photometry_output = self._make_output(associations.getCcdImageList(),
753 photometry.model,
754 "toPhotoCalib")
755 else:
756 photometry_output = None
758 return pipeBase.Struct(outputWcs=astrometry_output,
759 outputPhotoCalib=photometry_output,
760 job=self.job,
761 astrometryRefObjLoader=self.astrometryRefObjLoader,
762 photometryRefObjLoader=self.photometryRefObjLoader)
764 def _make_schema_table(self):
765 """Return an afw SourceTable to use as a base for creating the
766 SourceCatalog to insert values from the dataFrame into.
768 Returns
769 -------
770 table : `lsst.afw.table.SourceTable`
771 Table with schema and slots to use to make SourceCatalogs.
772 """
773 schema = lsst.afw.table.SourceTable.makeMinimalSchema()
774 schema.addField("centroid_x", "D")
775 schema.addField("centroid_y", "D")
776 schema.addField("centroid_xErr", "F")
777 schema.addField("centroid_yErr", "F")
778 schema.addField("shape_xx", "D")
779 schema.addField("shape_yy", "D")
780 schema.addField("shape_xy", "D")
781 schema.addField("flux_instFlux", "D")
782 schema.addField("flux_instFluxErr", "D")
783 table = lsst.afw.table.SourceTable.make(schema)
784 table.defineCentroid("centroid")
785 table.defineShape("shape")
786 return table
788 def _extract_detector_catalog_from_visit_catalog(self, table, visitCatalog, detectorId):
789 """Return an afw SourceCatalog extracted from a visit-level dataframe,
790 limited to just one detector.
792 Parameters
793 ----------
794 table : `lsst.afw.table.SourceTable`
795 Table factory to use to make the SourceCatalog that will be
796 populated with data from ``visitCatalog``.
797 visitCatalog : `pandas.DataFrame`
798 DataFrame to extract a detector catalog from.
799 detectorId : `int`
800 Numeric id of the detector to extract from ``visitCatalog``.
802 Returns
803 -------
804 catalog : `lsst.afw.table.SourceCatalog`
805 Detector-level catalog extracted from ``visitCatalog``.
806 """
807 # map from dataFrame column to afw table column
808 mapping = {'sourceId': 'id',
809 'x': 'centroid_x',
810 'y': 'centroid_y',
811 'xErr': 'centroid_xErr',
812 'yErr': 'centroid_yErr',
813 'Ixx': 'shape_xx',
814 'Iyy': 'shape_yy',
815 'Ixy': 'shape_xy',
816 f'{self.config.sourceFluxType}_instFlux': 'flux_instFlux',
817 f'{self.config.sourceFluxType}_instFluxErr': 'flux_instFluxErr',
818 }
819 # If the DataFrame we're reading was generated by a task running with
820 # Gen2 middleware, the column for the detector will be "ccd" for at
821 # least HSC (who knows what it might be in general!); that would be
822 # true even if the data repo is later converted to Gen3, because that
823 # doesn't touch the files themselves. In Gen3, the column for the
824 # detector will always be "detector".
825 detector_column = "detector" if "detector" in visitCatalog.columns else "ccd"
826 catalog = lsst.afw.table.SourceCatalog(table)
827 matched = visitCatalog[detector_column] == detectorId
828 catalog.resize(sum(matched))
829 view = visitCatalog.loc[matched]
830 for dfCol, afwCol in mapping.items():
831 catalog[afwCol] = view[dfCol]
833 self.log.debug("%d sources selected in visit %d detector %d",
834 len(catalog),
835 view['visit'].iloc[0], # all visits in this catalog are the same, so take the first
836 detectorId)
837 return catalog
839 def _load_data(self, inputSourceTableVisit, inputVisitSummary, associations,
840 jointcalControl, camera):
841 """Read the data that jointcal needs to run. (Gen3 version)
843 Modifies ``associations`` in-place with the loaded data.
845 Parameters
846 ----------
847 inputSourceTableVisit : `list` [`lsst.daf.butler.DeferredDatasetHandle`]
848 References to visit-level DataFrames to load the catalog data from.
849 inputVisitSummary : `list` [`lsst.daf.butler.DeferredDatasetHandle`]
850 Visit-level exposure summary catalog with metadata.
851 associations : `lsst.jointcal.Associations`
852 Object to add the loaded data to by constructing new CcdImages.
853 jointcalControl : `jointcal.JointcalControl`
854 Control object for C++ associations management.
855 camera : `lsst.afw.cameraGeom.Camera`
856 Camera object for detector geometry.
858 Returns
859 -------
860 oldWcsList: `list` [`lsst.afw.geom.SkyWcs`]
861 The original WCS of the input data, to aid in writing tests.
862 bands : `list` [`str`]
863 The filter bands of each input dataset.
864 """
865 oldWcsList = []
866 filters = []
867 load_cat_prof_file = 'jointcal_load_data.prof' if self.config.detailedProfile else ''
868 with pipeBase.cmdLineTask.profile(load_cat_prof_file):
869 table = self._make_schema_table() # every detector catalog has the same layout
870 # No guarantee that the input is in the same order of visits, so we have to map one of them.
871 catalogMap = {ref.dataId['visit']: i for i, ref in enumerate(inputSourceTableVisit)}
872 detectorDict = {detector.getId(): detector for detector in camera}
874 for visitSummaryRef in inputVisitSummary:
875 visitSummary = visitSummaryRef.get()
876 visitCatalog = inputSourceTableVisit[catalogMap[visitSummaryRef.dataId['visit']]].get()
877 selected = self.sourceSelector.run(visitCatalog)
879 # Build a CcdImage for each detector in this visit.
880 detectors = {id: index for index, id in enumerate(visitSummary['id'])}
881 for id, index in detectors.items():
882 catalog = self._extract_detector_catalog_from_visit_catalog(table, selected.sourceCat, id)
883 data = self._make_one_input_data(visitSummary[index], catalog, detectorDict)
884 result = self._build_ccdImage(data, associations, jointcalControl)
885 if result is not None:
886 oldWcsList.append(result.wcs)
887 # A visit has only one band, so we can just use the first.
888 filters.append(data.filter)
889 if len(filters) == 0:
890 raise RuntimeError("No data to process: did source selector remove all sources?")
891 filters = collections.Counter(filters)
893 return oldWcsList, filters
895 def _make_one_input_data(self, visitRecord, catalog, detectorDict):
896 """Return a data structure for this detector+visit."""
897 return JointcalInputData(visit=visitRecord['visit'],
898 catalog=catalog,
899 visitInfo=visitRecord.getVisitInfo(),
900 detector=detectorDict[visitRecord.getId()],
901 photoCalib=visitRecord.getPhotoCalib(),
902 wcs=visitRecord.getWcs(),
903 bbox=visitRecord.getBBox(),
904 # ExposureRecord doesn't have a FilterLabel yet,
905 # so we have to make one.
906 filter=lsst.afw.image.FilterLabel(band=visitRecord['band'],
907 physical=visitRecord['physical_filter']))
909 # We don't currently need to persist the metadata.
910 # If we do in the future, we will have to add appropriate dataset templates
911 # to each obs package (the metadata template should look like `jointcal_wcs`).
912 def _getMetadataName(self):
913 return None
915 @classmethod
916 def _makeArgumentParser(cls):
917 """Create an argument parser"""
918 parser = pipeBase.ArgumentParser(name=cls._DefaultName)
919 parser.add_id_argument("--id", "calexp", help="data ID, e.g. --id visit=6789 ccd=0..9",
920 ContainerClass=PerTractCcdDataIdContainer)
921 return parser
923 def _build_ccdImage(self, data, associations, jointcalControl):
924 """
925 Extract the necessary things from this catalog+metadata to add a new
926 ccdImage.
928 Parameters
929 ----------
930 data : `JointcalInputData`
931 The loaded input data.
932 associations : `lsst.jointcal.Associations`
933 Object to add the info to, to construct a new CcdImage
934 jointcalControl : `jointcal.JointcalControl`
935 Control object for associations management
937 Returns
938 ------
939 namedtuple or `None`
940 ``wcs``
941 The TAN WCS of this image, read from the calexp
942 (`lsst.afw.geom.SkyWcs`).
943 ``key``
944 A key to identify this dataRef by its visit and ccd ids
945 (`namedtuple`).
946 `None`
947 if there are no sources in the loaded catalog.
948 """
949 if len(data.catalog) == 0:
950 self.log.warn("No sources selected in visit %s ccd %s", data.visit, data.detector.getId())
951 return None
953 associations.createCcdImage(data.catalog,
954 data.wcs,
955 data.visitInfo,
956 data.bbox,
957 data.filter.physicalLabel,
958 data.photoCalib,
959 data.detector,
960 data.visit,
961 data.detector.getId(),
962 jointcalControl)
964 Result = collections.namedtuple('Result_from_build_CcdImage', ('wcs', 'key'))
965 Key = collections.namedtuple('Key', ('visit', 'ccd'))
966 return Result(data.wcs, Key(data.visit, data.detector.getId()))
968 def _readDataId(self, butler, dataId):
969 """Read all of the data for one dataId from the butler. (gen2 version)"""
970 # Not all instruments have `visit` in their dataIds.
971 if "visit" in dataId.keys():
972 visit = dataId["visit"]
973 else:
974 visit = butler.getButler().queryMetadata("calexp", ("visit"), butler.dataId)[0]
975 detector = butler.get('calexp_detector', dataId=dataId)
977 catalog = butler.get('src',
978 flags=lsst.afw.table.SOURCE_IO_NO_FOOTPRINTS,
979 dataId=dataId)
980 goodSrc = self.sourceSelector.run(catalog)
981 self.log.debug("%d sources selected in visit %d detector %d",
982 len(goodSrc.sourceCat),
983 visit,
984 detector.getId())
985 return JointcalInputData(visit=visit,
986 catalog=goodSrc.sourceCat,
987 visitInfo=butler.get('calexp_visitInfo', dataId=dataId),
988 detector=detector,
989 photoCalib=butler.get('calexp_photoCalib', dataId=dataId),
990 wcs=butler.get('calexp_wcs', dataId=dataId),
991 bbox=butler.get('calexp_bbox', dataId=dataId),
992 filter=butler.get('calexp_filterLabel', dataId=dataId))
994 def loadData(self, dataRefs, associations, jointcalControl):
995 """Read the data that jointcal needs to run. (Gen2 version)"""
996 visit_ccd_to_dataRef = {}
997 oldWcsList = []
998 filters = []
999 load_cat_prof_file = 'jointcal_loadData.prof' if self.config.detailedProfile else ''
1000 with pipeBase.cmdLineTask.profile(load_cat_prof_file):
1001 # Need the bounding-box of the focal plane (the same for all visits) for photometry visit models
1002 camera = dataRefs[0].get('camera', immediate=True)
1003 self.focalPlaneBBox = camera.getFpBBox()
1004 for dataRef in dataRefs:
1005 data = self._readDataId(dataRef.getButler(), dataRef.dataId)
1006 result = self._build_ccdImage(data, associations, jointcalControl)
1007 if result is None:
1008 continue
1009 oldWcsList.append(result.wcs)
1010 visit_ccd_to_dataRef[result.key] = dataRef
1011 filters.append(data.filter)
1012 if len(filters) == 0:
1013 raise RuntimeError("No data to process: did source selector remove all sources?")
1014 filters = collections.Counter(filters)
1016 return oldWcsList, filters, visit_ccd_to_dataRef
1018 def _getDebugPath(self, filename):
1019 """Constructs a path to filename using the configured debug path.
1020 """
1021 return os.path.join(self.config.debugOutputPath, filename)
1023 def _prep_sky(self, associations, filters):
1024 """Prepare on-sky and other data that must be computed after data has
1025 been read.
1026 """
1027 associations.computeCommonTangentPoint()
1029 boundingCircle = associations.computeBoundingCircle()
1030 center = lsst.geom.SpherePoint(boundingCircle.getCenter())
1031 radius = lsst.geom.Angle(boundingCircle.getOpeningAngle().asRadians(), lsst.geom.radians)
1033 self.log.info(f"Data has center={center} with radius={radius.asDegrees()} degrees.")
1035 # Determine a default filter band associated with the catalog. See DM-9093
1036 defaultFilter = filters.most_common(1)[0][0]
1037 self.log.debug("Using '%s' filter for reference flux", defaultFilter.physicalLabel)
1039 return boundingCircle, center, radius, defaultFilter
1041 @pipeBase.timeMethod
1042 def runDataRef(self, dataRefs):
1043 """
1044 Jointly calibrate the astrometry and photometry across a set of images.
1046 NOTE: this is for gen2 middleware only.
1048 Parameters
1049 ----------
1050 dataRefs : `list` of `lsst.daf.persistence.ButlerDataRef`
1051 List of data references to the exposures to be fit.
1053 Returns
1054 -------
1055 result : `lsst.pipe.base.Struct`
1056 Struct of metadata from the fit, containing:
1058 ``dataRefs``
1059 The provided data references that were fit (with updated WCSs)
1060 ``oldWcsList``
1061 The original WCS from each dataRef
1062 ``metrics``
1063 Dictionary of internally-computed metrics for testing/validation.
1064 """
1065 if len(dataRefs) == 0:
1066 raise ValueError('Need a non-empty list of data references!')
1068 exitStatus = 0 # exit status for shell
1070 sourceFluxField = "slot_%sFlux" % (self.config.sourceFluxType,)
1071 jointcalControl = lsst.jointcal.JointcalControl(sourceFluxField)
1072 associations = lsst.jointcal.Associations()
1074 oldWcsList, filters, visit_ccd_to_dataRef = self.loadData(dataRefs,
1075 associations,
1076 jointcalControl)
1078 boundingCircle, center, radius, defaultFilter = self._prep_sky(associations, filters)
1079 epoch = self._compute_proper_motion_epoch(associations.getCcdImageList())
1081 tract = dataRefs[0].dataId['tract']
1083 if self.config.doAstrometry:
1084 astrometry = self._do_load_refcat_and_fit(associations, defaultFilter, center, radius,
1085 name="astrometry",
1086 refObjLoader=self.astrometryRefObjLoader,
1087 referenceSelector=self.astrometryReferenceSelector,
1088 fit_function=self._fit_astrometry,
1089 tract=tract,
1090 epoch=epoch)
1091 self._write_astrometry_results(associations, astrometry.model, visit_ccd_to_dataRef)
1092 else:
1093 astrometry = Astrometry(None, None, None)
1095 if self.config.doPhotometry:
1096 photometry = self._do_load_refcat_and_fit(associations, defaultFilter, center, radius,
1097 name="photometry",
1098 refObjLoader=self.photometryRefObjLoader,
1099 referenceSelector=self.photometryReferenceSelector,
1100 fit_function=self._fit_photometry,
1101 tract=tract,
1102 epoch=epoch,
1103 reject_bad_fluxes=True)
1104 self._write_photometry_results(associations, photometry.model, visit_ccd_to_dataRef)
1105 else:
1106 photometry = Photometry(None, None)
1108 return pipeBase.Struct(dataRefs=dataRefs,
1109 oldWcsList=oldWcsList,
1110 job=self.job,
1111 astrometryRefObjLoader=self.astrometryRefObjLoader,
1112 photometryRefObjLoader=self.photometryRefObjLoader,
1113 defaultFilter=defaultFilter,
1114 epoch=epoch,
1115 exitStatus=exitStatus)
1117 def _get_refcat_coordinate_error_override(self, refCat, name):
1118 """Check whether we should override the refcat coordinate errors, and
1119 return the overridden error if necessary.
1121 Parameters
1122 ----------
1123 refCat : `lsst.afw.table.SimpleCatalog`
1124 The reference catalog to check for a ``coord_raErr`` field.
1125 name : `str`
1126 Whether we are doing "astrometry" or "photometry".
1128 Returns
1129 -------
1130 refCoordErr : `float`
1131 The refcat coordinate error to use, or NaN if we are not overriding
1132 those fields.
1134 Raises
1135 ------
1136 lsst.pex.config.FieldValidationError
1137 Raised if the refcat does not contain coordinate errors and
1138 ``config.astrometryReferenceErr`` is not set.
1139 """
1140 # This value doesn't matter for photometry, so just set something to
1141 # keep old refcats from causing problems.
1142 if name.lower() == "photometry":
1143 if 'coord_raErr' not in refCat.schema:
1144 return 100
1145 else:
1146 return float('nan')
1148 if self.config.astrometryReferenceErr is None and 'coord_raErr' not in refCat.schema:
1149 msg = ("Reference catalog does not contain coordinate errors, "
1150 "and config.astrometryReferenceErr not supplied.")
1151 raise pexConfig.FieldValidationError(JointcalConfig.astrometryReferenceErr,
1152 self.config,
1153 msg)
1155 if self.config.astrometryReferenceErr is not None and 'coord_raErr' in refCat.schema:
1156 self.log.warn("Overriding reference catalog coordinate errors with %f/coordinate [mas]",
1157 self.config.astrometryReferenceErr)
1159 if self.config.astrometryReferenceErr is None:
1160 return float('nan')
1161 else:
1162 return self.config.astrometryReferenceErr
1164 def _compute_proper_motion_epoch(self, ccdImageList):
1165 """Return the proper motion correction epoch of the provided images.
1167 Parameters
1168 ----------
1169 ccdImageList : `list` [`lsst.jointcal.CcdImage`]
1170 The images to compute the appropriate epoch for.
1172 Returns
1173 -------
1174 epoch : `astropy.time.Time`
1175 The date to use for proper motion corrections.
1176 """
1177 mjds = [ccdImage.getMjd() for ccdImage in ccdImageList]
1178 return astropy.time.Time(np.mean(mjds), format='mjd', scale="tai")
1180 def _do_load_refcat_and_fit(self, associations, defaultFilter, center, radius,
1181 tract="", match_cut=3.0,
1182 reject_bad_fluxes=False, *,
1183 name="", refObjLoader=None, referenceSelector=None,
1184 fit_function=None, epoch=None):
1185 """Load reference catalog, perform the fit, and return the result.
1187 Parameters
1188 ----------
1189 associations : `lsst.jointcal.Associations`
1190 The star/reference star associations to fit.
1191 defaultFilter : `lsst.afw.image.FilterLabel`
1192 filter to load from reference catalog.
1193 center : `lsst.geom.SpherePoint`
1194 ICRS center of field to load from reference catalog.
1195 radius : `lsst.geom.Angle`
1196 On-sky radius to load from reference catalog.
1197 name : `str`
1198 Name of thing being fit: "astrometry" or "photometry".
1199 refObjLoader : `lsst.meas.algorithms.LoadReferenceObjectsTask`
1200 Reference object loader to use to load a reference catalog.
1201 referenceSelector : `lsst.meas.algorithms.ReferenceSourceSelectorTask`
1202 Selector to use to pick objects from the loaded reference catalog.
1203 fit_function : callable
1204 Function to call to perform fit (takes Associations object).
1205 tract : `str`, optional
1206 Name of tract currently being fit.
1207 match_cut : `float`, optional
1208 Radius in arcseconds to find cross-catalog matches to during
1209 associations.associateCatalogs.
1210 reject_bad_fluxes : `bool`, optional
1211 Reject refCat sources with NaN/inf flux or NaN/0 fluxErr.
1212 epoch : `astropy.time.Time`, optional
1213 Epoch to which to correct refcat proper motion and parallax,
1214 or `None` to not apply such corrections.
1216 Returns
1217 -------
1218 result : `Photometry` or `Astrometry`
1219 Result of `fit_function()`
1220 """
1221 self.log.info("====== Now processing %s...", name)
1222 # TODO: this should not print "trying to invert a singular transformation:"
1223 # if it does that, something's not right about the WCS...
1224 associations.associateCatalogs(match_cut)
1225 add_measurement(self.job, 'jointcal.associated_%s_fittedStars' % name,
1226 associations.fittedStarListSize())
1228 applyColorterms = False if name.lower() == "astrometry" else self.config.applyColorTerms
1229 refCat, fluxField = self._load_reference_catalog(refObjLoader, referenceSelector,
1230 center, radius, defaultFilter,
1231 applyColorterms=applyColorterms,
1232 epoch=epoch)
1233 refCoordErr = self._get_refcat_coordinate_error_override(refCat, name)
1235 associations.collectRefStars(refCat,
1236 self.config.matchCut*lsst.geom.arcseconds,
1237 fluxField,
1238 refCoordinateErr=refCoordErr,
1239 rejectBadFluxes=reject_bad_fluxes)
1240 add_measurement(self.job, 'jointcal.collected_%s_refStars' % name,
1241 associations.refStarListSize())
1243 associations.prepareFittedStars(self.config.minMeasurements)
1245 self._check_star_lists(associations, name)
1246 add_measurement(self.job, 'jointcal.selected_%s_refStars' % name,
1247 associations.nFittedStarsWithAssociatedRefStar())
1248 add_measurement(self.job, 'jointcal.selected_%s_fittedStars' % name,
1249 associations.fittedStarListSize())
1250 add_measurement(self.job, 'jointcal.selected_%s_ccdImages' % name,
1251 associations.nCcdImagesValidForFit())
1253 load_cat_prof_file = 'jointcal_fit_%s.prof'%name if self.config.detailedProfile else ''
1254 dataName = "{}_{}".format(tract, defaultFilter.bandLabel)
1255 with pipeBase.cmdLineTask.profile(load_cat_prof_file):
1256 result = fit_function(associations, dataName)
1257 # TODO DM-12446: turn this into a "butler save" somehow.
1258 # Save reference and measurement chi2 contributions for this data
1259 if self.config.writeChi2FilesInitialFinal:
1260 baseName = self._getDebugPath(f"{name}_final_chi2-{dataName}")
1261 result.fit.saveChi2Contributions(baseName+"{type}")
1262 self.log.info("Wrote chi2 contributions files: %s", baseName)
1264 return result
1266 def _load_reference_catalog(self, refObjLoader, referenceSelector, center, radius, filterLabel,
1267 applyColorterms=False, epoch=None):
1268 """Load the necessary reference catalog sources, convert fluxes to
1269 correct units, and apply color term corrections if requested.
1271 Parameters
1272 ----------
1273 refObjLoader : `lsst.meas.algorithms.LoadReferenceObjectsTask`
1274 The reference catalog loader to use to get the data.
1275 referenceSelector : `lsst.meas.algorithms.ReferenceSourceSelectorTask`
1276 Source selector to apply to loaded reference catalog.
1277 center : `lsst.geom.SpherePoint`
1278 The center around which to load sources.
1279 radius : `lsst.geom.Angle`
1280 The radius around ``center`` to load sources in.
1281 filterLabel : `lsst.afw.image.FilterLabel`
1282 The camera filter to load fluxes for.
1283 applyColorterms : `bool`
1284 Apply colorterm corrections to the refcat for ``filterName``?
1285 epoch : `astropy.time.Time`, optional
1286 Epoch to which to correct refcat proper motion and parallax,
1287 or `None` to not apply such corrections.
1289 Returns
1290 -------
1291 refCat : `lsst.afw.table.SimpleCatalog`
1292 The loaded reference catalog.
1293 fluxField : `str`
1294 The name of the reference catalog flux field appropriate for ``filterName``.
1295 """
1296 skyCircle = refObjLoader.loadSkyCircle(center,
1297 radius,
1298 filterLabel.bandLabel,
1299 epoch=epoch)
1301 selected = referenceSelector.run(skyCircle.refCat)
1302 # Need memory contiguity to get reference filters as a vector.
1303 if not selected.sourceCat.isContiguous():
1304 refCat = selected.sourceCat.copy(deep=True)
1305 else:
1306 refCat = selected.sourceCat
1308 if applyColorterms:
1309 refCatName = refObjLoader.ref_dataset_name
1310 self.log.info("Applying color terms for physical filter=%r reference catalog=%s",
1311 filterLabel.physicalLabel, refCatName)
1312 colorterm = self.config.colorterms.getColorterm(filterLabel.physicalLabel,
1313 refCatName,
1314 doRaise=True)
1316 refMag, refMagErr = colorterm.getCorrectedMagnitudes(refCat)
1317 refCat[skyCircle.fluxField] = u.Magnitude(refMag, u.ABmag).to_value(u.nJy)
1318 # TODO: I didn't want to use this, but I'll deal with it in DM-16903
1319 refCat[skyCircle.fluxField+'Err'] = fluxErrFromABMagErr(refMagErr, refMag) * 1e9
1321 return refCat, skyCircle.fluxField
1323 def _check_star_lists(self, associations, name):
1324 # TODO: these should be len(blah), but we need this properly wrapped first.
1325 if associations.nCcdImagesValidForFit() == 0:
1326 raise RuntimeError('No images in the ccdImageList!')
1327 if associations.fittedStarListSize() == 0:
1328 raise RuntimeError('No stars in the {} fittedStarList!'.format(name))
1329 if associations.refStarListSize() == 0:
1330 raise RuntimeError('No stars in the {} reference star list!'.format(name))
1332 def _logChi2AndValidate(self, associations, fit, model, chi2Label, writeChi2Name=None):
1333 """Compute chi2, log it, validate the model, and return chi2.
1335 Parameters
1336 ----------
1337 associations : `lsst.jointcal.Associations`
1338 The star/reference star associations to fit.
1339 fit : `lsst.jointcal.FitterBase`
1340 The fitter to use for minimization.
1341 model : `lsst.jointcal.Model`
1342 The model being fit.
1343 chi2Label : `str`
1344 Label to describe the chi2 (e.g. "Initialized", "Final").
1345 writeChi2Name : `str`, optional
1346 Filename prefix to write the chi2 contributions to.
1347 Do not supply an extension: an appropriate one will be added.
1349 Returns
1350 -------
1351 chi2: `lsst.jointcal.Chi2Accumulator`
1352 The chi2 object for the current fitter and model.
1354 Raises
1355 ------
1356 FloatingPointError
1357 Raised if chi2 is infinite or NaN.
1358 ValueError
1359 Raised if the model is not valid.
1360 """
1361 if writeChi2Name is not None:
1362 fullpath = self._getDebugPath(writeChi2Name)
1363 fit.saveChi2Contributions(fullpath+"{type}")
1364 self.log.info("Wrote chi2 contributions files: %s", fullpath)
1366 chi2 = fit.computeChi2()
1367 self.log.info("%s %s", chi2Label, chi2)
1368 self._check_stars(associations)
1369 if not np.isfinite(chi2.chi2):
1370 raise FloatingPointError(f'{chi2Label} chi2 is invalid: {chi2}')
1371 if not model.validate(associations.getCcdImageList(), chi2.ndof):
1372 raise ValueError("Model is not valid: check log messages for warnings.")
1373 return chi2
1375 def _fit_photometry(self, associations, dataName=None):
1376 """
1377 Fit the photometric data.
1379 Parameters
1380 ----------
1381 associations : `lsst.jointcal.Associations`
1382 The star/reference star associations to fit.
1383 dataName : `str`
1384 Name of the data being processed (e.g. "1234_HSC-Y"), for
1385 identifying debugging files.
1387 Returns
1388 -------
1389 fit_result : `namedtuple`
1390 fit : `lsst.jointcal.PhotometryFit`
1391 The photometric fitter used to perform the fit.
1392 model : `lsst.jointcal.PhotometryModel`
1393 The photometric model that was fit.
1394 """
1395 self.log.info("=== Starting photometric fitting...")
1397 # TODO: should use pex.config.RegistryField here (see DM-9195)
1398 if self.config.photometryModel == "constrainedFlux":
1399 model = lsst.jointcal.ConstrainedFluxModel(associations.getCcdImageList(),
1400 self.focalPlaneBBox,
1401 visitOrder=self.config.photometryVisitOrder,
1402 errorPedestal=self.config.photometryErrorPedestal)
1403 # potentially nonlinear problem, so we may need a line search to converge.
1404 doLineSearch = self.config.allowLineSearch
1405 elif self.config.photometryModel == "constrainedMagnitude":
1406 model = lsst.jointcal.ConstrainedMagnitudeModel(associations.getCcdImageList(),
1407 self.focalPlaneBBox,
1408 visitOrder=self.config.photometryVisitOrder,
1409 errorPedestal=self.config.photometryErrorPedestal)
1410 # potentially nonlinear problem, so we may need a line search to converge.
1411 doLineSearch = self.config.allowLineSearch
1412 elif self.config.photometryModel == "simpleFlux":
1413 model = lsst.jointcal.SimpleFluxModel(associations.getCcdImageList(),
1414 errorPedestal=self.config.photometryErrorPedestal)
1415 doLineSearch = False # purely linear in model parameters, so no line search needed
1416 elif self.config.photometryModel == "simpleMagnitude":
1417 model = lsst.jointcal.SimpleMagnitudeModel(associations.getCcdImageList(),
1418 errorPedestal=self.config.photometryErrorPedestal)
1419 doLineSearch = False # purely linear in model parameters, so no line search needed
1421 fit = lsst.jointcal.PhotometryFit(associations, model)
1422 # TODO DM-12446: turn this into a "butler save" somehow.
1423 # Save reference and measurement chi2 contributions for this data
1424 if self.config.writeChi2FilesInitialFinal:
1425 baseName = f"photometry_initial_chi2-{dataName}"
1426 else:
1427 baseName = None
1428 if self.config.writeInitialModel:
1429 fullpath = self._getDebugPath("initialPhotometryModel.txt")
1430 writeModel(model, fullpath, self.log)
1431 self._logChi2AndValidate(associations, fit, model, "Initialized", writeChi2Name=baseName)
1433 def getChi2Name(whatToFit):
1434 if self.config.writeChi2FilesOuterLoop:
1435 return f"photometry_init-%s_chi2-{dataName}" % whatToFit
1436 else:
1437 return None
1439 # The constrained model needs the visit transform fit first; the chip
1440 # transform is initialized from the singleFrame PhotoCalib, so it's close.
1441 dumpMatrixFile = self._getDebugPath("photometry_preinit") if self.config.writeInitMatrix else ""
1442 if self.config.photometryModel.startswith("constrained"):
1443 # no line search: should be purely (or nearly) linear,
1444 # and we want a large step size to initialize with.
1445 fit.minimize("ModelVisit", dumpMatrixFile=dumpMatrixFile)
1446 self._logChi2AndValidate(associations, fit, model, "Initialize ModelVisit",
1447 writeChi2Name=getChi2Name("ModelVisit"))
1448 dumpMatrixFile = "" # so we don't redo the output on the next step
1450 fit.minimize("Model", doLineSearch=doLineSearch, dumpMatrixFile=dumpMatrixFile)
1451 self._logChi2AndValidate(associations, fit, model, "Initialize Model",
1452 writeChi2Name=getChi2Name("Model"))
1454 fit.minimize("Fluxes") # no line search: always purely linear.
1455 self._logChi2AndValidate(associations, fit, model, "Initialize Fluxes",
1456 writeChi2Name=getChi2Name("Fluxes"))
1458 fit.minimize("Model Fluxes", doLineSearch=doLineSearch)
1459 self._logChi2AndValidate(associations, fit, model, "Initialize ModelFluxes",
1460 writeChi2Name=getChi2Name("ModelFluxes"))
1462 model.freezeErrorTransform()
1463 self.log.debug("Photometry error scales are frozen.")
1465 chi2 = self._iterate_fit(associations,
1466 fit,
1467 self.config.maxPhotometrySteps,
1468 "photometry",
1469 "Model Fluxes",
1470 doRankUpdate=self.config.photometryDoRankUpdate,
1471 doLineSearch=doLineSearch,
1472 dataName=dataName)
1474 add_measurement(self.job, 'jointcal.photometry_final_chi2', chi2.chi2)
1475 add_measurement(self.job, 'jointcal.photometry_final_ndof', chi2.ndof)
1476 return Photometry(fit, model)
1478 def _fit_astrometry(self, associations, dataName=None):
1479 """
1480 Fit the astrometric data.
1482 Parameters
1483 ----------
1484 associations : `lsst.jointcal.Associations`
1485 The star/reference star associations to fit.
1486 dataName : `str`
1487 Name of the data being processed (e.g. "1234_HSC-Y"), for
1488 identifying debugging files.
1490 Returns
1491 -------
1492 fit_result : `namedtuple`
1493 fit : `lsst.jointcal.AstrometryFit`
1494 The astrometric fitter used to perform the fit.
1495 model : `lsst.jointcal.AstrometryModel`
1496 The astrometric model that was fit.
1497 sky_to_tan_projection : `lsst.jointcal.ProjectionHandler`
1498 The model for the sky to tangent plane projection that was used in the fit.
1499 """
1501 self.log.info("=== Starting astrometric fitting...")
1503 associations.deprojectFittedStars()
1505 # NOTE: need to return sky_to_tan_projection so that it doesn't get garbage collected.
1506 # TODO: could we package sky_to_tan_projection and model together so we don't have to manage
1507 # them so carefully?
1508 sky_to_tan_projection = lsst.jointcal.OneTPPerVisitHandler(associations.getCcdImageList())
1510 if self.config.astrometryModel == "constrained":
1511 model = lsst.jointcal.ConstrainedAstrometryModel(associations.getCcdImageList(),
1512 sky_to_tan_projection,
1513 chipOrder=self.config.astrometryChipOrder,
1514 visitOrder=self.config.astrometryVisitOrder)
1515 elif self.config.astrometryModel == "simple":
1516 model = lsst.jointcal.SimpleAstrometryModel(associations.getCcdImageList(),
1517 sky_to_tan_projection,
1518 self.config.useInputWcs,
1519 nNotFit=0,
1520 order=self.config.astrometrySimpleOrder)
1522 fit = lsst.jointcal.AstrometryFit(associations, model, self.config.positionErrorPedestal)
1523 # TODO DM-12446: turn this into a "butler save" somehow.
1524 # Save reference and measurement chi2 contributions for this data
1525 if self.config.writeChi2FilesInitialFinal:
1526 baseName = f"astrometry_initial_chi2-{dataName}"
1527 else:
1528 baseName = None
1529 if self.config.writeInitialModel:
1530 fullpath = self._getDebugPath("initialAstrometryModel.txt")
1531 writeModel(model, fullpath, self.log)
1532 self._logChi2AndValidate(associations, fit, model, "Initial", writeChi2Name=baseName)
1534 def getChi2Name(whatToFit):
1535 if self.config.writeChi2FilesOuterLoop:
1536 return f"astrometry_init-%s_chi2-{dataName}" % whatToFit
1537 else:
1538 return None
1540 dumpMatrixFile = self._getDebugPath("astrometry_preinit") if self.config.writeInitMatrix else ""
1541 # The constrained model needs the visit transform fit first; the chip
1542 # transform is initialized from the detector's cameraGeom, so it's close.
1543 if self.config.astrometryModel == "constrained":
1544 fit.minimize("DistortionsVisit", dumpMatrixFile=dumpMatrixFile)
1545 self._logChi2AndValidate(associations, fit, model, "Initialize DistortionsVisit",
1546 writeChi2Name=getChi2Name("DistortionsVisit"))
1547 dumpMatrixFile = "" # so we don't redo the output on the next step
1549 fit.minimize("Distortions", dumpMatrixFile=dumpMatrixFile)
1550 self._logChi2AndValidate(associations, fit, model, "Initialize Distortions",
1551 writeChi2Name=getChi2Name("Distortions"))
1553 fit.minimize("Positions")
1554 self._logChi2AndValidate(associations, fit, model, "Initialize Positions",
1555 writeChi2Name=getChi2Name("Positions"))
1557 fit.minimize("Distortions Positions")
1558 self._logChi2AndValidate(associations, fit, model, "Initialize DistortionsPositions",
1559 writeChi2Name=getChi2Name("DistortionsPositions"))
1561 chi2 = self._iterate_fit(associations,
1562 fit,
1563 self.config.maxAstrometrySteps,
1564 "astrometry",
1565 "Distortions Positions",
1566 doRankUpdate=self.config.astrometryDoRankUpdate,
1567 dataName=dataName)
1569 add_measurement(self.job, 'jointcal.astrometry_final_chi2', chi2.chi2)
1570 add_measurement(self.job, 'jointcal.astrometry_final_ndof', chi2.ndof)
1572 return Astrometry(fit, model, sky_to_tan_projection)
1574 def _check_stars(self, associations):
1575 """Count measured and reference stars per ccd and warn/log them."""
1576 for ccdImage in associations.getCcdImageList():
1577 nMeasuredStars, nRefStars = ccdImage.countStars()
1578 self.log.debug("ccdImage %s has %s measured and %s reference stars",
1579 ccdImage.getName(), nMeasuredStars, nRefStars)
1580 if nMeasuredStars < self.config.minMeasuredStarsPerCcd:
1581 self.log.warn("ccdImage %s has only %s measuredStars (desired %s)",
1582 ccdImage.getName(), nMeasuredStars, self.config.minMeasuredStarsPerCcd)
1583 if nRefStars < self.config.minRefStarsPerCcd:
1584 self.log.warn("ccdImage %s has only %s RefStars (desired %s)",
1585 ccdImage.getName(), nRefStars, self.config.minRefStarsPerCcd)
1587 def _iterate_fit(self, associations, fitter, max_steps, name, whatToFit,
1588 dataName="",
1589 doRankUpdate=True,
1590 doLineSearch=False):
1591 """Run fitter.minimize up to max_steps times, returning the final chi2.
1593 Parameters
1594 ----------
1595 associations : `lsst.jointcal.Associations`
1596 The star/reference star associations to fit.
1597 fitter : `lsst.jointcal.FitterBase`
1598 The fitter to use for minimization.
1599 max_steps : `int`
1600 Maximum number of steps to run outlier rejection before declaring
1601 convergence failure.
1602 name : {'photometry' or 'astrometry'}
1603 What type of data are we fitting (for logs and debugging files).
1604 whatToFit : `str`
1605 Passed to ``fitter.minimize()`` to define the parameters to fit.
1606 dataName : `str`, optional
1607 Descriptive name for this dataset (e.g. tract and filter),
1608 for debugging.
1609 doRankUpdate : `bool`, optional
1610 Do an Eigen rank update during minimization, or recompute the full
1611 matrix and gradient?
1612 doLineSearch : `bool`, optional
1613 Do a line search for the optimum step during minimization?
1615 Returns
1616 -------
1617 chi2: `lsst.jointcal.Chi2Statistic`
1618 The final chi2 after the fit converges, or is forced to end.
1620 Raises
1621 ------
1622 FloatingPointError
1623 Raised if the fitter fails with a non-finite value.
1624 RuntimeError
1625 Raised if the fitter fails for some other reason;
1626 log messages will provide further details.
1627 """
1628 dumpMatrixFile = self._getDebugPath(f"{name}_postinit") if self.config.writeInitMatrix else ""
1629 oldChi2 = lsst.jointcal.Chi2Statistic()
1630 oldChi2.chi2 = float("inf")
1631 for i in range(max_steps):
1632 if self.config.writeChi2FilesOuterLoop:
1633 writeChi2Name = f"{name}_iterate_{i}_chi2-{dataName}"
1634 else:
1635 writeChi2Name = None
1636 result = fitter.minimize(whatToFit,
1637 self.config.outlierRejectSigma,
1638 doRankUpdate=doRankUpdate,
1639 doLineSearch=doLineSearch,
1640 dumpMatrixFile=dumpMatrixFile)
1641 dumpMatrixFile = "" # clear it so we don't write the matrix again.
1642 chi2 = self._logChi2AndValidate(associations, fitter, fitter.getModel(),
1643 f"Fit iteration {i}", writeChi2Name=writeChi2Name)
1645 if result == MinimizeResult.Converged:
1646 if doRankUpdate:
1647 self.log.debug("fit has converged - no more outliers - redo minimization "
1648 "one more time in case we have lost accuracy in rank update.")
1649 # Redo minimization one more time in case we have lost accuracy in rank update
1650 result = fitter.minimize(whatToFit, self.config.outlierRejectSigma)
1651 chi2 = self._logChi2AndValidate(associations, fitter, fitter.getModel(), "Fit completed")
1653 # log a message for a large final chi2, TODO: DM-15247 for something better
1654 if chi2.chi2/chi2.ndof >= 4.0:
1655 self.log.error("Potentially bad fit: High chi-squared/ndof.")
1657 break
1658 elif result == MinimizeResult.Chi2Increased:
1659 self.log.warn("Still some outliers remaining but chi2 increased - retry")
1660 # Check whether the increase was large enough to cause trouble.
1661 chi2Ratio = chi2.chi2 / oldChi2.chi2
1662 if chi2Ratio > 1.5:
1663 self.log.warn('Significant chi2 increase by a factor of %.4g / %.4g = %.4g',
1664 chi2.chi2, oldChi2.chi2, chi2Ratio)
1665 # Based on a variety of HSC jointcal logs (see DM-25779), it
1666 # appears that chi2 increases more than a factor of ~2 always
1667 # result in the fit diverging rapidly and ending at chi2 > 1e10.
1668 # Using 10 as the "failure" threshold gives some room between
1669 # leaving a warning and bailing early.
1670 if chi2Ratio > 10:
1671 msg = ("Large chi2 increase between steps: fit likely cannot converge."
1672 " Try setting one or more of the `writeChi2*` config fields and looking"
1673 " at how individual star chi2-values evolve during the fit.")
1674 raise RuntimeError(msg)
1675 oldChi2 = chi2
1676 elif result == MinimizeResult.NonFinite:
1677 filename = self._getDebugPath("{}_failure-nonfinite_chi2-{}.csv".format(name, dataName))
1678 # TODO DM-12446: turn this into a "butler save" somehow.
1679 fitter.saveChi2Contributions(filename+"{type}")
1680 msg = "Nonfinite value in chi2 minimization, cannot complete fit. Dumped star tables to: {}"
1681 raise FloatingPointError(msg.format(filename))
1682 elif result == MinimizeResult.Failed:
1683 raise RuntimeError("Chi2 minimization failure, cannot complete fit.")
1684 else:
1685 raise RuntimeError("Unxepected return code from minimize().")
1686 else:
1687 self.log.error("%s failed to converge after %d steps"%(name, max_steps))
1689 return chi2
1691 def _make_output(self, ccdImageList, model, func):
1692 """Return the internal jointcal models converted to the afw
1693 structures that will be saved to disk.
1695 Parameters
1696 ----------
1697 ccdImageList : `lsst.jointcal.CcdImageList`
1698 The list of CcdImages to get the output for.
1699 model : `lsst.jointcal.AstrometryModel` or `lsst.jointcal.PhotometryModel`
1700 The internal jointcal model to convert for each `lsst.jointcal.CcdImage`.
1701 func : `str`
1702 The name of the function to call on ``model`` to get the converted
1703 structure. Must accept an `lsst.jointcal.CcdImage`.
1705 Returns
1706 -------
1707 output : `dict` [`tuple`, `lsst.jointcal.AstrometryModel`] or
1708 `dict` [`tuple`, `lsst.jointcal.PhotometryModel`]
1709 The data to be saved, keyed on (visit, detector).
1710 """
1711 output = {}
1712 for ccdImage in ccdImageList:
1713 ccd = ccdImage.ccdId
1714 visit = ccdImage.visit
1715 self.log.debug("%s for visit: %d, ccd: %d", func, visit, ccd)
1716 output[(visit, ccd)] = getattr(model, func)(ccdImage)
1717 return output
1719 def _write_astrometry_results(self, associations, model, visit_ccd_to_dataRef):
1720 """
1721 Write the fitted astrometric results to a new 'jointcal_wcs' dataRef.
1723 Parameters
1724 ----------
1725 associations : `lsst.jointcal.Associations`
1726 The star/reference star associations to fit.
1727 model : `lsst.jointcal.AstrometryModel`
1728 The astrometric model that was fit.
1729 visit_ccd_to_dataRef : `dict` of Key: `lsst.daf.persistence.ButlerDataRef`
1730 Dict of ccdImage identifiers to dataRefs that were fit.
1731 """
1732 ccdImageList = associations.getCcdImageList()
1733 output = self._make_output(ccdImageList, model, "makeSkyWcs")
1734 for key, skyWcs in output.items():
1735 dataRef = visit_ccd_to_dataRef[key]
1736 try:
1737 dataRef.put(skyWcs, 'jointcal_wcs')
1738 except pexExceptions.Exception as e:
1739 self.log.fatal('Failed to write updated Wcs: %s', str(e))
1740 raise e
1742 def _write_photometry_results(self, associations, model, visit_ccd_to_dataRef):
1743 """
1744 Write the fitted photometric results to a new 'jointcal_photoCalib' dataRef.
1746 Parameters
1747 ----------
1748 associations : `lsst.jointcal.Associations`
1749 The star/reference star associations to fit.
1750 model : `lsst.jointcal.PhotometryModel`
1751 The photoometric model that was fit.
1752 visit_ccd_to_dataRef : `dict` of Key: `lsst.daf.persistence.ButlerDataRef`
1753 Dict of ccdImage identifiers to dataRefs that were fit.
1754 """
1756 ccdImageList = associations.getCcdImageList()
1757 output = self._make_output(ccdImageList, model, "toPhotoCalib")
1758 for key, photoCalib in output.items():
1759 dataRef = visit_ccd_to_dataRef[key]
1760 try:
1761 dataRef.put(photoCalib, 'jointcal_photoCalib')
1762 except pexExceptions.Exception as e:
1763 self.log.fatal('Failed to write updated PhotoCalib: %s', str(e))
1764 raise e