Coverage for python/lsst/drp/tasks/gbdesAstrometricFit.py: 10%
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1# This file is part of drp_tasks.
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4# This product includes software developed by the
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22import astropy.coordinates
23import astropy.time
24import astropy.units as u
25import astshim
26import lsst.afw.geom as afwgeom
27import lsst.afw.table
28import lsst.geom
29import lsst.pex.config as pexConfig
30import lsst.pipe.base as pipeBase
31import lsst.sphgeom
32import numpy as np
33import wcsfit
34import yaml
35from lsst.meas.algorithms import (
36 LoadReferenceObjectsConfig,
37 ReferenceObjectLoader,
38 ReferenceSourceSelectorTask,
39)
40from lsst.meas.algorithms.sourceSelector import sourceSelectorRegistry
42__all__ = ["GbdesAstrometricFitConnections", "GbdesAstrometricFitConfig", "GbdesAstrometricFitTask"]
45def _make_ref_covariance_matrix(
46 refCat, inputUnit=u.radian, outputCoordUnit=u.marcsec, outputPMUnit=u.marcsec, version=1
47):
48 """Make a covariance matrix for the reference catalog including proper
49 motion and parallax.
51 The output is flattened to one dimension to match the format expected by
52 `gbdes`.
54 Parameters
55 ----------
56 refCat : `lsst.afw.table.SimpleCatalog`
57 Catalog including proper motion and parallax measurements.
58 inputUnit : `astropy.unit.core.Unit`
59 Units of the input catalog
60 outputCoordUnit : `astropy.unit.core.Unit`
61 Units required for the coordinates in the covariance matrix. `gbdes`
62 expects milliarcseconds.
63 outputPMUnit : `astropy.unit.core.Unit`
64 Units required for the proper motion/parallax in the covariance matrix.
65 `gbdes` expects milliarcseconds.
66 version : `int`
67 Version of the reference catalog. Version 2 includes covariance
68 measurements.
69 Returns
70 -------
71 cov : `list` of `float`
72 Flattened output covariance matrix.
73 """
74 cov = np.zeros((len(refCat), 25))
75 if version == 1:
76 # Here is the standard ordering of components in the cov matrix,
77 # to match the PM enumeration in C++ code of gbdes package's Match.
78 # Each tuple gives: the array holding the 1d error,
79 # the string in Gaia column names for this
80 # the ordering in the Gaia catalog
81 # and the ordering of the tuples is the order we want in our cov matrix
82 raErr = (refCat["coord_raErr"] * inputUnit).to(outputCoordUnit).to_value()
83 decErr = (refCat["coord_decErr"] * inputUnit).to(outputCoordUnit).to_value()
84 raPMErr = (refCat["pm_raErr"] * inputUnit).to(outputPMUnit).to_value()
85 decPMErr = (refCat["pm_decErr"] * inputUnit).to(outputPMUnit).to_value()
86 parallaxErr = (refCat["parallaxErr"] * inputUnit).to(outputPMUnit).to_value()
87 stdOrder = (
88 (raErr, "ra", 0),
89 (decErr, "dec", 1),
90 (raPMErr, "pmra", 3),
91 (decPMErr, "pmdec", 4),
92 (parallaxErr, "parallax", 2),
93 )
95 k = 0
96 for i, pr1 in enumerate(stdOrder):
97 for j, pr2 in enumerate(stdOrder):
98 if pr1[2] < pr2[2]:
99 cov[:, k] = 0
100 elif pr1[2] > pr2[2]:
101 cov[:, k] = 0
102 else:
103 # diagnonal element
104 cov[:, k] = pr1[0] * pr2[0]
105 k = k + 1
107 elif version == 2:
108 positionParameters = ["coord_ra", "coord_dec", "pm_ra", "pm_dec", "parallax"]
109 units = [outputCoordUnit, outputCoordUnit, outputPMUnit, outputPMUnit, outputPMUnit]
110 k = 0
111 for i, pi in enumerate(positionParameters):
112 for j, pj in enumerate(positionParameters):
113 if i == j:
114 cov[:, k] = (refCat[f"{pi}Err"] ** 2 * inputUnit**2).to_value(units[j] * units[j])
115 elif i > j:
116 cov[:, k] = (refCat[f"{pj}_{pi}_Cov"] * inputUnit**2).to_value(units[i] * units[j])
117 else:
118 cov[:, k] = (refCat[f"{pi}_{pj}_Cov"] * inputUnit**2).to_value(units[i] * units[j])
120 k += 1
121 return cov
124def _nCoeffsFromDegree(degree):
125 """Get the number of coefficients for a polynomial of a certain degree with
126 two variables.
128 This uses the general formula that the number of coefficients for a
129 polynomial of degree d with n variables is (n + d) choose d, where in this
130 case n is fixed to 2.
132 Parameters
133 ----------
134 degree : `int`
135 Degree of the polynomial in question.
137 Returns
138 -------
139 nCoeffs : `int`
140 Number of coefficients for the polynomial in question.
141 """
142 nCoeffs = int((degree + 2) * (degree + 1) / 2)
143 return nCoeffs
146def _degreeFromNCoeffs(nCoeffs):
147 """Get the degree for a polynomial with two variables and a certain number
148 of coefficients.
150 This is done by applying the quadratic formula to the
151 formula for calculating the number of coefficients of the polynomial.
153 Parameters
154 ----------
155 nCoeffs : `int`
156 Number of coefficients for the polynomial in question.
158 Returns
159 -------
160 degree : `int`
161 Degree of the polynomial in question.
162 """
163 degree = int(-1.5 + 0.5 * (1 + 8 * nCoeffs) ** 0.5)
164 return degree
167def _convert_to_ast_polymap_coefficients(coefficients):
168 """Convert vector of polynomial coefficients from the format used in
169 `gbdes` into AST format (see Poly2d::vectorIndex(i, j) in
170 gbdes/gbutil/src/Poly2d.cpp). This assumes two input and two output
171 coordinates.
173 Parameters
174 ----------
175 coefficients : `list`
176 Coefficients of the polynomials.
177 degree : `int`
178 Degree of the polynomial.
180 Returns
181 -------
182 astPoly : `astshim.PolyMap`
183 Coefficients in AST polynomial format.
184 """
185 polyArray = np.zeros((len(coefficients), 4))
186 N = len(coefficients) / 2
187 degree = _degreeFromNCoeffs(N)
189 for outVar in [1, 2]:
190 for i in range(degree + 1):
191 for j in range(degree + 1):
192 if (i + j) > degree:
193 continue
194 vectorIndex = int(((i + j) * (i + j + 1)) / 2 + j + N * (outVar - 1))
195 polyArray[vectorIndex, 0] = coefficients[vectorIndex]
196 polyArray[vectorIndex, 1] = outVar
197 polyArray[vectorIndex, 2] = i
198 polyArray[vectorIndex, 3] = j
200 astPoly = astshim.PolyMap(polyArray, 2, options="IterInverse=1,NIterInverse=10,TolInverse=1e-7")
201 return astPoly
204class GbdesAstrometricFitConnections(
205 pipeBase.PipelineTaskConnections, dimensions=("skymap", "tract", "instrument", "physical_filter")
206):
207 """Middleware input/output connections for task data."""
209 inputCatalogRefs = pipeBase.connectionTypes.Input(
210 doc="Source table in parquet format, per visit.",
211 name="preSourceTable_visit",
212 storageClass="DataFrame",
213 dimensions=("instrument", "visit"),
214 deferLoad=True,
215 multiple=True,
216 )
217 inputVisitSummaries = pipeBase.connectionTypes.Input(
218 doc=(
219 "Per-visit consolidated exposure metadata built from calexps. "
220 "These catalogs use detector id for the id and must be sorted for "
221 "fast lookups of a detector."
222 ),
223 name="visitSummary",
224 storageClass="ExposureCatalog",
225 dimensions=("instrument", "visit"),
226 multiple=True,
227 )
228 referenceCatalog = pipeBase.connectionTypes.PrerequisiteInput(
229 doc="The astrometry reference catalog to match to loaded input catalog sources.",
230 name="gaia_dr3_20230707",
231 storageClass="SimpleCatalog",
232 dimensions=("skypix",),
233 deferLoad=True,
234 multiple=True,
235 )
236 outputWcs = pipeBase.connectionTypes.Output(
237 doc=(
238 "Per-tract, per-visit world coordinate systems derived from the fitted model."
239 " These catalogs only contain entries for detectors with an output, and use"
240 " the detector id for the catalog id, sorted on id for fast lookups of a detector."
241 ),
242 name="gbdesAstrometricFitSkyWcsCatalog",
243 storageClass="ExposureCatalog",
244 dimensions=("instrument", "visit", "skymap", "tract"),
245 multiple=True,
246 )
247 outputCatalog = pipeBase.connectionTypes.Output(
248 doc=(
249 "Source table with stars used in fit, along with residuals in pixel coordinates and tangent "
250 "plane coordinates and chisq values."
251 ),
252 name="gbdesAstrometricFit_fitStars",
253 storageClass="ArrowNumpyDict",
254 dimensions=("instrument", "skymap", "tract", "physical_filter"),
255 )
256 starCatalog = pipeBase.connectionTypes.Output(
257 doc="Star catalog.",
258 name="gbdesAstrometricFit_starCatalog",
259 storageClass="ArrowNumpyDict",
260 dimensions=("instrument", "skymap", "tract", "physical_filter"),
261 )
262 modelParams = pipeBase.connectionTypes.Output(
263 doc="WCS parameter covariance.",
264 name="gbdesAstrometricFit_modelParams",
265 storageClass="ArrowNumpyDict",
266 dimensions=("instrument", "skymap", "tract", "physical_filter"),
267 )
269 def getSpatialBoundsConnections(self):
270 return ("inputVisitSummaries",)
272 def __init__(self, *, config=None):
273 super().__init__(config=config)
275 if not self.config.saveModelParams:
276 self.outputs.remove("modelParams")
279class GbdesAstrometricFitConfig(
280 pipeBase.PipelineTaskConfig, pipelineConnections=GbdesAstrometricFitConnections
281):
282 """Configuration for GbdesAstrometricFitTask"""
284 sourceSelector = sourceSelectorRegistry.makeField(
285 doc="How to select sources for cross-matching.", default="science"
286 )
287 referenceSelector = pexConfig.ConfigurableField(
288 target=ReferenceSourceSelectorTask,
289 doc="How to down-select the loaded astrometry reference catalog.",
290 )
291 matchRadius = pexConfig.Field(
292 doc="Matching tolerance between associated objects (arcseconds).", dtype=float, default=1.0
293 )
294 minMatches = pexConfig.Field(
295 doc="Number of matches required to keep a source object.", dtype=int, default=2
296 )
297 allowSelfMatches = pexConfig.Field(
298 doc="Allow multiple sources from the same visit to be associated with the same object.",
299 dtype=bool,
300 default=False,
301 )
302 sourceFluxType = pexConfig.Field(
303 dtype=str,
304 doc="Source flux field to use in source selection and to get fluxes from the catalog.",
305 default="apFlux_12_0",
306 )
307 systematicError = pexConfig.Field(
308 dtype=float,
309 doc=(
310 "Systematic error padding added in quadrature for the science catalogs (marcsec). The default"
311 "value is equivalent to 0.02 pixels for HSC."
312 ),
313 default=0.0034,
314 )
315 referenceSystematicError = pexConfig.Field(
316 dtype=float,
317 doc="Systematic error padding added in quadrature for the reference catalog (marcsec).",
318 default=0.0,
319 )
320 modelComponents = pexConfig.ListField(
321 dtype=str,
322 doc=(
323 "List of mappings to apply to transform from pixels to sky, in order of their application."
324 "Supported options are 'INSTRUMENT/DEVICE' and 'EXPOSURE'."
325 ),
326 default=["INSTRUMENT/DEVICE", "EXPOSURE"],
327 )
328 deviceModel = pexConfig.ListField(
329 dtype=str,
330 doc=(
331 "List of mappings to apply to transform from detector pixels to intermediate frame. Map names"
332 "should match the format 'BAND/DEVICE/<map name>'."
333 ),
334 default=["BAND/DEVICE/poly"],
335 )
336 exposureModel = pexConfig.ListField(
337 dtype=str,
338 doc=(
339 "List of mappings to apply to transform from intermediate frame to sky coordinates. Map names"
340 "should match the format 'EXPOSURE/<map name>'."
341 ),
342 default=["EXPOSURE/poly"],
343 )
344 devicePolyOrder = pexConfig.Field(dtype=int, doc="Order of device polynomial model.", default=4)
345 exposurePolyOrder = pexConfig.Field(dtype=int, doc="Order of exposure polynomial model.", default=6)
346 fitProperMotion = pexConfig.Field(dtype=bool, doc="Fit the proper motions of the objects.", default=False)
347 excludeNonPMObjects = pexConfig.Field(
348 dtype=bool, doc="Exclude reference objects without proper motion/parallax information.", default=True
349 )
350 fitReserveFraction = pexConfig.Field(
351 dtype=float, default=0.2, doc="Fraction of objects to reserve from fit for validation."
352 )
353 fitReserveRandomSeed = pexConfig.Field(
354 dtype=int,
355 doc="Set the random seed for selecting data points to reserve from the fit for validation.",
356 default=1234,
357 )
358 saveModelParams = pexConfig.Field(
359 dtype=bool,
360 doc=(
361 "Save the parameters and covariance of the WCS model. Default to "
362 "false because this can be very large."
363 ),
364 default=False,
365 )
367 def setDefaults(self):
368 # Use only stars because aperture fluxes of galaxies are biased and
369 # depend on seeing.
370 self.sourceSelector["science"].doUnresolved = True
371 self.sourceSelector["science"].unresolved.name = "extendedness"
373 # Use only isolated sources.
374 self.sourceSelector["science"].doIsolated = True
375 self.sourceSelector["science"].isolated.parentName = "parentSourceId"
376 self.sourceSelector["science"].isolated.nChildName = "deblend_nChild"
377 # Do not use either flux or centroid measurements with flags,
378 # chosen from the usual QA flags for stars.
379 self.sourceSelector["science"].doFlags = True
380 badFlags = [
381 "pixelFlags_edge",
382 "pixelFlags_saturated",
383 "pixelFlags_interpolatedCenter",
384 "pixelFlags_interpolated",
385 "pixelFlags_crCenter",
386 "pixelFlags_bad",
387 "hsmPsfMoments_flag",
388 f"{self.sourceFluxType}_flag",
389 ]
390 self.sourceSelector["science"].flags.bad = badFlags
392 # Use only primary sources.
393 self.sourceSelector["science"].doRequirePrimary = True
395 def validate(self):
396 super().validate()
398 # Check if all components of the device and exposure models are
399 # supported.
400 for component in self.deviceModel:
401 if not (("poly" in component.lower()) or ("identity" in component.lower())):
402 raise pexConfig.FieldValidationError(
403 GbdesAstrometricFitConfig.deviceModel,
404 self,
405 f"deviceModel component {component} is not supported.",
406 )
408 for component in self.exposureModel:
409 if not (("poly" in component.lower()) or ("identity" in component.lower())):
410 raise pexConfig.FieldValidationError(
411 GbdesAstrometricFitConfig.exposureModel,
412 self,
413 f"exposureModel component {component} is not supported.",
414 )
417class GbdesAstrometricFitTask(pipeBase.PipelineTask):
418 """Calibrate the WCS across multiple visits of the same field using the
419 GBDES package.
420 """
422 ConfigClass = GbdesAstrometricFitConfig
423 _DefaultName = "gbdesAstrometricFit"
425 def __init__(self, **kwargs):
426 super().__init__(**kwargs)
427 self.makeSubtask("sourceSelector")
428 self.makeSubtask("referenceSelector")
430 def runQuantum(self, butlerQC, inputRefs, outputRefs):
431 # We override runQuantum to set up the refObjLoaders
432 inputs = butlerQC.get(inputRefs)
434 instrumentName = butlerQC.quantum.dataId["instrument"]
436 # Ensure the inputs are in a consistent order
437 inputCatVisits = np.array([inputCat.dataId["visit"] for inputCat in inputs["inputCatalogRefs"]])
438 inputs["inputCatalogRefs"] = [inputs["inputCatalogRefs"][v] for v in inputCatVisits.argsort()]
439 inputSumVisits = np.array([inputSum[0]["visit"] for inputSum in inputs["inputVisitSummaries"]])
440 inputs["inputVisitSummaries"] = [inputs["inputVisitSummaries"][v] for v in inputSumVisits.argsort()]
441 inputRefHtm7s = np.array([inputRefCat.dataId["htm7"] for inputRefCat in inputRefs.referenceCatalog])
442 inputRefCatRefs = [inputRefs.referenceCatalog[htm7] for htm7 in inputRefHtm7s.argsort()]
443 inputRefCats = np.array([inputRefCat.dataId["htm7"] for inputRefCat in inputs["referenceCatalog"]])
444 inputs["referenceCatalog"] = [inputs["referenceCatalog"][v] for v in inputRefCats.argsort()]
446 sampleRefCat = inputs["referenceCatalog"][0].get()
447 refEpoch = sampleRefCat[0]["epoch"]
449 refConfig = LoadReferenceObjectsConfig()
450 refConfig.anyFilterMapsToThis = "phot_g_mean"
451 refConfig.requireProperMotion = True
452 refObjectLoader = ReferenceObjectLoader(
453 dataIds=[ref.datasetRef.dataId for ref in inputRefCatRefs],
454 refCats=inputs.pop("referenceCatalog"),
455 config=refConfig,
456 log=self.log,
457 )
459 output = self.run(
460 **inputs, instrumentName=instrumentName, refEpoch=refEpoch, refObjectLoader=refObjectLoader
461 )
463 wcsOutputRefDict = {outWcsRef.dataId["visit"]: outWcsRef for outWcsRef in outputRefs.outputWcs}
464 for visit, outputWcs in output.outputWCSs.items():
465 butlerQC.put(outputWcs, wcsOutputRefDict[visit])
466 butlerQC.put(output.outputCatalog, outputRefs.outputCatalog)
467 butlerQC.put(output.starCatalog, outputRefs.starCatalog)
468 if self.config.saveModelParams:
469 butlerQC.put(output.modelParams, outputRefs.modelParams)
471 def run(
472 self, inputCatalogRefs, inputVisitSummaries, instrumentName="", refEpoch=None, refObjectLoader=None
473 ):
474 """Run the WCS fit for a given set of visits
476 Parameters
477 ----------
478 inputCatalogRefs : `list`
479 List of `DeferredDatasetHandle`s pointing to visit-level source
480 tables.
481 inputVisitSummaries : `list` of `lsst.afw.table.ExposureCatalog`
482 List of catalogs with per-detector summary information.
483 instrumentName : `str`, optional
484 Name of the instrument used. This is only used for labelling.
485 refEpoch : `float`
486 Epoch of the reference objects in MJD.
487 refObjectLoader : instance of
488 `lsst.meas.algorithms.loadReferenceObjects.ReferenceObjectLoader`
489 Referencef object loader instance.
491 Returns
492 -------
493 result : `lsst.pipe.base.Struct`
494 ``outputWCSs`` : `list` of `lsst.afw.table.ExposureCatalog`
495 List of exposure catalogs (one per visit) with the WCS for each
496 detector set by the new fitted WCS.
497 ``fitModel`` : `wcsfit.WCSFit`
498 Model-fitting object with final model parameters.
499 ``outputCatalog`` : `pyarrow.Table`
500 Catalog with fit residuals of all sources used.
501 """
502 if (len(inputVisitSummaries) == 1) and self.config.deviceModel and self.config.exposureModel:
503 raise RuntimeError(
504 "More than one exposure is necessary to break the degeneracy between the "
505 "device model and the exposure model."
506 )
507 self.log.info("Gathering instrument, exposure, and field info")
508 # Set up an instrument object
509 instrument = wcsfit.Instrument(instrumentName)
511 # Get RA, Dec, MJD, etc., for the input visits
512 exposureInfo, exposuresHelper, extensionInfo = self._get_exposure_info(
513 inputVisitSummaries, instrument
514 )
516 # Get information about the extent of the input visits
517 fields, fieldCenter, fieldRadius = self._prep_sky(inputVisitSummaries, exposureInfo.medianEpoch)
519 self.log.info("Load catalogs and associate sources")
520 # Set up class to associate sources into matches using a
521 # friends-of-friends algorithm
522 associations = wcsfit.FoFClass(
523 fields,
524 [instrument],
525 exposuresHelper,
526 [fieldRadius.asDegrees()],
527 (self.config.matchRadius * u.arcsec).to(u.degree).value,
528 )
530 # Add the reference catalog to the associator
531 medianEpoch = astropy.time.Time(exposureInfo.medianEpoch, format="decimalyear").mjd
532 refObjects, refCovariance = self._load_refcat(
533 associations, refObjectLoader, fieldCenter, fieldRadius, extensionInfo, epoch=medianEpoch
534 )
536 # Add the science catalogs and associate new sources as they are added
537 sourceIndices, usedColumns = self._load_catalogs_and_associate(
538 associations, inputCatalogRefs, extensionInfo
539 )
540 self._check_degeneracies(associations, extensionInfo)
542 self.log.info("Fit the WCSs")
543 # Set up a YAML-type string using the config variables and a sample
544 # visit
545 inputYAML, mapTemplate = self.make_yaml(inputVisitSummaries[0])
547 # Set the verbosity level for WCSFit from the task log level.
548 # TODO: DM-36850, Add lsst.log to gbdes so that log messages are
549 # properly propagated.
550 loglevel = self.log.getEffectiveLevel()
551 if loglevel >= self.log.WARNING:
552 verbose = 0
553 elif loglevel == self.log.INFO:
554 verbose = 1
555 else:
556 verbose = 2
558 # Set up the WCS-fitting class using the results of the FOF associator
559 wcsf = wcsfit.WCSFit(
560 fields,
561 [instrument],
562 exposuresHelper,
563 extensionInfo.visitIndex,
564 extensionInfo.detectorIndex,
565 inputYAML,
566 extensionInfo.wcs,
567 associations.sequence,
568 associations.extn,
569 associations.obj,
570 sysErr=self.config.systematicError,
571 refSysErr=self.config.referenceSystematicError,
572 usePM=self.config.fitProperMotion,
573 verbose=verbose,
574 )
576 # Add the science and reference sources
577 self._add_objects(wcsf, inputCatalogRefs, sourceIndices, extensionInfo, usedColumns)
578 self._add_ref_objects(wcsf, refObjects, refCovariance, extensionInfo)
580 # There must be at least as many sources per visit as the number of
581 # free parameters in the per-visit mapping. Set minFitExposures to be
582 # the number of free parameters, so that visits with fewer visits are
583 # dropped.
584 nCoeffVisitModel = _nCoeffsFromDegree(self.config.exposurePolyOrder)
585 # Do the WCS fit
586 wcsf.fit(
587 reserveFraction=self.config.fitReserveFraction,
588 randomNumberSeed=self.config.fitReserveRandomSeed,
589 minFitExposures=nCoeffVisitModel,
590 )
591 self.log.info("WCS fitting done")
593 outputWCSs = self._make_outputs(wcsf, inputVisitSummaries, exposureInfo, mapTemplate=mapTemplate)
594 outputCatalog = wcsf.getOutputCatalog()
595 starCatalog = wcsf.getStarCatalog()
596 modelParams = self._compute_model_params(wcsf) if self.config.saveModelParams else None
598 return pipeBase.Struct(
599 outputWCSs=outputWCSs,
600 fitModel=wcsf,
601 outputCatalog=outputCatalog,
602 starCatalog=starCatalog,
603 modelParams=modelParams,
604 )
606 def _prep_sky(self, inputVisitSummaries, epoch, fieldName="Field"):
607 """Get center and radius of the input tract. This assumes that all
608 visits will be put into the same `wcsfit.Field` and fit together.
610 Paramaters
611 ----------
612 inputVisitSummaries : `list` of `lsst.afw.table.ExposureCatalog`
613 List of catalogs with per-detector summary information.
614 epoch : float
615 Reference epoch.
616 fieldName : str
617 Name of the field, used internally.
619 Returns
620 -------
621 fields : `wcsfit.Fields`
622 Object with field information.
623 center : `lsst.geom.SpherePoint`
624 Center of the field.
625 radius : `lsst.sphgeom._sphgeom.Angle`
626 Radius of the bounding circle of the tract.
627 """
628 allDetectorCorners = []
629 for visSum in inputVisitSummaries:
630 detectorCorners = [
631 lsst.geom.SpherePoint(ra, dec, lsst.geom.degrees).getVector()
632 for (ra, dec) in zip(visSum["raCorners"].ravel(), visSum["decCorners"].ravel())
633 if (np.isfinite(ra) and (np.isfinite(dec)))
634 ]
635 allDetectorCorners.extend(detectorCorners)
636 boundingCircle = lsst.sphgeom.ConvexPolygon.convexHull(allDetectorCorners).getBoundingCircle()
637 center = lsst.geom.SpherePoint(boundingCircle.getCenter())
638 ra = center.getRa().asDegrees()
639 dec = center.getDec().asDegrees()
640 radius = boundingCircle.getOpeningAngle()
642 # wcsfit.Fields describes a list of fields, but we assume all
643 # observations will be fit together in one field.
644 fields = wcsfit.Fields([fieldName], [ra], [dec], [epoch])
646 return fields, center, radius
648 def _get_exposure_info(
649 self, inputVisitSummaries, instrument, fieldNumber=0, instrumentNumber=0, refEpoch=None
650 ):
651 """Get various information about the input visits to feed to the
652 fitting routines.
654 Parameters
655 ----------
656 inputVisitSummaries : `list` of `lsst.afw.table.ExposureCatalog`
657 Tables for each visit with information for detectors.
658 instrument : `wcsfit.Instrument`
659 Instrument object to which detector information is added.
660 fieldNumber : `int`
661 Index of the field for these visits. Should be zero if all data is
662 being fit together.
663 instrumentNumber : `int`
664 Index of the instrument for these visits. Should be zero if all
665 data comes from the same instrument.
666 refEpoch : `float`
667 Epoch of the reference objects in MJD.
669 Returns
670 -------
671 exposureInfo : `lsst.pipe.base.Struct`
672 Struct containing general properties for the visits:
673 ``visits`` : `list`
674 List of visit names.
675 ``detectors`` : `list`
676 List of all detectors in any visit.
677 ``ras`` : `list` of float
678 List of boresight RAs for each visit.
679 ``decs`` : `list` of float
680 List of borseight Decs for each visit.
681 ``medianEpoch`` : float
682 Median epoch of all visits in decimal-year format.
683 exposuresHelper : `wcsfit.ExposuresHelper`
684 Object containing information about the input visits.
685 extensionInfo : `lsst.pipe.base.Struct`
686 Struct containing properties for each extension:
687 ``visit`` : `np.ndarray`
688 Name of the visit for this extension.
689 ``detector`` : `np.ndarray`
690 Name of the detector for this extension.
691 ``visitIndex` : `np.ndarray` of `int`
692 Index of visit for this extension.
693 ``detectorIndex`` : `np.ndarray` of `int`
694 Index of the detector for this extension.
695 ``wcss`` : `np.ndarray` of `lsst.afw.geom.SkyWcs`
696 Initial WCS for this extension.
697 ``extensionType`` : `np.ndarray` of `str`
698 "SCIENCE" or "REFERENCE".
699 """
700 exposureNames = []
701 ras = []
702 decs = []
703 visits = []
704 detectors = []
705 airmasses = []
706 exposureTimes = []
707 mjds = []
708 observatories = []
709 wcss = []
711 extensionType = []
712 extensionVisitIndices = []
713 extensionDetectorIndices = []
714 extensionVisits = []
715 extensionDetectors = []
716 # Get information for all the science visits
717 for v, visitSummary in enumerate(inputVisitSummaries):
718 visitInfo = visitSummary[0].getVisitInfo()
719 visit = visitSummary[0]["visit"]
720 visits.append(visit)
721 exposureNames.append(str(visit))
722 raDec = visitInfo.getBoresightRaDec()
723 ras.append(raDec.getRa().asRadians())
724 decs.append(raDec.getDec().asRadians())
725 airmasses.append(visitInfo.getBoresightAirmass())
726 exposureTimes.append(visitInfo.getExposureTime())
727 obsDate = visitInfo.getDate()
728 obsMJD = obsDate.get(obsDate.MJD)
729 mjds.append(obsMJD)
730 # Get the observatory ICRS position for use in fitting parallax
731 obsLon = visitInfo.observatory.getLongitude().asDegrees()
732 obsLat = visitInfo.observatory.getLatitude().asDegrees()
733 obsElev = visitInfo.observatory.getElevation()
734 earthLocation = astropy.coordinates.EarthLocation.from_geodetic(obsLon, obsLat, obsElev)
735 observatory_gcrs = earthLocation.get_gcrs(astropy.time.Time(obsMJD, format="mjd"))
736 observatory_icrs = observatory_gcrs.transform_to(astropy.coordinates.ICRS())
737 # We want the position in AU in Cartesian coordinates
738 observatories.append(observatory_icrs.cartesian.xyz.to(u.AU).value)
740 for row in visitSummary:
741 detector = row["id"]
743 wcs = row.getWcs()
744 if wcs is None:
745 self.log.warning(
746 "WCS is None for visit %d, detector %d: this extension (visit/detector) will be "
747 "dropped.",
748 visit,
749 detector,
750 )
751 continue
752 else:
753 wcsRA = wcs.getSkyOrigin().getRa().asRadians()
754 wcsDec = wcs.getSkyOrigin().getDec().asRadians()
755 tangentPoint = wcsfit.Gnomonic(wcsRA, wcsDec)
756 mapping = wcs.getFrameDict().getMapping("PIXELS", "IWC")
757 gbdes_wcs = wcsfit.Wcs(wcsfit.ASTMap(mapping), tangentPoint)
758 wcss.append(gbdes_wcs)
760 if detector not in detectors:
761 detectors.append(detector)
762 detectorBounds = wcsfit.Bounds(
763 row["bbox_min_x"], row["bbox_max_x"], row["bbox_min_y"], row["bbox_max_y"]
764 )
765 instrument.addDevice(str(detector), detectorBounds)
767 detectorIndex = np.flatnonzero(detector == np.array(detectors))[0]
768 extensionVisitIndices.append(v)
769 extensionDetectorIndices.append(detectorIndex)
770 extensionVisits.append(visit)
771 extensionDetectors.append(detector)
772 extensionType.append("SCIENCE")
774 fieldNumbers = list(np.ones(len(exposureNames), dtype=int) * fieldNumber)
775 instrumentNumbers = list(np.ones(len(exposureNames), dtype=int) * instrumentNumber)
777 # Set the reference epoch to be the median of the science visits.
778 # The reference catalog will be shifted to this date.
779 medianMJD = np.median(mjds)
780 medianEpoch = astropy.time.Time(medianMJD, format="mjd").decimalyear
782 # Add information for the reference catalog. Most of the values are
783 # not used.
784 exposureNames.append("REFERENCE")
785 visits.append(-1)
786 fieldNumbers.append(0)
787 if self.config.fitProperMotion:
788 instrumentNumbers.append(-2)
789 else:
790 instrumentNumbers.append(-1)
791 ras.append(0.0)
792 decs.append(0.0)
793 airmasses.append(0.0)
794 exposureTimes.append(0)
795 mjds.append((refEpoch if (refEpoch is not None) else medianMJD))
796 observatories.append(np.array([0, 0, 0]))
797 identity = wcsfit.IdentityMap()
798 icrs = wcsfit.SphericalICRS()
799 refWcs = wcsfit.Wcs(identity, icrs, "Identity", np.pi / 180.0)
800 wcss.append(refWcs)
802 extensionVisitIndices.append(len(exposureNames) - 1)
803 extensionDetectorIndices.append(-1) # REFERENCE device must be -1
804 extensionVisits.append(-1)
805 extensionDetectors.append(-1)
806 extensionType.append("REFERENCE")
808 # Make a table of information to use elsewhere in the class
809 extensionInfo = pipeBase.Struct(
810 visit=np.array(extensionVisits),
811 detector=np.array(extensionDetectors),
812 visitIndex=np.array(extensionVisitIndices),
813 detectorIndex=np.array(extensionDetectorIndices),
814 wcs=np.array(wcss),
815 extensionType=np.array(extensionType),
816 )
818 # Make the exposureHelper object to use in the fitting routines
819 exposuresHelper = wcsfit.ExposuresHelper(
820 exposureNames,
821 fieldNumbers,
822 instrumentNumbers,
823 ras,
824 decs,
825 airmasses,
826 exposureTimes,
827 mjds,
828 observatories,
829 )
831 exposureInfo = pipeBase.Struct(
832 visits=visits, detectors=detectors, ras=ras, decs=decs, medianEpoch=medianEpoch
833 )
835 return exposureInfo, exposuresHelper, extensionInfo
837 def _load_refcat(
838 self, associations, refObjectLoader, center, radius, extensionInfo, epoch=None, fieldIndex=0
839 ):
840 """Load the reference catalog and add reference objects to the
841 `wcsfit.FoFClass` object.
843 Parameters
844 ----------
845 associations : `wcsfit.FoFClass`
846 Object to which to add the catalog of reference objects.
847 refObjectLoader :
848 `lsst.meas.algorithms.loadReferenceObjects.ReferenceObjectLoader`
849 Object set up to load reference catalog objects.
850 center : `lsst.geom.SpherePoint`
851 Center of the circle in which to load reference objects.
852 radius : `lsst.sphgeom._sphgeom.Angle`
853 Radius of the circle in which to load reference objects.
854 extensionInfo : `lsst.pipe.base.Struct`
855 Struct containing properties for each extension.
856 epoch : `float`
857 MJD to which to correct the object positions.
858 fieldIndex : `int`
859 Index of the field. Should be zero if all the data is fit together.
861 Returns
862 -------
863 refObjects : `dict`
864 Position and error information of reference objects.
865 refCovariance : `list` of `float`
866 Flattened output covariance matrix.
867 """
868 formattedEpoch = astropy.time.Time(epoch, format="mjd")
870 refFilter = refObjectLoader.config.anyFilterMapsToThis
871 skyCircle = refObjectLoader.loadSkyCircle(center, radius, refFilter, epoch=formattedEpoch)
873 selected = self.referenceSelector.run(skyCircle.refCat)
874 # Need memory contiguity to get reference filters as a vector.
875 if not selected.sourceCat.isContiguous():
876 refCat = selected.sourceCat.copy(deep=True)
877 else:
878 refCat = selected.sourceCat
880 # In Gaia DR3, missing values are denoted by NaNs.
881 finiteInd = np.isfinite(refCat["coord_ra"]) & np.isfinite(refCat["coord_dec"])
882 refCat = refCat[finiteInd]
884 if self.config.excludeNonPMObjects:
885 # Gaia DR2 has zeros for missing data, while Gaia DR3 has NaNs:
886 hasPM = (
887 (refCat["pm_raErr"] != 0) & np.isfinite(refCat["pm_raErr"]) & np.isfinite(refCat["pm_decErr"])
888 )
889 refCat = refCat[hasPM]
891 ra = (refCat["coord_ra"] * u.radian).to(u.degree).to_value().tolist()
892 dec = (refCat["coord_dec"] * u.radian).to(u.degree).to_value().tolist()
893 raCov = ((refCat["coord_raErr"] * u.radian).to(u.degree).to_value() ** 2).tolist()
894 decCov = ((refCat["coord_decErr"] * u.radian).to(u.degree).to_value() ** 2).tolist()
896 # Get refcat version from refcat metadata
897 refCatMetadata = refObjectLoader.refCats[0].get().getMetadata()
898 refCatVersion = refCatMetadata["REFCAT_FORMAT_VERSION"]
899 if refCatVersion == 2:
900 raDecCov = (
901 (refCat["coord_ra_coord_dec_Cov"] * u.radian**2).to(u.degree**2).to_value().tolist()
902 )
903 else:
904 raDecCov = np.zeros(len(ra))
906 refObjects = {"ra": ra, "dec": dec, "raCov": raCov, "decCov": decCov, "raDecCov": raDecCov}
907 refCovariance = []
909 if self.config.fitProperMotion:
910 raPM = (refCat["pm_ra"] * u.radian).to(u.marcsec).to_value().tolist()
911 decPM = (refCat["pm_dec"] * u.radian).to(u.marcsec).to_value().tolist()
912 parallax = (refCat["parallax"] * u.radian).to(u.marcsec).to_value().tolist()
913 cov = _make_ref_covariance_matrix(refCat, version=refCatVersion)
914 pmDict = {"raPM": raPM, "decPM": decPM, "parallax": parallax}
915 refObjects.update(pmDict)
916 refCovariance = cov
918 extensionIndex = np.flatnonzero(extensionInfo.extensionType == "REFERENCE")[0]
919 visitIndex = extensionInfo.visitIndex[extensionIndex]
920 detectorIndex = extensionInfo.detectorIndex[extensionIndex]
921 instrumentIndex = -1 # -1 indicates the reference catalog
922 refWcs = extensionInfo.wcs[extensionIndex]
924 associations.addCatalog(
925 refWcs,
926 "STELLAR",
927 visitIndex,
928 fieldIndex,
929 instrumentIndex,
930 detectorIndex,
931 extensionIndex,
932 np.ones(len(refCat), dtype=bool),
933 ra,
934 dec,
935 np.arange(len(ra)),
936 )
938 return refObjects, refCovariance
940 @staticmethod
941 def _find_extension_index(extensionInfo, visit, detector):
942 """Find the index for a given extension from its visit and detector
943 number.
945 If no match is found, None is returned.
947 Parameters
948 ----------
949 extensionInfo : `lsst.pipe.base.Struct`
950 Struct containing properties for each extension.
951 visit : `int`
952 Visit number
953 detector : `int`
954 Detector number
956 Returns
957 -------
958 extensionIndex : `int` or None
959 Index of this extension
960 """
961 findExtension = np.flatnonzero((extensionInfo.visit == visit) & (extensionInfo.detector == detector))
962 if len(findExtension) == 0:
963 extensionIndex = None
964 else:
965 extensionIndex = findExtension[0]
966 return extensionIndex
968 def _load_catalogs_and_associate(
969 self, associations, inputCatalogRefs, extensionInfo, fieldIndex=0, instrumentIndex=0
970 ):
971 """Load the science catalogs and add the sources to the associator
972 class `wcsfit.FoFClass`, associating them into matches as you go.
974 Parameters
975 ----------
976 associations : `wcsfit.FoFClass`
977 Object to which to add the catalog of source and which performs
978 the source association.
979 inputCatalogRefs : `list`
980 List of DeferredDatasetHandles pointing to visit-level source
981 tables.
982 extensionInfo : `lsst.pipe.base.Struct`
983 Struct containing properties for each extension.
984 fieldIndex : `int`
985 Index of the field for these catalogs. Should be zero assuming all
986 data is being fit together.
987 instrumentIndex : `int`
988 Index of the instrument for these catalogs. Should be zero
989 assuming all data comes from the same instrument.
991 Returns
992 -------
993 sourceIndices : `list`
994 List of boolean arrays used to select sources.
995 columns : `list` of `str`
996 List of columns needed from source tables.
997 """
998 columns = [
999 "detector",
1000 "sourceId",
1001 "x",
1002 "xErr",
1003 "y",
1004 "yErr",
1005 "ixx",
1006 "iyy",
1007 "ixy",
1008 f"{self.config.sourceFluxType}_instFlux",
1009 f"{self.config.sourceFluxType}_instFluxErr",
1010 ]
1011 if self.sourceSelector.config.doFlags:
1012 columns.extend(self.sourceSelector.config.flags.bad)
1013 if self.sourceSelector.config.doUnresolved:
1014 columns.append(self.sourceSelector.config.unresolved.name)
1015 if self.sourceSelector.config.doIsolated:
1016 columns.append(self.sourceSelector.config.isolated.parentName)
1017 columns.append(self.sourceSelector.config.isolated.nChildName)
1018 if self.sourceSelector.config.doRequirePrimary:
1019 columns.append(self.sourceSelector.config.requirePrimary.primaryColName)
1021 sourceIndices = [None] * len(extensionInfo.visit)
1022 for inputCatalogRef in inputCatalogRefs:
1023 visit = inputCatalogRef.dataId["visit"]
1024 inputCatalog = inputCatalogRef.get(parameters={"columns": columns})
1025 # Get a sorted array of detector names
1026 detectors = np.unique(inputCatalog["detector"])
1028 for detector in detectors:
1029 detectorSources = inputCatalog[inputCatalog["detector"] == detector]
1030 xCov = detectorSources["xErr"] ** 2
1031 yCov = detectorSources["yErr"] ** 2
1032 xyCov = (
1033 detectorSources["ixy"] * (xCov + yCov) / (detectorSources["ixx"] + detectorSources["iyy"])
1034 )
1035 # Remove sources with bad shape measurements
1036 goodShapes = xyCov**2 <= (xCov * yCov)
1037 selected = self.sourceSelector.run(detectorSources)
1038 goodInds = selected.selected & goodShapes
1040 isStar = np.ones(goodInds.sum())
1041 extensionIndex = self._find_extension_index(extensionInfo, visit, detector)
1042 if extensionIndex is None:
1043 # This extension does not have information necessary for
1044 # fit. Skip the detections from this detector for this
1045 # visit.
1046 continue
1047 detectorIndex = extensionInfo.detectorIndex[extensionIndex]
1048 visitIndex = extensionInfo.visitIndex[extensionIndex]
1050 sourceIndices[extensionIndex] = goodInds
1052 wcs = extensionInfo.wcs[extensionIndex]
1053 associations.reprojectWCS(wcs, fieldIndex)
1055 associations.addCatalog(
1056 wcs,
1057 "STELLAR",
1058 visitIndex,
1059 fieldIndex,
1060 instrumentIndex,
1061 detectorIndex,
1062 extensionIndex,
1063 isStar,
1064 detectorSources[goodInds]["x"].to_list(),
1065 detectorSources[goodInds]["y"].to_list(),
1066 np.arange(goodInds.sum()),
1067 )
1069 associations.sortMatches(
1070 fieldIndex, minMatches=self.config.minMatches, allowSelfMatches=self.config.allowSelfMatches
1071 )
1073 return sourceIndices, columns
1075 def _check_degeneracies(self, associations, extensionInfo):
1076 """Check that the minimum number of visits and sources needed to
1077 constrain the model are present.
1079 This does not guarantee that the Hessian matrix of the chi-square,
1080 which is used to fit the model, will be positive-definite, but if the
1081 checks here do not pass, the matrix is certain to not be
1082 positive-definite and the model cannot be fit.
1084 Parameters
1085 ----------
1086 associations : `wcsfit.FoFClass`
1087 Object holding the source association information.
1088 extensionInfo : `lsst.pipe.base.Struct`
1089 Struct containing properties for each extension.
1090 """
1091 # As a baseline, need to have more stars per detector than per-detector
1092 # parameters, and more stars per visit than per-visit parameters.
1093 whichExtension = np.array(associations.extn)
1094 whichDetector = np.zeros(len(whichExtension))
1095 whichVisit = np.zeros(len(whichExtension))
1097 for extension, (detector, visit) in enumerate(zip(extensionInfo.detector, extensionInfo.visit)):
1098 ex_ind = whichExtension == extension
1099 whichDetector[ex_ind] = detector
1100 whichVisit[ex_ind] = visit
1102 if "BAND/DEVICE/poly" in self.config.deviceModel:
1103 nCoeffDetectorModel = _nCoeffsFromDegree(self.config.devicePolyOrder)
1104 unconstrainedDetectors = []
1105 for detector in np.unique(extensionInfo.detector):
1106 numSources = (whichDetector == detector).sum()
1107 if numSources < nCoeffDetectorModel:
1108 unconstrainedDetectors.append(str(detector))
1110 if unconstrainedDetectors:
1111 raise RuntimeError(
1112 "The model is not constrained. The following detectors do not have enough "
1113 f"sources ({nCoeffDetectorModel} required): ",
1114 ", ".join(unconstrainedDetectors),
1115 )
1117 def make_yaml(self, inputVisitSummary, inputFile=None):
1118 """Make a YAML-type object that describes the parameters of the fit
1119 model.
1121 Parameters
1122 ----------
1123 inputVisitSummary : `lsst.afw.table.ExposureCatalog`
1124 Catalog with per-detector summary information.
1125 inputFile : `str`
1126 Path to a file that contains a basic model.
1128 Returns
1129 -------
1130 inputYAML : `wcsfit.YAMLCollector`
1131 YAML object containing the model description.
1132 inputDict : `dict` [`str`, `str`]
1133 Dictionary containing the model description.
1134 """
1135 if inputFile is not None:
1136 inputYAML = wcsfit.YAMLCollector(inputFile, "PixelMapCollection")
1137 else:
1138 inputYAML = wcsfit.YAMLCollector("", "PixelMapCollection")
1139 inputDict = {}
1140 modelComponents = ["INSTRUMENT/DEVICE", "EXPOSURE"]
1141 baseMap = {"Type": "Composite", "Elements": modelComponents}
1142 inputDict["EXPOSURE/DEVICE/base"] = baseMap
1144 xMin = str(inputVisitSummary["bbox_min_x"].min())
1145 xMax = str(inputVisitSummary["bbox_max_x"].max())
1146 yMin = str(inputVisitSummary["bbox_min_y"].min())
1147 yMax = str(inputVisitSummary["bbox_max_y"].max())
1149 deviceModel = {"Type": "Composite", "Elements": self.config.deviceModel.list()}
1150 inputDict["INSTRUMENT/DEVICE"] = deviceModel
1151 for component in self.config.deviceModel:
1152 if "poly" in component.lower():
1153 componentDict = {
1154 "Type": "Poly",
1155 "XPoly": {"OrderX": self.config.devicePolyOrder, "SumOrder": True},
1156 "YPoly": {"OrderX": self.config.devicePolyOrder, "SumOrder": True},
1157 "XMin": xMin,
1158 "XMax": xMax,
1159 "YMin": yMin,
1160 "YMax": yMax,
1161 }
1162 elif "identity" in component.lower():
1163 componentDict = {"Type": "Identity"}
1165 inputDict[component] = componentDict
1167 exposureModel = {"Type": "Composite", "Elements": self.config.exposureModel.list()}
1168 inputDict["EXPOSURE"] = exposureModel
1169 for component in self.config.exposureModel:
1170 if "poly" in component.lower():
1171 componentDict = {
1172 "Type": "Poly",
1173 "XPoly": {"OrderX": self.config.exposurePolyOrder, "SumOrder": "true"},
1174 "YPoly": {"OrderX": self.config.exposurePolyOrder, "SumOrder": "true"},
1175 }
1176 elif "identity" in component.lower():
1177 componentDict = {"Type": "Identity"}
1179 inputDict[component] = componentDict
1181 inputYAML.addInput(yaml.dump(inputDict))
1182 inputYAML.addInput("Identity:\n Type: Identity\n")
1184 return inputYAML, inputDict
1186 def _add_objects(self, wcsf, inputCatalogRefs, sourceIndices, extensionInfo, columns):
1187 """Add science sources to the wcsfit.WCSFit object.
1189 Parameters
1190 ----------
1191 wcsf : `wcsfit.WCSFit`
1192 WCS-fitting object.
1193 inputCatalogRefs : `list`
1194 List of DeferredDatasetHandles pointing to visit-level source
1195 tables.
1196 sourceIndices : `list`
1197 List of boolean arrays used to select sources.
1198 extensionInfo : `lsst.pipe.base.Struct`
1199 Struct containing properties for each extension.
1200 columns : `list` of `str`
1201 List of columns needed from source tables.
1202 """
1203 for inputCatalogRef in inputCatalogRefs:
1204 visit = inputCatalogRef.dataId["visit"]
1205 inputCatalog = inputCatalogRef.get(parameters={"columns": columns})
1206 detectors = np.unique(inputCatalog["detector"])
1208 for detector in detectors:
1209 detectorSources = inputCatalog[inputCatalog["detector"] == detector]
1211 extensionIndex = self._find_extension_index(extensionInfo, visit, detector)
1212 if extensionIndex is None:
1213 # This extension does not have information necessary for
1214 # fit. Skip the detections from this detector for this
1215 # visit.
1216 continue
1218 sourceCat = detectorSources[sourceIndices[extensionIndex]]
1220 xCov = sourceCat["xErr"] ** 2
1221 yCov = sourceCat["yErr"] ** 2
1222 xyCov = sourceCat["ixy"] * (xCov + yCov) / (sourceCat["ixx"] + sourceCat["iyy"])
1223 # TODO: add correct xyErr if DM-7101 is ever done.
1225 d = {
1226 "x": sourceCat["x"].to_numpy(),
1227 "y": sourceCat["y"].to_numpy(),
1228 "xCov": xCov.to_numpy(),
1229 "yCov": yCov.to_numpy(),
1230 "xyCov": xyCov.to_numpy(),
1231 }
1233 wcsf.setObjects(extensionIndex, d, "x", "y", ["xCov", "yCov", "xyCov"])
1235 def _add_ref_objects(self, wcsf, refObjects, refCovariance, extensionInfo):
1236 """Add reference sources to the wcsfit.WCSFit object.
1238 Parameters
1239 ----------
1240 wcsf : `wcsfit.WCSFit`
1241 WCS-fitting object.
1242 refObjects : `dict`
1243 Position and error information of reference objects.
1244 refCovariance : `list` of `float`
1245 Flattened output covariance matrix.
1246 extensionInfo : `lsst.pipe.base.Struct`
1247 Struct containing properties for each extension.
1248 """
1249 extensionIndex = np.flatnonzero(extensionInfo.extensionType == "REFERENCE")[0]
1251 if self.config.fitProperMotion:
1252 wcsf.setObjects(
1253 extensionIndex,
1254 refObjects,
1255 "ra",
1256 "dec",
1257 ["raCov", "decCov", "raDecCov"],
1258 pmDecKey="decPM",
1259 pmRaKey="raPM",
1260 parallaxKey="parallax",
1261 pmCovKey="fullCov",
1262 pmCov=refCovariance,
1263 )
1264 else:
1265 wcsf.setObjects(extensionIndex, refObjects, "ra", "dec", ["raCov", "decCov", "raDecCov"])
1267 def _make_afw_wcs(self, mapDict, centerRA, centerDec, doNormalizePixels=False, xScale=1, yScale=1):
1268 """Make an `lsst.afw.geom.SkyWcs` from a dictionary of mappings.
1270 Parameters
1271 ----------
1272 mapDict : `dict`
1273 Dictionary of mapping parameters.
1274 centerRA : `lsst.geom.Angle`
1275 RA of the tangent point.
1276 centerDec : `lsst.geom.Angle`
1277 Declination of the tangent point.
1278 doNormalizePixels : `bool`
1279 Whether to normalize pixels so that range is [-1,1].
1280 xScale : `float`
1281 Factor by which to normalize x-dimension. Corresponds to width of
1282 detector.
1283 yScale : `float`
1284 Factor by which to normalize y-dimension. Corresponds to height of
1285 detector.
1287 Returns
1288 -------
1289 outWCS : `lsst.afw.geom.SkyWcs`
1290 WCS constructed from the input mappings
1291 """
1292 # Set up pixel frames
1293 pixelFrame = astshim.Frame(2, "Domain=PIXELS")
1294 normedPixelFrame = astshim.Frame(2, "Domain=NORMEDPIXELS")
1296 if doNormalizePixels:
1297 # Pixels will need to be rescaled before going into the mappings
1298 normCoefficients = [-1.0, 2.0 / xScale, 0, -1.0, 0, 2.0 / yScale]
1299 normMap = _convert_to_ast_polymap_coefficients(normCoefficients)
1300 else:
1301 normMap = astshim.UnitMap(2)
1303 # All of the detectors for one visit map to the same tangent plane
1304 tangentPoint = lsst.geom.SpherePoint(centerRA, centerDec)
1305 cdMatrix = afwgeom.makeCdMatrix(1.0 * lsst.geom.degrees, 0 * lsst.geom.degrees, True)
1306 iwcToSkyWcs = afwgeom.makeSkyWcs(lsst.geom.Point2D(0, 0), tangentPoint, cdMatrix)
1307 iwcToSkyMap = iwcToSkyWcs.getFrameDict().getMapping("PIXELS", "SKY")
1308 skyFrame = iwcToSkyWcs.getFrameDict().getFrame("SKY")
1310 frameDict = astshim.FrameDict(pixelFrame)
1311 frameDict.addFrame("PIXELS", normMap, normedPixelFrame)
1313 currentFrameName = "NORMEDPIXELS"
1315 # Dictionary values are ordered according to the maps' application.
1316 for m, mapElement in enumerate(mapDict.values()):
1317 mapType = mapElement["Type"]
1319 if mapType == "Poly":
1320 mapCoefficients = mapElement["Coefficients"]
1321 astMap = _convert_to_ast_polymap_coefficients(mapCoefficients)
1322 elif mapType == "Identity":
1323 astMap = astshim.UnitMap(2)
1324 else:
1325 raise ValueError(f"Converting map type {mapType} to WCS is not supported")
1327 if m == len(mapDict) - 1:
1328 newFrameName = "IWC"
1329 else:
1330 newFrameName = "INTERMEDIATE" + str(m)
1331 newFrame = astshim.Frame(2, f"Domain={newFrameName}")
1332 frameDict.addFrame(currentFrameName, astMap, newFrame)
1333 currentFrameName = newFrameName
1334 frameDict.addFrame("IWC", iwcToSkyMap, skyFrame)
1336 outWCS = afwgeom.SkyWcs(frameDict)
1337 return outWCS
1339 def _make_outputs(self, wcsf, visitSummaryTables, exposureInfo, mapTemplate=None):
1340 """Make a WCS object out of the WCS models.
1342 Parameters
1343 ----------
1344 wcsf : `wcsfit.WCSFit`
1345 WCSFit object, assumed to have fit model.
1346 visitSummaryTables : `list` of `lsst.afw.table.ExposureCatalog`
1347 Catalogs with per-detector summary information from which to grab
1348 detector information.
1349 extensionInfo : `lsst.pipe.base.Struct`
1350 Struct containing properties for each extension.
1352 Returns
1353 -------
1354 catalogs : `dict` of [`str`, `lsst.afw.table.ExposureCatalog`]
1355 Dictionary of `lsst.afw.table.ExposureCatalog` objects with the WCS
1356 set to the WCS fit in wcsf, keyed by the visit name.
1357 """
1358 # Get the parameters of the fit models
1359 mapParams = wcsf.mapCollection.getParamDict()
1361 # Set up the schema for the output catalogs
1362 schema = lsst.afw.table.ExposureTable.makeMinimalSchema()
1363 schema.addField("visit", type="L", doc="Visit number")
1365 # Pixels will need to be rescaled before going into the mappings
1366 sampleDetector = visitSummaryTables[0][0]
1367 xscale = sampleDetector["bbox_max_x"] - sampleDetector["bbox_min_x"]
1368 yscale = sampleDetector["bbox_max_y"] - sampleDetector["bbox_min_y"]
1370 catalogs = {}
1371 for v, visitSummary in enumerate(visitSummaryTables):
1372 visit = visitSummary[0]["visit"]
1374 visitMap = wcsf.mapCollection.orderAtoms(f"{visit}")[0]
1375 visitMapType = wcsf.mapCollection.getMapType(visitMap)
1376 if (visitMap not in mapParams) and (visitMapType != "Identity"):
1377 self.log.warning("Visit %d was dropped because of an insufficient number of sources.", visit)
1378 continue
1380 catalog = lsst.afw.table.ExposureCatalog(schema)
1381 catalog.resize(len(exposureInfo.detectors))
1382 catalog["visit"] = visit
1384 for d, detector in enumerate(visitSummary["id"]):
1385 mapName = f"{visit}/{detector}"
1386 if mapName in wcsf.mapCollection.allMapNames():
1387 mapElements = wcsf.mapCollection.orderAtoms(f"{mapName}/base")
1388 else:
1389 # This extension was not fit, but the WCS can be recovered
1390 # using the maps fit from sources on other visits but the
1391 # same detector and from sources on other detectors from
1392 # this visit.
1393 genericElements = mapTemplate["EXPOSURE/DEVICE/base"]["Elements"]
1394 mapElements = []
1395 instrument = visitSummary[0].getVisitInfo().instrumentLabel
1396 # Go through the generic map components to build the names
1397 # of the specific maps for this extension.
1398 for component in genericElements:
1399 elements = mapTemplate[component]["Elements"]
1400 for element in elements:
1401 # TODO: DM-42519, gbdes sets the "BAND" to the
1402 # instrument name currently. This will need to be
1403 # disambiguated if we run on multiple bands at
1404 # once.
1405 element = element.replace("BAND", str(instrument))
1406 element = element.replace("EXPOSURE", str(visit))
1407 element = element.replace("DEVICE", str(detector))
1408 mapElements.append(element)
1409 mapDict = {}
1410 for m, mapElement in enumerate(mapElements):
1411 mapType = wcsf.mapCollection.getMapType(mapElement)
1412 mapDict[mapElement] = {"Type": mapType}
1414 if mapType == "Poly":
1415 mapCoefficients = mapParams[mapElement]
1416 mapDict[mapElement]["Coefficients"] = mapCoefficients
1418 # The RA and Dec of the visit are needed for the last step of
1419 # the mapping from the visit tangent plane to RA and Dec
1420 outWCS = self._make_afw_wcs(
1421 mapDict,
1422 exposureInfo.ras[v] * lsst.geom.radians,
1423 exposureInfo.decs[v] * lsst.geom.radians,
1424 doNormalizePixels=True,
1425 xScale=xscale,
1426 yScale=yscale,
1427 )
1429 catalog[d].setId(detector)
1430 catalog[d].setWcs(outWCS)
1431 catalog.sort()
1432 catalogs[visit] = catalog
1434 return catalogs
1436 def _compute_model_params(self, wcsf):
1437 """Get the WCS model parameters and covariance and convert to a
1438 dictionary that will be readable as a pandas dataframe or other table.
1440 Parameters
1441 ----------
1442 wcsf : `wcsfit.WCSFit`
1443 WCSFit object, assumed to have fit model.
1445 Returns
1446 -------
1447 modelParams : `dict`
1448 Parameters and covariance of the best-fit WCS model.
1449 """
1450 modelParamDict = wcsf.mapCollection.getParamDict()
1451 modelCovariance = wcsf.getModelCovariance()
1453 modelParams = {k: [] for k in ["mapName", "coordinate", "parameter", "coefficientNumber"]}
1454 i = 0
1455 for mapName, params in modelParamDict.items():
1456 nCoeffs = len(params)
1457 # There are an equal number of x and y coordinate parameters
1458 nCoordCoeffs = nCoeffs // 2
1459 modelParams["mapName"].extend([mapName] * nCoeffs)
1460 modelParams["coordinate"].extend(["x"] * nCoordCoeffs)
1461 modelParams["coordinate"].extend(["y"] * nCoordCoeffs)
1462 modelParams["parameter"].extend(params)
1463 modelParams["coefficientNumber"].extend(np.arange(nCoordCoeffs))
1464 modelParams["coefficientNumber"].extend(np.arange(nCoordCoeffs))
1466 for p in range(nCoeffs):
1467 if p < nCoordCoeffs:
1468 coord = "x"
1469 else:
1470 coord = "y"
1471 modelParams[f"{mapName}_{coord}_{p}_cov"] = modelCovariance[i]
1472 i += 1
1474 # Convert the dictionary values from lists to numpy arrays.
1475 for key, value in modelParams.items():
1476 modelParams[key] = np.array(value)
1478 return modelParams