lsst.pipe.tasks gdf62c121a3+1fdf519404
finalizeCharacterization.py
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22"""Task to run a finalized image characterization, using additional data.
23"""
24import numpy as np
25import esutil
26import pandas as pd
27
28import lsst.pex.config as pexConfig
29import lsst.pipe.base as pipeBase
30import lsst.daf.base as dafBase
31import lsst.afw.table as afwTable
32import lsst.meas.algorithms as measAlg
33import lsst.meas.extensions.piff.piffPsfDeterminer # noqa: F401
34from lsst.meas.algorithms import MeasureApCorrTask
35from lsst.meas.base import SingleFrameMeasurementTask, ApplyApCorrTask
36from lsst.meas.algorithms.sourceSelector import sourceSelectorRegistry
37
38from .reserveIsolatedStars import ReserveIsolatedStarsTask
39
40__all__ = ['FinalizeCharacterizationConnections',
41 'FinalizeCharacterizationConfig',
42 'FinalizeCharacterizationTask']
43
44
45class FinalizeCharacterizationConnections(pipeBase.PipelineTaskConnections,
46 dimensions=('instrument', 'visit',),
47 defaultTemplates={}):
48 src_schema = pipeBase.connectionTypes.InitInput(
49 doc='Input schema used for src catalogs.',
50 name='src_schema',
51 storageClass='SourceCatalog',
52 )
53 srcs = pipeBase.connectionTypes.Input(
54 doc='Source catalogs for the visit',
55 name='src',
56 storageClass='SourceCatalog',
57 dimensions=('instrument', 'visit', 'detector'),
58 deferLoad=True,
59 multiple=True,
60 )
61 calexps = pipeBase.connectionTypes.Input(
62 doc='Calexps for the visit',
63 name='calexp',
64 storageClass='ExposureF',
65 dimensions=('instrument', 'visit', 'detector'),
66 deferLoad=True,
67 multiple=True,
68 )
69 isolated_star_cats = pipeBase.connectionTypes.Input(
70 doc=('Catalog of isolated stars with average positions, number of associated '
71 'sources, and indexes to the isolated_star_sources catalogs.'),
72 name='isolated_star_cat',
73 storageClass='DataFrame',
74 dimensions=('instrument', 'tract', 'skymap'),
75 deferLoad=True,
76 multiple=True,
77 )
78 isolated_star_sources = pipeBase.connectionTypes.Input(
79 doc=('Catalog of isolated star sources with sourceIds, and indexes to the '
80 'isolated_star_cats catalogs.'),
81 name='isolated_star_sources',
82 storageClass='DataFrame',
83 dimensions=('instrument', 'tract', 'skymap'),
84 deferLoad=True,
85 multiple=True,
86 )
87 finalized_psf_ap_corr_cat = pipeBase.connectionTypes.Output(
88 doc=('Per-visit finalized psf models and aperture corrections. This '
89 'catalog uses detector id for the id and are sorted for fast '
90 'lookups of a detector.'),
91 name='finalized_psf_ap_corr_catalog',
92 storageClass='ExposureCatalog',
93 dimensions=('instrument', 'visit'),
94 )
95 finalized_src_table = pipeBase.connectionTypes.Output(
96 doc=('Per-visit catalog of measurements for psf/flag/etc.'),
97 name='finalized_src_table',
98 storageClass='DataFrame',
99 dimensions=('instrument', 'visit'),
100 )
101
102
103class FinalizeCharacterizationConfig(pipeBase.PipelineTaskConfig,
104 pipelineConnections=FinalizeCharacterizationConnections):
105 """Configuration for FinalizeCharacterizationTask."""
106 source_selector = sourceSelectorRegistry.makeField(
107 doc="How to select sources",
108 default="science"
109 )
110 id_column = pexConfig.Field(
111 doc='Name of column in isolated_star_sources with source id.',
112 dtype=str,
113 default='sourceId',
114 )
115 reserve_selection = pexConfig.ConfigurableField(
116 target=ReserveIsolatedStarsTask,
117 doc='Task to select reserved stars',
118 )
119 make_psf_candidates = pexConfig.ConfigurableField(
120 target=measAlg.MakePsfCandidatesTask,
121 doc='Task to make psf candidates from selected stars.',
122 )
123 psf_determiner = measAlg.psfDeterminerRegistry.makeField(
124 'PSF Determination algorithm',
125 default='piff'
126 )
127 measurement = pexConfig.ConfigurableField(
128 target=SingleFrameMeasurementTask,
129 doc='Measure sources for aperture corrections'
130 )
131 measure_ap_corr = pexConfig.ConfigurableField(
132 target=MeasureApCorrTask,
133 doc="Subtask to measure aperture corrections"
134 )
135 apply_ap_corr = pexConfig.ConfigurableField(
136 target=ApplyApCorrTask,
137 doc="Subtask to apply aperture corrections"
138 )
139
140 def setDefaults(self):
141 super().setDefaults()
142
143 source_selector = self.source_selectorsource_selector['science']
144 source_selector.setDefaults()
145
146 # We use the source selector only to select out flagged objects
147 # and signal-to-noise. Isolated, unresolved sources are handled
148 # by the isolated star catalog.
149
150 source_selector.doFlags = True
151 source_selector.doSignalToNoise = True
152 source_selector.doFluxLimit = False
153 source_selector.doUnresolved = False
154 source_selector.doIsolated = False
155
156 source_selector.signalToNoise.minimum = 20.0
157 source_selector.signalToNoise.maximum = 1000.0
158
159 source_selector.signalToNoise.fluxField = 'base_GaussianFlux_instFlux'
160 source_selector.signalToNoise.errField = 'base_GaussianFlux_instFluxErr'
161
162 source_selector.flags.bad = ['base_PixelFlags_flag_edge',
163 'base_PixelFlags_flag_interpolatedCenter',
164 'base_PixelFlags_flag_saturatedCenter',
165 'base_PixelFlags_flag_crCenter',
166 'base_PixelFlags_flag_bad',
167 'base_PixelFlags_flag_interpolated',
168 'base_PixelFlags_flag_saturated',
169 'slot_Centroid_flag',
170 'base_GaussianFlux_flag']
171
172 self.measure_ap_corrmeasure_ap_corr.sourceSelector['flagged'].field = 'final_psf_used'
173
174 import lsst.meas.modelfit # noqa: F401
175 import lsst.meas.extensions.photometryKron # noqa: F401
176 import lsst.meas.extensions.convolved # noqa: F401
177 import lsst.meas.extensions.gaap # noqa: F401
178 import lsst.meas.extensions.shapeHSM # noqa: F401
179
180 # Set up measurement defaults
181 self.measurementmeasurement.plugins.names = [
182 'base_PsfFlux',
183 'base_GaussianFlux',
184 'modelfit_DoubleShapeletPsfApprox',
185 'modelfit_CModel',
186 'ext_photometryKron_KronFlux',
187 'ext_convolved_ConvolvedFlux',
188 'ext_gaap_GaapFlux',
189 'ext_shapeHSM_HsmShapeRegauss',
190 'ext_shapeHSM_HsmSourceMoments',
191 'ext_shapeHSM_HsmPsfMoments',
192 'ext_shapeHSM_HsmSourceMomentsRound',
193 ]
194 self.measurementmeasurement.slots.modelFlux = 'modelfit_CModel'
195 self.measurementmeasurement.plugins['ext_convolved_ConvolvedFlux'].seeing.append(8.0)
196 self.measurementmeasurement.plugins['ext_gaap_GaapFlux'].sigmas = [
197 0.5,
198 0.7,
199 1.0,
200 1.5,
201 2.5,
202 3.0
203 ]
204 self.measurementmeasurement.plugins['ext_gaap_GaapFlux'].doPsfPhotometry = True
205 self.measurementmeasurement.slots.shape = 'ext_shapeHSM_HsmSourceMoments'
206 self.measurementmeasurement.slots.psfShape = 'exp_shapeHSM_HsmPsfMoments'
207 self.measurementmeasurement.plugins['ext_shapeHSM_HsmShapeRegauss'].deblendNChild = ""
208 # Turn off slot setting for measurement for centroid and shape
209 # (for which we use the input src catalog measurements)
210 self.measurementmeasurement.slots.centroid = None
211 self.measurementmeasurement.slots.apFlux = None
212 self.measurementmeasurement.slots.calibFlux = None
213
214 names = self.measurementmeasurement.plugins['ext_convolved_ConvolvedFlux'].getAllResultNames()
215 self.measure_ap_corrmeasure_ap_corr.allowFailure += names
216 names = self.measurementmeasurement.plugins["ext_gaap_GaapFlux"].getAllGaapResultNames()
217 self.measure_ap_corrmeasure_ap_corr.allowFailure += names
218
219
220class FinalizeCharacterizationTask(pipeBase.PipelineTask):
221 """Run final characterization on exposures."""
222 ConfigClass = FinalizeCharacterizationConfig
223 _DefaultName = 'finalize_characterization'
224
225 def __init__(self, initInputs=None, **kwargs):
226 super().__init__(initInputs=initInputs, **kwargs)
227
228 self.schema_mapper, self.schemaschema = self._make_output_schema_mapper_make_output_schema_mapper(
229 initInputs['src_schema'].schema
230 )
231
232 self.makeSubtask('reserve_selection')
233 self.makeSubtask('source_selector')
234 self.makeSubtask('make_psf_candidates')
235 self.makeSubtask('psf_determiner')
236 self.makeSubtask('measurement', schema=self.schemaschema)
237 self.makeSubtask('measure_ap_corr', schema=self.schemaschema)
238 self.makeSubtask('apply_ap_corr', schema=self.schemaschema)
239
240 # Only log warning and fatal errors from the source_selector
241 self.source_selector.log.setLevel(self.source_selector.log.WARN)
242
243 def runQuantum(self, butlerQC, inputRefs, outputRefs):
244 input_handle_dict = butlerQC.get(inputRefs)
245
246 band = butlerQC.quantum.dataId['band']
247 visit = butlerQC.quantum.dataId['visit']
248
249 src_dict_temp = {handle.dataId['detector']: handle
250 for handle in input_handle_dict['srcs']}
251 calexp_dict_temp = {handle.dataId['detector']: handle
252 for handle in input_handle_dict['calexps']}
253 isolated_star_cat_dict_temp = {handle.dataId['tract']: handle
254 for handle in input_handle_dict['isolated_star_cats']}
255 isolated_star_source_dict_temp = {handle.dataId['tract']: handle
256 for handle in input_handle_dict['isolated_star_sources']}
257 # TODO: Sort until DM-31701 is done and we have deterministic
258 # dataset ordering.
259 src_dict = {detector: src_dict_temp[detector] for
260 detector in sorted(src_dict_temp.keys())}
261 calexp_dict = {detector: calexp_dict_temp[detector] for
262 detector in sorted(calexp_dict_temp.keys())}
263 isolated_star_cat_dict = {tract: isolated_star_cat_dict_temp[tract] for
264 tract in sorted(isolated_star_cat_dict_temp.keys())}
265 isolated_star_source_dict = {tract: isolated_star_source_dict_temp[tract] for
266 tract in sorted(isolated_star_source_dict_temp.keys())}
267
268 struct = self.runrun(visit,
269 band,
270 isolated_star_cat_dict,
271 isolated_star_source_dict,
272 src_dict,
273 calexp_dict)
274
275 butlerQC.put(struct.psf_ap_corr_cat,
276 outputRefs.finalized_psf_ap_corr_cat)
277 butlerQC.put(pd.DataFrame(struct.output_table),
278 outputRefs.finalized_src_table)
279
280 def run(self, visit, band, isolated_star_cat_dict, isolated_star_source_dict, src_dict, calexp_dict):
281 """
282 Run the FinalizeCharacterizationTask.
283
284 Parameters
285 ----------
286 visit : `int`
287 Visit number. Used in the output catalogs.
288 band : `str`
289 Band name. Used to select reserved stars.
290 isolated_star_cat_dict : `dict`
291 Per-tract dict of isolated star catalog handles.
292 isolated_star_source_dict : `dict`
293 Per-tract dict of isolated star source catalog handles.
294 src_dict : `dict`
295 Per-detector dict of src catalog handles.
296 calexp_dict : `dict`
297 Per-detector dict of calibrated exposure handles.
298
299 Returns
300 -------
301 struct : `lsst.pipe.base.struct`
302 Struct with outputs for persistence.
303 """
304 # We do not need the isolated star table in this task.
305 # However, it is used in tests to confirm consistency of indexes.
306 _, isolated_source_table = self.concat_isolated_star_catsconcat_isolated_star_cats(
307 band,
308 isolated_star_cat_dict,
309 isolated_star_source_dict
310 )
311
312 exposure_cat_schema = afwTable.ExposureTable.makeMinimalSchema()
313 exposure_cat_schema.addField('visit', type='I', doc='Visit number')
314
315 metadata = dafBase.PropertyList()
316 metadata.add("COMMENT", "Catalog id is detector id, sorted.")
317 metadata.add("COMMENT", "Only detectors with data have entries.")
318
319 psf_ap_corr_cat = afwTable.ExposureCatalog(exposure_cat_schema)
320 psf_ap_corr_cat.setMetadata(metadata)
321
322 measured_src_tables = []
323
324 for detector in src_dict:
325 src = src_dict[detector].get()
326 exposure = calexp_dict[detector].get()
327
328 psf, ap_corr_map, measured_src = self.compute_psf_and_ap_corr_mapcompute_psf_and_ap_corr_map(
329 visit,
330 detector,
331 exposure,
332 src,
333 isolated_source_table
334 )
335
336 # And now we package it together...
337 record = psf_ap_corr_cat.addNew()
338 record['id'] = int(detector)
339 record['visit'] = visit
340 if psf is not None:
341 record.setPsf(psf)
342 if ap_corr_map is not None:
343 record.setApCorrMap(ap_corr_map)
344
345 measured_src['visit'][:] = visit
346 measured_src['detector'][:] = detector
347
348 measured_src_tables.append(measured_src.asAstropy().as_array())
349
350 measured_src_table = np.concatenate(measured_src_tables)
351
352 return pipeBase.Struct(psf_ap_corr_cat=psf_ap_corr_cat,
353 output_table=measured_src_table)
354
355 def _make_output_schema_mapper(self, input_schema):
356 """Make the schema mapper from the input schema to the output schema.
357
358 Parameters
359 ----------
360 input_schema : `lsst.afw.table.Schema`
361 Input schema.
362
363 Returns
364 -------
366 Schema mapper
367 output_schema : `lsst.afw.table.Schema`
368 Output schema (with alias map)
369 """
370 mapper = afwTable.SchemaMapper(input_schema)
371 mapper.addMinimalSchema(afwTable.SourceTable.makeMinimalSchema())
372 mapper.addMapping(input_schema['slot_Centroid_x'].asKey())
373 mapper.addMapping(input_schema['slot_Centroid_y'].asKey())
374
375 # The aperture fields may be used by the psf determiner.
376 aper_fields = input_schema.extract('base_CircularApertureFlux_*')
377 for field, item in aper_fields.items():
378 mapper.addMapping(item.key)
379
380 # The following two may be redundant, but then the mapping is a no-op.
381 apflux_fields = input_schema.extract('slot_ApFlux_*')
382 for field, item in apflux_fields.items():
383 mapper.addMapping(item.key)
384
385 calibflux_fields = input_schema.extract('slot_CalibFlux_*')
386 for field, item in calibflux_fields.items():
387 mapper.addMapping(item.key)
388
389 mapper.addMapping(
390 input_schema[self.config.source_selector.active.signalToNoise.fluxField].asKey(),
391 'final_psf_selection_flux')
392 mapper.addMapping(
393 input_schema[self.config.source_selector.active.signalToNoise.errField].asKey(),
394 'final_psf_selection_flux_err')
395
396 output_schema = mapper.getOutputSchema()
397
398 output_schema.addField(
399 'final_psf_candidate',
400 type='Flag',
401 doc=('set if the source was a candidate for PSF determination, '
402 'as determined from FinalizeCharacterizationTask.'),
403 )
404 output_schema.addField(
405 'final_psf_reserved',
406 type='Flag',
407 doc=('set if source was reserved from PSF determination by '
408 'FinalizeCharacterizationTask.'),
409 )
410 output_schema.addField(
411 'final_psf_used',
412 type='Flag',
413 doc=('set if source was used in the PSF determination by '
414 'FinalizeCharacterizationTask.'),
415 )
416 output_schema.addField(
417 'visit',
418 type=np.int32,
419 doc='Visit number for the sources.',
420 )
421 output_schema.addField(
422 'detector',
423 type=np.int32,
424 doc='Detector number for the sources.',
425 )
426
427 alias_map = input_schema.getAliasMap()
428 alias_map_output = afwTable.AliasMap()
429 alias_map_output.set('slot_Centroid', alias_map.get('slot_Centroid'))
430 alias_map_output.set('slot_ApFlux', alias_map.get('slot_ApFlux'))
431 alias_map_output.set('slot_CalibFlux', alias_map.get('slot_CalibFlux'))
432
433 output_schema.setAliasMap(alias_map_output)
434
435 return mapper, output_schema
436
437 def _make_selection_schema_mapper(self, input_schema):
438 """Make the schema mapper from the input schema to the selection schema.
439
440 Parameters
441 ----------
442 input_schema : `lsst.afw.table.Schema`
443 Input schema.
444
445 Returns
446 -------
448 Schema mapper
449 selection_schema : `lsst.afw.table.Schema`
450 Selection schema (with alias map)
451 """
452 mapper = afwTable.SchemaMapper(input_schema)
453 mapper.addMinimalSchema(input_schema)
454
455 selection_schema = mapper.getOutputSchema()
456
457 selection_schema.addField(
458 'final_psf_candidate',
459 type='Flag',
460 doc=('set if the source was a candidate for PSF determination, '
461 'as determined from FinalizeCharacterizationTask.'),
462 )
463 selection_schema.addField(
464 'final_psf_reserved',
465 type='Flag',
466 doc=('set if source was reserved from PSF determination by '
467 'FinalizeCharacterizationTask.'),
468 )
469 selection_schema.addField(
470 'final_psf_used',
471 type='Flag',
472 doc=('set if source was used in the PSF determination by '
473 'FinalizeCharacterizationTask.'),
474 )
475
476 selection_schema.setAliasMap(input_schema.getAliasMap())
477
478 return mapper, selection_schema
479
480 def concat_isolated_star_cats(self, band, isolated_star_cat_dict, isolated_star_source_dict):
481 """
482 Concatenate isolated star catalogs and make reserve selection.
483
484 Parameters
485 ----------
486 band : `str`
487 Band name. Used to select reserved stars.
488 isolated_star_cat_dict : `dict`
489 Per-tract dict of isolated star catalog handles.
490 isolated_star_source_dict : `dict`
491 Per-tract dict of isolated star source catalog handles.
492
493 Returns
494 -------
495 isolated_table : `np.ndarray` (N,)
496 Table of isolated stars, with indexes to isolated sources.
497 isolated_source_table : `np.ndarray` (M,)
498 Table of isolated sources, with indexes to isolated stars.
499 """
500 isolated_tables = []
501 isolated_sources = []
502 merge_cat_counter = 0
503 merge_source_counter = 0
504
505 for tract in isolated_star_cat_dict:
506 df_cat = isolated_star_cat_dict[tract].get()
507 table_cat = df_cat.to_records()
508
509 df_source = isolated_star_source_dict[tract].get(
510 parameters={'columns': [self.config.id_column,
511 'obj_index']}
512 )
513 table_source = df_source.to_records()
514
515 # Cut isolated star table to those observed in this band, and adjust indexes
516 (use_band,) = (table_cat[f'nsource_{band}'] > 0).nonzero()
517
518 # With the following matching:
519 # table_source[b] <-> table_cat[use_band[a]]
520 obj_index = table_source['obj_index'][:]
521 a, b = esutil.numpy_util.match(use_band, obj_index)
522
523 # Update indexes and cut to band-selected stars/sources
524 table_source['obj_index'][b] = a
525 _, index_new = np.unique(a, return_index=True)
526 table_cat[f'source_cat_index_{band}'][use_band] = index_new
527
528 # After the following cuts, the catalogs have the following properties:
529 # - table_cat only contains isolated stars that have at least one source
530 # in ``band``.
531 # - table_source only contains ``band`` sources.
532 # - The slice table_cat["source_cat_index_{band}"]: table_cat["source_cat_index_{band}"]
533 # + table_cat["nsource_{band}]
534 # applied to table_source will give all the sources associated with the star.
535 # - For each source, table_source["obj_index"] points to the index of the associated
536 # isolated star.
537 table_source = table_source[b]
538 table_cat = table_cat[use_band]
539
540 # Add reserved flag column to tables
541 table_cat = np.lib.recfunctions.append_fields(
542 table_cat,
543 'reserved',
544 np.zeros(table_cat.size, dtype=bool),
545 usemask=False
546 )
547 table_source = np.lib.recfunctions.append_fields(
548 table_source,
549 'reserved',
550 np.zeros(table_source.size, dtype=bool),
551 usemask=False
552 )
553
554 # Get reserve star flags
555 table_cat['reserved'][:] = self.reserve_selection.run(
556 len(table_cat),
557 extra=f'{band}_{tract}',
558 )
559 table_source['reserved'][:] = table_cat['reserved'][table_source['obj_index']]
560
561 # Offset indexes to account for tract merging
562 table_cat[f'source_cat_index_{band}'] += merge_source_counter
563 table_source['obj_index'] += merge_cat_counter
564
565 isolated_tables.append(table_cat)
566 isolated_sources.append(table_source)
567
568 merge_cat_counter += len(table_cat)
569 merge_source_counter += len(table_source)
570
571 isolated_table = np.concatenate(isolated_tables)
572 isolated_source_table = np.concatenate(isolated_sources)
573
574 return isolated_table, isolated_source_table
575
576 def compute_psf_and_ap_corr_map(self, visit, detector, exposure, src, isolated_source_table):
577 """Compute psf model and aperture correction map for a single exposure.
578
579 Parameters
580 ----------
581 visit : `int`
582 Visit number (for logging).
583 detector : `int`
584 Detector number (for logging).
585 exposure : `lsst.afw.image.ExposureF`
587 isolated_source_table : `np.ndarray`
588
589 Returns
590 -------
592 PSF Model
593 ap_corr_map : `lsst.afw.image.ApCorrMap`
594 Aperture correction map.
595 measured_src : `lsst.afw.table.SourceCatalog`
596 Updated source catalog with measurements, flags and aperture corrections.
597 """
598 # Apply source selector (s/n, flags, etc.)
599 good_src = self.source_selector.selectSources(src)
600
601 # Cut down input src to the selected sources
602 # We use a separate schema/mapper here than for the output/measurement catalog because of
603 # clashes between fields that were previously run and those that need to be rerun with
604 # the new psf model. This may be slightly inefficient but keeps input
605 # and output values cleanly separated.
606 selection_mapper, selection_schema = self._make_selection_schema_mapper_make_selection_schema_mapper(src.schema)
607
608 selected_src = afwTable.SourceCatalog(selection_schema)
609 selected_src.reserve(good_src.selected.sum())
610 selected_src.extend(src[good_src.selected], mapper=selection_mapper)
611
612 # Find the isolated sources and set flags
613 matched_src, matched_iso = esutil.numpy_util.match(
614 selected_src['id'],
615 isolated_source_table[self.config.id_column]
616 )
617
618 matched_arr = np.zeros(len(selected_src), dtype=bool)
619 matched_arr[matched_src] = True
620 selected_src['final_psf_candidate'] = matched_arr
621
622 reserved_arr = np.zeros(len(selected_src), dtype=bool)
623 reserved_arr[matched_src] = isolated_source_table['reserved'][matched_iso]
624 selected_src['final_psf_reserved'] = reserved_arr
625
626 selected_src = selected_src[selected_src['final_psf_candidate']].copy(deep=True)
627
628 # Make the measured source catalog as well, based on the selected catalog.
629 measured_src = afwTable.SourceCatalog(self.schemaschema)
630 measured_src.reserve(len(selected_src))
631 measured_src.extend(selected_src, mapper=self.schema_mapper)
632
633 # We need to copy over the final_psf flags because they were not in the mapper
634 measured_src['final_psf_candidate'] = selected_src['final_psf_candidate']
635 measured_src['final_psf_reserved'] = selected_src['final_psf_reserved']
636
637 # Select the psf candidates from the selection catalog
638 try:
639 psf_selection_result = self.make_psf_candidates.run(selected_src, exposure=exposure)
640 except Exception as e:
641 self.log.warning('Failed to make psf candidates for visit %d, detector %d: %s',
642 visit, detector, e)
643 return None, None, measured_src
644
645 psf_cand_cat = psf_selection_result.goodStarCat
646
647 # Make list of psf candidates to send to the determiner
648 # (omitting those marked as reserved)
649 psf_determiner_list = [cand for cand, use
650 in zip(psf_selection_result.psfCandidates,
651 ~psf_cand_cat['final_psf_reserved']) if use]
652 flag_key = psf_cand_cat.schema['final_psf_used'].asKey()
653 try:
654 psf, cell_set = self.psf_determiner.determinePsf(exposure,
655 psf_determiner_list,
656 self.metadata,
657 flagKey=flag_key)
658 except Exception as e:
659 self.log.warning('Failed to determine psf for visit %d, detector %d: %s',
660 visit, detector, e)
661 return None, None, measured_src
662
663 # At this point, we need to transfer the psf used flag from the selection
664 # catalog to the measurement catalog.
665 matched_selected, matched_measured = esutil.numpy_util.match(
666 selected_src['id'],
667 measured_src['id']
668 )
669 measured_used = np.zeros(len(measured_src), dtype=bool)
670 measured_used[matched_measured] = selected_src['final_psf_used'][matched_selected]
671 measured_src['final_psf_used'] = measured_used
672
673 # Next, we do the measurement on all the psf candidate, used, and reserved stars.
674 try:
675 self.measurement.run(measCat=measured_src, exposure=exposure)
676 except Exception as e:
677 self.log.warning('Failed to make measurements for visit %d, detector %d: %s',
678 visit, detector, e)
679 return psf, None, measured_src
680
681 # And finally the ap corr map.
682 try:
683 ap_corr_map = self.measure_ap_corr.run(exposure=exposure,
684 catalog=measured_src).apCorrMap
685 except Exception as e:
686 self.log.warning('Failed to compute aperture corrections for visit %d, detector %d: %s',
687 visit, detector, e)
688 return psf, None, measured_src
689
690 self.apply_ap_corr.run(catalog=measured_src, apCorrMap=ap_corr_map)
691
692 return psf, ap_corr_map, measured_src
def compute_psf_and_ap_corr_map(self, visit, detector, exposure, src, isolated_source_table)
def concat_isolated_star_cats(self, band, isolated_star_cat_dict, isolated_star_source_dict)
def run(self, visit, band, isolated_star_cat_dict, isolated_star_source_dict, src_dict, calexp_dict)