lsst.pipe.tasks gb1d6de0934+c320316d7a
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 = 'calib_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='L', 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 'calib_psf_selection_flux')
392 mapper.addMapping(
393 input_schema[self.config.source_selector.active.signalToNoise.errField].asKey(),
394 'calib_psf_selection_flux_err')
395
396 output_schema = mapper.getOutputSchema()
397
398 output_schema.addField(
399 'calib_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 'calib_psf_reserved',
406 type='Flag',
407 doc=('set if source was reserved from PSF determination by '
408 'FinalizeCharacterizationTask.'),
409 )
410 output_schema.addField(
411 'calib_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.int64,
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.setAliasMap(input_schema.getAliasMap())
458
459 return mapper, selection_schema
460
461 def concat_isolated_star_cats(self, band, isolated_star_cat_dict, isolated_star_source_dict):
462 """
463 Concatenate isolated star catalogs and make reserve selection.
464
465 Parameters
466 ----------
467 band : `str`
468 Band name. Used to select reserved stars.
469 isolated_star_cat_dict : `dict`
470 Per-tract dict of isolated star catalog handles.
471 isolated_star_source_dict : `dict`
472 Per-tract dict of isolated star source catalog handles.
473
474 Returns
475 -------
476 isolated_table : `np.ndarray` (N,)
477 Table of isolated stars, with indexes to isolated sources.
478 isolated_source_table : `np.ndarray` (M,)
479 Table of isolated sources, with indexes to isolated stars.
480 """
481 isolated_tables = []
482 isolated_sources = []
483 merge_cat_counter = 0
484 merge_source_counter = 0
485
486 for tract in isolated_star_cat_dict:
487 df_cat = isolated_star_cat_dict[tract].get()
488 table_cat = df_cat.to_records()
489
490 df_source = isolated_star_source_dict[tract].get(
491 parameters={'columns': [self.config.id_column,
492 'obj_index']}
493 )
494 table_source = df_source.to_records()
495
496 # Cut isolated star table to those observed in this band, and adjust indexes
497 (use_band,) = (table_cat[f'nsource_{band}'] > 0).nonzero()
498
499 # With the following matching:
500 # table_source[b] <-> table_cat[use_band[a]]
501 obj_index = table_source['obj_index'][:]
502 a, b = esutil.numpy_util.match(use_band, obj_index)
503
504 # Update indexes and cut to band-selected stars/sources
505 table_source['obj_index'][b] = a
506 _, index_new = np.unique(a, return_index=True)
507 table_cat[f'source_cat_index_{band}'][use_band] = index_new
508
509 # After the following cuts, the catalogs have the following properties:
510 # - table_cat only contains isolated stars that have at least one source
511 # in ``band``.
512 # - table_source only contains ``band`` sources.
513 # - The slice table_cat["source_cat_index_{band}"]: table_cat["source_cat_index_{band}"]
514 # + table_cat["nsource_{band}]
515 # applied to table_source will give all the sources associated with the star.
516 # - For each source, table_source["obj_index"] points to the index of the associated
517 # isolated star.
518 table_source = table_source[b]
519 table_cat = table_cat[use_band]
520
521 # Add reserved flag column to tables
522 table_cat = np.lib.recfunctions.append_fields(
523 table_cat,
524 'reserved',
525 np.zeros(table_cat.size, dtype=bool),
526 usemask=False
527 )
528 table_source = np.lib.recfunctions.append_fields(
529 table_source,
530 'reserved',
531 np.zeros(table_source.size, dtype=bool),
532 usemask=False
533 )
534
535 # Get reserve star flags
536 table_cat['reserved'][:] = self.reserve_selection.run(
537 len(table_cat),
538 extra=f'{band}_{tract}',
539 )
540 table_source['reserved'][:] = table_cat['reserved'][table_source['obj_index']]
541
542 # Offset indexes to account for tract merging
543 table_cat[f'source_cat_index_{band}'] += merge_source_counter
544 table_source['obj_index'] += merge_cat_counter
545
546 isolated_tables.append(table_cat)
547 isolated_sources.append(table_source)
548
549 merge_cat_counter += len(table_cat)
550 merge_source_counter += len(table_source)
551
552 isolated_table = np.concatenate(isolated_tables)
553 isolated_source_table = np.concatenate(isolated_sources)
554
555 return isolated_table, isolated_source_table
556
557 def compute_psf_and_ap_corr_map(self, visit, detector, exposure, src, isolated_source_table):
558 """Compute psf model and aperture correction map for a single exposure.
559
560 Parameters
561 ----------
562 visit : `int`
563 Visit number (for logging).
564 detector : `int`
565 Detector number (for logging).
566 exposure : `lsst.afw.image.ExposureF`
568 isolated_source_table : `np.ndarray`
569
570 Returns
571 -------
573 PSF Model
574 ap_corr_map : `lsst.afw.image.ApCorrMap`
575 Aperture correction map.
576 measured_src : `lsst.afw.table.SourceCatalog`
577 Updated source catalog with measurements, flags and aperture corrections.
578 """
579 # Apply source selector (s/n, flags, etc.)
580 good_src = self.source_selector.selectSources(src)
581
582 # Cut down input src to the selected sources
583 # We use a separate schema/mapper here than for the output/measurement catalog because of
584 # clashes between fields that were previously run and those that need to be rerun with
585 # the new psf model. This may be slightly inefficient but keeps input
586 # and output values cleanly separated.
587 selection_mapper, selection_schema = self._make_selection_schema_mapper_make_selection_schema_mapper(src.schema)
588
589 selected_src = afwTable.SourceCatalog(selection_schema)
590 selected_src.reserve(good_src.selected.sum())
591 selected_src.extend(src[good_src.selected], mapper=selection_mapper)
592
593 # The calib flags have been copied from the input table,
594 # and we reset them here just to ensure they aren't propagated.
595 selected_src['calib_psf_candidate'] = np.zeros(len(selected_src), dtype=bool)
596 selected_src['calib_psf_used'] = np.zeros(len(selected_src), dtype=bool)
597 selected_src['calib_psf_reserved'] = np.zeros(len(selected_src), dtype=bool)
598
599 # Find the isolated sources and set flags
600 matched_src, matched_iso = esutil.numpy_util.match(
601 selected_src['id'],
602 isolated_source_table[self.config.id_column]
603 )
604
605 matched_arr = np.zeros(len(selected_src), dtype=bool)
606 matched_arr[matched_src] = True
607 selected_src['calib_psf_candidate'] = matched_arr
608
609 reserved_arr = np.zeros(len(selected_src), dtype=bool)
610 reserved_arr[matched_src] = isolated_source_table['reserved'][matched_iso]
611 selected_src['calib_psf_reserved'] = reserved_arr
612
613 selected_src = selected_src[selected_src['calib_psf_candidate']].copy(deep=True)
614
615 # Make the measured source catalog as well, based on the selected catalog.
616 measured_src = afwTable.SourceCatalog(self.schemaschema)
617 measured_src.reserve(len(selected_src))
618 measured_src.extend(selected_src, mapper=self.schema_mapper)
619
620 # We need to copy over the calib_psf flags because they were not in the mapper
621 measured_src['calib_psf_candidate'] = selected_src['calib_psf_candidate']
622 measured_src['calib_psf_reserved'] = selected_src['calib_psf_reserved']
623
624 # Select the psf candidates from the selection catalog
625 try:
626 psf_selection_result = self.make_psf_candidates.run(selected_src, exposure=exposure)
627 except Exception as e:
628 self.log.warning('Failed to make psf candidates for visit %d, detector %d: %s',
629 visit, detector, e)
630 return None, None, measured_src
631
632 psf_cand_cat = psf_selection_result.goodStarCat
633
634 # Make list of psf candidates to send to the determiner
635 # (omitting those marked as reserved)
636 psf_determiner_list = [cand for cand, use
637 in zip(psf_selection_result.psfCandidates,
638 ~psf_cand_cat['calib_psf_reserved']) if use]
639 flag_key = psf_cand_cat.schema['calib_psf_used'].asKey()
640 try:
641 psf, cell_set = self.psf_determiner.determinePsf(exposure,
642 psf_determiner_list,
643 self.metadata,
644 flagKey=flag_key)
645 except Exception as e:
646 self.log.warning('Failed to determine psf for visit %d, detector %d: %s',
647 visit, detector, e)
648 return None, None, measured_src
649
650 # At this point, we need to transfer the psf used flag from the selection
651 # catalog to the measurement catalog.
652 matched_selected, matched_measured = esutil.numpy_util.match(
653 selected_src['id'],
654 measured_src['id']
655 )
656 measured_used = np.zeros(len(measured_src), dtype=bool)
657 measured_used[matched_measured] = selected_src['calib_psf_used'][matched_selected]
658 measured_src['calib_psf_used'] = measured_used
659
660 # Next, we do the measurement on all the psf candidate, used, and reserved stars.
661 try:
662 self.measurement.run(measCat=measured_src, exposure=exposure)
663 except Exception as e:
664 self.log.warning('Failed to make measurements for visit %d, detector %d: %s',
665 visit, detector, e)
666 return psf, None, measured_src
667
668 # And finally the ap corr map.
669 try:
670 ap_corr_map = self.measure_ap_corr.run(exposure=exposure,
671 catalog=measured_src).apCorrMap
672 except Exception as e:
673 self.log.warning('Failed to compute aperture corrections for visit %d, detector %d: %s',
674 visit, detector, e)
675 return psf, None, measured_src
676
677 self.apply_ap_corr.run(catalog=measured_src, apCorrMap=ap_corr_map)
678
679 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)