Coverage for python/lsst/pipe/tasks/finalizeCharacterization.py: 17%
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1#
2# LSST Data Management System
3# Copyright 2008-2022 AURA/LSST.
4#
5# This product includes software developed by the
6# LSST Project (http://www.lsst.org/).
7#
8# This program is free software: you can redistribute it and/or modify
9# it under the terms of the GNU General Public License as published by
10# the Free Software Foundation, either version 3 of the License, or
11# (at your option) any later version.
12#
13# This program is distributed in the hope that it will be useful,
14# but WITHOUT ANY WARRANTY; without even the implied warranty of
15# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
16# GNU General Public License for more details.
17#
18# You should have received a copy of the LSST License Statement and
19# the GNU General Public License along with this program. If not,
20# see <http://www.lsstcorp.org/LegalNotices/>.
21#
22"""Task to run a finalized image characterization, using additional data.
23"""
24import numpy as np
25import esutil
26import pandas as pd
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
38from .reserveIsolatedStars import ReserveIsolatedStarsTask
40__all__ = ['FinalizeCharacterizationConnections',
41 'FinalizeCharacterizationConfig',
42 'FinalizeCharacterizationTask']
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 )
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 )
140 def setDefaults(self):
141 super().setDefaults()
143 source_selector = self.source_selector['science']
144 source_selector.setDefaults()
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.
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
156 source_selector.signalToNoise.minimum = 20.0
157 source_selector.signalToNoise.maximum = 1000.0
159 source_selector.signalToNoise.fluxField = 'base_GaussianFlux_instFlux'
160 source_selector.signalToNoise.errField = 'base_GaussianFlux_instFluxErr'
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']
172 self.measure_ap_corr.sourceSelector['flagged'].field = 'calib_psf_used'
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
180 # Set up measurement defaults
181 self.measurement.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.measurement.slots.modelFlux = 'modelfit_CModel'
195 self.measurement.plugins['ext_convolved_ConvolvedFlux'].seeing.append(8.0)
196 self.measurement.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.measurement.plugins['ext_gaap_GaapFlux'].doPsfPhotometry = True
205 self.measurement.slots.shape = 'ext_shapeHSM_HsmSourceMoments'
206 self.measurement.slots.psfShape = 'exp_shapeHSM_HsmPsfMoments'
207 self.measurement.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.measurement.slots.centroid = None
211 self.measurement.slots.apFlux = None
212 self.measurement.slots.calibFlux = None
214 names = self.measurement.plugins['ext_convolved_ConvolvedFlux'].getAllResultNames()
215 self.measure_ap_corr.allowFailure += names
216 names = self.measurement.plugins["ext_gaap_GaapFlux"].getAllGaapResultNames()
217 self.measure_ap_corr.allowFailure += names
220class FinalizeCharacterizationTask(pipeBase.PipelineTask):
221 """Run final characterization on exposures."""
222 ConfigClass = FinalizeCharacterizationConfig
223 _DefaultName = 'finalize_characterization'
225 def __init__(self, initInputs=None, **kwargs):
226 super().__init__(initInputs=initInputs, **kwargs)
228 self.schema_mapper, self.schema = self._make_output_schema_mapper(
229 initInputs['src_schema'].schema
230 )
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.schema)
237 self.makeSubtask('measure_ap_corr', schema=self.schema)
238 self.makeSubtask('apply_ap_corr', schema=self.schema)
240 # Only log warning and fatal errors from the source_selector
241 self.source_selector.log.setLevel(self.source_selector.log.WARN)
243 def runQuantum(self, butlerQC, inputRefs, outputRefs):
244 input_handle_dict = butlerQC.get(inputRefs)
246 band = butlerQC.quantum.dataId['band']
247 visit = butlerQC.quantum.dataId['visit']
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())}
268 struct = self.run(visit,
269 band,
270 isolated_star_cat_dict,
271 isolated_star_source_dict,
272 src_dict,
273 calexp_dict)
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)
280 def run(self, visit, band, isolated_star_cat_dict, isolated_star_source_dict, src_dict, calexp_dict):
281 """
282 Run the FinalizeCharacterizationTask.
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.
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_cats(
307 band,
308 isolated_star_cat_dict,
309 isolated_star_source_dict
310 )
312 exposure_cat_schema = afwTable.ExposureTable.makeMinimalSchema()
313 exposure_cat_schema.addField('visit', type='L', doc='Visit number')
315 metadata = dafBase.PropertyList()
316 metadata.add("COMMENT", "Catalog id is detector id, sorted.")
317 metadata.add("COMMENT", "Only detectors with data have entries.")
319 psf_ap_corr_cat = afwTable.ExposureCatalog(exposure_cat_schema)
320 psf_ap_corr_cat.setMetadata(metadata)
322 measured_src_tables = []
324 for detector in src_dict:
325 src = src_dict[detector].get()
326 exposure = calexp_dict[detector].get()
328 psf, ap_corr_map, measured_src = self.compute_psf_and_ap_corr_map(
329 visit,
330 detector,
331 exposure,
332 src,
333 isolated_source_table
334 )
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)
345 measured_src['visit'][:] = visit
346 measured_src['detector'][:] = detector
348 measured_src_tables.append(measured_src.asAstropy().as_array())
350 measured_src_table = np.concatenate(measured_src_tables)
352 return pipeBase.Struct(psf_ap_corr_cat=psf_ap_corr_cat,
353 output_table=measured_src_table)
355 def _make_output_schema_mapper(self, input_schema):
356 """Make the schema mapper from the input schema to the output schema.
358 Parameters
359 ----------
360 input_schema : `lsst.afw.table.Schema`
361 Input schema.
363 Returns
364 -------
365 mapper : `lsst.afw.table.SchemaMapper`
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())
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)
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)
385 calibflux_fields = input_schema.extract('slot_CalibFlux_*')
386 for field, item in calibflux_fields.items():
387 mapper.addMapping(item.key)
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')
396 output_schema = mapper.getOutputSchema()
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 )
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'))
433 output_schema.setAliasMap(alias_map_output)
435 return mapper, output_schema
437 def _make_selection_schema_mapper(self, input_schema):
438 """Make the schema mapper from the input schema to the selection schema.
440 Parameters
441 ----------
442 input_schema : `lsst.afw.table.Schema`
443 Input schema.
445 Returns
446 -------
447 mapper : `lsst.afw.table.SchemaMapper`
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)
455 selection_schema = mapper.getOutputSchema()
457 selection_schema.setAliasMap(input_schema.getAliasMap())
459 return mapper, selection_schema
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.
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.
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
486 for tract in isolated_star_cat_dict:
487 df_cat = isolated_star_cat_dict[tract].get()
488 table_cat = df_cat.to_records()
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()
496 # Cut isolated star table to those observed in this band, and adjust indexes
497 (use_band,) = (table_cat[f'nsource_{band}'] > 0).nonzero()
499 if len(use_band) == 0:
500 # There are no sources in this band in this tract.
501 self.log.info("No sources found in %s band in tract %d.", band, tract)
502 continue
504 # With the following matching:
505 # table_source[b] <-> table_cat[use_band[a]]
506 obj_index = table_source['obj_index'][:]
507 a, b = esutil.numpy_util.match(use_band, obj_index)
509 # Update indexes and cut to band-selected stars/sources
510 table_source['obj_index'][b] = a
511 _, index_new = np.unique(a, return_index=True)
512 table_cat[f'source_cat_index_{band}'][use_band] = index_new
514 # After the following cuts, the catalogs have the following properties:
515 # - table_cat only contains isolated stars that have at least one source
516 # in ``band``.
517 # - table_source only contains ``band`` sources.
518 # - The slice table_cat["source_cat_index_{band}"]: table_cat["source_cat_index_{band}"]
519 # + table_cat["nsource_{band}]
520 # applied to table_source will give all the sources associated with the star.
521 # - For each source, table_source["obj_index"] points to the index of the associated
522 # isolated star.
523 table_source = table_source[b]
524 table_cat = table_cat[use_band]
526 # Add reserved flag column to tables
527 table_cat = np.lib.recfunctions.append_fields(
528 table_cat,
529 'reserved',
530 np.zeros(table_cat.size, dtype=bool),
531 usemask=False
532 )
533 table_source = np.lib.recfunctions.append_fields(
534 table_source,
535 'reserved',
536 np.zeros(table_source.size, dtype=bool),
537 usemask=False
538 )
540 # Get reserve star flags
541 table_cat['reserved'][:] = self.reserve_selection.run(
542 len(table_cat),
543 extra=f'{band}_{tract}',
544 )
545 table_source['reserved'][:] = table_cat['reserved'][table_source['obj_index']]
547 # Offset indexes to account for tract merging
548 table_cat[f'source_cat_index_{band}'] += merge_source_counter
549 table_source['obj_index'] += merge_cat_counter
551 isolated_tables.append(table_cat)
552 isolated_sources.append(table_source)
554 merge_cat_counter += len(table_cat)
555 merge_source_counter += len(table_source)
557 isolated_table = np.concatenate(isolated_tables)
558 isolated_source_table = np.concatenate(isolated_sources)
560 return isolated_table, isolated_source_table
562 def compute_psf_and_ap_corr_map(self, visit, detector, exposure, src, isolated_source_table):
563 """Compute psf model and aperture correction map for a single exposure.
565 Parameters
566 ----------
567 visit : `int`
568 Visit number (for logging).
569 detector : `int`
570 Detector number (for logging).
571 exposure : `lsst.afw.image.ExposureF`
572 src : `lsst.afw.table.SourceCatalog`
573 isolated_source_table : `np.ndarray`
575 Returns
576 -------
577 psf : `lsst.meas.algorithms.ImagePsf`
578 PSF Model
579 ap_corr_map : `lsst.afw.image.ApCorrMap`
580 Aperture correction map.
581 measured_src : `lsst.afw.table.SourceCatalog`
582 Updated source catalog with measurements, flags and aperture corrections.
583 """
584 # Apply source selector (s/n, flags, etc.)
585 good_src = self.source_selector.selectSources(src)
587 # Cut down input src to the selected sources
588 # We use a separate schema/mapper here than for the output/measurement catalog because of
589 # clashes between fields that were previously run and those that need to be rerun with
590 # the new psf model. This may be slightly inefficient but keeps input
591 # and output values cleanly separated.
592 selection_mapper, selection_schema = self._make_selection_schema_mapper(src.schema)
594 selected_src = afwTable.SourceCatalog(selection_schema)
595 selected_src.reserve(good_src.selected.sum())
596 selected_src.extend(src[good_src.selected], mapper=selection_mapper)
598 # The calib flags have been copied from the input table,
599 # and we reset them here just to ensure they aren't propagated.
600 selected_src['calib_psf_candidate'] = np.zeros(len(selected_src), dtype=bool)
601 selected_src['calib_psf_used'] = np.zeros(len(selected_src), dtype=bool)
602 selected_src['calib_psf_reserved'] = np.zeros(len(selected_src), dtype=bool)
604 # Find the isolated sources and set flags
605 matched_src, matched_iso = esutil.numpy_util.match(
606 selected_src['id'],
607 isolated_source_table[self.config.id_column]
608 )
610 matched_arr = np.zeros(len(selected_src), dtype=bool)
611 matched_arr[matched_src] = True
612 selected_src['calib_psf_candidate'] = matched_arr
614 reserved_arr = np.zeros(len(selected_src), dtype=bool)
615 reserved_arr[matched_src] = isolated_source_table['reserved'][matched_iso]
616 selected_src['calib_psf_reserved'] = reserved_arr
618 selected_src = selected_src[selected_src['calib_psf_candidate']].copy(deep=True)
620 # Make the measured source catalog as well, based on the selected catalog.
621 measured_src = afwTable.SourceCatalog(self.schema)
622 measured_src.reserve(len(selected_src))
623 measured_src.extend(selected_src, mapper=self.schema_mapper)
625 # We need to copy over the calib_psf flags because they were not in the mapper
626 measured_src['calib_psf_candidate'] = selected_src['calib_psf_candidate']
627 measured_src['calib_psf_reserved'] = selected_src['calib_psf_reserved']
629 # Select the psf candidates from the selection catalog
630 try:
631 psf_selection_result = self.make_psf_candidates.run(selected_src, exposure=exposure)
632 except Exception as e:
633 self.log.warning('Failed to make psf candidates for visit %d, detector %d: %s',
634 visit, detector, e)
635 return None, None, measured_src
637 psf_cand_cat = psf_selection_result.goodStarCat
639 # Make list of psf candidates to send to the determiner
640 # (omitting those marked as reserved)
641 psf_determiner_list = [cand for cand, use
642 in zip(psf_selection_result.psfCandidates,
643 ~psf_cand_cat['calib_psf_reserved']) if use]
644 flag_key = psf_cand_cat.schema['calib_psf_used'].asKey()
645 try:
646 psf, cell_set = self.psf_determiner.determinePsf(exposure,
647 psf_determiner_list,
648 self.metadata,
649 flagKey=flag_key)
650 except Exception as e:
651 self.log.warning('Failed to determine psf for visit %d, detector %d: %s',
652 visit, detector, e)
653 return None, None, measured_src
655 # At this point, we need to transfer the psf used flag from the selection
656 # catalog to the measurement catalog.
657 matched_selected, matched_measured = esutil.numpy_util.match(
658 selected_src['id'],
659 measured_src['id']
660 )
661 measured_used = np.zeros(len(measured_src), dtype=bool)
662 measured_used[matched_measured] = selected_src['calib_psf_used'][matched_selected]
663 measured_src['calib_psf_used'] = measured_used
665 # Next, we do the measurement on all the psf candidate, used, and reserved stars.
666 try:
667 self.measurement.run(measCat=measured_src, exposure=exposure)
668 except Exception as e:
669 self.log.warning('Failed to make measurements for visit %d, detector %d: %s',
670 visit, detector, e)
671 return psf, None, measured_src
673 # And finally the ap corr map.
674 try:
675 ap_corr_map = self.measure_ap_corr.run(exposure=exposure,
676 catalog=measured_src).apCorrMap
677 except Exception as e:
678 self.log.warning('Failed to compute aperture corrections for visit %d, detector %d: %s',
679 visit, detector, e)
680 return psf, None, measured_src
682 self.apply_ap_corr.run(catalog=measured_src, apCorrMap=ap_corr_map)
684 return psf, ap_corr_map, measured_src