Coverage for python/lsst/pipe/tasks/isolatedStarAssociation.py: 14%
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« prev ^ index » next coverage.py v7.2.3, created at 2023-04-22 03:00 -0700
1# This file is part of pipe_tasks.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <https://www.gnu.org/licenses/>.
22__all__ = ['IsolatedStarAssociationConnections',
23 'IsolatedStarAssociationConfig',
24 'IsolatedStarAssociationTask']
26import numpy as np
27import pandas as pd
28from smatch.matcher import Matcher
30import lsst.pex.config as pexConfig
31import lsst.pipe.base as pipeBase
32from lsst.skymap import BaseSkyMap
33from lsst.meas.algorithms.sourceSelector import sourceSelectorRegistry
36class IsolatedStarAssociationConnections(pipeBase.PipelineTaskConnections,
37 dimensions=('instrument', 'tract', 'skymap',),
38 defaultTemplates={}):
39 source_table_visit = pipeBase.connectionTypes.Input(
40 doc='Source table in parquet format, per visit',
41 name='sourceTable_visit',
42 storageClass='DataFrame',
43 dimensions=('instrument', 'visit'),
44 deferLoad=True,
45 multiple=True,
46 )
47 skymap = pipeBase.connectionTypes.Input(
48 doc="Input definition of geometry/bbox and projection/wcs for warped exposures",
49 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
50 storageClass='SkyMap',
51 dimensions=('skymap',),
52 )
53 isolated_star_sources = pipeBase.connectionTypes.Output(
54 doc='Catalog of individual sources for the isolated stars',
55 name='isolated_star_sources',
56 storageClass='DataFrame',
57 dimensions=('instrument', 'tract', 'skymap'),
58 )
59 isolated_star_cat = pipeBase.connectionTypes.Output(
60 doc='Catalog of isolated star positions',
61 name='isolated_star_cat',
62 storageClass='DataFrame',
63 dimensions=('instrument', 'tract', 'skymap'),
64 )
67class IsolatedStarAssociationConfig(pipeBase.PipelineTaskConfig,
68 pipelineConnections=IsolatedStarAssociationConnections):
69 """Configuration for IsolatedStarAssociationTask."""
71 inst_flux_field = pexConfig.Field(
72 doc=('Full name of instFlux field to use for s/n selection and persistence. '
73 'The associated flag will be implicity included in bad_flags. '
74 'Note that this is expected to end in ``instFlux``.'),
75 dtype=str,
76 default='apFlux_12_0_instFlux',
77 )
78 match_radius = pexConfig.Field(
79 doc='Match radius (arcseconds)',
80 dtype=float,
81 default=1.0,
82 )
83 isolation_radius = pexConfig.Field(
84 doc=('Isolation radius (arcseconds). Any stars with average centroids '
85 'within this radius of another star will be rejected from the final '
86 'catalog. This radius should be at least 2x match_radius.'),
87 dtype=float,
88 default=2.0,
89 )
90 band_order = pexConfig.ListField(
91 doc=(('Ordered list of bands to use for matching/storage. '
92 'Any bands not listed will not be matched.')),
93 dtype=str,
94 default=['i', 'z', 'r', 'g', 'y', 'u'],
95 )
96 id_column = pexConfig.Field(
97 doc='Name of column with source id.',
98 dtype=str,
99 default='sourceId',
100 )
101 ra_column = pexConfig.Field(
102 doc='Name of column with right ascension.',
103 dtype=str,
104 default='ra',
105 )
106 dec_column = pexConfig.Field(
107 doc='Name of column with declination.',
108 dtype=str,
109 default='decl',
110 )
111 physical_filter_column = pexConfig.Field(
112 doc='Name of column with physical filter name',
113 dtype=str,
114 default='physical_filter',
115 )
116 band_column = pexConfig.Field(
117 doc='Name of column with band name',
118 dtype=str,
119 default='band',
120 )
121 extra_columns = pexConfig.ListField(
122 doc='Extra names of columns to read and persist (beyond instFlux and error).',
123 dtype=str,
124 default=['x',
125 'y',
126 'apFlux_17_0_instFlux',
127 'apFlux_17_0_instFluxErr',
128 'apFlux_17_0_flag',
129 'localBackground_instFlux',
130 'localBackground_flag']
131 )
132 source_selector = sourceSelectorRegistry.makeField(
133 doc='How to select sources. Under normal usage this should not be changed.',
134 default='science'
135 )
137 def setDefaults(self):
138 super().setDefaults()
140 source_selector = self.source_selector['science']
141 source_selector.setDefaults()
143 source_selector.doFlags = True
144 source_selector.doUnresolved = True
145 source_selector.doSignalToNoise = True
146 source_selector.doIsolated = True
147 source_selector.doRequireFiniteRaDec = True
149 source_selector.signalToNoise.minimum = 10.0
150 source_selector.signalToNoise.maximum = 1000.0
152 flux_flag_name = self.inst_flux_field.replace("instFlux", "flag")
154 source_selector.flags.bad = ['pixelFlags_edge',
155 'pixelFlags_interpolatedCenter',
156 'pixelFlags_saturatedCenter',
157 'pixelFlags_crCenter',
158 'pixelFlags_bad',
159 'pixelFlags_interpolated',
160 'pixelFlags_saturated',
161 'centroid_flag',
162 flux_flag_name]
164 source_selector.signalToNoise.fluxField = self.inst_flux_field
165 source_selector.signalToNoise.errField = self.inst_flux_field + 'Err'
167 source_selector.isolated.parentName = 'parentSourceId'
168 source_selector.isolated.nChildName = 'deblend_nChild'
170 source_selector.unresolved.maximum = 0.5
171 source_selector.unresolved.name = 'extendedness'
173 source_selector.requireFiniteRaDec.raColName = self.ra_column
174 source_selector.requireFiniteRaDec.decColName = self.dec_column
177class IsolatedStarAssociationTask(pipeBase.PipelineTask):
178 """Associate sources into isolated star catalogs.
179 """
180 ConfigClass = IsolatedStarAssociationConfig
181 _DefaultName = 'isolatedStarAssociation'
183 def __init__(self, **kwargs):
184 super().__init__(**kwargs)
186 self.makeSubtask('source_selector')
187 # Only log warning and fatal errors from the source_selector
188 self.source_selector.log.setLevel(self.source_selector.log.WARN)
190 def runQuantum(self, butlerQC, inputRefs, outputRefs):
191 input_ref_dict = butlerQC.get(inputRefs)
193 tract = butlerQC.quantum.dataId['tract']
195 source_table_refs = input_ref_dict['source_table_visit']
197 self.log.info('Running with %d source_table_visit dataRefs',
198 len(source_table_refs))
200 source_table_ref_dict_temp = {source_table_ref.dataId['visit']: source_table_ref for
201 source_table_ref in source_table_refs}
203 bands = {source_table_ref.dataId['band'] for source_table_ref in source_table_refs}
204 for band in bands:
205 if band not in self.config.band_order:
206 self.log.warning('Input data has data from band %s but that band is not '
207 'configured for matching', band)
209 # TODO: Sort by visit until DM-31701 is done and we have deterministic
210 # dataset ordering.
211 source_table_ref_dict = {visit: source_table_ref_dict_temp[visit] for
212 visit in sorted(source_table_ref_dict_temp.keys())}
214 struct = self.run(input_ref_dict['skymap'], tract, source_table_ref_dict)
216 butlerQC.put(pd.DataFrame(struct.star_source_cat),
217 outputRefs.isolated_star_sources)
218 butlerQC.put(pd.DataFrame(struct.star_cat),
219 outputRefs.isolated_star_cat)
221 def run(self, skymap, tract, source_table_ref_dict):
222 """Run the isolated star association task.
224 Parameters
225 ----------
226 skymap : `lsst.skymap.SkyMap`
227 Skymap object.
228 tract : `int`
229 Tract number.
230 source_table_ref_dict : `dict`
231 Dictionary of source_table refs. Key is visit, value is dataref.
233 Returns
234 -------
235 struct : `lsst.pipe.base.struct`
236 Struct with outputs for persistence.
237 """
238 star_source_cat = self._make_all_star_sources(skymap[tract], source_table_ref_dict)
240 primary_bands = self.config.band_order
242 # Do primary matching
243 primary_star_cat = self._match_primary_stars(primary_bands, star_source_cat)
245 if len(primary_star_cat) == 0:
246 return pipeBase.Struct(star_source_cat=np.zeros(0, star_source_cat.dtype),
247 star_cat=np.zeros(0, primary_star_cat.dtype))
249 # Remove neighbors
250 primary_star_cat = self._remove_neighbors(primary_star_cat)
252 if len(primary_star_cat) == 0:
253 return pipeBase.Struct(star_source_cat=np.zeros(0, star_source_cat.dtype),
254 star_cat=np.zeros(0, primary_star_cat.dtype))
256 # Crop to inner tract region
257 inner_tract_ids = skymap.findTractIdArray(primary_star_cat[self.config.ra_column],
258 primary_star_cat[self.config.dec_column],
259 degrees=True)
260 use = (inner_tract_ids == tract)
261 self.log.info('Total of %d isolated stars in inner tract.', use.sum())
263 primary_star_cat = primary_star_cat[use]
265 if len(primary_star_cat) == 0:
266 return pipeBase.Struct(star_source_cat=np.zeros(0, star_source_cat.dtype),
267 star_cat=np.zeros(0, primary_star_cat.dtype))
269 # Set the unique ids.
270 primary_star_cat['isolated_star_id'] = self._compute_unique_ids(skymap,
271 tract,
272 len(primary_star_cat))
274 # Match to sources.
275 star_source_cat, primary_star_cat = self._match_sources(primary_bands,
276 star_source_cat,
277 primary_star_cat)
279 return pipeBase.Struct(star_source_cat=star_source_cat,
280 star_cat=primary_star_cat)
282 def _make_all_star_sources(self, tract_info, source_table_ref_dict):
283 """Make a catalog of all the star sources.
285 Parameters
286 ----------
287 tract_info : `lsst.skymap.TractInfo`
288 Information about the tract.
289 source_table_ref_dict : `dict`
290 Dictionary of source_table refs. Key is visit, value is dataref.
292 Returns
293 -------
294 star_source_cat : `np.ndarray`
295 Catalog of star sources.
296 """
297 # Internally, we use a numpy recarray, they are by far the fastest
298 # option in testing for relatively narrow tables.
299 # (have not tested wide tables)
300 all_columns, persist_columns = self._get_source_table_visit_column_names()
301 poly = tract_info.outer_sky_polygon
303 tables = []
304 for visit in source_table_ref_dict:
305 source_table_ref = source_table_ref_dict[visit]
306 df = source_table_ref.get(parameters={'columns': all_columns})
307 df.reset_index(inplace=True)
309 goodSrc = self.source_selector.selectSources(df)
311 table = df[persist_columns][goodSrc.selected].to_records()
313 # Append columns that include the row in the source table
314 # and the matched object index (to be filled later).
315 table = np.lib.recfunctions.append_fields(table,
316 ['source_row',
317 'obj_index'],
318 [np.where(goodSrc.selected)[0],
319 np.zeros(goodSrc.selected.sum(), dtype=np.int32)],
320 dtypes=['i4', 'i4'],
321 usemask=False)
323 # We cut to the outer tract polygon to ensure consistent matching
324 # from tract to tract.
325 tract_use = poly.contains(np.deg2rad(table[self.config.ra_column]),
326 np.deg2rad(table[self.config.dec_column]))
328 tables.append(table[tract_use])
330 # Combine tables
331 star_source_cat = np.concatenate(tables)
333 return star_source_cat
335 def _get_source_table_visit_column_names(self):
336 """Get the list of sourceTable_visit columns from the config.
338 Returns
339 -------
340 all_columns : `list` [`str`]
341 All columns to read
342 persist_columns : `list` [`str`]
343 Columns to persist (excluding selection columns)
344 """
345 columns = [self.config.id_column,
346 'visit', 'detector',
347 self.config.ra_column, self.config.dec_column,
348 self.config.physical_filter_column, self.config.band_column,
349 self.config.inst_flux_field, self.config.inst_flux_field + 'Err']
350 columns.extend(self.config.extra_columns)
352 all_columns = columns.copy()
353 if self.source_selector.config.doFlags:
354 all_columns.extend(self.source_selector.config.flags.bad)
355 if self.source_selector.config.doUnresolved:
356 all_columns.append(self.source_selector.config.unresolved.name)
357 if self.source_selector.config.doIsolated:
358 all_columns.append(self.source_selector.config.isolated.parentName)
359 all_columns.append(self.source_selector.config.isolated.nChildName)
361 return all_columns, columns
363 def _match_primary_stars(self, primary_bands, star_source_cat):
364 """Match primary stars.
366 Parameters
367 ----------
368 primary_bands : `list` [`str`]
369 Ordered list of primary bands.
370 star_source_cat : `np.ndarray`
371 Catalog of star sources.
373 Returns
374 -------
375 primary_star_cat : `np.ndarray`
376 Catalog of primary star positions
377 """
378 ra_col = self.config.ra_column
379 dec_col = self.config.dec_column
381 dtype = self._get_primary_dtype(primary_bands)
383 primary_star_cat = None
384 for primary_band in primary_bands:
385 use = (star_source_cat['band'] == primary_band)
387 ra = star_source_cat[ra_col][use]
388 dec = star_source_cat[dec_col][use]
390 with Matcher(ra, dec) as matcher:
391 try:
392 # New smatch API
393 idx = matcher.query_groups(self.config.match_radius/3600., min_match=1)
394 except AttributeError:
395 # Old smatch API
396 idx = matcher.query_self(self.config.match_radius/3600., min_match=1)
398 count = len(idx)
400 if count == 0:
401 self.log.info('Found 0 primary stars in %s band.', primary_band)
402 continue
404 band_cat = np.zeros(count, dtype=dtype)
405 band_cat['primary_band'] = primary_band
407 # If the tract cross ra=0 (that is, it has both low ra and high ra)
408 # then we need to remap all ra values from [0, 360) to [-180, 180)
409 # before doing any position averaging.
410 remapped = False
411 if ra.min() < 60.0 and ra.max() > 300.0:
412 ra_temp = (ra + 180.0) % 360. - 180.
413 remapped = True
414 else:
415 ra_temp = ra
417 # Compute mean position for each primary star
418 for i, row in enumerate(idx):
419 row = np.array(row)
420 band_cat[ra_col][i] = np.mean(ra_temp[row])
421 band_cat[dec_col][i] = np.mean(dec[row])
423 if remapped:
424 # Remap ra back to [0, 360)
425 band_cat[ra_col] %= 360.0
427 # Match to previous band catalog(s), and remove duplicates.
428 if primary_star_cat is None or len(primary_star_cat) == 0:
429 primary_star_cat = band_cat
430 else:
431 with Matcher(band_cat[ra_col], band_cat[dec_col]) as matcher:
432 idx = matcher.query_radius(primary_star_cat[ra_col],
433 primary_star_cat[dec_col],
434 self.config.match_radius/3600.)
435 # Any object with a match should be removed.
436 match_indices = np.array([i for i in range(len(idx)) if len(idx[i]) > 0])
437 if len(match_indices) > 0:
438 band_cat = np.delete(band_cat, match_indices)
440 primary_star_cat = np.append(primary_star_cat, band_cat)
441 self.log.info('Found %d primary stars in %s band.', len(band_cat), primary_band)
443 # If everything was cut, we still want the correct datatype.
444 if primary_star_cat is None:
445 primary_star_cat = np.zeros(0, dtype=dtype)
447 return primary_star_cat
449 def _remove_neighbors(self, primary_star_cat):
450 """Remove neighbors from the primary star catalog.
452 Parameters
453 ----------
454 primary_star_cat : `np.ndarray`
455 Primary star catalog.
457 Returns
458 -------
459 primary_star_cat_cut : `np.ndarray`
460 Primary star cat with neighbors removed.
461 """
462 ra_col = self.config.ra_column
463 dec_col = self.config.dec_column
465 with Matcher(primary_star_cat[ra_col], primary_star_cat[dec_col]) as matcher:
466 # By setting min_match=2 objects that only match to themselves
467 # will not be recorded.
468 try:
469 # New smatch API
470 idx = matcher.query_groups(self.config.isolation_radius/3600., min_match=2)
471 except AttributeError:
472 # Old smatch API
473 idx = matcher.query_self(self.config.isolation_radius/3600., min_match=2)
475 try:
476 neighbor_indices = np.concatenate(idx)
477 except ValueError:
478 neighbor_indices = np.zeros(0, dtype=int)
480 if len(neighbor_indices) > 0:
481 neighbored = np.unique(neighbor_indices)
482 self.log.info('Cutting %d objects with close neighbors.', len(neighbored))
483 primary_star_cat = np.delete(primary_star_cat, neighbored)
485 return primary_star_cat
487 def _match_sources(self, bands, star_source_cat, primary_star_cat):
488 """Match individual sources to primary stars.
490 Parameters
491 ----------
492 bands : `list` [`str`]
493 List of bands.
494 star_source_cat : `np.ndarray`
495 Array of star sources.
496 primary_star_cat : `np.ndarray`
497 Array of primary stars.
499 Returns
500 -------
501 star_source_cat_sorted : `np.ndarray`
502 Sorted and cropped array of star sources.
503 primary_star_cat : `np.ndarray`
504 Catalog of isolated stars, with indexes to star_source_cat_cut.
505 """
506 ra_col = self.config.ra_column
507 dec_col = self.config.dec_column
509 # We match sources per-band because it allows us to have sorted
510 # sources for easy retrieval of per-band matches.
511 n_source_per_band_per_obj = np.zeros((len(bands),
512 len(primary_star_cat)),
513 dtype=np.int32)
514 band_uses = []
515 idxs = []
516 with Matcher(primary_star_cat[ra_col], primary_star_cat[dec_col]) as matcher:
517 for b, band in enumerate(bands):
518 band_use, = np.where(star_source_cat['band'] == band)
520 idx = matcher.query_radius(star_source_cat[ra_col][band_use],
521 star_source_cat[dec_col][band_use],
522 self.config.match_radius/3600.)
523 n_source_per_band_per_obj[b, :] = np.array([len(row) for row in idx])
524 idxs.append(idx)
525 band_uses.append(band_use)
527 n_source_per_obj = np.sum(n_source_per_band_per_obj, axis=0)
529 primary_star_cat['nsource'] = n_source_per_obj
530 primary_star_cat['source_cat_index'][1:] = np.cumsum(n_source_per_obj)[:-1]
532 n_tot_source = primary_star_cat['source_cat_index'][-1] + primary_star_cat['nsource'][-1]
534 # Temporary arrays until we crop/sort the source catalog
535 source_index = np.zeros(n_tot_source, dtype=np.int32)
536 obj_index = np.zeros(n_tot_source, dtype=np.int32)
538 ctr = 0
539 for i in range(len(primary_star_cat)):
540 obj_index[ctr: ctr + n_source_per_obj[i]] = i
541 for b in range(len(bands)):
542 source_index[ctr: ctr + n_source_per_band_per_obj[b, i]] = band_uses[b][idxs[b][i]]
543 ctr += n_source_per_band_per_obj[b, i]
545 source_cat_index_band_offset = np.cumsum(n_source_per_band_per_obj, axis=0)
547 for b, band in enumerate(bands):
548 primary_star_cat[f'nsource_{band}'] = n_source_per_band_per_obj[b, :]
549 if b == 0:
550 # The first band listed is the same as the overall star
551 primary_star_cat[f'source_cat_index_{band}'] = primary_star_cat['source_cat_index']
552 else:
553 # Other band indices are offset from the previous band
554 primary_star_cat[f'source_cat_index_{band}'] = (primary_star_cat['source_cat_index']
555 + source_cat_index_band_offset[b - 1, :])
557 star_source_cat = star_source_cat[source_index]
558 star_source_cat['obj_index'] = obj_index
560 return star_source_cat, primary_star_cat
562 def _compute_unique_ids(self, skymap, tract, nstar):
563 """Compute unique star ids.
565 This is a simple hash of the tract and star to provide an
566 id that is unique for a given processing.
568 Parameters
569 ----------
570 skymap : `lsst.skymap.Skymap`
571 Skymap object.
572 tract : `int`
573 Tract id number.
574 nstar : `int`
575 Number of stars.
577 Returns
578 -------
579 ids : `np.ndarray`
580 Array of unique star ids.
581 """
582 # The end of the id will be big enough to hold the tract number
583 mult = 10**(int(np.log10(len(skymap))) + 1)
585 return (np.arange(nstar) + 1)*mult + tract
587 def _get_primary_dtype(self, primary_bands):
588 """Get the numpy datatype for the primary star catalog.
590 Parameters
591 ----------
592 primary_bands : `list` [`str`]
593 List of primary bands.
595 Returns
596 -------
597 dtype : `numpy.dtype`
598 Datatype of the primary catalog.
599 """
600 max_len = max([len(primary_band) for primary_band in primary_bands])
602 dtype = [('isolated_star_id', 'i8'),
603 (self.config.ra_column, 'f8'),
604 (self.config.dec_column, 'f8'),
605 ('primary_band', f'U{max_len}'),
606 ('source_cat_index', 'i4'),
607 ('nsource', 'i4')]
609 for band in primary_bands:
610 dtype.append((f'source_cat_index_{band}', 'i4'))
611 dtype.append((f'nsource_{band}', 'i4'))
613 return dtype