Coverage for tests/test_association_task.py : 10%

Hot-keys on this page
r m x p toggle line displays
j k next/prev highlighted chunk
0 (zero) top of page
1 (one) first highlighted chunk
1# This file is part of ap_association.
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/>.
22import numpy as np
23import pandas as pd
24import unittest
26from lsst.afw.cameraGeom.testUtils import DetectorWrapper
27import lsst.afw.geom as afwGeom
28import lsst.afw.image as afwImage
29import lsst.afw.image.utils as afwImageUtils
30import lsst.afw.table as afwTable
31import lsst.daf.base as dafBase
32import lsst.geom as geom
33import lsst.sphgeom as sphgeom
34import lsst.utils.tests
36from lsst.ap.association import \
37 AssociationTask, \
38 make_dia_source_schema, \
39 make_dia_object_schema
42def create_test_points(point_locs_deg,
43 wcs=None,
44 start_id=0,
45 schema=None,
46 scatter_arcsec=1.0,
47 indexer_ids=None,
48 associated_ids=None):
49 """Create dummy DIASources or DIAObjects for use in our tests.
50 Parameters
51 ----------
52 point_locs_deg : array-like (N, 2) of `float`s
53 Positions of the test points to create in RA, DEC.
54 wcs : `lsst.afw.geom.SkyWcs`
55 Wcs to convert RA/Dec to x/y if provided.
56 start_id : `int`
57 Unique id of the first object to create. The remaining sources are
58 incremented by one from the first id.
59 schema : `lsst.afw.table.Schema`
60 Schema of the objects to create. Defaults to the DIASource schema.
61 scatter_arcsec : `float`
62 Scatter to add to the position of each DIASource.
63 indexer_ids : `list` of `ints`s
64 Id numbers of pixelization indexer to store. Must be the same length
65 as the first dimension of point_locs_deg.
66 associated_ids : `list` of `ints`s
67 Id numbers of associated DIAObjects to store. Must be the same length
68 as the first dimension of point_locs_deg.
69 Returns
70 -------
71 test_points : `lsst.afw.table.SourceCatalog`
72 Catalog of points to test.
73 """
74 if schema is None:
75 schema = make_dia_source_schema()
76 sources = afwTable.SourceCatalog(schema)
78 for src_idx, (ra, dec,) in enumerate(point_locs_deg):
79 src = sources.addNew()
80 src['id'] = src_idx + start_id
81 coord = geom.SpherePoint(ra, dec, geom.degrees)
82 if scatter_arcsec > 0.0:
83 coord = coord.offset(
84 np.random.rand() * 360 * geom.degrees,
85 np.random.rand() * scatter_arcsec * geom.arcseconds)
86 if indexer_ids is not None:
87 src['pixelId'] = indexer_ids[src_idx]
88 if associated_ids is not None:
89 src['diaObjectId'] = associated_ids[src_idx]
90 src.setCoord(coord)
92 if wcs is not None:
93 xyCentroid = wcs.skykToPixel(coord)
94 src.set("x", xyCentroid.getX())
95 src.set("y", xyCentroid.getY())
97 return sources
100def create_test_points_pandas(point_locs_deg,
101 wcs=None,
102 start_id=0,
103 schema=None,
104 scatter_arcsec=1.0,
105 indexer_ids=None,
106 associated_ids=None):
107 """Create dummy DIASources or DIAObjects for use in our tests.
108 Parameters
109 ----------
110 point_locs_deg : array-like (N, 2) of `float`s
111 Positions of the test points to create in RA, DEC.
112 wcs : `lsst.afw.geom.SkyWcs`
113 Wcs to convert RA/Dec to x/y if provided.
114 start_id : `int`
115 Unique id of the first object to create. The remaining sources are
116 incremented by one from the first id.
117 schema : `lsst.afw.table.Schema`
118 Schema of the objects to create. Defaults to the DIASource schema.
119 scatter_arcsec : `float`
120 Scatter to add to the position of each DIASource.
121 indexer_ids : `list` of `ints`s
122 Id numbers of pixelization indexer to store. Must be the same length
123 as the first dimension of point_locs_deg.
124 associated_ids : `list` of `ints`s
125 Id numbers of associated DIAObjects to store. Must be the same length
126 as the first dimension of point_locs_deg.
127 Returns
128 -------
129 test_points : `pandas.DataFrame`
130 Catalog of points to test.
131 """
132 if schema is None:
133 schema = make_dia_source_schema()
134 sources = afwTable.SourceCatalog(schema)
136 for src_idx, (ra, dec,) in enumerate(point_locs_deg):
137 src = sources.addNew()
138 src['id'] = src_idx + start_id
139 coord = geom.SpherePoint(ra, dec, geom.degrees)
140 if scatter_arcsec > 0.0:
141 coord = coord.offset(
142 np.random.rand() * 360 * geom.degrees,
143 np.random.rand() * scatter_arcsec * geom.arcseconds)
144 if indexer_ids is not None:
145 src['pixelId'] = indexer_ids[src_idx]
146 if associated_ids is not None:
147 src['diaObjectId'] = associated_ids[src_idx]
148 src.setCoord(coord)
150 if wcs is not None:
151 xyCentroid = wcs.skykToPixel(coord)
152 src.set("x", xyCentroid.getX())
153 src.set("y", xyCentroid.getY())
155 sources = sources.asAstropy().to_pandas()
157 return sources
160class TestAssociationTask(unittest.TestCase):
162 def setUp(self):
163 """Create a sqlite3 database with default tables and schemas.
164 """
165 # CFHT Filters from the camera mapper.
166 self.filter_names = ["u", "g", "r", "i", "z"]
167 afwImageUtils.resetFilters()
168 afwImageUtils.defineFilter('u', lambdaEff=374, alias="u.MP9301")
169 afwImageUtils.defineFilter('g', lambdaEff=487, alias="g.MP9401")
170 afwImageUtils.defineFilter('r', lambdaEff=628, alias="r.MP9601")
171 afwImageUtils.defineFilter('i', lambdaEff=778, alias="i.MP9701")
172 afwImageUtils.defineFilter('z', lambdaEff=1170, alias="z.MP9801")
174 self.dia_object_schema = make_dia_object_schema()
176 # metadata taken from CFHT data
177 # v695856-e0/v695856-e0-c000-a00.sci_img.fits
179 self.metadata = dafBase.PropertySet()
181 self.metadata.set("SIMPLE", "T")
182 self.metadata.set("BITPIX", -32)
183 self.metadata.set("NAXIS", 2)
184 self.metadata.set("NAXIS1", 1024)
185 self.metadata.set("NAXIS2", 1153)
186 self.metadata.set("RADECSYS", 'FK5')
187 self.metadata.set("EQUINOX", 2000.)
189 self.metadata.setDouble("CRVAL1", 215.604025685476)
190 self.metadata.setDouble("CRVAL2", 53.1595451514076)
191 self.metadata.setDouble("CRPIX1", 1109.99981456774)
192 self.metadata.setDouble("CRPIX2", 560.018167811613)
193 self.metadata.set("CTYPE1", 'RA---SIN')
194 self.metadata.set("CTYPE2", 'DEC--SIN')
196 self.metadata.setDouble("CD1_1", 5.10808596133527E-05)
197 self.metadata.setDouble("CD1_2", 1.85579539217196E-07)
198 self.metadata.setDouble("CD2_2", -5.10281493481982E-05)
199 self.metadata.setDouble("CD2_1", -8.27440751733828E-07)
201 self.wcs = afwGeom.makeSkyWcs(self.metadata)
202 self.exposure = afwImage.makeExposure(
203 afwImage.makeMaskedImageFromArrays(np.ones((1024, 1153))),
204 self.wcs)
205 detector = DetectorWrapper(id=23, bbox=self.exposure.getBBox()).detector
206 visit = afwImage.VisitInfo(
207 exposureId=1234,
208 exposureTime=200.,
209 date=dafBase.DateTime("2014-05-13T17:00:00.000000000",
210 dafBase.DateTime.Timescale.TAI))
211 self.exposure.setDetector(detector)
212 self.exposure.getInfo().setVisitInfo(visit)
213 self.exposure.setFilter(afwImage.Filter('g'))
214 self.flux0 = 10000
215 self.flux0_err = 100
216 self.exposure.setPhotoCalib(
217 afwImage.PhotoCalib(self.flux0, self.flux0_err))
219 bbox = geom.Box2D(self.exposure.getBBox())
220 wcs = self.exposure.getWcs()
222 self.pixelator = sphgeom.HtmPixelization(20)
223 region = sphgeom.ConvexPolygon([wcs.pixelToSky(pp).getVector()
224 for pp in bbox.getCorners()])
226 indices = self.pixelator.envelope(region, 64)
227 # Index types must be cast to int to work with dax_apdb.
228 self.index_ranges = indices.ranges()
230 def tearDown(self):
231 """Delete the database after we are done with it.
232 """
233 del self.metadata
234 del self.wcs
235 del self.exposure
237 def test_run(self):
238 """Test the run method with a database that already exists and
239 contains DIAObjects and Sources.
240 """
241 dia_objects = self._run_association_and_retrieve_objects(True)
242 not_updated_idx = 0
243 updated_idx_start = 1
244 new_idx_start = 5
245 total_expected_dia_objects = 10
246 self.assertEqual(len(dia_objects), total_expected_dia_objects)
248 # Test to make sure the number of DIAObjects have been properly
249 # associated within the db.
250 for obj_idx, (df_idx, dia_object) in enumerate(dia_objects.iterrows()):
251 if df_idx == not_updated_idx:
252 # Test the DIAObject we expect to not be associated with any
253 # new DIASources.
254 self.assertEqual(dia_object['gPSFluxNdata'], 1)
255 self.assertEqual(dia_object['rPSFluxNdata'], 1)
256 self.assertEqual(dia_object['nDiaSources'], 2)
257 self.assertEqual(df_idx, obj_idx)
258 elif updated_idx_start <= df_idx < new_idx_start:
259 # Test that associating to the existing DIAObjects went
260 # as planned and test that the IDs of the newly associated
261 # DIASources is correct.
262 self.assertEqual(dia_object['gPSFluxNdata'], 2)
263 self.assertEqual(dia_object['rPSFluxNdata'], 1)
264 self.assertEqual(dia_object['nDiaSources'], 3)
265 self.assertEqual(df_idx, obj_idx)
266 else:
267 self.assertEqual(dia_object['gPSFluxNdata'], 1)
268 self.assertEqual(dia_object['nDiaSources'], 1)
269 self.assertEqual(df_idx, obj_idx + 4 + 5)
271 def test_run_no_existing_objects(self):
272 """Test the run method with a completely empty database.
273 """
274 dia_objects = self._run_association_and_retrieve_objects(False)
275 total_expected_dia_objects = 9
276 self.assertEqual(len(dia_objects),
277 total_expected_dia_objects)
278 for obj_idx, (df_idx, output_dia_object) in enumerate(dia_objects.iterrows()):
279 self.assertEqual(output_dia_object['gPSFluxNdata'], 1)
280 self.assertEqual(df_idx, obj_idx + 10)
282 def _run_association_and_retrieve_objects(self, create_objects=False):
283 """Convenience method for testing the Association run method.
285 Parameters
286 ----------
287 create_objects : `bool`
288 Boolean specifying if seed DIAObjects and DIASources should be
289 inserted into the database before association.
291 Return
292 ------
293 dia_objects : `lsst.afw.table.SourceCatalog`
294 Final set of DIAObjects to be tested.
295 """
296 if create_objects:
297 diaObjects, diaSourceHistory = \
298 self._create_dia_objects_and_sources()
299 else:
300 diaObjects = pd.DataFrame(columns=["diaObjectId"])
301 diaSourceHistory = pd.DataFrame(columns=["diaObjectId",
302 "filterName",
303 "diaSourceId"])
304 diaObjects.set_index("diaObjectId",
305 inplace=True,
306 drop=False)
307 diaSourceHistory.set_index(["diaObjectId",
308 "filterName",
309 "diaSourceId"],
310 inplace=True,
311 drop=False)
313 source_centers = [
314 [self.wcs.pixelToSky(idx, idx).getRa().asDegrees(),
315 self.wcs.pixelToSky(idx, idx).getDec().asDegrees()]
316 for idx in np.linspace(1, 1000, 10)[1:]]
317 dia_sources = create_test_points(
318 point_locs_deg=source_centers,
319 start_id=10,
320 scatter_arcsec=-1)
321 for dia_source in dia_sources:
322 self._set_source_values(
323 dia_source=dia_source,
324 flux=10000,
325 fluxErr=100,
326 filterName=self.exposure.getFilter().getName(),
327 filterId=self.exposure.getFilter().getId(),
328 ccdVisitId=self.exposure.getInfo().getVisitInfo().getExposureId(),
329 midPointTai=self.exposure.getInfo().getVisitInfo().getDate().get(system=dafBase.DateTime.MJD))
331 assoc_task = AssociationTask()
333 diaSources = dia_sources.asAstropy().to_pandas()
334 diaSources.rename(columns={"coord_ra": "ra",
335 "coord_dec": "decl",
336 "id": "diaSourceId",
337 "parent": "parentDiaSourceId"},
338 inplace=True)
339 diaSources["ra"] = np.degrees(diaSources["ra"])
340 diaSources["decl"] = np.degrees(diaSources["decl"])
342 if len(diaObjects) == 0:
343 diaSourceHistory = pd.DataFrame(columns=["diaObjectId",
344 "filterName",
345 "diaSourceId"])
346 diaSourceHistory.set_index(
347 ["diaObjectId", "filterName", "diaSourceId"],
348 drop=False,
349 inplace=True)
351 results = assoc_task.run(diaSources,
352 diaObjects,
353 diaSourceHistory)
354 return results.diaObjects
356 def _set_source_values(self, dia_source, flux, fluxErr, filterName,
357 filterId, ccdVisitId, midPointTai):
358 """Set fluxes and visit info for DiaSources.
360 Parameters
361 ----------
362 dia_source : `lsst.afw.table.SourceRecord`
363 SourceRecord object to edit.
364 flux : `double`
365 Flux of DiaSource
366 fluxErr : `double`
367 Flux error of DiaSource
368 filterName : `string`
369 Name of filter for flux.
370 filterId : `int`
371 Unique id of filter.
372 ccdVisitId : `int`
373 Integer id of this ccd/visit.
374 midPointTai : `double`
375 Time of observation
376 """
377 dia_source['ccdVisitId'] = ccdVisitId
378 dia_source["midPointTai"] = midPointTai
379 dia_source["psFlux"] = flux / self.flux0
380 dia_source["psFluxErr"] = np.sqrt(
381 (fluxErr / self.flux0) ** 2
382 + (flux * self.flux0_err / self.flux0 ** 2) ** 2)
383 dia_source["apFlux"] = flux / self.flux0
384 dia_source["apFluxErr"] = np.sqrt(
385 (fluxErr / self.flux0) ** 2
386 + (flux * self.flux0_err / self.flux0 ** 2) ** 2)
387 dia_source["totFlux"] = flux / self.flux0
388 dia_source["totFluxErr"] = np.sqrt(
389 (fluxErr / self.flux0) ** 2
390 + (flux * self.flux0_err / self.flux0 ** 2) ** 2)
391 dia_source["filterName"] = filterName
392 dia_source["filterId"] = filterId
393 dia_source["x"] = 0.
394 dia_source["y"] = 0.
396 def _create_dia_objects_and_sources(self):
397 """Method for storing a set of test DIAObjects and sources into
398 the L1 database.
399 """
401 # This should create a DB of 5 DIAObjects with 2 DIASources associated
402 # to them. The DIASources are "observed" in g and r.
404 # Create DIObjects, give them fluxes, and store them
405 n_objects = 5
406 object_centers = np.array([
407 [self.wcs.pixelToSky(idx, idx).getRa().asDegrees(),
408 self.wcs.pixelToSky(idx, idx).getDec().asDegrees()]
409 for idx in np.linspace(1, 1000, 10)])
410 dia_objects = create_test_points(
411 point_locs_deg=object_centers[:n_objects],
412 start_id=0,
413 schema=self.dia_object_schema,
414 scatter_arcsec=-1,)
415 # Set the DIAObject fluxes and number of associated sources.
416 for dia_object in dia_objects:
417 dia_object["nDiaSources"] = 2
418 for filter_name in self.filter_names:
419 sphPoint = geom.SpherePoint(dia_object.getCoord())
420 htmIndex = self.pixelator.index(sphPoint.getVector())
421 dia_object["pixelId"] = htmIndex
422 dia_object['%sPSFluxMean' % filter_name] = 1
423 dia_object['%sPSFluxMeanErr' % filter_name] = 1
424 dia_object['%sPSFluxSigma' % filter_name] = 1
425 dia_object['%sPSFluxNdata' % filter_name] = 1
426 dia_objects = dia_objects.asAstropy().to_pandas()
427 dia_objects.rename(columns={"coord_ra": "ra",
428 "coord_dec": "decl",
429 "id": "diaObjectId"},
430 inplace=True)
431 dia_objects["ra"] = np.degrees(dia_objects["ra"])
432 dia_objects["decl"] = np.degrees(dia_objects["decl"])
434 dateTime = dafBase.DateTime("2014-05-13T16:00:00.000000000",
435 dafBase.DateTime.Timescale.TAI)
437 # Create DIASources, update their ccdVisitId and fluxes, and store
438 # them.
439 dia_sources = create_test_points(
440 point_locs_deg=np.concatenate(
441 [object_centers[:n_objects], object_centers[:n_objects]]),
442 start_id=0,
443 scatter_arcsec=-1,
444 associated_ids=[0, 1, 2, 3, 4,
445 0, 1, 2, 3, 4])
446 for src_idx, dia_source in enumerate(dia_sources):
447 if src_idx < n_objects:
448 self._set_source_values(
449 dia_source=dia_source,
450 flux=10000,
451 fluxErr=100,
452 filterName='g',
453 filterId=1,
454 ccdVisitId=1232,
455 midPointTai=dateTime.get(system=dafBase.DateTime.MJD))
456 else:
457 self._set_source_values(
458 dia_source=dia_source,
459 flux=10000,
460 fluxErr=100,
461 filterName='r',
462 filterId=2,
463 ccdVisitId=1233,
464 midPointTai=dateTime.get(system=dafBase.DateTime.MJD))
465 dia_sources = dia_sources.asAstropy().to_pandas()
466 dia_sources.rename(columns={"coord_ra": "ra",
467 "coord_dec": "decl",
468 "id": "diaSourceId",
469 "parent": "parentDiaSourceId"},
470 inplace=True)
471 dia_sources["ra"] = np.degrees(dia_sources["ra"])
472 dia_sources["decl"] = np.degrees(dia_sources["decl"])
473 return dia_objects, dia_sources
475 def test_associate_sources(self):
476 """Test the performance of the associate_sources method in
477 AssociationTask.
478 """
479 n_objects = 5
480 dia_objects = create_test_points_pandas(
481 point_locs_deg=[[0.04 * obj_idx, 0.04 * obj_idx]
482 for obj_idx in range(n_objects)],
483 start_id=0,
484 schema=self.dia_object_schema,
485 scatter_arcsec=-1,)
486 dia_objects.rename(columns={"coord_ra": "ra",
487 "coord_dec": "decl",
488 "id": "diaObjectId"},
489 inplace=True)
491 n_sources = 5
492 dia_sources = create_test_points_pandas(
493 point_locs_deg=[
494 [0.04 * (src_idx + 1),
495 0.04 * (src_idx + 1)]
496 for src_idx in range(n_sources)],
497 start_id=n_objects,
498 scatter_arcsec=0.1)
499 dia_sources.rename(columns={"coord_ra": "ra",
500 "coord_dec": "decl",
501 "id": "diaSourceId"},
502 inplace=True)
504 assoc_task = AssociationTask()
505 assoc_result = assoc_task.associate_sources(
506 dia_objects, dia_sources)
508 for test_obj_id, expected_obj_id in zip(
509 assoc_result.associated_dia_object_ids,
510 [1, 2, 3, 4, 9]):
511 self.assertEqual(test_obj_id, expected_obj_id)
513 def test_score_and_match(self):
514 """Test association between a set of sources and an existing
515 DIAObjectCollection.
517 This also tests that a DIASource that can't be associated within
518 tolerance is appended to the DIAObjectCollection as a new
519 DIAObject.
520 """
522 assoc_task = AssociationTask()
523 # Create a set of DIAObjects that contain only one DIASource
524 n_objects = 5
525 dia_objects = create_test_points_pandas(
526 point_locs_deg=[[0.04 * obj_idx, 0.04 * obj_idx]
527 for obj_idx in range(n_objects)],
528 start_id=0,
529 schema=self.dia_object_schema,
530 scatter_arcsec=-1,)
531 dia_objects.rename(columns={"coord_ra": "ra",
532 "coord_dec": "decl",
533 "id": "diaObjectId"},
534 inplace=True)
536 n_sources = 5
537 dia_sources = create_test_points_pandas(
538 point_locs_deg=[
539 [0.04 * (src_idx + 1),
540 0.04 * (src_idx + 1)]
541 for src_idx in range(n_sources)],
542 start_id=n_objects,
543 scatter_arcsec=-1)
544 dia_sources.rename(columns={"coord_ra": "ra",
545 "coord_dec": "decl",
546 "id": "diaSourceId"},
547 inplace=True)
549 score_struct = assoc_task.score(dia_objects,
550 dia_sources,
551 1.0 * geom.arcseconds)
552 self.assertFalse(np.isfinite(score_struct.scores[-1]))
553 for src_idx in range(4):
554 # Our scores should be extremely close to 0 but not exactly so due
555 # to machine noise.
556 self.assertAlmostEqual(score_struct.scores[src_idx], 0.0,
557 places=16)
559 # After matching each DIAObject should now contain 2 DIASources
560 # except the last DIAObject in this collection which should be
561 # newly created during the matching step and contain only one
562 # DIASource.
563 match_result = assoc_task.match(dia_objects, dia_sources, score_struct)
564 updated_ids = match_result.associated_dia_object_ids
565 self.assertEqual(len(updated_ids), 5)
566 self.assertEqual(match_result.n_updated_dia_objects, 4)
567 self.assertEqual(match_result.n_new_dia_objects, 1)
568 self.assertEqual(match_result.n_unassociated_dia_objects, 1)
570 # Test updating all DiaObjects
571 n_objects = 4
572 dia_objects = create_test_points_pandas(
573 point_locs_deg=[[0.04 * obj_idx, 0.04 * obj_idx]
574 for obj_idx in range(n_objects)],
575 start_id=0,
576 schema=self.dia_object_schema,
577 scatter_arcsec=-1,)
578 dia_objects.rename(columns={"coord_ra": "ra",
579 "coord_dec": "decl",
580 "id": "diaObjectId"},
581 inplace=True)
583 n_sources = 4
584 dia_sources = create_test_points_pandas(
585 point_locs_deg=[
586 [0.04 * src_idx,
587 0.04 * src_idx]
588 for src_idx in range(n_sources)],
589 start_id=n_objects,
590 scatter_arcsec=-1)
592 dia_sources.rename(columns={"coord_ra": "ra",
593 "coord_dec": "decl",
594 "id": "diaSourceId"},
595 inplace=True)
596 score_struct = assoc_task.score(dia_objects[1:],
597 dia_sources[:-1],
598 1.0 * geom.arcseconds)
599 match_result = assoc_task.match(dia_objects, dia_sources, score_struct)
600 updated_ids = match_result.associated_dia_object_ids
601 self.assertEqual(len(updated_ids), 4)
603 def test_remove_nan_dia_sources(self):
604 n_sources = 6
605 dia_sources = create_test_points_pandas(
606 point_locs_deg=[
607 [0.04 * (src_idx + 1),
608 0.04 * (src_idx + 1)]
609 for src_idx in range(n_sources)],
610 start_id=0,
611 scatter_arcsec=-1)
612 dia_sources.rename(columns={"coord_ra": "ra",
613 "coord_dec": "decl",
614 "id": "diaSourceId"},
615 inplace=True)
617 dia_sources.loc[2, "ra"] = np.nan
618 dia_sources.loc[3, "decl"] = np.nan
619 dia_sources.loc[4, "ra"] = np.nan
620 dia_sources.loc[4, "decl"] = np.nan
621 assoc_task = AssociationTask()
622 out_dia_sources = assoc_task.check_dia_source_radec(dia_sources)
623 self.assertEqual(len(out_dia_sources), n_sources - 3)
626class MemoryTester(lsst.utils.tests.MemoryTestCase):
627 pass
630def setup_module(module):
631 lsst.utils.tests.init()
634if __name__ == "__main__": 634 ↛ 635line 634 didn't jump to line 635, because the condition on line 634 was never true
635 lsst.utils.tests.init()
636 unittest.main()