def test_score_and_match(self):
"""Test association between a set of sources and an existing
DIAObjectCollection.
"""
assoc_task = AssociationTask()
score_struct = assoc_task.score(self.diaObjects,
self.diaSourceZeroScatter,
1.0 * geom.arcseconds)
self.assertFalse(np.isfinite(score_struct.scores[0]))
for src_idx in range(1, len(self.diaSources)):
# Our scores should be extremely close to 0 but not exactly so due
# to machine noise.
self.assertAlmostEqual(score_struct.scores[src_idx],
0.0,
places=16)
# After matching each DIAObject should now contain 2 DIASources
# except the last DIAObject in this collection which should be
# newly created during the matching step and contain only one
# DIASource.
match_result = assoc_task.match(self.diaObjects, self.diaSources,
score_struct)
self.assertEqual(match_result.nUpdatedDiaObjects, 4)
self.assertEqual(match_result.nUnassociatedDiaObjects, 1)
def test_score_and_match(self):
"""Test association between a set of sources and an existing
DIAObjectCollection.
This also tests that a DIASource that can't be associated within
tolerance is appended to the DIAObjectCollection as a new
DIAObject.
"""
assoc_task = AssociationTask()
# Create a set of DIAObjects that contain only one DIASource
n_objects = 5
dia_objects = create_test_points_pandas(
point_locs_deg=[[0.04 * obj_idx, 0.04 * obj_idx]
for obj_idx in range(n_objects)],
start_id=0,
schema=self.dia_object_schema,
scatter_arcsec=-1,)
dia_objects.rename(columns={"coord_ra": "ra",
"coord_dec": "decl",
"id": "diaObjectId"},
inplace=True)
n_sources = 5
dia_sources = create_test_points_pandas(
point_locs_deg=[
[0.04 * (src_idx + 1),
0.04 * (src_idx + 1)]
for src_idx in range(n_sources)],
start_id=n_objects,
scatter_arcsec=-1)
dia_sources.rename(columns={"coord_ra": "ra",
"coord_dec": "decl",
"id": "diaSourceId"},
inplace=True)
score_struct = assoc_task.score(dia_objects,
dia_sources,
1.0 * geom.arcseconds)
self.assertFalse(np.isfinite(score_struct.scores[-1]))
for src_idx in range(4):
# Our scores should be extremely close to 0 but not exactly so due
# to machine noise.
self.assertAlmostEqual(score_struct.scores[src_idx], 0.0,
places=16)
# After matching each DIAObject should now contain 2 DIASources
# except the last DIAObject in this collection which should be
# newly created during the matching step and contain only one
# DIASource.
match_result = assoc_task.match(dia_objects, dia_sources, score_struct)
updated_ids = match_result.associated_dia_object_ids
self.assertEqual(len(updated_ids), 5)
self.assertEqual(match_result.n_updated_dia_objects, 4)
self.assertEqual(match_result.n_new_dia_objects, 1)
self.assertEqual(match_result.n_unassociated_dia_objects, 1)
# Test updating all DiaObjects
n_objects = 4
dia_objects = create_test_points_pandas(
point_locs_deg=[[0.04 * obj_idx, 0.04 * obj_idx]
for obj_idx in range(n_objects)],
start_id=0,
schema=self.dia_object_schema,
scatter_arcsec=-1,)
dia_objects.rename(columns={"coord_ra": "ra",
"coord_dec": "decl",
"id": "diaObjectId"},
inplace=True)
n_sources = 4
dia_sources = create_test_points_pandas(
point_locs_deg=[
[0.04 * src_idx,
0.04 * src_idx]
for src_idx in range(n_sources)],
start_id=n_objects,
scatter_arcsec=-1)
dia_sources.rename(columns={"coord_ra": "ra",
"coord_dec": "decl",
"id": "diaSourceId"},
inplace=True)
score_struct = assoc_task.score(dia_objects[1:],
dia_sources[:-1],
1.0 * geom.arcseconds)
match_result = assoc_task.match(dia_objects, dia_sources, score_struct)
updated_ids = match_result.associated_dia_object_ids
self.assertEqual(len(updated_ids), 4)