def test_stitcher_with_identity(real_tracklets):
# Add fake IDs
for i in range(3):
tracklet = real_tracklets[i]
for v in tracklet.values():
v[:, -1] = i
stitcher = TrackletStitcher.from_dict_of_dict(real_tracklets, n_tracks=3)
tracklets = sorted(stitcher, key=lambda t: t.identity)
assert all(tracklet.identity == i for i, tracklet in enumerate(tracklets))
# Split all tracklets in half
tracklets = [
t for track in stitcher for t in stitcher.split_tracklet(track, [25])
]
stitcher = TrackletStitcher(tracklets, n_tracks=3)
assert len(stitcher) == 6
stitcher.build_graph()
weight = stitcher.G.edges[('0out', '3in')]['weight']
def weight_func(t1, t2):
w = 0.01 if t1.identity == t2.identity else 1
return w * t1.distance_to(t2)
stitcher.build_graph(weight_func=weight_func)
assert stitcher.G.number_of_edges() == 27
new_weight = stitcher.G.edges[('0out', '3in')]['weight']
assert new_weight == weight // 100
stitcher.stitch()
assert len(stitcher.tracks) == 3
assert all(len(track) == 50 for track in stitcher.tracks)
assert all(0.998 <= track.likelihood <= 1 for track in stitcher.tracks)
tracks = sorted(stitcher.tracks, key=lambda t: t.identity)
assert all(track.identity == i for i, track in enumerate(tracks))
def test_stitcher_montblanc(real_tracklets_montblanc):
stitcher = TrackletStitcher.from_dict_of_dict(
real_tracklets_montblanc,
n_tracks=3,
)
assert len(stitcher) == 5
assert all(tracklet.is_continuous for tracklet in stitcher.tracklets)
assert all(tracklet.identity == -1 for tracklet in stitcher.tracklets)
assert len(stitcher.residuals) == 1
assert len(stitcher.residuals[0]) == 2
assert stitcher.compute_max_gap(stitcher.tracklets) == 5
stitcher.build_graph()
assert stitcher.G.number_of_edges() == 18
weights = [w for *_, w in stitcher.G.edges.data("weight") if w]
assert weights == [2453, 24498, 5428]
stitcher.stitch()
assert len(stitcher.tracks) == 3
assert all(len(track) >= 176 for track in stitcher.tracks)
assert all(0.996 <= track.likelihood <= 1 for track in stitcher.tracks)
df_gt = pd.read_hdf('tests/data/montblanc_tracks.h5')
df = stitcher.format_df()
np.testing.assert_equal(df.to_numpy(), df_gt.to_numpy())
def test_stitcher_real(tmpdir_factory, real_tracklets):
stitcher = TrackletStitcher.from_dict_of_dict(real_tracklets, n_tracks=3)
assert len(stitcher) == 3
assert all(tracklet.is_continuous for tracklet in stitcher.tracklets)
assert not stitcher.residuals
assert stitcher.compute_max_gap() == 0
stitcher.build_graph()
assert stitcher.G.number_of_edges() == 9
assert all(weight is None for *_, weight in stitcher.G.edges.data("weight"))
stitcher.stitch()
assert len(stitcher.tracks) == 3
assert all(len(track) == 50 for track in stitcher.tracks)
assert all(0.998 <= track.likelihood <= 1 for track in stitcher.tracks)
output_name = tmpdir_factory.mktemp("data").join("fake.h5")
stitcher.write_tracks(output_name, ["mickey", "minnie", "bianca"])