def test_segment_basic_segments_correctly(self):
"""
A few example time-serieses to be segmented.
"""
ts = np.array([False, False, True, True, False, False, True, False])
starts_correct, ends_correct = (np.array([2, 6]), np.array([4, 7]))
starts, ends = time_series.segment_basic(ts)
np.testing.assert_array_equal(starts, starts_correct)
np.testing.assert_array_equal(ends, ends_correct)
ts = np.array([False, False, True, True, False, False, True, True])
starts_correct, ends_correct = (np.array([2, 6]), np.array([4, 8]))
starts, ends = time_series.segment_basic(ts)
np.testing.assert_array_equal(starts, starts_correct)
np.testing.assert_array_equal(ends, ends_correct)
ts = np.array([True, False, True, True, False, False, True, True])
starts_correct, ends_correct = (np.array([0, 2, 6]), np.array([1, 4, 8]))
starts, ends = time_series.segment_basic(ts)
np.testing.assert_array_equal(starts, starts_correct)
np.testing.assert_array_equal(ends, ends_correct)
def clean_traj(traj, cleaning_params):
"""
Return the start and end timepoints of the clean portions of a trajectory.
:param traj: a Trajectory
:param cleaning_params: cleaning parameter dictionary
:return: list of tuples giving start and end timepoint ids
"""
speed_threshold = cleaning_params['speed_threshold']
dist_from_wall_threshold = cleaning_params['dist_from_wall_threshold']
min_pause_length = cleaning_params['min_pause_length']
min_trajectory_length = cleaning_params['min_trajectory_length']
# get relevant trajectory information
stp_id, etp_id = traj.start_timepoint_id, traj.end_timepoint_id
speeds = traj.velocities_a(session)
dists_from_wall = traj.distances_from_wall(session)
# get mask of all timepoints below speed and distance from wall threshold
paused_mask = (speeds < speed_threshold) * (dists_from_wall <
dist_from_wall_threshold)
# get start and end idxs of pauses
pause_starts, pause_ends = time_series.segment_basic(paused_mask)
# set paused_mask elements to false when pause is below minimum duration
for pd_ctr, pause_duration in enumerate(pause_ends - pause_starts):
if pause_duration < min_pause_length:
paused_mask[pause_starts[pd_ctr]:pause_ends[pd_ctr]] = False
# get starts and ends of active portions of trajectories
clean_portions = np.transpose(
time_series.segment_basic(~paused_mask,
t=np.arange(stp_id, etp_id + 1)))
# decrement end timepoint ids since SQL uses inclusive ends
clean_portions[:, 1] -= 1
# return only portions that are sufficiently long
return clean_portions[(clean_portions[:, 1] -
clean_portions[:, 0]) > min_trajectory_length]
def test_segment_basic_segments_correctly_with_external_idxs(self):
t = np.arange(100, 200)
ts = np.array([False, False, True, True, False, False, True, False])
starts_correct, ends_correct = (np.array([2, 6]) + 100, np.array([4, 7]) + 100)
starts, ends = time_series.segment_basic(ts, t)
np.testing.assert_array_equal(starts, starts_correct)
np.testing.assert_array_equal(ends, ends_correct)
ts = np.array([False, False, True, True, False, False, True, True])
starts_correct, ends_correct = (np.array([2, 6]) + 100, np.array([4, 8]) + 100)
starts, ends = time_series.segment_basic(ts, t)
np.testing.assert_array_equal(starts, starts_correct)
np.testing.assert_array_equal(ends, ends_correct)
ts = np.array([True, False, True, True, False, False, True, True])
starts_correct, ends_correct = (np.array([0, 2, 6]) + 100, np.array([1, 4, 8]) + 100)
starts, ends = time_series.segment_basic(ts, t)
np.testing.assert_array_equal(starts, starts_correct)
np.testing.assert_array_equal(ends, ends_correct)
