def plot_velocity_histogram(data):
delta_time = extract_delta_time(data)
velocities = compute_velocities(data)
plots.plot(delta_time)
plots.show()
plots.hist(velocities*ms_to_mph, 100)
plots.show()
def merge_trajectories(raw_trajectories_folder, results_file):
onlyfiles = [f for f in os.listdir(raw_trajectories_folder)
if os.path.isfile(os.path.join(raw_trajectories_folder, f))]
contents = []
for name in onlyfiles:
full_name = os.path.join(raw_trajectories_folder, name)
contents.append(read_compressed_trajectory(full_name))
contents.sort(key=lambda p: p[0, 0], reverse=False)
merged_traj = np.vstack(contents)
merged_traj = apply_filter(merged_traj, DuplicateTimeFilter())
dt = extract_delta_time(merged_traj)
assert((dt > 0).all())
write_compressed_trajectory(merged_traj, results_file)
def find_endpoints_batch(data):
# get delta time array in seconds
delta_time = extract_delta_time(data)
# get indices on the trajectory where we spend a lot of time still
stationary_threshold = (60*60) * 3 # hours
stationary_points = np.where(delta_time>stationary_threshold)[0]
# filter out stationary points that are driving-distance (1km) close to each other
is_index_close = lambda index1, index2: distance(data[index1], data[index2]) < 1000
unique_locations = [stationary_points[0]]
for s in stationary_points:
candidates = [u for u in unique_locations
if is_index_close(s, u)]
# location is unique if no candidates found
if len(candidates) == 0:
unique_locations.append(s)
return unique_locations
