def get_stats_ds(self, vector_ds, sample_distance):
#TODO: get angular movement for ALL user, store in list structure
dd = sample_distance
stats_ds = []
for vec in vector_ds:
stats = []
for idx in range(len(vec)):
if idx < dd + 2:
stats.append([vec[idx][0], 0, 0, 0])
continue
dt = vec[idx][0] - vec[idx - dd][0]
theta = head_orientation_lib.angle_between(
vec[idx][1], vec[idx - dd][1])
v = theta * 1.0 / dt
# vec[idx][2] = v
# dv = vec[idx][2] - vec[idx-dd][2]
# a = dv * 1.0 / dt
# vec[idx][3] = a
item = [vec[idx][0], theta, v, 0]
stats.append(item)
stats = np.array(stats)
t_list, dtheta_list, _, _ = (stats[:, i] for i in range(4))
#stats[:, 2] = np.gradient(dtheta_list, t_list) #calculate velocity incorrectly
stats[:, 3] = np.gradient(stats[:, 2], t_list)
stats_ds.append(stats)
return stats_ds
def get_stats_ds0(self, vector_ds, sample_distance):
# TODO: get angular movement for ALL user, store in list structure
dd = sample_distance
stats_ds = []
for vec in vector_ds:
stats = []
for idx in range(len(vec)):
if idx < dd+2:
continue
dt = vec[idx][0] - vec[idx-dd][0]
theta = head_orientation_lib.angle_between(vec[idx][1], vec[idx-dd][1])
v = theta * 1.0 / dt
vec[idx][2] = v
dv = vec[idx][2] - vec[idx-dd][2]
a = dv * 1.0 / dt
vec[idx][3] = a
item = [vec[idx][0], theta, v, a]
stats.append(item)
stats = np.array(stats)
stats_ds.append(stats)
return stats_ds