for trial in range(len(session_trial[day])):
beh_file_name = 'mouse_' + f'{mouse}' + '_session_' + f'{session}' + '_trial_' + \
f'{session_trial[day][trial]}' + '_likelihood_0.75.npy'
beh_path = behaviour_path + beh_file_name
if not os.path.isfile(beh_path):
print('ERROR: Behaviour file not found:' + beh_path)
else:
tracking = np.load(beh_path)
init_trial = int(timeline[trial * 2])
end_trial = int(timeline[trial * 2 + 1])
duration = np.min((tracking.shape[0], end_trial - init_trial))
cm = np.zeros((tracking.shape[0], 2))
body_part = 1
x = tracking[:, body_part * 2]
y = tracking[:, body_part * 2 + 1]
x_new, y_new = beh_func.interpolate_positions(x, y)
cm[:, 0] = signal.medfilt(x_new, 7)
cm[:, 1] = signal.medfilt(y_new, 7)
cm_vector[init_trial:init_trial + cm.shape[0], :] = cm[
0:cm_vector[init_trial:init_trial +
cm.shape[0], :].shape[0], :]
output_tracking_file = 'mouse_' + f'{mouse}' + '_session_' + f'{session}' + '_day_' + \
f'{day + 1}' + '_likelihood_0.75.npy'
output_tracking_path = category_path + output_tracking_file
np.save(output_tracking_path, cm_vector)
day = day + 1
beh_file_name = 'mouse_' + f'{mouse}' + '_session_' + f'{session}' + '_trial_' + \
f'{session_trial[day][trial]}' + '_likelihood_0.75.npy'
beh_path = behaviour_path + beh_file_name
if not os.path.isfile(beh_path):
print('ERROR: Behaviour file not found:' + beh_path)
else:
tracking = np.load(beh_path)
init_trial = int(timeline[trial * 2])
end_trial = int(timeline[trial * 2 + 1])
duration = np.min((tracking.shape[0], end_trial - init_trial))
x_positions_pre_nose = tracking[:, 0].T
y_positions_pre_nose = tracking[:, 1].T
x_positions_inter_nose, y_positions_inter_nose = beh_func.interpolate_positions(
x_positions_pre_nose, y_positions_pre_nose)
x_positions_nose = x_positions_inter_nose # gaussian_filter(x_positions_inter_nose, sigma=2)
y_positions_nose = y_positions_inter_nose # gaussian_filter(y_positions_inter_nose, sigma=2)
x_positions_pre_head = tracking[:, 2].T
y_positions_pre_head = tracking[:, 3].T
x_positions_inter_head, y_positions_inter_head = beh_func.interpolate_positions(
x_positions_pre_head, y_positions_pre_head)
x_positions_head = x_positions_inter_head # gaussian_filter(x_positions_inter_head, sigma=2)
y_positions_head = y_positions_inter_head # gaussian_filter(y_positions_inter_head, sigma=2)
position = np.array([x_positions_head, y_positions_head]).T
position_nose = np.array([x_positions_nose,
y_positions_nose]).T
vx = np.zeros((x_positions_head.shape[0], 1))
vy = np.zeros((y_positions_head.shape[0], 1))
def get_parameters(tracking, coordinates1, coordinates2):
positions_list = [
beh_func.interpolate_positions(signal1=tracking[:, i * 2].T,
signal2=tracking[:, i * 2 + 1].T)
for i in range(int(tracking.shape[1] / 2))
]
positions = np.array(positions_list)
cm = np.mean(positions, axis=0)
d1 = [distance(cm[:, i], coordinates1[i, :]) for i in range(cm.shape[1])]
d2 = [distance(cm[:, i], coordinates2[i, :]) for i in range(cm.shape[1])]
d1 = np.array(d1)
d2 = np.array(d2)
vx = np.zeros((cm.shape[1], 1))
vy = np.zeros((cm.shape[1], 1))
vx[1:, 0] = np.diff(cm[0, :])
vy[1:, 0] = np.diff(cm[1, :])
speed = np.squeeze(np.sqrt(vx * vx + vy * vy))
hd_list = [
head_direction(positions[:, :, i]) for i in range(positions.shape[2])
]
hd = np.array(hd_list)
## get object one direction
x_difference = [
coordinates1[i, 0] - tracking[i, 0] for i in range(tracking.shape[0])
]
y_difference = [
coordinates1[i, 1] - tracking[i, 1] for i in range(tracking.shape[0])
]
direction = np.array([np.array(x_difference), np.array(y_difference)]).T
direction = direction / npalg.norm(direction)
angle1_list = [
angle_between(hd[i], direction[i, :]) for i in range(len(hd))
]
angle1 = np.array(angle1_list)
x_difference = [
coordinates2[i, 0] - tracking[i, 0] for i in range(tracking.shape[0])
]
y_difference = [
coordinates2[i, 1] - tracking[i, 1] for i in range(tracking.shape[0])
]
direction2 = np.array([np.array(x_difference), np.array(y_difference)]).T
direction2 = direction2 / npalg.norm(direction2)
angle2_list = [
angle_between(hd[i], direction2[i, :]) for i in range(len(hd))
]
angle2 = np.array(angle2_list)
parameters = {
'cm': cm,
'head_direction': hd,
'speed': speed,
'angle1': angle1,
'angle2': angle2,
'dist_obj1': d1,
'dist_obj2': d2
}
return parameters