return gaze
limit_seconds1 = [0, +2.5]
epoch_length1 = limit_seconds1[1] - limit_seconds1[0]
common.Number.frames1 = int(epoch_length1 * common.FS.eye)
gaze = np.empty([
common.Number.frames1, common.Number.axes, common.Number.trials,
common.Number.players, common.Number.sessions
])
gaze[:] = np.nan
for SESSION in np.array(range(0, 20)):
common.print_stars()
print(SESSION)
if SESSION == 1:
print('missing eye data...')
continue
for PLAYER in range(0, common.Number.players):
SUBJECT = PLAYER + SESSION * 2
print(SUBJECT)
filename = '..\\eye_tracking\\preprocess_eye_data2\\' + common.Labels.session[
SESSION] + '.' + common.Labels.player[
PLAYER] + ".processedEye.mat"
print(filename)
def export_endpoints_and_trajectories():
results_mat = scipy.io.loadmat(
"..\\behavioural_performance\\endpoint_accuracy\\results.mat")
curvature_mat = scipy.io.loadmat(
"..\\behavioural_performance\\curvature_runner\\collated_curvature_results.mat"
)
collated_curvature_results = curvature_mat[
'collated_curvature_results'] # (960, 3, 20)
# print(collated_curvature_results.shape)
def get_endpoints_and_trajectories(SESSION, verbose=False):
session = np.repeat(SESSION + 1, common.Number.trials) # (960,)
trial = np.array((range(1, common.Number.trials + 1))) # (960,)
control = results_mat['results'][SESSION]['control'][0].flatten(
) # (960,)
control = np.array(control, dtype=object)
control[control == 1] = common.Labels.control[0]
control[control == 2] = common.Labels.control[1]
target_position = results_mat['results'][SESSION]['target_position'][
0].flatten() # (960,)
endpoint = results_mat['results'][SESSION]['endpoint'][
0] # (2, 960, 3)
endpoint_displacement = results_mat['results'][SESSION][
'endpoint_displacement'][0] # (960, 3)
trajectory = results_mat['results'][SESSION]['trajectory'][
0] # (2, 1008, 960, 3)
# sort by best performer on endpoint displacement
for TRIAL in range(1, common.Number.trials):
index = np.argsort(endpoint_displacement[TRIAL, 0:2])
if verbose:
print(endpoint_displacement[TRIAL, 0:2])
print(index)
endpoint_displacement[TRIAL, 0:2] = endpoint_displacement[TRIAL,
index]
endpoint[:, TRIAL, 0:2] = endpoint[:, TRIAL, index]
endpoint_HP_solo_x = endpoint[0, :, 0]
endpoint_HP_solo_y = endpoint[1, :, 0]
endpoint_LP_solo_x = endpoint[0, :, 1]
endpoint_LP_solo_y = endpoint[1, :, 1]
endpoint_Joint_x = endpoint[0, :, 2]
endpoint_Joint_y = endpoint[1, :, 2]
columns = [
'session', 'trial', 'control', 'target_position',
'endpoint_HP_solo_x', 'endpoint_HP_solo_y', 'endpoint_LP_solo_x',
'endpoint_LP_solo_y', 'endpoint_Joint_x', 'endpoint_Joint_y'
]
data = np.transpose(
np.array([
session, trial, control, target_position, endpoint_HP_solo_x,
endpoint_HP_solo_y, endpoint_LP_solo_x, endpoint_LP_solo_y,
endpoint_Joint_x, endpoint_Joint_y
]))
df_endpoint = pd.DataFrame(data, columns=columns)
curvature = collated_curvature_results[:, :, SESSION]
if SESSION == 20:
curvature[705:common.Number.
trials] = np.nan # controller disconnected
# sort by best performer on trajectory displacement
for TRIAL in range(1, common.Number.trials):
index = np.argsort(curvature[TRIAL, 0:2])
curvature[TRIAL, 0:2] = curvature[TRIAL, index]
trajectory[:, :, TRIAL, 0:2] = trajectory[:, :, TRIAL, index]
trajectory_HP_solo_x = trajectory[0, :, :,
0].flatten('K') # (2, 1008, 960, 3)
trajectory_HP_solo_y = trajectory[1, :, :,
0].flatten('K') # (2, 1008, 960, 3)
trajectory_LP_solo_x = trajectory[0, :, :,
1].flatten('K') # (2, 1008, 960, 3)
trajectory_LP_solo_y = trajectory[1, :, :,
1].flatten('K') # (2, 1008, 960, 3)
trajectory_Joint_x = trajectory[0, :, :,
2].flatten('K') # (2, 1008, 960, 3)
trajectory_Joint_y = trajectory[1, :, :,
2].flatten('K') # (2, 1008, 960, 3)
session = np.repeat(SESSION + 1, common.Number.frames *
common.Number.trials) # (967680,)
trial = np.repeat(np.array((range(1, common.Number.trials + 1))),
common.Number.frames) # (967680,)
frame = np.resize(range(1, common.Number.frames + 1),
common.Number.frames *
common.Number.trials) # (967680,)
control = np.repeat(
results_mat['results'][SESSION]['control'][0].flatten(),
common.Number.frames) # (967680,)
control = np.array(control, dtype=object)
control[control == 1] = common.Labels.control[0]
control[control == 2] = common.Labels.control[1]
target_position = np.repeat(
results_mat['results'][SESSION]['target_position'][0].flatten(),
common.Number.frames) # (967680,)
columns = [
'session', 'trial', 'control', 'target_position', 'frame',
'trajectory_HP_solo_x', 'trajectory_HP_solo_y',
'trajectory_LP_solo_x', 'trajectory_LP_solo_y',
'trajectory_Joint_x', 'trajectory_Joint_y'
]
data = np.transpose(
np.array([
session, trial, control, target_position, frame,
trajectory_HP_solo_x, trajectory_HP_solo_y,
trajectory_LP_solo_x, trajectory_LP_solo_y, trajectory_Joint_x,
trajectory_Joint_y
]))
df_trajectory = pd.DataFrame(data, columns=columns)
if verbose:
print(control.shape)
print(target_position.shape)
print(endpoint.shape)
print(endpoint_displacement.shape)
print(trajectory.shape)
print(session.shape)
print(trial.shape)
print(data.shape)
print(curvature.shape)
return df_endpoint, df_trajectory
df_endpoint_list = []
df_trajectory_list = []
for SESSION in range(0, common.Number.sessions):
common.print_stars()
print(SESSION)
df_endpoint, df_trajectory = get_endpoints_and_trajectories(
SESSION, False)
df_endpoint_list.append(df_endpoint)
df_trajectory_list.append(df_trajectory)
df_endpoint = pd.concat(df_endpoint_list)
df_trajectory = pd.concat(df_trajectory_list)
df_endpoint.to_csv(output_directory + 'Fig. 3c.csv')
df_trajectory.to_csv(output_directory + 'Fig. 3a.csv')
## plot endpoints
endpoint_plot(df_endpoint)
## plot trajectories
trajectory_plot(df_trajectory)