def cluster(self, gaze_points_list, inplace=False):
"""
Find clusters of input gaze data and label clustered points as smooth pursuit.
Labels (sets the 'EYE_MOVEMENT_TYPE' field) the clusters of data points as 'SP',
other samples as 'NOISE_CLUSTER'.
New column 'CLUSTER_ID' is added into the @DATA section of each arff object in @gaze_points_list,
indicating cluster group ID.
:param gaze_points_list: a list of arff objects (dictionary with fields such as 'data' and 'metadata')
:param inplace: whether to modify the original input gaze data with gaze data after clustering or use a copy
:return: gaze data after clustering in the same form as the input data.
"""
if not inplace:
gaze_points_list = copy.deepcopy(gaze_points_list)
# add global indexing to be able to reference the particular sample even after clustering all in one structure
for ind in xrange(len(gaze_points_list)):
ArffHelper.add_column(gaze_points_list[ind], name='global_index', dtype='INTEGER', default_value=-1)
gaze_points_list[ind]['data']['global_index'] = np.arange(gaze_points_list[ind]['data'].shape[0])
self._setup_internal_parameters(gaze_points_list)
self._data_set = self._aggregate_data(gaze_points_list)
# has to be a copy, so that is is placed continuously in memory
self._timestamps = self._data_set['time'].copy()
current_cluster_id = 0
for i in xrange(len(self._data_set)):
if self._data_set[i]['visited_flag'] == 1:
continue
else:
self._data_set[i]['visited_flag'] = 1
neighbourhood = self._get_neighbourhood(i)
if self._validate_neighbourhood(neighbourhood):
# if not: mark current point as NOISE
self._expand_cluster(i, neighbourhood, current_cluster_id)
current_cluster_id += 1
# create a new column in gaze_points_list for CLUSTER_ID
for i in xrange(len(gaze_points_list)):
ArffHelper.add_column(gaze_points_list[i], 'CLUSTER_ID', 'NUMERIC', -1)
# label data in gaze_points_list as SP according to CLUSTER_ID
for i in xrange(len(self._data_set)):
observer_id = int(self._data_set[i]['observer_id'])
global_index = self._data_set[i]['global_index']
if self._data_set[i]['CLUSTER_ID'] != -1:
gaze_points_list[observer_id]['data']['EYE_MOVEMENT_TYPE'][global_index] = 'SP'
gaze_points_list[observer_id]['data']['CLUSTER_ID'][global_index] = self._data_set[i]['CLUSTER_ID']
else:
gaze_points_list[observer_id]['data']['EYE_MOVEMENT_TYPE'][global_index] = 'NOISE_CLUSTER'
# can now remove the global_index column
for ind in xrange(len(gaze_points_list)):
ArffHelper.remove_column(gaze_points_list[ind], name='global_index')
return gaze_points_list
def add_eye_movement_attribute(arff_object):
"""
Add the EYE_MOVEMENT_TYPE attribute to the @arff_object. If already present, do nothing.
:param arff_object: arff object
:return: arff object with added column for eye movement type
"""
from recording_processor import EM_TYPE_ATTRIBUTE_NAME, EM_TYPE_ARFF_DATA_TYPE, EM_TYPE_DEFAULT_VALUE
if 'EYE_MOVEMENT_TYPE' not in arff_object['data'].dtype.names:
ArffHelper.add_column(arff_object, EM_TYPE_ATTRIBUTE_NAME,
EM_TYPE_ARFF_DATA_TYPE, EM_TYPE_DEFAULT_VALUE)
return arff_object
def test_add_column(self):
name = 'EM_type'
dtype = ['UNKNOWN', 'FIX', 'SACCADE', 'SP', 'NOISE']
a = ArffHelper.load(open('test_data/arff_data_example.arff'))
b = ArffHelper.add_column(a, name, dtype, 'UNKNOWN')
a['attributes'].append((name, dtype))
self.assertEqual(a['attributes'], b['attributes'])
def detect(self, gaze_points, inplace=False):
"""
This method labels saccades (also noise) in the provided gaze_points, which should be an arff object
:param gaze_points: gaze recording data, an arff object (i.e. a dictionary with 'data', 'metadata'
and etc. keys)
:param inplace: whether to replace the data inside @gaze_points or create a new structure
:return: gaze points with added labels SACCADE, NOISE
"""
if not inplace:
gaze_points = copy.deepcopy(gaze_points)
# also keep track of saccadic and intersaccadic intervals
detected_saccades_count = 0
if 'SACC_INTERVAL_INDEX' not in gaze_points['data'].dtype.names:
ArffHelper.add_column(gaze_points, 'SACC_INTERVAL_INDEX',
'INTEGER', -1)
# a virtual saccade that finished before the recording for uniform processing
last_saccade_end = -1
intersaccadic_intervals_count = 0
if 'INTERSACC_INTERVAL_INDEX' not in gaze_points['data'].dtype.names:
ArffHelper.add_column(gaze_points, 'INTERSACC_INTERVAL_INDEX',
'INTEGER', -1)
# verify that the timestamps are sorted!
times = gaze_points['data']['time']
assert all(times[i] <= times[i + 1] for i in xrange(len(times) - 1)), \
'Timestamps are not sorted in {}'.format(gaze_points['metadata']['filename'])
# -1 so that the exact value ends up on the right of the searched timestamp
searchable_timestamps = times - self.VELOCITY_INTEGRAL_INTERVAL_MICROSEC - 1
# find the indices of the first
prev_indices = np.searchsorted(times,
searchable_timestamps,
side='right')
cur_indices = np.arange(len(prev_indices))
# if the index after search points towards this very data point, take the previous one
prev_indices[prev_indices == cur_indices] -= 1
# except for the very first sample
prev_indices[0] = 0
# computing velocities
x_shifts = gaze_points['data']['x'][cur_indices] - gaze_points['data'][
'x'][prev_indices]
y_shifts = gaze_points['data']['y'][cur_indices] - gaze_points['data'][
'y'][prev_indices]
shifts = np.linalg.norm(np.vstack([x_shifts, y_shifts]), axis=0)
time_shifts = gaze_points['data']['time'][cur_indices] - gaze_points[
'data']['time'][prev_indices]
# keep it above 0, the shifts are 0 there anyway
time_shifts[time_shifts == 0] += 1
velocities = shifts / time_shifts # pixels per microsecond
ppd = util.calculate_ppd(gaze_points)
velocities /= ppd # degree per microsecond
velocities *= 1e6 # degree per second
# How many samples back is it reasonable to go?
time_step = np.diff(times).mean()
# a big margin of error, 10 times as many samples as would normally need
extra_samples_count = int(
np.round((self.MAX_DURATION_MICROSEC * 10) / time_step))
# Glitch detection: glitches are defined by one of several features.
#
# (1) Coordinates far outside the calibrated region (what constitutes far is defined
# by the tolerance parameter) are assumed to be erroneous.
is_glitch = np.zeros(gaze_points['data'].shape[0], dtype=np.bool)
is_glitch[gaze_points['data']['x'] <
-gaze_points['metadata']['width_px'] * self.TOLERANCE] = True
is_glitch[
gaze_points['data']['y'] < -gaze_points['metadata']['height_px'] *
self.TOLERANCE] = True
is_glitch[
gaze_points['data']['x'] > gaze_points['metadata']['width_px'] *
(1 + self.TOLERANCE)] = True
is_glitch[
gaze_points['data']['y'] > gaze_points['metadata']['height_px'] *
(1 + self.TOLERANCE)] = True
# (2) If the @gaze_points supports the estimate of a confidence
# measure for samples, a confidence lower than 0.1 also indicates
# a glitch here.
if 'confidence' in gaze_points['data'].dtype.names:
is_glitch[gaze_points['data']['confidence'] < 0.1] = True
# (3) Finally, velocities that exceed \a maxSpeed (default currently
# set to ~1000 degrees/s) are regarded as glitches as well and labelled as noise
is_glitch[velocities > self.MAX_SPEED_DEGREE_PER_SEC] = True
gaze_points['data']['EYE_MOVEMENT_TYPE'][
velocities > self.MAX_SPEED_DEGREE_PER_SEC] = 'NOISE'
# Remember first sample after glitch:
# to prevent saccade detection at the first non-glitch sample
# that follows, saccade detection is inhibited for that first sample.
post_glitch = np.diff(is_glitch.astype(int)) == -1
post_glitch = np.hstack(([False], post_glitch))
# Remember last sample before glitch:
# since we normally would suspend the other criteria (incl. speed) if we are inside glitch, we try to avoid
# border effects in both next-after and last-before glitch samples
pre_glitch = np.diff(is_glitch.astype(int)) == 1
pre_glitch = np.hstack((pre_glitch, [False]))
all_glitch = is_glitch + post_glitch + pre_glitch
# we will assign glitch samples' labels to NOISE after the saccades have been detected
# recompute speeds for post-glitch samples
pre_glitch_indices = np.nonzero(pre_glitch)[0]
for i in np.nonzero(post_glitch)[0]:
# find the corresponding start of the glitch
corresponding_pre_glitch = np.searchsorted(pre_glitch_indices,
i) - 1
if corresponding_pre_glitch < 0:
# no correspondence found, it's the glitch from the beginning of recording ==> set velocity to 0
velocities[i] = 0
else:
# found a completed glitch
velocities[i] = np.linalg.norm([
gaze_points['data']['x'][i] -
gaze_points['data']['x'][corresponding_pre_glitch],
gaze_points['data']['y'][i] -
gaze_points['data']['y'][corresponding_pre_glitch]
]) / (times[i] - times[corresponding_pre_glitch]
) # pixels per microsecond
velocities[i] /= ppd # degrees per microsecond
velocities[i] *= 1e6 # degrees per second
# Looking for saccade seed points
# saccade seed point should
# (1) exceed the fast threshold
# (2) be biologically plausible
# (3) not be inside a glitch
saccade_seeds = (velocities > self.THRESHOLD_ONSET_FAST_DEGREE_PER_SEC) * \
(velocities < self.MAX_SPEED_DEGREE_PER_SEC) * \
(1 - all_glitch)
saccade_seed_indices = np.nonzero(saccade_seeds)[0]
for potential_seed_index in saccade_seed_indices:
if gaze_points['data']['EYE_MOVEMENT_TYPE'][
potential_seed_index] != 'UNKNOWN':
# already labelled this before, ex. as a saccade that started from another seed point
continue
if self.verbose == 2:
print >> sys.stderr, 'potential seed index', potential_seed_index
# Looking for onset:
# (1) should be above slow threshold speed
# (2) should not be a glitch
# (3) does not yet have a label
onset_candidates_check = (velocities[max(0, potential_seed_index - extra_samples_count):potential_seed_index] >=
self.THRESHOLD_ONSET_SLOW_DEGREE_PER_SEC) * \
(1 - is_glitch[max(0, potential_seed_index - extra_samples_count):potential_seed_index]) * \
(gaze_points['data']['EYE_MOVEMENT_TYPE'][
max(0, potential_seed_index - extra_samples_count):potential_seed_index
] == 'UNKNOWN')
# find the last zero (the next sample after it is the beginning of the last uninterrupted 1-sequence,
# i.e. the saccade onset
try:
last_zero_index = np.nonzero(1 - onset_candidates_check)[0][-1]
except IndexError:
# not found
continue
saccade_onset_index = last_zero_index + 1 + max(
0, potential_seed_index -
extra_samples_count) # shift accordingly
# also this should not be the glitch or post/pre-glitch sample
while all_glitch[saccade_onset_index]:
saccade_onset_index += 1
# looking for offset
# (1) should be above offset speed threshold
# (2) should not exceed biologically plausible duration threshold
# (3) should not yet have a label (i.e. not NOISE labelled above)
offset_candidates_check = (velocities[potential_seed_index:potential_seed_index + extra_samples_count] >=
self.THRESHOLD_OFFSET_DEGREE_PER_SEC) * \
(times[potential_seed_index:potential_seed_index + extra_samples_count] -
times[saccade_onset_index] <= self.MAX_DURATION_MICROSEC)
# we ignore the criterion around the glitch
offset_candidates_check += is_glitch[
potential_seed_index:potential_seed_index +
extra_samples_count]
offset_candidates_check += post_glitch[
potential_seed_index:potential_seed_index +
extra_samples_count]
# but there should not yet be a label present, i.e. it's not the NOISE labelled above
offset_candidates_check *= (
gaze_points['data']['EYE_MOVEMENT_TYPE']
[potential_seed_index:potential_seed_index +
extra_samples_count] == 'UNKNOWN')
# find the first zero (this is the first sample with speed below the threshold, i.e. the saccade offset
try:
saccade_offset_index = np.nonzero(
1 - offset_candidates_check)[0][0]
except IndexError:
# no offset found
continue
# the index was starting at potential_seed_index
saccade_offset_index += potential_seed_index
# if we are finished inside the glitch, we have reached a biological limit of some sorts ==> discard
if is_glitch[saccade_offset_index]:
continue
if self.verbose == 2:
print >> sys.stderr, 'Found onset/offset indices', saccade_onset_index, saccade_offset_index
# now validate the saccade parameters
# (1) it spans at least the minimal necessary interval
saccade_time = times[saccade_offset_index] - times[
saccade_onset_index]
if saccade_time < self.MIN_DURATION_MICROSEC:
# If the resulting saccade is shorter than
# a minDuration, we assume that we have only encountered
# some noise impulse and discard this saccade.
gaze_points['data']['EYE_MOVEMENT_TYPE'][
saccade_onset_index:saccade_offset_index + 1] = 'NOISE'
if self.verbose == 2:
print >> sys.stderr, 'Discarding due to low duration: needed {}, had {}'.\
format(self.MIN_DURATION_MICROSEC, saccade_time)
continue
# (2) mean velocity is not below the slow onset threshold
saccade_displacement = np.linalg.norm([
gaze_points['data']['x'][saccade_offset_index] -
gaze_points['data']['x'][saccade_onset_index],
gaze_points['data']['y'][saccade_offset_index] -
gaze_points['data']['y'][saccade_onset_index],
])
mean_speed = saccade_displacement / saccade_time # pixels per microsecond
mean_speed /= ppd # degrees per microsecond
mean_speed *= 1e6 # degrees per second
if mean_speed < self.THRESHOLD_ONSET_SLOW_DEGREE_PER_SEC:
# Saccades where the average velocity drops below the offset threshold
# are also discarded (those are often due to some high-velocity samples
# going in one direction, then jumping back - which is unbiological).
if self.verbose == 2:
print >> sys.stderr, 'Discarding due to low average speed: needed {}, had {}'.format(
self.THRESHOLD_ONSET_SLOW_DEGREE_PER_SEC, mean_speed)
continue
# If all is okay, we detected a whole saccade
gaze_points['data']['EYE_MOVEMENT_TYPE'][
saccade_onset_index:saccade_offset_index + 1] = 'SACCADE'
# write the saccade index into the appropriate field and update the global count
gaze_points['data']['SACC_INTERVAL_INDEX'][saccade_onset_index:saccade_offset_index + 1] = \
detected_saccades_count
detected_saccades_count += 1
# from the end of last saccade till the beginning of this one, put appropriate intersaccadic interval index
# also update the global count of intersaccadic intervals
gaze_points['data']['INTERSACC_INTERVAL_INDEX'][last_saccade_end + 1:saccade_onset_index] = \
intersaccadic_intervals_count
intersaccadic_intervals_count += 1
last_saccade_end = saccade_offset_index
if self.verbose:
print >> sys.stderr, '{0} {1:0.1f} {2:0.1f} {3} {4:0.1f} {5:0.1f}'.format(
gaze_points['data'][saccade_onset_index]['time'],
gaze_points['data'][saccade_onset_index]['x'],
gaze_points['data'][saccade_onset_index]['y'],
gaze_points['data'][saccade_offset_index]['time'],
gaze_points['data'][saccade_offset_index]['x'],
gaze_points['data'][saccade_offset_index]['y'],
)
# final intersaccadic interval, if there is one
gaze_points['data']['INTERSACC_INTERVAL_INDEX'][last_saccade_end + 1:] = \
intersaccadic_intervals_count
intersaccadic_intervals_count += 1
# Override erroneous samples' labels
gaze_points['data']['EYE_MOVEMENT_TYPE'][is_glitch] = 'NOISE'
return gaze_points
def detect(self, gaze_points, inplace=False):
"""
Identify and label fixation intervals as 'FIX' and some others as 'NOISE'.
Fixation identification includes the following steps:
- First, all inter-saccadic intervals with a dispersion of less than
a certain spread threshold (@self.PREFILTERING_INTERVAL_SPREAD_THRESHOLD_DEGREES) are marked as fixations.
- Then, a temporal window (@self.SLIDING_WINDOW_WIDTH_MICROSEC ms) is shifted across the
remaining data and a non-fixation onset (offset) is marked every
time speed rises above (fell below) threshold (@self.SPEED_THRESHOLD_DEGREES_PER_SEC).
- There are two ways for speed calculation: spread and speed.
-'speed': speed from start point to end point is larger than
threshold.
-'spread': maximum moving speed of either x or y is larger than
threshold.
Data with speed below threshold are labeled as 'FIX'.
- Finally, non-fixation episodes longer than @self.MINIMAL_SP_DURATION_MICROSEC are kept as 'UNKNOWN',
the shorter ones are labeled as 'NOISE' (these are fairly dynamic episodes that however should not be SP).
:param gaze_points: arff object with saccades detected (and intersaccadic intervals labelled)
:param inplace: whether to replace the data inside @gaze_points or create a new structure
:return: arff object with data labeled as 'FIX' and 'NOISE'. Some 'UNKNOWN' labels are kept for the next stage.
"""
if not inplace:
gaze_points = copy.deepcopy(gaze_points)
# add a global index column (to keep track of where we are even if working within an intersaccadic interval)
gaze_points = ArffHelper.add_column(gaze_points,
name='global_index',
dtype='INTEGER',
default_value=-1)
gaze_points['data']['global_index'] = np.arange(
gaze_points['data'].shape[0])
# I. First step of fixation removal: rough prefiltering
#
# Convert constants to pixels per second
ppd = util.calculate_ppd(gaze_points)
speed_thd = ppd * self.SPEED_THRESHOLD_DEGREES_PER_SEC
prefiltering_spread_thd = ppd * self.PREFILTERING_INTERVAL_SPREAD_THRESHOLD_DEGREES
# record intersaccadic interval indices of those intervals that are not labelled as FIX by the prefiltering
unknown_interval_index = []
unknown_interval_masks = []
for i in xrange(
max(gaze_points['data']['INTERSACC_INTERVAL_INDEX']) + 1):
mask = gaze_points['data']['INTERSACC_INTERVAL_INDEX'] == i
intersacc_interval = gaze_points['data'][mask]
if len(intersacc_interval) == 0:
continue
dispersion = [
max(intersacc_interval['x']) - min(intersacc_interval['x']),
max(intersacc_interval['y']) - min(intersacc_interval['y'])
]
if any(thd >= prefiltering_spread_thd for thd in dispersion):
unknown_interval_index.append(i) # keep unknown
unknown_interval_masks.append(
mask.copy()) # cache the indexing
else:
gaze_points['data']['EYE_MOVEMENT_TYPE'][mask] = 'FIX'
# II. Second step of fixation removal: finer prefiltering
#
for i, interval_mask in zip(unknown_interval_index,
unknown_interval_masks):
# We record the borders of the non-FIX episodes to validate their duration. If the non-FIX episode is very
# short, we mark it as NOISE (not enough duration for a candidate for smooth pursuit)
onset_timestamp = None
onset_index = None
intersacc_interval = gaze_points['data'][interval_mask]
intersacc_interval = util.get_xy_moving_average(
intersacc_interval,
self.NORMALIZATION_SLIDING_WINDOW_SIZE_SAMPLES,
inplace=False)
# for intervals shorter than @self.INTERSACCADIC_INTERVAL_DURATION_THRESHOLD_MICROSEC:
# cannot do further filtering. The label remains 'UNKNOWN'
if intersacc_interval['time'][-1] - intersacc_interval['time'][0] < \
self.INTERSACCADIC_INTERVAL_DURATION_THRESHOLD_MICROSEC:
continue
# for intervals that longer than self.SLIDING_WINDOW_WIDTH_MICROSEC: do further pre-filtering.
# Label data as 'FIX' or 'NOISE', or keep 'UNKNOWN'
else:
# window is shifted by 1 sample every time
for index, item in enumerate(intersacc_interval):
x_start = item['x']
y_start = item['y']
shift_window_interval = intersacc_interval[
(intersacc_interval['time'] >= item['time']) *
(intersacc_interval['time'] <=
item['time'] + self.SLIDING_WINDOW_WIDTH_MICROSEC)]
# if distance between current data and the end of interval is shorter than
# self.SLIDING_WINDOW_WIDTH_MICROSEC (i.e. if the end of the window matches the end of the
# intersaccadic interval), we keep the previous label if it was FIX, otherwise keep UNKNOWN
if shift_window_interval['time'][-1] == intersacc_interval[
'time'][-1]:
if intersacc_interval['EYE_MOVEMENT_TYPE'][index -
1] == 'FIX':
gaze_points['data']['EYE_MOVEMENT_TYPE'][(
gaze_points['data']['time'] == item['time']
)] = 'FIX'
# we do not keep track of the non-fixation interval anymore since it will be all fixation
# until the end of the intersaccadic interval
onset_timestamp = None
onset_index = None
else:
# new non-fixation interval is starting
onset_timestamp = item['time']
onset_index = item['global_index']
# if distance between current data and the end of interval is larger than window size, continue
# with the process
else:
# get window duration in seconds
period = (shift_window_interval['time'][-1] -
shift_window_interval['time'][0]) * 1e-6
# is the fixation criterion satisfied?
fixation_flag = True
if self.SLIDING_WINDOW_CRITERION == 'speed':
# if the current speed is larger than speed threshold --
# mark as onset(UNKNOWN, NOISE). else -- mark as offset(FIX)
x_end = shift_window_interval['x'][-1]
y_end = shift_window_interval['y'][-1]
if math.sqrt(
(x_start - x_end)**2 +
(y_start - y_end)**2) >= speed_thd * period:
# will not be a fixation
fixation_flag = False
else: # spread
# if either x_max - x_min or y_max - y_min is larger than speed threshold * time --
# mark as onset. else -- mark as offset
x_max = max(shift_window_interval['x'])
x_min = min(shift_window_interval['x'])
y_max = max(shift_window_interval['y'])
y_min = min(shift_window_interval['y'])
if max(x_max - x_min,
y_max - y_min) >= speed_thd * period:
# will not be a fixation
fixation_flag = False
if fixation_flag:
gaze_points['data']['EYE_MOVEMENT_TYPE'][
item['global_index']] = 'FIX'
# either a fixation start or the whole interval end
if fixation_flag or index == len(
intersacc_interval) - 1:
# if we had a non-fixation interval going on before, check it's duration
if onset_index is not None:
# onset episode larger than 50ms: UNKNOWN. else: NOISE
if item['time'] - onset_timestamp < self.MINIMAL_SP_DURATION_MICROSEC:
offset_timestamp = item['time'] - 1
offset_index = item['global_index'] - 1
# if this is not the beginning of fixation,
# the last item also should be labelled as NOISE
if not fixation_flag:
offset_timestamp += 1
offset_index += 1
gaze_points['data'][onset_index:(
offset_index +
1)]['EYE_MOVEMENT_TYPE'] = 'NOISE'
# episode is finished
onset_timestamp = None
onset_index = None
else:
# if new non-fixation interval started
if onset_timestamp is None:
onset_timestamp = item['time']
onset_index = item['global_index']
# otherwise it just continues, don't have to do anything
# can now remove the global_index column
gaze_points = ArffHelper.remove_column(gaze_points, 'global_index')
return gaze_points