def check_trial_event_consistency(self):
meta = load_stimuli_metadata(self.data_root, self.stimuli_version)
sfreq = self.raw.info['sfreq']
n_errors = 0
for i, event in enumerate(self.trial_events[:-1]):
event_id = event[2]
start = event[0]
if event_id < 1000:
stim_id, cond = decode_event_id(event_id)
if cond in [1, 2]:
field = 'length_with_cue'
else:
field = 'length_without_cue'
sample_len = sfreq * meta[stim_id][field]
else:
sample_len = 1
next_start = self.trial_events[i + 1, 0]
if next_start < start + sample_len:
expected_len = sample_len / float(sfreq)
event_len = (next_start - start) / float(sfreq)
log.warn('warning: event {} starts before expected end of {}'.
format(self.trial_events[i + 1], event))
log.warn(
'expected length: {:.3f}s, real length: {:.3f}s, delta: {:.3f}s'
.format(expected_len, event_len, expected_len - event_len))
n_errors += 1
log.info('{} problems detected.'.format(n_errors))
def check_trial_event_consistency(self):
meta = load_stimuli_metadata(self.data_root, self.stimuli_version)
sfreq = self.raw.info['sfreq']
n_errors = 0
for i, event in enumerate(self.trial_events[:-1]):
event_id = event[2]
start = event[0]
if event_id < 1000:
stim_id, cond = decode_event_id(event_id)
if cond in [1,2]:
field = 'length_with_cue'
else:
field = 'length_without_cue'
sample_len = sfreq * meta[stim_id][field]
else:
sample_len = 1
next_start = self.trial_events[i+1, 0]
if next_start < start + sample_len:
expected_len = sample_len / float(sfreq)
event_len = (next_start - start) / float(sfreq)
log.warn('warning: event {} starts before expected end of {}'.format(self.trial_events[i+1], event))
log.warn('expected length: {:.3f}s, real length: {:.3f}s, delta: {:.3f}s'.format(
expected_len, event_len, expected_len-event_len))
n_errors += 1
log.info('{} problems detected.'.format(n_errors))
def generate_beat_events(
trial_events, # base events as stored in raw fif files
include_cue_beats=True, # generate events for cue beats as well?
use_audio_onset=True, # use the more precise audio onset marker (code 1000) if present
exclude_stimulus_ids=[],
exclude_condition_ids=[],
beat_event_id_generator=default_beat_event_id_generator,
sr=512.0, # sample rate, correct value important to compute event frames
verbose=False,
version=None):
## prepare return value
beat_events = []
## get stimuli meta information
meta = load_stimuli_metadata_map(version=version)
beats = load_stimuli_metadata_map('beats',
verbose=verbose,
version=version)
if include_cue_beats:
cue_beats = load_stimuli_metadata_map('cue_beats')
## determine the number of cue beats
num_cue_beats = dict()
for stimulus_id in STIMULUS_IDS:
num_cue_beats[stimulus_id] = \
meta[stimulus_id]['beats_per_bar'] * meta[stimulus_id]['cue_bars']
if verbose:
print(num_cue_beats)
## helper function to add a single beat event
def add_beat_event(etime, stimulus_id, condition, beat_count, cue=False):
etype = beat_event_id_generator(stimulus_id, condition, cue,
beat_count)
beat_events.append([etime, 0, etype])
if verbose:
print(beat_events[-1])
## helper function to add a batch of beat events
def add_beat_events(etimes, stimulus_id, condition, cue=False):
beats_per_bar = meta[stimulus_id]['beats_per_bar']
for i, etime in enumerate(etimes):
beat_count = (i % beats_per_bar) + 1
add_beat_event(etime, stimulus_id, condition, beat_count, cue)
for i, event in enumerate(trial_events):
etype = event[2]
etime = event[0]
if verbose:
print('{:4d} at {:8d}'.format(etype, etime))
if etype >= 1000: # stimulus_id + condition
continue
stimulus_id, condition = decode_event_id(etype)
if stimulus_id in exclude_stimulus_ids or condition in exclude_condition_ids:
continue # skip excluded
trial_start = etime # default: use trial onset
if use_audio_onset and condition < 3:
# Note: conditions 3 and 4 have no audio cues
next_event = trial_events[i + 1]
if next_event[2] == 1000: # only use if audio onset
trial_start = next_event[0]
if verbose:
print('Trial start at {}'.format(trial_start))
if condition < 3: # cued
offset = sr * meta[stimulus_id]['length_of_cue']
if include_cue_beats:
cue_beat_times = trial_start + np.floor(
sr * cue_beats[stimulus_id])
cue_beat_times = cue_beat_times[:num_cue_beats[
stimulus_id]] # truncate at num_cue_beats
cue_beat_times = np.asarray(cue_beat_times, dtype=int)
if verbose:
print(cue_beat_times)
add_beat_events(cue_beat_times,
stimulus_id,
condition,
cue=True)
else:
offset = 0 # no cue
beat_times = trial_start + offset + np.floor(sr * beats[stimulus_id])
beat_times = np.asarray(beat_times, dtype=int)
if verbose:
print(beat_times[:5], '...')
add_beat_events(beat_times, stimulus_id, condition)
beat_events = np.asarray(beat_events, dtype=int)
return beat_events
def generate_beat_events(trial_events, # base events as stored in raw fif files
include_cue_beats=True, # generate events for cue beats as well?
use_audio_onset=True, # use the more precise audio onset marker (code 1000) if present
exclude_stimulus_ids=[],
exclude_condition_ids=[],
beat_event_id_generator=default_beat_event_id_generator,
sr=512.0, # sample rate, correct value important to compute event frames
verbose=False,
version=None):
## prepare return value
beat_events = []
## get stimuli meta information
meta = load_stimuli_metadata_map(version=version)
beats = load_stimuli_metadata_map('beats', verbose=verbose, version=version)
if include_cue_beats:
cue_beats = load_stimuli_metadata_map('cue_beats')
## determine the number of cue beats
num_cue_beats = dict()
for stimulus_id in STIMULUS_IDS:
num_cue_beats[stimulus_id] = \
meta[stimulus_id]['beats_per_bar'] * meta[stimulus_id]['cue_bars']
if verbose:
print num_cue_beats
## helper function to add a single beat event
def add_beat_event(etime, stimulus_id, condition, beat_count, cue=False):
etype = beat_event_id_generator(stimulus_id, condition, cue, beat_count)
beat_events.append([etime, 0, etype])
if verbose:
print beat_events[-1]
## helper function to add a batch of beat events
def add_beat_events(etimes, stimulus_id, condition, cue=False):
beats_per_bar = meta[stimulus_id]['beats_per_bar']
for i, etime in enumerate(etimes):
beat_count = (i % beats_per_bar) + 1
add_beat_event(etime, stimulus_id, condition, beat_count, cue)
for i, event in enumerate(trial_events):
etype = event[2]
etime = event[0]
if verbose:
print '{:4d} at {:8d}'.format(etype, etime)
if etype >= 1000: # stimulus_id + condition
continue
stimulus_id, condition = decode_event_id(etype)
if stimulus_id in exclude_stimulus_ids or condition in exclude_condition_ids:
continue # skip excluded
trial_start = etime # default: use trial onset
if use_audio_onset and condition < 3:
# Note: conditions 3 and 4 have no audio cues
next_event = trial_events[i+1]
if next_event[2] == 1000: # only use if audio onset
trial_start = next_event[0]
if verbose:
print 'Trial start at {}'.format(trial_start)
if condition < 3: # cued
offset = sr * meta[stimulus_id]['length_of_cue']
if include_cue_beats:
cue_beat_times = trial_start + np.floor(sr * cue_beats[stimulus_id])
cue_beat_times = cue_beat_times[:num_cue_beats[stimulus_id]] # truncate at num_cue_beats
cue_beat_times = np.asarray(cue_beat_times, dtype=int)
if verbose:
print cue_beat_times
add_beat_events(cue_beat_times, stimulus_id, condition, cue=True)
else:
offset = 0 # no cue
beat_times = trial_start + offset + np.floor(sr * beats[stimulus_id])
beat_times = np.asarray(beat_times, dtype=int)
if verbose:
print beat_times[:5], '...'
add_beat_events(beat_times, stimulus_id, condition)
beat_events = np.asarray(beat_events, dtype=int)
return beat_events
