def epochs2mat(data_dir,
channel_picks,
event_id,
tmin,
tmax,
merge_epochs=False,
spfilter=None,
spchannels=None):
if merge_epochs:
# load all raw files in the directory and merge epochs
fiflist = []
for data_file in qc.get_file_list(data_dir, fullpath=True):
if data_file[-4:] != '.fif':
continue
fiflist.append(data_file)
raw, events = pu.load_multi(fiflist,
spfilter=spfilter,
spchannels=spchannels)
matfile = data_dir + '/epochs_all.mat'
save_mat(raw, events, channel_picks, event_id, tmin, tmax, matfile)
else:
# process individual raw file separately
for data_file in qc.get_file_list(data_dir, fullpath=True):
if data_file[-4:] != '.fif':
continue
[base, fname, fext] = qc.parse_path_list(data_file)
matfile = '%s/%s-epochs.mat' % (base, fname)
raw, events = pu.load_raw(data_file)
save_mat(raw, events, channel_picks, event_id, tmin, tmax, matfile)
logger.info('Exported to %s' % matfile)
def run(fif_file):
print('Loading "%s"' % fif_file)
raw, events = pu.load_raw(fif_file)
print('Raw info: %s' % raw)
print('Channels: %s' % ', '.join(raw.ch_names))
print('Events: %s' % set(events[:, 2]))
print('Sampling freq: %.3f Hz' % raw.info['sfreq'])
qc.print_c('\n>> Interactive mode start. Type quit or Ctrl+D to finish',
'g')
qc.print_c('>> Variables: raw, events\n', 'g')
embed()
def fif_resample(fif_dir, sfreq_target):
out_dir = fif_dir + '/fif_resample%d' % sfreq_target
qc.make_dirs(out_dir)
for f in qc.get_file_list(fif_dir):
pp = qc.parse_path(f)
if pp.ext != 'fif':
continue
logger.info('Resampling %s' % f)
raw, events = pu.load_raw(f)
raw.resample(sfreq_target)
fif_out = '%s/%s.fif' % (out_dir, pp.name)
raw.save(fif_out)
logger.info('Exported to %s' % fif_out)
def fif2mat(data_dir):
out_dir = '%s/mat_files' % data_dir
qc.make_dirs(out_dir)
for rawfile in qc.get_file_list(data_dir, fullpath=True):
if rawfile[-4:] != '.fif': continue
raw, events = pu.load_raw(rawfile)
events[:, 0] += 1 # MATLAB uses 1-based indexing
sfreq = raw.info['sfreq']
data = dict(signals=raw._data,
events=events,
sfreq=sfreq,
ch_names=raw.ch_names)
fname = qc.parse_path(rawfile).name
matfile = '%s/%s.mat' % (out_dir, fname)
scipy.io.savemat(matfile, data)
logger.info('Exported to %s' % matfile)
logger.info('Finished exporting.')
def merge_events(trigger_file, events, rawfile_in, rawfile_out):
tdef = trigger_def(trigger_file)
raw, eve = pu.load_raw(rawfile_in)
logger.info('=== Before merging ===')
notfounds = []
for key in np.unique(eve[:, 2]):
if key in tdef.by_value:
logger.info(
'%s: %d events' %
(tdef.by_value[key], len(np.where(eve[:, 2] == key)[0])))
else:
logger.info('%d: %d events' %
(key, len(np.where(eve[:, 2] == key)[0])))
notfounds.append(key)
if notfounds:
for key in notfounds:
logger.warning('Key %d was not found in the definition file.' %
key)
for key in events:
ev_src = events[key]
ev_out = tdef.by_name[key]
x = []
for e in ev_src:
x.append(np.where(eve[:, 2] == tdef.by_name[e])[0])
eve[np.concatenate(x), 2] = ev_out
# sanity check
dups = np.where(0 == np.diff(eve[:, 0]))[0]
assert len(dups) == 0
# reset trigger channel
raw._data[0] *= 0
raw.add_events(eve, 'TRIGGER')
raw.save(rawfile_out, overwrite=True)
logger.info('=== After merging ===')
for key in np.unique(eve[:, 2]):
if key in tdef.by_value:
logger.info(
'%s: %d events' %
(tdef.by_value[key], len(np.where(eve[:, 2] == key)[0])))
else:
logger.info('%s: %d events' %
(key, len(np.where(eve[:, 2] == key)[0])))
def fix_channel_names(fif_dir, new_channel_names):
'''
Change channel names of fif files in a given directory.
Input
-----
@fif_dir: path to fif files
@new_channel_names: list of new channel names
Output
------
Modified fif files are saved in fif_dir/corrected/
Kyuhwa Lee, 2019.
'''
flist = []
for f in qc.get_file_list(fif_dir):
if qc.parse_path(f).ext == 'fif':
flist.append(f)
if len(flist) > 0:
qc.make_dirs('%s/corrected' % fif_dir)
for f in qc.get_file_list(fif_dir):
logger.info('\nLoading %s' % f)
p = qc.parse_path(f)
if p.ext == 'fif':
raw, eve = pu.load_raw(f)
if len(raw.ch_names) != len(new_channel_names):
raise RuntimeError('The number of new channels do not matach that of fif file.')
raw.info['ch_names'] = new_channel_names
for ch, new_ch in zip(raw.info['chs'], new_channel_names):
ch['ch_name'] = new_ch
out_fif = '%s/corrected/%s.fif' % (p.dir, p.name)
logger.info('Exporting to %s' % out_fif)
raw.save(out_fif)
else:
logger.warning('No fif files found in %s' % fif_dir)
def raw2psd(rawfile=None,
fmin=1,
fmax=40,
wlen=0.5,
wstep=1,
tmin=0.0,
tmax=None,
channel_picks=None,
excludes=[],
n_jobs=1):
"""
Compute PSD features over a sliding window on the entire raw file.
Leading edge of the window is the time reference, i.e. do not use future data.
Input
=====
rawfile: fif file.
channel_picks: None or list of channel names
tmin (sec): start time of the PSD window relative to the event onset.
tmax (sec): end time of the PSD window relative to the event onset. None = until the end.
fmin (Hz): minimum PSD frequency
fmax (Hz): maximum PSD frequency
wlen (sec): sliding window length for computing PSD (sec)
wstep (int): sliding window step (time samples)
excludes (list): list of channels to exclude
"""
raw, eve = pu.load_raw(rawfile)
sfreq = raw.info['sfreq']
wframes = int(round(sfreq * wlen))
raw_eeg = raw.pick_types(meg=False, eeg=True, stim=False, exclude=excludes)
if channel_picks is None:
rawdata = raw_eeg._data
chlist = raw.ch_names
else:
chlist = []
for ch in channel_picks:
chlist.append(raw.ch_names.index(ch))
rawdata = raw_eeg._data[np.array(chlist)]
if tmax is None:
t_end = rawdata.shape[1]
else:
t_end = int(round(tmax * sfreq))
t_start = int(round(tmin * sfreq)) + wframes
psde = mne.decoding.PSDEstimator(sfreq, fmin=fmin, fmax=fmax, n_jobs=1,\
bandwidth=None, low_bias=True, adaptive=False, normalization='length',
verbose=None)
print('[PID %d] %s' % (os.getpid(), rawfile))
psd_all = []
evelist = []
times = []
t_len = t_end - t_start
last_eve = 0
y_i = 0
t_last = t_start
tm = qc.Timer()
for t in range(t_start, t_end, wstep):
# compute PSD
window = rawdata[:, t - wframes:t]
psd = psde.transform(
window.reshape((1, window.shape[0], window.shape[1])))
psd = psd.reshape(psd.shape[1], psd.shape[2])
psd_all.append(psd)
times.append(t)
# matching events at the current window
if y_i < eve.shape[0] and t >= eve[y_i][0]:
last_eve = eve[y_i][2]
y_i += 1
evelist.append(last_eve)
if tm.sec() >= 1:
perc = (t - t_start) / t_len
fps = (t - t_last) / wstep
est = (t_end - t) / wstep / fps
logger.info('[PID %d] %.1f%% (%.1f FPS, %ds left)' %
(os.getpid(), perc * 100.0, fps, est))
t_last = t
tm.reset()
logger.info('Finished.')
# export data
try:
chnames = [raw.ch_names[ch] for ch in chlist]
psd_all = np.array(psd_all)
[basedir, fname, fext] = qc.parse_path_list(rawfile)
fout_header = '%s/psd-%s-header.pkl' % (basedir, fname)
fout_psd = '%s/psd-%s-data.npy' % (basedir, fname)
header = {
'psdfile': fout_psd,
'times': np.array(times),
'sfreq': sfreq,
'channels': chnames,
'wframes': wframes,
'events': evelist
}
qc.save_obj(fout_header, header)
np.save(fout_psd, psd_all)
logger.info('Exported to:\n(header) %s\n(numpy array) %s' %
(fout_header, fout_psd))
except:
logger.exception('(%s) Unexpected error occurred while exporting data. Dropping you into a shell for recovery.' %\
os.path.basename(__file__))
embed()
def stream_player(server_name,
fif_file,
chunk_size,
auto_restart=True,
wait_start=True,
repeat=np.float('inf'),
high_resolution=False,
trigger_file=None):
"""
Input
=====
server_name: LSL server name.
fif_file: fif file to replay.
chunk_size: number of samples to send at once (usually 16-32 is good enough).
auto_restart: play from beginning again after reaching the end.
wait_start: wait for user to start in the beginning.
repeat: number of loops to play.
high_resolution: use perf_counter() instead of sleep() for higher time resolution
but uses much more cpu due to polling.
trigger_file: used to convert event numbers into event strings for readability.
Note: Run neurodecode.set_log_level('DEBUG') to print out the relative time stamps since started.
"""
raw, events = pu.load_raw(fif_file)
sfreq = raw.info['sfreq'] # sampling frequency
n_channels = len(raw.ch_names) # number of channels
if trigger_file is not None:
tdef = trigger_def(trigger_file)
try:
event_ch = raw.ch_names.index('TRIGGER')
except ValueError:
event_ch = None
if raw is not None:
logger.info_green('Successfully loaded %s' % fif_file)
logger.info('Server name: %s' % server_name)
logger.info('Sampling frequency %.3f Hz' % sfreq)
logger.info('Number of channels : %d' % n_channels)
logger.info('Chunk size : %d' % chunk_size)
for i, ch in enumerate(raw.ch_names):
logger.info('%d %s' % (i, ch))
logger.info('Trigger channel : %s' % event_ch)
else:
raise RuntimeError('Error while loading %s' % fif_file)
# set server information
sinfo = pylsl.StreamInfo(server_name, channel_count=n_channels, channel_format='float32',\
nominal_srate=sfreq, type='EEG', source_id=server_name)
desc = sinfo.desc()
channel_desc = desc.append_child("channels")
for ch in raw.ch_names:
channel_desc.append_child('channel').append_child_value('label', str(ch))\
.append_child_value('type','EEG').append_child_value('unit','microvolts')
desc.append_child('amplifier').append_child('settings').append_child_value(
'is_slave', 'false')
desc.append_child('acquisition').append_child_value(
'manufacturer',
'NeuroDecode').append_child_value('serial_number', 'N/A')
outlet = pylsl.StreamOutlet(sinfo, chunk_size=chunk_size)
if wait_start:
input('Press Enter to start streaming.')
logger.info('Streaming started')
idx_chunk = 0
t_chunk = chunk_size / sfreq
finished = False
if high_resolution:
t_start = time.perf_counter()
else:
t_start = time.time()
# start streaming
played = 1
while played < repeat:
idx_current = idx_chunk * chunk_size
chunk = raw._data[:, idx_current:idx_current + chunk_size]
data = chunk.transpose().tolist()
if idx_current >= raw._data.shape[1] - chunk_size:
finished = True
if high_resolution:
# if a resolution over 2 KHz is needed
t_sleep_until = t_start + idx_chunk * t_chunk
while time.perf_counter() < t_sleep_until:
pass
else:
# time.sleep() can have 500 us resolution using the tweak tool provided.
t_wait = t_start + idx_chunk * t_chunk - time.time()
if t_wait > 0.001:
time.sleep(t_wait)
outlet.push_chunk(data)
logger.debug('[%8.3fs] sent %d samples (LSL %8.3f)' %
(time.perf_counter(), len(data), pylsl.local_clock()))
if event_ch is not None:
event_values = set(chunk[event_ch]) - set([0])
if len(event_values) > 0:
if trigger_file is None:
logger.info('Events: %s' % event_values)
else:
for event in event_values:
if event in tdef.by_value:
logger.info('Events: %s (%s)' %
(event, tdef.by_value[event]))
else:
logger.info('Events: %s (Undefined event)' % event)
idx_chunk += 1
if finished:
if auto_restart is False:
input(
'Reached the end of data. Press Enter to restart or Ctrl+C to stop.'
)
else:
logger.info('Reached the end of data. Restarting.')
idx_chunk = 0
finished = False
if high_resolution:
t_start = time.perf_counter()
else:
t_start = time.time()
played += 1
def get_tfr(cfg, recursive=False, n_jobs=1):
'''
@params:
tfr_type: 'multitaper' or 'morlet'
recursive: if True, load raw files in sub-dirs recursively
export_path: path to save plots
n_jobs: number of cores to run in parallel
'''
cfg = check_config(cfg)
tfr_type = cfg.TFR_TYPE
export_path = cfg.EXPORT_PATH
t_buffer = cfg.T_BUFFER
if tfr_type == 'multitaper':
tfr = mne.time_frequency.tfr_multitaper
elif tfr_type == 'morlet':
tfr = mne.time_frequency.tfr_morlet
elif tfr_type == 'butter':
butter_order = 4 # TODO: parameterize
tfr = lfilter
elif tfr_type == 'fir':
raise NotImplementedError
else:
raise ValueError('Wrong TFR type %s' % tfr_type)
n_jobs = cfg.N_JOBS
if n_jobs is None:
n_jobs = mp.cpu_count()
if hasattr(cfg, 'DATA_PATHS'):
if export_path is None:
raise ValueError(
'For multiple directories, cfg.EXPORT_PATH cannot be None')
else:
outpath = export_path
# custom event file
if hasattr(cfg, 'EVENT_FILE') and cfg.EVENT_FILE is not None:
events = mne.read_events(cfg.EVENT_FILE)
file_prefix = 'grandavg'
# load and merge files from all directories
flist = []
for ddir in cfg.DATA_PATHS:
ddir = ddir.replace('\\', '/')
if ddir[-1] != '/': ddir += '/'
for f in qc.get_file_list(ddir, fullpath=True,
recursive=recursive):
if qc.parse_path(f).ext in ['fif', 'bdf', 'gdf']:
flist.append(f)
raw, events = pu.load_multi(flist)
else:
logger.info('Loading %s' % cfg.DATA_FILE)
raw, events = pu.load_raw(cfg.DATA_FILE)
# custom events
if hasattr(cfg, 'EVENT_FILE') and cfg.EVENT_FILE is not None:
events = mne.read_events(cfg.EVENT_FILE)
if export_path is None:
[outpath, file_prefix, _] = qc.parse_path_list(cfg.DATA_FILE)
else:
file_prefix = qc.parse_path(cfg.DATA_FILE).name
outpath = export_path
file_prefix = qc.parse_path(cfg.DATA_FILE).name
# re-referencing
if cfg.REREFERENCE is not None:
pu.rereference(raw, cfg.REREFERENCE[1], cfg.REREFERENCE[0])
assert cfg.REREFERENCE[0] in raw.ch_names
sfreq = raw.info['sfreq']
# set channels of interest
picks = pu.channel_names_to_index(raw, cfg.CHANNEL_PICKS)
spchannels = pu.channel_names_to_index(raw, cfg.SP_CHANNELS)
if max(picks) > len(raw.info['ch_names']):
msg = 'ERROR: "picks" has a channel index %d while there are only %d channels.' %\
(max(picks), len(raw.info['ch_names']))
raise RuntimeError(msg)
# Apply filters
raw = pu.preprocess(raw,
spatial=cfg.SP_FILTER,
spatial_ch=spchannels,
spectral=cfg.TP_FILTER,
spectral_ch=picks,
notch=cfg.NOTCH_FILTER,
notch_ch=picks,
multiplier=cfg.MULTIPLIER,
n_jobs=n_jobs)
# Read epochs
classes = {}
for t in cfg.TRIGGERS:
if t in set(events[:, -1]):
if hasattr(cfg, 'tdef'):
classes[cfg.tdef.by_value[t]] = t
else:
classes[str(t)] = t
if len(classes) == 0:
raise ValueError('No desired event was found from the data.')
try:
tmin = cfg.EPOCH[0]
tmin_buffer = tmin - t_buffer
raw_tmax = raw._data.shape[1] / sfreq - 0.1
if cfg.EPOCH[1] is None:
if cfg.POWER_AVERAGED:
raise ValueError(
'EPOCH value cannot have None for grand averaged TFR')
else:
if len(cfg.TRIGGERS) > 1:
raise ValueError(
'If the end time of EPOCH is None, only a single event can be defined.'
)
t_ref = events[np.where(
events[:, 2] == list(cfg.TRIGGERS)[0])[0][0], 0] / sfreq
tmax = raw_tmax - t_ref - t_buffer
else:
tmax = cfg.EPOCH[1]
tmax_buffer = tmax + t_buffer
if tmax_buffer > raw_tmax:
raise ValueError(
'Epoch length with buffer (%.3f) is larger than signal length (%.3f)'
% (tmax_buffer, raw_tmax))
epochs_all = mne.Epochs(raw,
events,
classes,
tmin=tmin_buffer,
tmax=tmax_buffer,
proj=False,
picks=picks,
baseline=None,
preload=True)
if epochs_all.drop_log_stats() > 0:
logger.error(
'\n** Bad epochs found. Dropping into a Python shell.')
logger.error(epochs_all.drop_log)
logger.error('tmin = %.1f, tmax = %.1f, tmin_buffer = %.1f, tmax_buffer = %.1f, raw length = %.1f' % \
(tmin, tmax, tmin_buffer, tmax_buffer, raw._data.shape[1] / sfreq))
logger.error('\nType exit to continue.\n')
pdb.set_trace()
except:
logger.critical(
'\n*** (tfr_export) Unknown error occurred while epoching ***')
logger.critical('tmin = %.1f, tmax = %.1f, tmin_buffer = %.1f, tmax_buffer = %.1f, raw length = %.1f' % \
(tmin, tmax, tmin_buffer, tmax_buffer, raw._data.shape[1] / sfreq))
pdb.set_trace()
power = {}
for evname in classes:
export_dir = outpath
qc.make_dirs(export_dir)
logger.info('>> Processing %s' % evname)
freqs = cfg.FREQ_RANGE # define frequencies of interest
n_cycles = freqs / 2. # different number of cycle per frequency
if cfg.POWER_AVERAGED:
# grand-average TFR
epochs = epochs_all[evname][:]
if len(epochs) == 0:
logger.WARNING('No %s epochs. Skipping.' % evname)
continue
if tfr_type == 'butter':
b, a = butter_bandpass(cfg.FREQ_RANGE[0],
cfg.FREQ_RANGE[-1],
sfreq,
order=butter_order)
tfr_filtered = lfilter(b, a, epochs, axis=2)
tfr_hilbert = hilbert(tfr_filtered)
tfr_power = abs(tfr_hilbert)
tfr_data = np.mean(tfr_power, axis=0)
elif tfr_type == 'fir':
raise NotImplementedError
else:
power[evname] = tfr(epochs,
freqs=freqs,
n_cycles=n_cycles,
use_fft=False,
return_itc=False,
decim=1,
n_jobs=n_jobs)
power[evname] = power[evname].crop(tmin=tmin, tmax=tmax)
tfr_data = power[evname].data
if cfg.EXPORT_MATLAB is True:
# export all channels to MATLAB
mout = '%s/%s-%s-%s.mat' % (export_dir, file_prefix,
cfg.SP_FILTER, evname)
scipy.io.savemat(
mout, {
'tfr': tfr_data,
'chs': epochs.ch_names,
'events': events,
'sfreq': sfreq,
'tmin': tmin,
'tmax': tmax,
'epochs': cfg.EPOCH,
'freqs': cfg.FREQ_RANGE
})
logger.info('Exported %s' % mout)
if cfg.EXPORT_PNG is True:
# Inspect power for each channel
for ch in np.arange(len(picks)):
chname = raw.ch_names[picks[ch]]
title = 'Peri-event %s - Channel %s' % (evname, chname)
# mode= None | 'logratio' | 'ratio' | 'zscore' | 'mean' | 'percent'
fig = power[evname].plot([ch],
baseline=cfg.BS_TIMES,
mode=cfg.BS_MODE,
show=False,
colorbar=True,
title=title,
vmin=cfg.VMIN,
vmax=cfg.VMAX,
dB=False)
fout = '%s/%s-%s-%s-%s.png' % (
export_dir, file_prefix, cfg.SP_FILTER, evname, chname)
fig.savefig(fout)
plt.close()
logger.info('Exported to %s' % fout)
else:
# TFR per event
for ep in range(len(epochs_all[evname])):
epochs = epochs_all[evname][ep]
if len(epochs) == 0:
logger.WARNING('No %s epochs. Skipping.' % evname)
continue
power[evname] = tfr(epochs,
freqs=freqs,
n_cycles=n_cycles,
use_fft=False,
return_itc=False,
decim=1,
n_jobs=n_jobs)
power[evname] = power[evname].crop(tmin=tmin, tmax=tmax)
if cfg.EXPORT_MATLAB is True:
# export all channels to MATLAB
mout = '%s/%s-%s-%s-ep%02d.mat' % (
export_dir, file_prefix, cfg.SP_FILTER, evname, ep + 1)
scipy.io.savemat(
mout, {
'tfr': power[evname].data,
'chs': power[evname].ch_names,
'events': events,
'sfreq': sfreq,
'tmin': tmin,
'tmax': tmax,
'epochs': cfg.EPOCH,
'freqs': cfg.FREQ_RANGE
})
logger.info('Exported %s' % mout)
if cfg.EXPORT_PNG is True:
# Inspect power for each channel
for ch in np.arange(len(picks)):
chname = raw.ch_names[picks[ch]]
title = 'Peri-event %s - Channel %s, Trial %d' % (
evname, chname, ep + 1)
# mode= None | 'logratio' | 'ratio' | 'zscore' | 'mean' | 'percent'
fig = power[evname].plot([ch],
baseline=cfg.BS_TIMES,
mode=cfg.BS_MODE,
show=False,
colorbar=True,
title=title,
vmin=cfg.VMIN,
vmax=cfg.VMAX,
dB=False)
fout = '%s/%s-%s-%s-%s-ep%02d.png' % (
export_dir, file_prefix, cfg.SP_FILTER, evname,
chname, ep + 1)
fig.savefig(fout)
plt.close()
logger.info('Exported %s' % fout)
if hasattr(cfg, 'POWER_DIFF'):
export_dir = '%s/diff' % outpath
qc.make_dirs(export_dir)
labels = classes.keys()
df = power[labels[0]] - power[labels[1]]
df.data = np.log(np.abs(df.data))
# Inspect power diff for each channel
for ch in np.arange(len(picks)):
chname = raw.ch_names[picks[ch]]
title = 'Peri-event %s-%s - Channel %s' % (labels[0], labels[1],
chname)
# mode= None | 'logratio' | 'ratio' | 'zscore' | 'mean' | 'percent'
fig = df.plot([ch],
baseline=cfg.BS_TIMES,
mode=cfg.BS_MODE,
show=False,
colorbar=True,
title=title,
vmin=3.0,
vmax=-3.0,
dB=False)
fout = '%s/%s-%s-diff-%s-%s-%s.jpg' % (export_dir, file_prefix,
cfg.SP_FILTER, labels[0],
labels[1], chname)
logger.info('Exporting to %s' % fout)
fig.savefig(fout)
plt.close()
logger.info('Finished !')
def get_tfr(fif_file, cfg, tfr, n_jobs=1):
raw, events = pu.load_raw(fif_file)
p = qc.parse_path(fif_file)
fname = p.name
outpath = p.dir
export_dir = '%s/plot_%s' % (outpath, fname)
qc.make_dirs(export_dir)
# set channels of interest
picks = pu.channel_names_to_index(raw, cfg.CHANNEL_PICKS)
spchannels = pu.channel_names_to_index(raw, cfg.SP_CHANNELS)
if max(picks) > len(raw.info['ch_names']):
msg = 'ERROR: "picks" has a channel index %d while there are only %d channels.' %\
(max(picks), len(raw.info['ch_names']))
raise RuntimeError(msg)
# Apply filters
raw = pu.preprocess(raw,
spatial=cfg.SP_FILTER,
spatial_ch=spchannels,
spectral=cfg.TP_FILTER,
spectral_ch=picks,
notch=cfg.NOTCH_FILTER,
notch_ch=picks,
multiplier=cfg.MULTIPLIER,
n_jobs=n_jobs)
# MNE TFR functions do not support Raw instances yet, so convert to Epoch
if cfg.EVENT_START is None:
raw._data[0][0] = 1
events = np.array([[0, 0, 1]])
classes = None
else:
classes = {'START': cfg.EVENT_START}
tmax = (raw._data.shape[1] - 1) / raw.info['sfreq']
epochs_all = mne.Epochs(raw,
events,
classes,
tmin=0,
tmax=tmax,
picks=picks,
baseline=None,
preload=True)
logger.info('\n>> Processing %s' % fif_file)
freqs = cfg.FREQ_RANGE # define frequencies of interest
n_cycles = freqs / 2. # different number of cycle per frequency
power = tfr(epochs_all,
freqs=freqs,
n_cycles=n_cycles,
use_fft=False,
return_itc=False,
decim=1,
n_jobs=n_jobs)
if cfg.EXPORT_MATLAB is True:
# export all channels to MATLAB
mout = '%s/%s-%s.mat' % (export_dir, fname, cfg.SP_FILTER)
scipy.io.savemat(
mout, {
'tfr': power.data,
'chs': power.ch_names,
'events': events,
'sfreq': raw.info['sfreq'],
'freqs': cfg.FREQ_RANGE
})
if cfg.EXPORT_PNG is True:
# Plot power of each channel
for ch in np.arange(len(picks)):
ch_name = raw.ch_names[picks[ch]]
title = 'Channel %s' % (ch_name)
# mode= None | 'logratio' | 'ratio' | 'zscore' | 'mean' | 'percent'
fig = power.plot([ch],
baseline=cfg.BS_TIMES,
mode=cfg.BS_MODE,
show=False,
colorbar=True,
title=title,
vmin=cfg.VMIN,
vmax=cfg.VMAX,
dB=False)
fout = '%s/%s-%s-%s.png' % (export_dir, fname, cfg.SP_FILTER,
ch_name)
fig.savefig(fout)
logger.info('Exported %s' % fout)
logger.info('Finished !')