def any2fif(filename, interactive=False, outdir=None, channel_file=None):
"""
Generic file format converter
"""
p = qc.parse_path(filename)
if outdir is not None:
qc.make_dirs(outdir)
if p.ext == 'pcl':
eve_file = '%s/%s.txt' % (p.dir, p.name.replace('raw', 'eve'))
if os.path.exists(eve_file):
logger.info('Adding events from %s' % eve_file)
else:
eve_file = None
pcl2fif(filename,
interactive=interactive,
outdir=outdir,
external_event=eve_file)
elif p.ext == 'eeg':
eeg2fif(filename, interactive=interactive, outdir=outdir)
elif p.ext in ['edf', 'bdf']:
bdf2fif(filename, interactive=interactive, outdir=outdir)
elif p.ext == 'gdf':
gdf2fif(filename,
interactive=interactive,
outdir=outdir,
channel_file=channel_file)
elif p.ext == 'xdf':
xdf2fif(filename, interactive=interactive, outdir=outdir)
else: # unknown format
logger.error(
'Ignored unrecognized file extension %s. It should be [.pcl | .eeg | .gdf | .bdf]'
% p.ext)
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 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 main(input_dir, channel_file=None):
count = 0
for f in qc.get_file_list(input_dir, fullpath=True, recursive=True):
p = qc.parse_path(f)
outdir = p.dir + '/fif/'
if p.ext in ['pcl', 'bdf', 'edf', 'gdf', 'eeg', 'xdf']:
logger.info('Converting %s' % f)
any2fif(f,
interactive=True,
outdir=outdir,
channel_file=channel_file)
count += 1
logger.info('%d files converted.' % count)
def load_raw(rawfile, spfilter=None, spchannels=None, events_ext=None, multiplier=1, verbose='ERROR'):
"""
Loads data from a fif-format file.
You can convert non-fif files (.eeg, .bdf, .gdf, .pcl) to fif format.
Parameters:
rawfile: (absolute) data file path
spfilter: 'car' | 'laplacian' | None
spchannels: None | list (for CAR) | dict (for LAPLACIAN)
'car': channel indices used for CAR filtering. If None, use all channels except
the trigger channel (index 0).
'laplacian': {channel:[neighbor1, neighbor2, ...], ...}
*** Note ***
Since neurodecode puts trigger channel as index 0, data channel starts from index 1.
events_ext: Add externally recorded events.
[ [sample_index1, 0, event_value1],... ]
multiplier: Multiply all values except triggers (to convert unit).
Returns:
raw: mne.io.RawArray object. First channel (index 0) is always trigger channel.
events: mne-compatible events numpy array object (N x [frame, 0, type])
spfilter= {None | 'car' | 'laplacian'}
"""
if not os.path.exists(rawfile):
logger.error('File %s not found' % rawfile)
raise IOError
if not os.path.isfile(rawfile):
logger.error('%s is not a file' % rawfile)
raise IOError
extension = qc.parse_path(rawfile).ext
assert extension in ['fif', 'fiff'], 'only fif format is supported'
raw = mne.io.Raw(rawfile, preload=True, verbose=verbose)
if spfilter is not None or multiplier is not 1:
preprocess(raw, spatial=spfilter, spatial_ch=spchannels, multiplier=multiplier)
if events_ext is not None:
events = mne.read_events(events_ext)
else:
tch = find_event_channel(raw)
if tch is not None:
events = mne.find_events(raw, stim_channel=raw.ch_names[tch], shortest_event=1, uint_cast=True, consecutive='increasing')
# MNE's annoying hidden cockroach: first_samp
events[:, 0] -= raw.first_samp
else:
events = np.array([], dtype=np.int64)
return raw, events
def trigger_def(ini_file, verbose=False):
class TriggerDef(object):
def __init__(self, items):
self.by_name = {}
self.by_value = {}
for key, value in items:
value = int(value)
setattr(self, key, value)
self.by_name[key] = value
self.by_value[value] = key
# show all possible trigger values
def check_data(self):
print('Attributes of the final class')
for attr in dir(self):
if not callable(getattr(self, attr)) and not attr.startswith("__"):
print(attr, getattr(self, attr))
if not os.path.exists(ini_file):
search_path = []
path_ini = qc.parse_path(ini_file)
path_self = qc.parse_path(__file__)
search_path.append(ini_file + '.ini')
search_path.append('%s/%s' % (path_self.dir, path_ini.name))
search_path.append('%s/%s.ini' % (path_self.dir, path_ini.name))
for ini_file in search_path:
if os.path.exists(ini_file):
if verbose:
logger.info('Found trigger definition file %s' % ini_file)
break
else:
raise IOError('Trigger event definition file %s not found' % ini_file)
config = ConfigParser(inline_comment_prefixes=('#', ';'))
config.optionxform = str
config.read(ini_file)
return TriggerDef(config.items('events'))
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.')
# 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])))
# sample code
if __name__ == '__main__':
fif_dir = r'D:\data\STIMO\GO004\offline\all'
trigger_file = 'triggerdef_gait_chuv.ini'
events = {'BOTH_GO': ['LEFT_GO', 'RIGHT_GO']}
out_dir = fif_dir + '/merged'
qc.make_dirs(out_dir)
for rawfile_in in qc.get_file_list(fif_dir):
p = qc.parse_path(rawfile_in)
if p.ext != 'fif':
continue
rawfile_out = '%s/%s.%s' % (out_dir, p.name, p.ext)
merge_events(trigger_file, events, rawfile_in, rawfile_out)
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 !')
# console logger handler
c_handler = logging.StreamHandler(stream)
c_handler.setFormatter(neurodecodeFormatter())
logger.addHandler(c_handler)
# minimum possible level of all handlers
#logger.setLevel(logging.DEBUG)
set_log_level(logger, verbosity, -1)
return logger
def set_log_level(logger, verbosity, handler_id=0):
'''
hander ID 0 is always stdout, followed by user-defined handlers.
'''
logger.handlers[handler_id].level = LOG_LEVELS[verbosity]
# init scripts
ROOT = qc.parse_path(os.path.realpath(__file__)).dir
for d in qc.get_dir_list(ROOT):
if os.path.exists('%s/__init__.py' % d):
exe_package = 'import neurodecode.%s' % d.replace(ROOT + '/', '')
exec(exe_package)
# set loggers
logging.getLogger('matplotlib').setLevel(logging.ERROR)
logger = logging.getLogger('neurodecode')
logger.propagate = False
init_logger(logger)
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 !')
def feature_importances_topo(featfile,
topo_layout_file=None,
channels=None,
channel_name_show=None):
"""
Compute feature importances across frequency bands and channels
@params
topo_laytout_file: if not None, topography map images will be generated and saved.
channel_name_show: list of channel names to show on topography map.
"""
logger.info('Loading %s' % featfile)
if channels is None:
channel_set = set()
with open(featfile) as f:
f.readline()
for l in f:
ch = l.strip().split('\t')[1]
channel_set.add(ch)
channels = list(channel_set)
# channel index lookup table
ch2index = {ch: i for i, ch in enumerate(channels)}
data_delta = np.zeros(len(channels))
data_theta = np.zeros(len(channels))
data_mu = np.zeros(len(channels))
data_beta = np.zeros(len(channels))
data_beta1 = np.zeros(len(channels))
data_beta2 = np.zeros(len(channels))
data_beta3 = np.zeros(len(channels))
data_lgamma = np.zeros(len(channels))
data_hgamma = np.zeros(len(channels))
data_per_ch = np.zeros(len(channels))
f = open(featfile)
f.readline()
for l in f:
token = l.strip().split('\t')
importance = float(token[0])
ch = token[1]
fq = float(token[2])
if fq <= 3:
data_delta[ch2index[ch]] += importance
elif fq <= 7:
data_theta[ch2index[ch]] += importance
elif fq <= 12:
data_mu[ch2index[ch]] += importance
elif fq <= 30:
data_beta[ch2index[ch]] += importance
elif fq <= 70:
data_lgamma[ch2index[ch]] += importance
else:
data_hgamma[ch2index[ch]] += importance
if 12.5 <= fq <= 16:
data_beta1[ch2index[ch]] += importance
elif fq <= 20:
data_beta2[ch2index[ch]] += importance
elif fq <= 28:
data_beta3[ch2index[ch]] += importance
data_per_ch[ch2index[ch]] += importance
hlen = 18 + len(channels) * 7
result = '>> Feature importance distribution\n'
result += 'bands ' + qc.list2string(channels,
'%6s') + ' | ' + 'per band\n'
result += '-' * hlen + '\n'
result += 'delta ' + qc.list2string(
data_delta, '%6.2f') + ' | %6.2f\n' % np.sum(data_delta)
result += 'theta ' + qc.list2string(
data_theta, '%6.2f') + ' | %6.2f\n' % np.sum(data_theta)
result += 'mu ' + qc.list2string(
data_mu, '%6.2f') + ' | %6.2f\n' % np.sum(data_mu)
#result += 'beta ' + qc.list2string(data_beta, '%6.2f') + ' | %6.2f\n' % np.sum(data_beta)
result += 'beta1 ' + qc.list2string(
data_beta1, '%6.2f') + ' | %6.2f\n' % np.sum(data_beta1)
result += 'beta2 ' + qc.list2string(
data_beta2, '%6.2f') + ' | %6.2f\n' % np.sum(data_beta2)
result += 'beta3 ' + qc.list2string(
data_beta3, '%6.2f') + ' | %6.2f\n' % np.sum(data_beta3)
result += 'lgamma ' + qc.list2string(
data_lgamma, '%6.2f') + ' | %6.2f\n' % np.sum(data_lgamma)
result += 'hgamma ' + qc.list2string(
data_hgamma, '%6.2f') + ' | %6.2f\n' % np.sum(data_hgamma)
result += '-' * hlen + '\n'
result += 'per_ch ' + qc.list2string(data_per_ch, '%6.2f') + ' | 100.00\n'
print(result)
p = qc.parse_path(featfile)
open('%s/%s_summary.txt' % (p.dir, p.name), 'w').write(result)
# export topo maps
if topo_layout_file is not None:
# default visualization setting
res = 64
contours = 6
# select channel names to show
if channel_name_show is None:
channel_name_show = channels
chan_vis = [''] * len(channels)
for ch in channel_name_show:
chan_vis[channels.index(ch)] = ch
# set channel locations and reverse lookup table
chanloc = {}
if not os.path.exists(topo_layout_file):
topo_layout_file = NEUROD_ROOT + '/layout/' + topo_layout_file
if not os.path.exists(topo_layout_file):
raise FileNotFoundError('Layout file %s not found.' %
topo_layout_file)
logger.info('Using layout %s' % topo_layout_file)
for l in open(topo_layout_file):
token = l.strip().split('\t')
ch = token[5]
x = float(token[1])
y = float(token[2])
chanloc[ch] = [x, y]
pos = np.zeros((len(channels), 2))
for i, ch in enumerate(channels):
pos[i] = chanloc[ch]
vmin = min(data_per_ch)
vmax = max(data_per_ch)
total = sum(data_per_ch)
rate_delta = sum(data_delta) * 100.0 / total
rate_theta = sum(data_theta) * 100.0 / total
rate_mu = sum(data_mu) * 100.0 / total
rate_beta = sum(data_beta) * 100.0 / total
rate_beta1 = sum(data_beta1) * 100.0 / total
rate_beta2 = sum(data_beta2) * 100.0 / total
rate_beta3 = sum(data_beta3) * 100.0 / total
rate_lgamma = sum(data_lgamma) * 100.0 / total
rate_hgamma = sum(data_hgamma) * 100.0 / total
export_topo(data_per_ch,
pos,
'features_topo_all.png',
xlabel='all bands 1-40 Hz',
chan_vis=chan_vis)
export_topo(data_delta,
pos,
'features_topo_delta.png',
xlabel='delta 1-3 Hz (%.1f%%)' % rate_delta,
chan_vis=chan_vis)
export_topo(data_theta,
pos,
'features_topo_theta.png',
xlabel='theta 4-7 Hz (%.1f%%)' % rate_theta,
chan_vis=chan_vis)
export_topo(data_mu,
pos,
'features_topo_mu.png',
xlabel='mu 8-12 Hz (%.1f%%)' % rate_mu,
chan_vis=chan_vis)
export_topo(data_beta,
pos,
'features_topo_beta.png',
xlabel='beta 13-30 Hz (%.1f%%)' % rate_beta,
chan_vis=chan_vis)
export_topo(data_beta1,
pos,
'features_topo_beta1.png',
xlabel='beta 12.5-16 Hz (%.1f%%)' % rate_beta1,
chan_vis=chan_vis)
export_topo(data_beta2,
pos,
'features_topo_beta2.png',
xlabel='beta 16-20 Hz (%.1f%%)' % rate_beta2,
chan_vis=chan_vis)
export_topo(data_beta3,
pos,
'features_topo_beta3.png',
xlabel='beta 20-28 Hz (%.1f%%)' % rate_beta3,
chan_vis=chan_vis)
export_topo(data_lgamma,
pos,
'features_topo_lowgamma.png',
xlabel='low gamma 31-40 Hz (%.1f%%)' % rate_lgamma,
chan_vis=chan_vis)
