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
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)
print('\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)
print('Exported %s' % fout)
print('Finished !')
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_cfg(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_DIRS'):
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_DIRS:
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:
print('Loading', 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:
outpath = export_path
# re-referencing
if cfg.REREFERENCE is not None:
pu.rereference(raw, cfg.REREFERENCE[1], cfg.REREFERENCE[0])
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
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))
#print('Epoch tmin = %.1f, tmax = %.1f, raw length = %.1f' % (tmin, tmax, 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:
print('\n** Bad epochs found. Dropping into a Python shell.')
print(epochs_all.drop_log)
print('tmin = %.1f, tmax = %.1f, tmin_buffer = %.1f, tmax_buffer = %.1f, raw length = %.1f' % \
(tmin, tmax, tmin_buffer, tmax_buffer, raw._data.shape[1] / sfreq))
print('\nType exit to continue.\n')
pdb.set_trace()
except:
print('\n*** (tfr_export) ERROR OCCURRED WHILE EPOCHING ***')
traceback.print_exc()
print('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 = '%s/plot_%s' % (outpath, evname)
export_dir = outpath
qc.make_dirs(export_dir)
print('\n>> 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:
print('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, 'epochs':cfg.EPOCH, 'freqs':cfg.FREQ_RANGE})
print('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)
fig.clf()
print('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:
print('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, 'freqs':cfg.FREQ_RANGE})
print('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)
fig.clf()
print('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)
print('Exporting to %s' % fout)
fig.savefig(fout)
fig.clf()
print('Finished !')