def apply_ica_select_brain_response(
fname_clean_raw, n_pca_components=None, conditions=["trigger"], event_id=1, include=None
):
""" Performs ICA recomposition with selected brain response components to a list of (ICA) files.
fname_clean_raw: raw data after ECG and EOG rejection.
n_pca_commonents: ICA's recomposition parameter.
conditions: the event kind to recompose the raw data, it can be 'trigger',
'response' or include both conditions.
"""
fnlist = get_files_from_list(fname_clean_raw)
# loop across all filenames
for fn_clean in fnlist:
# basename = fn_ctps_ics.rsplit('ctps')[0].rstrip(',')
basename = fn_clean.split(ext_raw)[0]
fnfilt = fn_clean
fnarica = basename + ext_ica
# load filtered and artefact removed data
meg_raw = mne.io.Raw(fnfilt, preload=True)
picks = mne.pick_types(meg_raw.info, meg=True, exclude="bads")
# ICA decomposition
ica = mne.preprocessing.read_ica(fnarica)
# loop across different event IDs
ctps_ics = []
descrip_id = ""
for event in conditions:
fn_ics_eve = basename + prefix_ctps + event + "-ic_selection.txt"
ctps_ics_eve = np.loadtxt(fn_ics_eve, dtype=int, delimiter=",")
ctps_ics += list(ctps_ics_eve - 1)
descrip_id += "," + event
# To keep the index unique
ctps_ics = list(set(ctps_ics))
fnclean_eve = fn_ics_eve.split(",ctpsbr")[0] + "%s,ctpsbr-raw.fif" % descrip_id
# clean and save MEG data
if n_pca_components:
npca = n_pca_components
else:
npca = picks.size
meg_clean = ica.apply(meg_raw, include=ctps_ics, n_pca_components=npca, copy=True)
if not meg_clean.info["description"]:
meg_clean.info["description"] = ""
meg_clean.info[
"description"
] += "Raw recomposed from ctps selected\
ICA components for brain\
responses only."
meg_clean.save(fnclean_eve, overwrite=True)
plot_compare_brain_responses(fname_clean_raw, fnclean_eve, event_id=event_id)
def apply_ica(fname_filtered, n_components=0.99, decim=None,
reject={'mag': 5e-12}, ica_method='fastica',
flow=None, fhigh=None, verbose=True):
''' Applies ICA to a list of (filtered) raw files. '''
from mne.preprocessing import ICA
fnfilt = get_files_from_list(fname_filtered)
# loop across all filenames
for fname in fnfilt:
name = os.path.split(fname)[1]
print ">>>> perform ICA signal decomposition on : " + name
# load filtered data
raw = mne.io.Raw(fname, preload=True)
picks = mne.pick_types(raw.info, meg=True, ref_meg=False, exclude='bads')
# check if data to estimate the optimal
# de-mixing matrix should be filtered
if flow or fhigh:
from jumeg.filter import jumeg_filter
# define filter type
if not flow:
filter_type = 'lp'
filter_info = " --> filter parameter : filter type=low pass %dHz" % flow
elif not fhigh:
filter_type = 'hp'
filter_info = " --> filter parameter : filter type=high pass %dHz" % flow
else:
filter_type = 'bp'
filter_info = " --> filter parameter: filter type=band pass %d-%dHz" % (flow, fhigh)
if verbose:
print ">>>> NOTE: Optimal cleaning parameter are estimated from filtered data!"
print filter_info
fi_mne_notch = jumeg_filter(fcut1=flow, fcut2=fhigh, filter_type=filter_type,
remove_dcoffset=False,
sampling_frequency=raw.info['sfreq'])
fi_mne_notch.apply_filter(raw._data, picks=picks)
# ICA decomposition
ica = ICA(method=ica_method, n_components=n_components,
max_pca_components=None)
ica.fit(raw, picks=picks, decim=decim, reject=reject)
# save ICA object
fnica_out = fname[:fname.rfind(ext_raw)] + ext_ica
# fnica_out = fname[0:len(fname)-4]+'-ica.fif'
ica.save(fnica_out)
def compute_forward_solution(fname_raw, subjects_dir=None, spacing='ico4',
mindist=5, eeg=False, overwrite=False):
'''Performs forward solution computation using mne_do_foward_solution
(uses MNE-C binaries).
Requires bem sol files, and raw meas file)
Input
-----
fname_raw : str or list
List of raw files for which to compute the forward operator.
Returns
-------
None. Forward operator -fwd.fif will be saved.
'''
from mne import do_forward_solution
fnames = get_files_from_list(fname_raw)
subjects_dir = check_env_variables(subjects_dir, key='SUBJECTS_DIR')
for fname in fnames:
print 'Computing fwd solution for %s' % (fname)
basename = os.path.basename(fname).split('-raw.fif')[0]
subject = basename.split('_')[0]
meas_fname = os.path.basename(fname)
fwd_fname = meas_fname.rsplit('-raw.fif')[0] + '-fwd.fif'
src_fname = subject + '-ico-4-src.fif'
bem_fname = subject + '-5120-5120-5120-bem-sol.fif'
trans_fname = subject + '-trans.fif'
fwd = do_forward_solution(subject, meas_fname, fname=fwd_fname, src=src_fname,
spacing=spacing, mindist=mindist, bem=bem_fname, mri=trans_fname,
eeg=eeg, overwrite=overwrite, subjects_dir=subjects_dir)
# fwd['surf_ori'] = True
# to read forward solutions
# fwd = mne.read_forward_solution(fwd_fname)
print 'Forward operator saved in file %s' % (fwd_fname)
def apply_filter(fname_raw, flow=1, fhigh=45, order=4, njobs=4):
''' Applies the MNE butterworth filter to a list of raw files. '''
filter_type = 'butter'
filt_method = 'fft'
fnraw = get_files_from_list(fname_raw)
# loop across all filenames
for fname in fnraw:
print ">>> filter raw data: %0.1f - %0.1f..." % (flow, fhigh)
# load raw data
raw = mne.io.Raw(fname, preload=True)
# filter raw data
raw.filter(flow, fhigh, n_jobs=njobs, method=filt_method)
# raw.filter(l_freq=flow_raw, h_freq=fhigh_raw, n_jobs=njobs, method='iir',
# iir_params={'ftype': filter_type, 'order': order})
print ">>>> writing filtered data to disk..."
name_raw = fname[:fname.rfind('-')] # fname.split('-')[0]
fnfilt = name_raw + prefix_filt + "%d-%d" % (flow, fhigh)
fnfilt = fnfilt + fname[fname.rfind('-'):] # fname.split('-')[1]
print 'saving: ' + fnfilt
raw.save(fnfilt, overwrite=True)
def apply_filter(fname_raw, flow=1, fhigh=45, order=4, njobs=4):
''' Applies the MNE butterworth filter to a list of raw files. '''
filter_type = 'butter'
filt_method = 'fft'
fnraw = get_files_from_list(fname_raw)
# loop across all filenames
for fname in fnraw:
print ">>> filter raw data: %0.1f - %0.1f..." % (flow, fhigh)
# load raw data
raw = mne.io.Raw(fname, preload=True)
# filter raw data
raw.filter(flow, fhigh, n_jobs=njobs, method=filt_method)
# raw.filter(l_freq=flow_raw, h_freq=fhigh_raw, n_jobs=njobs, method='iir',
# iir_params={'ftype': filter_type, 'order': order})
print ">>>> writing filtered data to disk..."
name_raw = fname[:fname.rfind('-')] # fname.split('-')[0]
fnfilt = name_raw + prefix_filt + "%d-%d" % (flow, fhigh)
fnfilt = fnfilt + fname[fname.rfind('-'):] # fname.split('-')[1]
print 'saving: ' + fnfilt
raw.save(fnfilt, overwrite=True)
def apply_average(filenames,
name_stim='STI 014',
event_id=None,
postfix=None,
tmin=-0.2,
tmax=0.4,
baseline=(None, 0),
proj=False,
save_plot=True,
show_plot=False):
''' Performs averaging to a list of raw files. '''
# Trigger or Response ?
if name_stim == 'STI 014': # trigger
trig_name = 'trigger'
else:
if name_stim == 'STI 013': # response
trig_name = 'response'
else:
trig_name = 'trigger'
fnlist = get_files_from_list(filenames)
# loop across raw files
fnavg = [] # collect output filenames
for fname in fnlist:
name = os.path.split(fname)[1]
print '>>> average raw data'
print name
# load raw data
raw = mne.io.Raw(fname, preload=True)
picks = mne.pick_types(raw.info,
meg=True,
ref_meg=False,
exclude='bads')
# stim events
stim_events = mne.find_events(raw,
stim_channel=name_stim,
consecutive=True)
nevents = len(stim_events)
if nevents > 0:
# for a specific event ID
if event_id:
ix = np.where(stim_events[:, 2] == event_id)[0]
stim_events = stim_events[ix, :]
else:
event_id = stim_events[0, 2]
epochs = mne.Epochs(raw,
events=stim_events,
event_id=event_id,
tmin=tmin,
tmax=tmax,
picks=picks,
preload=True,
baseline=baseline,
proj=proj)
avg = epochs.average()
# save averaged data
if (fname.rfind(ext_raw) > -1):
nchar = 8
else:
nchar = 4
if (postfix):
fnout = fname[0:len(fname) - nchar] + postfix + '.fif'
else:
fnout = fname[0:len(fname) - nchar] + ',' + trig_name + ext_ave
avg.save(fnout)
print 'saved:' + fnout
fnavg.append(fnout)
if (save_plot):
plot_average(fnavg, show_plot=show_plot)
else:
event_id = None
print '>>> Warning: Event not found in file: ' + fname
def apply_ctps(fname_ica,
freqs=[(1, 4), (4, 8), (8, 12), (12, 16), (16, 20)],
tmin=-0.2,
tmax=0.4,
name_stim='STI 014',
event_id=None,
baseline=(None, 0),
proj=False):
''' Applies CTPS to a list of ICA files. '''
from jumeg.filter import jumeg_filter
fiws = jumeg_filter(filter_method="bw")
fiws.filter_type = 'bp' # bp, lp, hp
fiws.dcoffset = True
fiws.filter_attenuation_factor = 1
nfreq = len(freqs)
print '>>> CTPS calculation on: ', freqs
# Trigger or Response ?
if name_stim == 'STI 014': # trigger
trig_name = 'trigger'
else:
if name_stim == 'STI 013': # response
trig_name = 'response'
else:
trig_name = 'auxillary'
fnlist = get_files_from_list(fname_ica)
# loop across all filenames
for fnica in fnlist:
name = os.path.split(fnica)[1]
#fname = fnica[0:len(fnica)-4]
basename = fnica[:fnica.rfind(ext_ica)]
fnraw = basename + ext_raw
#basename = os.path.splitext(os.path.basename(fnica))[0]
# load cleaned data
raw = mne.io.Raw(fnraw, preload=True)
picks = mne.pick_types(raw.info,
meg=True,
ref_meg=False,
exclude='bads')
# read (second) ICA
print ">>>> working on: " + basename
ica = mne.preprocessing.read_ica(fnica)
ica_picks = np.arange(ica.n_components_)
ncomp = len(ica_picks)
# stim events
stim_events = mne.find_events(raw,
stim_channel=name_stim,
consecutive=True)
nevents = len(stim_events)
if (nevents > 0):
# for a specific event ID
if event_id:
ix = np.where(stim_events[:, 2] == event_id)[0]
stim_events = stim_events[ix, :]
else:
event_id = stim_events[0, 2]
# create ctps dictionary
dctps = {
'fnica': fnica,
'basename': basename,
'stim_channel': name_stim,
'trig_name': trig_name,
'ncomp': ncomp,
'nevent': nevents,
'event_id': event_id,
'nfreq': nfreq,
'freqs': freqs,
}
# loop across all filenames
pkarr = []
ptarr = []
pkmax_arr = []
for ifreq in range(nfreq):
ica_raw = ica.get_sources(raw)
flow, fhigh = freqs[ifreq][0], freqs[ifreq][1]
bp = str(flow) + '_' + str(fhigh)
# filter ICA data and create epochs
#tw=0.1
# ica_raw.filter(l_freq=flow, h_freq=fhigh, picks=ica_picks,
# method='fft',l_trans_bandwidth=tw, h_trans_bandwidth=tw)
# ica_raw.filter(l_freq=flow, h_freq=fhigh, picks=ica_picks,
# method='fft')
# filter ws settings
# later we will make this as a one line call
data_length = raw._data[0, :].size
fiws.sampling_frequency = raw.info['sfreq']
fiws.fcut1 = flow
fiws.fcut2 = fhigh
#fiws.init_filter_kernel(data_length)
#fiws.init_filter(data_length)
for ichan in ica_picks:
fiws.apply_filter(ica_raw._data[ichan, :])
ica_epochs = mne.Epochs(ica_raw,
events=stim_events,
event_id=event_id,
tmin=tmin,
tmax=tmax,
verbose=False,
picks=ica_picks,
baseline=baseline,
proj=proj)
# compute CTPS
_, pk, pt = ctps.ctps(ica_epochs.get_data())
pkmax = pk.max(1)
times = ica_epochs.times * 1e3
pkarr.append(pk)
ptarr.append(pt)
pkmax_arr.append(pkmax)
pkarr = np.array(pkarr)
ptarr = np.array(ptarr)
pkmax_arr = np.array(pkmax_arr)
dctps['pk'] = np.float32(pkarr)
dctps['pt'] = np.float32(ptarr)
dctps['pkmax'] = np.float32(pkmax_arr)
dctps['nsamp'] = len(times)
dctps['times'] = np.float32(times)
dctps['tmin'] = np.float32(ica_epochs.tmin)
dctps['tmax'] = np.float32(ica_epochs.tmax)
fnctps = basename + prefix_ctps + trig_name
np.save(fnctps, dctps)
# Note; loading example: dctps = np.load(fnctps).items()
else:
event_id = None
def apply_ica_cleaning(fname_ica,
n_pca_components=None,
name_ecg='ECG 001',
flow_ecg=10,
fhigh_ecg=20,
name_eog_hor='EOG 001',
name_eog_ver='EOG 002',
flow_eog=1,
fhigh_eog=10,
threshold=0.3,
unfiltered=False,
notch_filter=True,
notch_freq=50,
notch_width=None):
''' Performs artifact rejection based on ICA to a list of (ICA) files. '''
fnlist = get_files_from_list(fname_ica)
# loop across all filenames
for fnica in fnlist:
name = os.path.split(fnica)[1]
#basename = fnica[0:len(fnica)-4]
basename = fnica[:fnica.rfind(ext_ica)]
fnfilt = basename + ext_raw
fnclean = basename + ext_clean
fnica_ar = basename + ext_icap
print ">>>> perform artifact rejection on :"
print ' ' + name
# load filtered data
meg_raw = mne.io.Raw(fnfilt, preload=True)
picks = mne.pick_types(meg_raw.info,
meg=True,
ref_meg=False,
exclude='bads')
# ICA decomposition
ica = mne.preprocessing.read_ica(fnica)
# get ECG and EOG related components
ic_ecg = get_ics_cardiac(meg_raw,
ica,
flow=flow_ecg,
fhigh=fhigh_ecg,
thresh=threshold)
ic_eog = get_ics_ocular(meg_raw,
ica,
flow=flow_eog,
fhigh=fhigh_eog,
thresh=threshold)
ica.exclude += list(ic_ecg) + list(ic_eog)
# ica.plot_topomap(ic_artefacts)
ica.save(fnica) # save again to store excluded
# clean and save MEG data
if n_pca_components:
npca = n_pca_components
else:
npca = picks.size
# check if cleaning should be applied
# to unfiltered data
if unfiltered:
# adjust filenames to unfiltered data
# FIXME breaks when noise reduced file is used with basename + ',nr'
# temporarily fixed by checking for fibp filter suffix
if basename.find(',fibp') != -1:
basename = basename[:basename.rfind(',')]
else:
basename = basename
fnfilt = basename + ext_raw
fnclean = basename + ext_clean
fnica_ar = basename + ext_icap
# load raw unfiltered data
meg_raw = mne.io.Raw(fnfilt, preload=True)
# apply notch filter
if notch_filter:
from jumeg.filter import jumeg_filter
# generate and apply filter
# check if array of frequencies is given
if type(notch_freq) in (tuple, list):
notch = np.array(notch_freq)
elif type(np.ndarray) == np.ndarray:
notch = notch_freq
# or a single frequency
else:
notch = np.array([])
fi_mne_notch = jumeg_filter(filter_method="mne",
filter_type='notch',
remove_dcoffset=False,
notch=notch,
notch_width=notch_width)
# if only a single frequency is given generate optimal
# filter parameter to also remove the harmonics
if not type(notch_freq) in (tuple, list, np.ndarray):
fi_mne_notch.calc_notches(notch_freq)
fi_mne_notch.apply_filter(meg_raw._data, picks=picks)
# apply cleaning
meg_clean = ica.apply(meg_raw.copy(),
exclude=ica.exclude,
n_pca_components=npca)
meg_clean.save(fnclean, overwrite=True)
# plot ECG, EOG averages before and after ICA
print ">>>> create performance image..."
plot_performance_artifact_rejection(meg_raw,
ica,
fnica_ar,
show=False,
verbose=False)
def apply_ica(fname_filtered,
n_components=0.99,
decim=None,
reject={'mag': 5e-12},
ica_method='fastica',
flow=None,
fhigh=None,
verbose=True):
''' Applies ICA to a list of (filtered) raw files. '''
from mne.preprocessing import ICA
fnfilt = get_files_from_list(fname_filtered)
# loop across all filenames
for fname in fnfilt:
name = os.path.split(fname)[1]
print ">>>> perform ICA signal decomposition on : " + name
# load filtered data
raw = mne.io.Raw(fname, preload=True)
picks = mne.pick_types(raw.info,
meg=True,
ref_meg=False,
exclude='bads')
# check if data to estimate the optimal
# de-mixing matrix should be filtered
if flow or fhigh:
from jumeg.filter import jumeg_filter
# define filter type
if not flow:
filter_type = 'lp'
filter_info = " --> filter parameter : filter type=low pass %dHz" % flow
elif not fhigh:
filter_type = 'hp'
filter_info = " --> filter parameter : filter type=high pass %dHz" % flow
else:
filter_type = 'bp'
filter_info = " --> filter parameter: filter type=band pass %d-%dHz" % (
flow, fhigh)
if verbose:
print ">>>> NOTE: Optimal cleaning parameter are estimated from filtered data!"
print filter_info
fi_mne_notch = jumeg_filter(fcut1=flow,
fcut2=fhigh,
filter_type=filter_type,
remove_dcoffset=False,
sampling_frequency=raw.info['sfreq'])
fi_mne_notch.apply_filter(raw._data, picks=picks)
# ICA decomposition
ica = ICA(method=ica_method,
n_components=n_components,
max_pca_components=None)
ica.fit(raw, picks=picks, decim=decim, reject=reject)
# save ICA object
fnica_out = fname[:fname.rfind(ext_raw)] + ext_ica
# fnica_out = fname[0:len(fname)-4]+'-ica.fif'
ica.save(fnica_out)
def apply_ica_select_brain_response(fname_clean_raw,
n_pca_components=None,
conditions=['trigger'],
event_id=1,
include=None):
''' Performs ICA recomposition with selected brain response components to a list of (ICA) files.
fname_clean_raw: raw data after ECG and EOG rejection.
n_pca_commonents: ICA's recomposition parameter.
conditions: the event kind to recompose the raw data, it can be 'trigger',
'response' or include both conditions.
'''
fnlist = get_files_from_list(fname_clean_raw)
# loop across all filenames
for fn_clean in fnlist:
#basename = fn_ctps_ics.rsplit('ctps')[0].rstrip(',')
basename = fn_clean.split(ext_raw)[0]
fnfilt = fn_clean
fnarica = basename + ext_ica
# load filtered and artefact removed data
meg_raw = mne.io.Raw(fnfilt, preload=True)
picks = mne.pick_types(meg_raw.info, meg=True, exclude='bads')
# ICA decomposition
ica = mne.preprocessing.read_ica(fnarica)
# loop across different event IDs
ctps_ics = []
descrip_id = ''
for event in conditions:
fn_ics_eve = basename + prefix_ctps + event + '-ic_selection.txt'
ctps_ics_eve = np.loadtxt(fn_ics_eve, dtype=int, delimiter=',')
ctps_ics += (list(ctps_ics_eve - 1))
descrip_id += ',' + event
#To keep the index unique
ctps_ics = list(set(ctps_ics))
fnclean_eve = fn_ics_eve.split(',ctpsbr')[0] +\
'%s,ctpsbr-raw.fif' % descrip_id
# clean and save MEG data
if n_pca_components:
npca = n_pca_components
else:
npca = picks.size
meg_clean = ica.apply(meg_raw,
include=ctps_ics,
n_pca_components=npca,
copy=True)
if not meg_clean.info['description']:
meg_clean.info['description'] = ''
meg_clean.info[
'description'] += 'Raw recomposed from ctps selected\
ICA components for brain\
responses only.'
meg_clean.save(fnclean_eve, overwrite=True)
plot_compare_brain_responses(fname_clean_raw,
fnclean_eve,
event_id=event_id)
def apply_average(filenames, name_stim='STI 014', event_id=None, postfix=None,
tmin=-0.2, tmax=0.4, baseline=(None, 0), proj=False,
save_plot=True, show_plot=False):
''' Performs averaging to a list of raw files. '''
# Trigger or Response ?
if name_stim == 'STI 014': # trigger
trig_name = 'trigger'
else:
if name_stim == 'STI 013': # response
trig_name = 'response'
else:
trig_name = 'trigger'
fnlist = get_files_from_list(filenames)
# loop across raw files
fnavg = [] # collect output filenames
for fname in fnlist:
name = os.path.split(fname)[1]
print '>>> average raw data'
print name
# load raw data
raw = mne.io.Raw(fname, preload=True)
picks = mne.pick_types(raw.info, meg=True, ref_meg=False,
exclude='bads')
# stim events
stim_events = mne.find_events(raw, stim_channel=name_stim,
consecutive=True)
nevents = len(stim_events)
if nevents > 0:
# for a specific event ID
if event_id:
ix = np.where(stim_events[:, 2] == event_id)[0]
stim_events = stim_events[ix, :]
else:
event_id = stim_events[0, 2]
epochs = mne.Epochs(raw, events=stim_events,
event_id=event_id, tmin=tmin, tmax=tmax,
picks=picks, preload=True, baseline=baseline,
proj=proj)
avg = epochs.average()
# save averaged data
if (fname.rfind(ext_raw) > -1):
nchar = 8
else:
nchar = 4
if (postfix):
fnout = fname[0:len(fname) - nchar] + postfix + '.fif'
else:
fnout = fname[0:len(fname) - nchar] + ',' + trig_name + ext_ave
avg.save(fnout)
print 'saved:' + fnout
fnavg.append(fnout)
if (save_plot):
plot_average(fnavg, show_plot=show_plot)
else:
event_id = None
print '>>> Warning: Event not found in file: ' + fname
def apply_ctps_surrogates(fname_ctps, fnout, nrepeat=1000,
mode='shuffle', save=True, n_jobs=4):
'''
Perform CTPS surrogate tests to estimate the significance level
for CTPS anaysis (a proper pK value ist estimated).
It is most likely that the statistical reliability of this test
is best improved by increasing the number of repetitions, while the
number of different experiments/subjects have minor effects only
Parameters
----------
fname_ctps: CTPS filename (or list of filenames)
fnout: Output (text) filename to store surrogate stats across all files
Options:
nrepeat: number of repetitions used to estimate the pk threshold
default is 1000
mode: 2 different modi are allowed.
'mode=shuffle' whill randomly shuffle the phase values. This is the default
'mode=shift' whill randomly shift the phase values
Return
------
info string array containing the statistical values about the surrogate analysis
'''
import os, time
from jumeg_utils import make_surrogates_ctps, get_stats_surrogates_ctps
fnlist = get_files_from_list(fname_ctps)
# loop across all filenames
ifile = 1
sep = '=========================================================================='
info = [sep,'#','# Statistical analysis on CTPS surrogates','#',sep]
for fnctps in fnlist:
path = os.path.dirname(fnctps)
basename = os.path.basename(fnctps)
name = os.path.splitext(basename)[0]
print '>>> calc. surrogates based on: ' + basename
# load CTPS data
dctps = np.load(fnctps).item()
phase_trials = dctps['pt'] # [nfreq, ntrials, nsources, nsamples]
# create surrogate tests
t_start = time.time()
pks = make_surrogates_ctps(phase_trials,nrepeat=nrepeat,
mode=mode,verbose=None,n_jobs=n_jobs)
# perform stats on surrogates
stats = get_stats_surrogates_ctps(pks, verbose=False)
info.append(sep)
info.append(path)
info.append(basename)
info.append('nfreq: '+ str(stats['nfreq']))
info.append('nrepeat: '+ str(stats['nrepeat']))
info.append('nsamples: '+ str(stats['nsamples']))
info.append('nsources: '+ str(stats['nsources']))
info.append('permutation mode: '+ mode)
# info for each freq. band of the current data set
info.append('# stats for each frequency band:')
line_f = 'freqs (Hz):'
line_max = 'pk max: '
line_mean = 'pk mean: '
line_min = 'pk min: '
for i in range(stats['nfreq']):
flow, fhigh = dctps['freqs'][i]
line_f += str('%5d-%d ' % (flow,fhigh))
line_max += str('%8.3f' % stats['pks_max'][i])
line_mean += str('%8.3f' % stats['pks_mean'][i])
line_min += str('%8.3f' % stats['pks_min'][i])
info.append(line_f)
info.append(line_min)
info.append(line_mean)
info.append(line_max)
# across freq. bands
pks_min = stats['pks_min_global']
pks_mean = stats['pks_mean_global']
pks_std = stats['pks_std_global']
pks_pct = stats['pks_pct99_global']
pks_max = stats['pks_max_global']
info.append('# stats across all frequency bands:')
info.append('pk min: '+ str('%8.3f' % pks_min))
info.append('pk mean: '+ str('%8.3f' % stats['pks_mean_global']))
info.append('pk std: '+ str('%8.3f' % stats['pks_std_global']))
info.append('pk pct99:'+ str('%8.3f' % stats['pks_pct99_global']))
info.append('pk pct99.90:'+ str('%8.3f' % stats['pks_pct999_global']))
info.append('pk pct99.99:'+ str('%8.3f' % stats['pks_pct9999_global']))
info.append('pk max: '+ str('%8.3f' % stats['pks_max_global']))
# combine global stats values of different ctps files into one global analysis
if (ifile > 1):
pks_all = np.concatenate((pks_all, pks.flatten()))
else:
pks_all = pks.flatten()
ifile += 1
if (ifile > 1):
info.append(sep)
info.append('#')
info.append('# stats across all files:')
info.append('#')
info.append('pk min: '+ str('%8.3f' % pks_all.min()))
info.append('pk mean: '+ str('%8.3f' % pks_all.mean()))
info.append('pk std: '+ str('%8.3f' % pks_all.std()))
info.append('pk pct99:'+ str('%8.3f' % np.percentile(pks_all,99)))
info.append('pk pct99.90:'+ str('%8.3f' % np.percentile(pks_all,99.9)))
info.append('pk pct99.99:'+ str('%8.3f' % np.percentile(pks_all,99.99)))
info.append('pk max: '+ str('%8.3f' % pks_all.max()))
info.append('#')
duration = (time.time() - t_start) / 60.0 # in minutes
info.append('duration [min]: %0.2f' % duration)
info.append('#')
info.append(sep)
# save surrogate stats
if (save):
np.savetxt(fnout, info, fmt='%s')
return info
def apply_ctps(fname_ica, freqs=[(1, 4), (4, 8), (8, 12), (12, 16), (16, 20)],
tmin=-0.2, tmax=0.4, name_stim='STI 014', event_id=None,
baseline=(None, 0), proj=False):
''' Applies CTPS to a list of ICA files. '''
from jumeg.filter import jumeg_filter
fiws = jumeg_filter(filter_method="bw")
fiws.filter_type = 'bp' # bp, lp, hp
fiws.dcoffset = True
fiws.filter_attenuation_factor = 1
nfreq = len(freqs)
print '>>> CTPS calculation on: ', freqs
# Trigger or Response ?
if name_stim == 'STI 014': # trigger
trig_name = 'trigger'
else:
if name_stim == 'STI 013': # response
trig_name = 'response'
else:
trig_name = 'auxillary'
fnlist = get_files_from_list(fname_ica)
# loop across all filenames
for fnica in fnlist:
name = os.path.split(fnica)[1]
#fname = fnica[0:len(fnica)-4]
basename = fnica[:fnica.rfind(ext_ica)]
fnraw = basename + ext_raw
#basename = os.path.splitext(os.path.basename(fnica))[0]
# load cleaned data
raw = mne.io.Raw(fnraw, preload=True)
picks = mne.pick_types(raw.info, meg=True, ref_meg=False, exclude='bads')
# read (second) ICA
print ">>>> working on: " + basename
ica = mne.preprocessing.read_ica(fnica)
ica_picks = np.arange(ica.n_components_)
ncomp = len(ica_picks)
# stim events
stim_events = mne.find_events(raw, stim_channel=name_stim, consecutive=True)
nevents = len(stim_events)
if (nevents > 0):
# for a specific event ID
if event_id:
ix = np.where(stim_events[:, 2] == event_id)[0]
stim_events = stim_events[ix, :]
else:
event_id = stim_events[0, 2]
# create ctps dictionary
dctps = {'fnica': fnica,
'basename': basename,
'stim_channel': name_stim,
'trig_name': trig_name,
'ncomp': ncomp,
'nevent': nevents,
'event_id': event_id,
'nfreq': nfreq,
'freqs': freqs,
}
# loop across all filenames
pkarr = []
ptarr = []
pkmax_arr = []
for ifreq in range(nfreq):
ica_raw = ica.get_sources(raw)
flow, fhigh = freqs[ifreq][0], freqs[ifreq][1]
bp = str(flow) + '_' + str(fhigh)
# filter ICA data and create epochs
#tw=0.1
# ica_raw.filter(l_freq=flow, h_freq=fhigh, picks=ica_picks,
# method='fft',l_trans_bandwidth=tw, h_trans_bandwidth=tw)
# ica_raw.filter(l_freq=flow, h_freq=fhigh, picks=ica_picks,
# method='fft')
# filter ws settings
# later we will make this as a one line call
data_length = raw._data[0, :].size
fiws.sampling_frequency = raw.info['sfreq']
fiws.fcut1 = flow
fiws.fcut2 = fhigh
#fiws.init_filter_kernel(data_length)
#fiws.init_filter(data_length)
for ichan in ica_picks:
fiws.apply_filter(ica_raw._data[ichan, :])
ica_epochs = mne.Epochs(ica_raw, events=stim_events,
event_id=event_id, tmin=tmin,
tmax=tmax, verbose=False,
picks=ica_picks, baseline=baseline,
proj=proj)
# compute CTPS
_, pk, pt = ctps.ctps(ica_epochs.get_data())
pkmax = pk.max(1)
times = ica_epochs.times * 1e3
pkarr.append(pk)
ptarr.append(pt)
pkmax_arr.append(pkmax)
pkarr = np.array(pkarr)
ptarr = np.array(ptarr)
pkmax_arr = np.array(pkmax_arr)
dctps['pk'] = np.float32(pkarr)
dctps['pt'] = np.float32(ptarr)
dctps['pkmax'] = np.float32(pkmax_arr)
dctps['nsamp'] = len(times)
dctps['times'] = np.float32(times)
dctps['tmin'] = np.float32(ica_epochs.tmin)
dctps['tmax'] = np.float32(ica_epochs.tmax)
fnctps = basename + prefix_ctps + trig_name
np.save(fnctps, dctps)
# Note; loading example: dctps = np.load(fnctps).items()
else:
event_id = None
def apply_ica_cleaning(fname_ica, n_pca_components=None,
name_ecg='ECG 001', flow_ecg=10, fhigh_ecg=20,
name_eog_hor='EOG 001', name_eog_ver='EOG 002',
flow_eog=1, fhigh_eog=10, threshold=0.3,
unfiltered=False, notch_filter=True, notch_freq=50,
notch_width=None):
''' Performs artifact rejection based on ICA to a list of (ICA) files. '''
fnlist = get_files_from_list(fname_ica)
# loop across all filenames
for fnica in fnlist:
name = os.path.split(fnica)[1]
#basename = fnica[0:len(fnica)-4]
basename = fnica[:fnica.rfind(ext_ica)]
fnfilt = basename + ext_raw
fnclean = basename + ext_clean
fnica_ar = basename + ext_icap
print ">>>> perform artifact rejection on :"
print ' ' + name
# load filtered data
meg_raw = mne.io.Raw(fnfilt, preload=True)
picks = mne.pick_types(meg_raw.info, meg=True, ref_meg=False, exclude='bads')
# ICA decomposition
ica = mne.preprocessing.read_ica(fnica)
# get ECG and EOG related components
ic_ecg = get_ics_cardiac(meg_raw, ica,
flow=flow_ecg, fhigh=fhigh_ecg, thresh=threshold)
ic_eog = get_ics_ocular(meg_raw, ica,
flow=flow_eog, fhigh=fhigh_eog, thresh=threshold)
ica.exclude += list(ic_ecg) + list(ic_eog)
# ica.plot_topomap(ic_artefacts)
ica.save(fnica) # save again to store excluded
# clean and save MEG data
if n_pca_components:
npca = n_pca_components
else:
npca = picks.size
# check if cleaning should be applied
# to unfiltered data
if unfiltered:
# adjust filenames to unfiltered data
# FIXME breaks when noise reduced file is used with basename + ',nr'
# temporarily fixed by checking for fibp filter suffix
if basename.find(',fibp') != -1:
basename = basename[:basename.rfind(',')]
else:
basename = basename
fnfilt = basename + ext_raw
fnclean = basename + ext_clean
fnica_ar = basename + ext_icap
# load raw unfiltered data
meg_raw = mne.io.Raw(fnfilt, preload=True)
# apply notch filter
if notch_filter:
from jumeg.filter import jumeg_filter
# generate and apply filter
# check if array of frequencies is given
if type(notch_freq) in (tuple, list):
notch = np.array(notch_freq)
elif type(np.ndarray) == np.ndarray:
notch = notch_freq
# or a single frequency
else:
notch = np.array([])
fi_mne_notch = jumeg_filter(filter_method="mne", filter_type='notch',
remove_dcoffset=False,
notch=notch, notch_width=notch_width)
# if only a single frequency is given generate optimal
# filter parameter to also remove the harmonics
if not type(notch_freq) in (tuple, list, np.ndarray):
fi_mne_notch.calc_notches(notch_freq)
fi_mne_notch.apply_filter(meg_raw._data, picks=picks)
# apply cleaning
meg_clean = ica.apply(meg_raw.copy(), exclude=ica.exclude,
n_pca_components=npca, copy=True)
meg_clean.save(fnclean, overwrite=True)
# plot ECG, EOG averages before and after ICA
print ">>>> create performance image..."
plot_performance_artifact_rejection(meg_raw, ica, fnica_ar,
show=False, verbose=False)
def apply_ctps_surrogates(fname_ctps,
fnout,
nrepeat=1000,
mode='shuffle',
save=True,
n_jobs=4):
'''
Perform CTPS surrogate tests to estimate the significance level
for CTPS anaysis (a proper pK value ist estimated).
It is most likely that the statistical reliability of this test
is best improved by increasing the number of repetitions, while the
number of different experiments/subjects have minor effects only
Parameters
----------
fname_ctps: CTPS filename (or list of filenames)
fnout: Output (text) filename to store surrogate stats across all files
Options:
nrepeat: number of repetitions used to estimate the pk threshold
default is 1000
mode: 2 different modi are allowed.
'mode=shuffle' whill randomly shuffle the phase values. This is the default
'mode=shift' whill randomly shift the phase values
Return
------
info string array containing the statistical values about the surrogate analysis
'''
import os, time
from jumeg_utils import make_surrogates_ctps, get_stats_surrogates_ctps
fnlist = get_files_from_list(fname_ctps)
# loop across all filenames
ifile = 1
sep = '=========================================================================='
info = [sep, '#', '# Statistical analysis on CTPS surrogates', '#', sep]
for fnctps in fnlist:
path = os.path.dirname(fnctps)
basename = os.path.basename(fnctps)
name = os.path.splitext(basename)[0]
print '>>> calc. surrogates based on: ' + basename
# load CTPS data
dctps = np.load(fnctps).item()
phase_trials = dctps['pt'] # [nfreq, ntrials, nsources, nsamples]
# create surrogate tests
t_start = time.time()
pks = make_surrogates_ctps(phase_trials,
nrepeat=nrepeat,
mode=mode,
verbose=None,
n_jobs=n_jobs)
# perform stats on surrogates
stats = get_stats_surrogates_ctps(pks, verbose=False)
info.append(sep)
info.append(path)
info.append(basename)
info.append('nfreq: ' + str(stats['nfreq']))
info.append('nrepeat: ' + str(stats['nrepeat']))
info.append('nsamples: ' + str(stats['nsamples']))
info.append('nsources: ' + str(stats['nsources']))
info.append('permutation mode: ' + mode)
# info for each freq. band of the current data set
info.append('# stats for each frequency band:')
line_f = 'freqs (Hz):'
line_max = 'pk max: '
line_mean = 'pk mean: '
line_min = 'pk min: '
for i in range(stats['nfreq']):
flow, fhigh = dctps['freqs'][i]
line_f += str('%5d-%d ' % (flow, fhigh))
line_max += str('%8.3f' % stats['pks_max'][i])
line_mean += str('%8.3f' % stats['pks_mean'][i])
line_min += str('%8.3f' % stats['pks_min'][i])
info.append(line_f)
info.append(line_min)
info.append(line_mean)
info.append(line_max)
# across freq. bands
pks_min = stats['pks_min_global']
pks_mean = stats['pks_mean_global']
pks_std = stats['pks_std_global']
pks_pct = stats['pks_pct99_global']
pks_max = stats['pks_max_global']
info.append('# stats across all frequency bands:')
info.append('pk min: ' + str('%8.3f' % pks_min))
info.append('pk mean: ' + str('%8.3f' % stats['pks_mean_global']))
info.append('pk std: ' + str('%8.3f' % stats['pks_std_global']))
info.append('pk pct99:' + str('%8.3f' % stats['pks_pct99_global']))
info.append('pk pct99.90:' + str('%8.3f' % stats['pks_pct999_global']))
info.append('pk pct99.99:' +
str('%8.3f' % stats['pks_pct9999_global']))
info.append('pk max: ' + str('%8.3f' % stats['pks_max_global']))
# combine global stats values of different ctps files into one global analysis
if (ifile > 1):
pks_all = np.concatenate((pks_all, pks.flatten()))
else:
pks_all = pks.flatten()
ifile += 1
if (ifile > 1):
info.append(sep)
info.append('#')
info.append('# stats across all files:')
info.append('#')
info.append('pk min: ' + str('%8.3f' % pks_all.min()))
info.append('pk mean: ' + str('%8.3f' % pks_all.mean()))
info.append('pk std: ' + str('%8.3f' % pks_all.std()))
info.append('pk pct99:' + str('%8.3f' % np.percentile(pks_all, 99)))
info.append('pk pct99.90:' +
str('%8.3f' % np.percentile(pks_all, 99.9)))
info.append('pk pct99.99:' +
str('%8.3f' % np.percentile(pks_all, 99.99)))
info.append('pk max: ' + str('%8.3f' % pks_all.max()))
info.append('#')
duration = (time.time() - t_start) / 60.0 # in minutes
info.append('duration [min]: %0.2f' % duration)
info.append('#')
info.append(sep)
# save surrogate stats
if (save):
np.savetxt(fnout, info, fmt='%s')
return info
def apply_ctps_select_ic(fname_ctps, threshold=0.1):
''' Select ICs based on CTPS analysis. '''
fnlist = get_files_from_list(fname_ctps)
# loop across all filenames
pl.ioff() # switch off (interactive) plot visualisation
ifile = 0
for fnctps in fnlist:
name = os.path.splitext(fnctps)[0]
basename = os.path.splitext(os.path.basename(fnctps))[0]
print '>>> working on: ' + basename
# load CTPS data
dctps = np.load(fnctps).item()
freqs = dctps['freqs']
nfreq = len(freqs)
ncomp = dctps['ncomp']
trig_name = dctps['trig_name']
times = dctps['times']
ic_sel = []
# loop acros all freq. bands
fig = pl.figure(ifile + 1, figsize=(16, 9), dpi=100)
pl.clf()
fig.subplots_adjust(left=0.08,
right=0.95,
bottom=0.05,
top=0.93,
wspace=0.2,
hspace=0.2)
fig.suptitle(basename, fontweight='bold')
nrow = np.ceil(float(nfreq) / 2)
for ifreq in range(nfreq):
pk = dctps['pk'][ifreq]
pt = dctps['pt'][ifreq]
pkmax = pk.max(1)
ixmax = np.where(pkmax == pkmax.max())[0]
ix = (np.where(pkmax >= threshold))[0]
if np.any(ix + 1):
if (ifreq > 0):
ic_sel = np.append(ic_sel, ix + 1)
else:
ic_sel = ix + 1
# do construct names for title, fnout_fig, fnout_ctps
frange = ' @' + str(freqs[ifreq][0]) + '-' + str(freqs[ifreq][1])
x = np.arange(ncomp) + 1
# do make bar plots for ctps thresh level plots
ax = fig.add_subplot(nrow, 2, ifreq + 1)
pl.bar(x, pkmax, color='steelblue')
pl.bar(x[ix], pkmax[ix], color='red')
pl.plot(x, np.repeat(threshold, ncomp), color='black')
pl.title(trig_name + frange, fontsize='small')
pl.xlim([1, ncomp + 1])
pl.ylim([0, 0.5])
pl.text(2, 0.45, 'ICs: ' + str(ix + 1))
ic_sel = np.unique(ic_sel)
nic = np.size(ic_sel)
fig.text(0.02,
0.98,
'pK threshold: ' + str(threshold),
transform=ax.transAxes)
info = 'ICs (all): ' + str(ic_sel).strip('[]')
fig.text(0.02, 0.01, info, transform=ax.transAxes)
# save CTPS components found
fntxt = name + '-ic_selection.txt'
ic_sel = np.reshape(ic_sel, [1, nic])
np.savetxt(fntxt, ic_sel, fmt='%i', delimiter=', ')
ifile += 1
# save figure
fnfig = name + '-ic_selection.png'
pl.savefig(fnfig, dpi=100)
pl.ion() # switch on (interactive) plot visualisation
def apply_ctps_select_ic(fname_ctps, threshold=0.1):
''' Select ICs based on CTPS analysis. '''
fnlist = get_files_from_list(fname_ctps)
# loop across all filenames
pl.ioff() # switch off (interactive) plot visualisation
ifile = 0
for fnctps in fnlist:
name = os.path.splitext(fnctps)[0]
basename = os.path.splitext(os.path.basename(fnctps))[0]
print '>>> working on: ' + basename
# load CTPS data
dctps = np.load(fnctps).item()
freqs = dctps['freqs']
nfreq = len(freqs)
ncomp = dctps['ncomp']
trig_name = dctps['trig_name']
times = dctps['times']
ic_sel = []
# loop acros all freq. bands
fig = pl.figure(ifile + 1, figsize=(16, 9), dpi=100)
pl.clf()
fig.subplots_adjust(left=0.08, right=0.95, bottom=0.05,
top=0.93, wspace=0.2, hspace=0.2)
fig.suptitle(basename, fontweight='bold')
nrow = np.ceil(float(nfreq) / 2)
for ifreq in range(nfreq):
pk = dctps['pk'][ifreq]
pt = dctps['pt'][ifreq]
pkmax = pk.max(1)
ixmax = np.where(pkmax == pkmax.max())[0]
ix = (np.where(pkmax >= threshold))[0]
if np.any(ix+1):
if (ifreq > 0):
ic_sel = np.append(ic_sel, ix + 1)
else:
ic_sel = ix + 1
# do construct names for title, fnout_fig, fnout_ctps
frange = ' @' + str(freqs[ifreq][0]) + '-' + str(freqs[ifreq][1])
x = np.arange(ncomp) + 1
# do make bar plots for ctps thresh level plots
ax = fig.add_subplot(nrow, 2, ifreq + 1)
pl.bar(x, pkmax, color='steelblue')
pl.bar(x[ix], pkmax[ix], color='red')
pl.plot(x,np.repeat(threshold,ncomp),color='black')
pl.title(trig_name + frange, fontsize='small')
pl.xlim([1, ncomp + 1])
pl.ylim([0, 0.5])
pl.text(2, 0.45, 'ICs: ' + str(ix + 1))
ic_sel = np.unique(ic_sel)
nic = np.size(ic_sel)
fig.text(0.02, 0.98, 'pK threshold: ' + str(threshold),
transform=ax.transAxes)
info = 'ICs (all): ' + str(ic_sel).strip('[]')
fig.text(0.02, 0.01, info, transform=ax.transAxes)
# save CTPS components found
fntxt = name + '-ic_selection.txt'
ic_sel = np.reshape(ic_sel, [1, nic])
np.savetxt(fntxt, ic_sel, fmt='%i', delimiter=', ')
ifile += 1
# save figure
fnfig = name + '-ic_selection.png'
pl.savefig(fnfig, dpi=100)
pl.ion() # switch on (interactive) plot visualisation
def apply_create_noise_covariance(fname_empty_room,
require_filter=False,
require_noise_reducer=False,
verbose=None):
'''
Creates the noise covariance matrix from an empty room file.
Parameters
----------
fname_empty_room : String containing the filename
of the empty room file (must be a fif-file)
File name should end with -raw.fif in order to have proper output filenames.
require_filter: bool
If true, the empy room file is filtered before calculating
the covariance matrix. (Beware, filter settings are fixed.)
require_noise_reducer: bool
If true, a noise reducer is applied on the empty room file.
The noise reducer frequencies are fixed to 50Hz, 60Hz and
to frequencies less than 5Hz i.e. the reference channels are filtered to
these frequency ranges and then signal obtained is removed from
the empty room raw data. For more information please check the jumeg noise reducer.
verbose : bool, str, int, or None
If not None, override default verbose level
(see mne.verbose).
default: verbose=None
'''
# -------------------------------------------
# import necessary modules
# -------------------------------------------
from mne import compute_raw_data_covariance as cp_covariance
from mne import write_cov, pick_types
from mne.io import Raw
from jumeg.jumeg_noise_reducer import noise_reducer
fner = get_files_from_list(fname_empty_room)
nfiles = len(fner)
# loop across all filenames
for ifile in range(nfiles):
fn_in = fner[ifile]
print ">>> create noise covariance using file: "
path_in, name = os.path.split(fn_in)
print name
if require_filter:
print "Filtering with preset settings..."
# filter empty room raw data
apply_filter(fn_in, flow=1, fhigh=45, order=4, njobs=4)
# reconstruct empty room file name accordingly
fn_in = fn_in[:fn_in.rfind(ext_empty_raw)] + ',fibp1-45-raw.fif'
if require_noise_reducer:
fn_empty_nr = fn_in[:fn_in.rfind(ext_empty_raw)] + ',nr-raw.fif'
noise_reducer(fn_in,
refnotch=50,
detrending=False,
fnout=fn_empty_nr)
noise_reducer(fn_empty_nr,
refnotch=60,
detrending=False,
fnout=fn_empty_nr)
noise_reducer(fn_empty_nr, reflp=5, fnout=fn_empty_nr)
fn_in = fn_empty_nr
# file name for saving noise_cov
fn_out = fn_in[:fn_in.rfind(ext_empty_raw)] + ext_empty_cov
# read in data
raw_empty = Raw(fn_in, verbose=verbose)
# pick MEG channels only
picks = pick_types(raw_empty.info,
meg=True,
ref_meg=False,
eeg=False,
stim=False,
eog=False,
exclude='bads')
# calculate noise-covariance matrix
noise_cov_mat = cp_covariance(raw_empty, picks=picks, verbose=verbose)
# write noise-covariance matrix to disk
write_cov(fn_out, noise_cov_mat)
def apply_create_noise_covariance(fname_empty_room, require_filter=False,
require_noise_reducer=False, verbose=None):
'''
Creates the noise covariance matrix from an empty room file.
Parameters
----------
fname_empty_room : String containing the filename
of the empty room file (must be a fif-file)
File name should end with -raw.fif in order to have proper output filenames.
require_filter: bool
If true, the empy room file is filtered before calculating
the covariance matrix. (Beware, filter settings are fixed.)
require_noise_reducer: bool
If true, a noise reducer is applied on the empty room file.
The noise reducer frequencies are fixed to 50Hz, 60Hz and
to frequencies less than 5Hz i.e. the reference channels are filtered to
these frequency ranges and then signal obtained is removed from
the empty room raw data. For more information please check the jumeg noise reducer.
verbose : bool, str, int, or None
If not None, override default verbose level
(see mne.verbose).
default: verbose=None
'''
# -------------------------------------------
# import necessary modules
# -------------------------------------------
from mne import compute_raw_data_covariance as cp_covariance
from mne import write_cov, pick_types
from mne.io import Raw
from jumeg.jumeg_noise_reducer import noise_reducer
fner = get_files_from_list(fname_empty_room)
nfiles = len(fner)
# loop across all filenames
for ifile in range(nfiles):
fn_in = fner[ifile]
print ">>> create noise covariance using file: "
path_in, name = os.path.split(fn_in)
print name
if require_filter:
print "Filtering with preset settings..."
# filter empty room raw data
apply_filter(fn_in, flow=1, fhigh=45, order=4, njobs=4)
# reconstruct empty room file name accordingly
fn_in = fn_in[:fn_in.rfind(ext_empty_raw)] + ',fibp1-45-raw.fif'
if require_noise_reducer:
fn_empty_nr = fn_in[:fn_in.rfind(ext_empty_raw)] + ',nr-raw.fif'
noise_reducer(fn_in, refnotch=50, detrending=False, fnout=fn_empty_nr)
noise_reducer(fn_empty_nr, refnotch=60, detrending=False, fnout=fn_empty_nr)
noise_reducer(fn_empty_nr, reflp=5, fnout=fn_empty_nr)
fn_in = fn_empty_nr
# file name for saving noise_cov
fn_out = fn_in[:fn_in.rfind(ext_empty_raw)] + ext_empty_cov
# read in data
raw_empty = Raw(fn_in, verbose=verbose)
# pick MEG channels only
picks = pick_types(raw_empty.info, meg=True, ref_meg=False, eeg=False,
stim=False, eog=False, exclude='bads')
# calculate noise-covariance matrix
noise_cov_mat = cp_covariance(raw_empty, picks=picks, verbose=verbose)
# write noise-covariance matrix to disk
write_cov(fn_out, noise_cov_mat)
