def apply_create_noise_covariance(fname_empty_room, require_filter=True, 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)
require_filter: bool
If true, the empy room file is filtered before calculating
the covariance matrix. (Beware, filter settings are fixed.)
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
from mne.io import Raw
from mne import pick_types
import os
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 == True:
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.split('-')[0] + ',bp1-45Hz-empty.fif'
# file name for saving noise_cov
fn_out = fn_in.split('-')[0] + ',empty-cov.fif'
# 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, verbose=None):
'''
Creates the noise covariance matrix from an empty room file.
Parameters
----------
fname_empty_room : String containing the filename
of the de-noise, empty room file (must be a fif-file)
require_filter: bool
If true, the empy room file is filtered before calculating
the covariance matrix. (Beware, filter settings are fixed.)
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)
ext_empty_raw = '-raw.fif'
ext_empty_cov = '-cov.fif'
# 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
fn_empty_nr = fn_in[:fn_in.rfind('-raw.fif')] + ',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)
# file name for saving noise_cov
fn_out = fn_empty_nr[:fn_empty_nr.rfind(ext_empty_raw)] + ext_empty_cov
# read in data
raw_empty = Raw(fn_empty_nr, preload=True, verbose=verbose)
raw_empty.interpolate_bads()
# 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_data(fname,raw=None,do_filter=True,filter_parameter=None,verbose=False,do_run=True,save=True ):
'''
Creates the noise covariance matrix from an empty room file.
Parameters
----------
fname : string
containing the filename of the empty room file (must be a fif-file)
do_filter: bool
If true, the empy room file is filtered before calculating
the covariance matrix. (Beware, filter settings are fixed.)
filter_parameter: dict
dict with filter parameter and flags
do_run: bool
execute this
save: bool
save output file
verbose : bool, str, int, or None
If not None, override default verbose level
(see mne.verbose).
default: verbose=None
RETURN
---------
full empty room filname as string
raw obj of input raw-obj or raw-empty-room obj
'''
# -------------------------------------------
# import necessary modules
# -------------------------------------------
from mne import compute_raw_data_covariance as cp_covariance
from mne import write_cov
from mne.io import Raw
from mne import pick_types
import os
mne.verbose = verbose
try:
(fname_empty_room,raw_empty) = jumeg_base.get_empty_room_fif(fname=fname,raw=raw,preload=do_run)
except:
return
if raw_empty :
#--- picks meg channels
filter_parameter.picks = jumeg_base.pick_meg_nobads(raw_empty)
#--- filter or get filter name
filter_parameter.do_run = do_filter
if do_filter :
print "Filtering empty room fif with noise variance settings...\n"
(fname_empty_room,raw_empty) = apply_filter_data(fname_empty_room,raw=raw_empty,**filter_parameter)
#--- update file name for saving noise_cov
fname_empty_room_cov = fname_empty_room.split('-')[0] + ',empty-cov.fif'
#--- calc nois-covariance matrix
if do_run :
noise_cov_mat = cp_covariance(raw_empty,picks=filter_parameter.picks,verbose=verbose)
#--- write noise-covariance matrix to disk
if save :
write_cov( fname_empty_room_cov, noise_cov_mat)
return fname_empty_room_cov
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, fname_out, 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)
fname_out : String containing the output filename
of the noise-covariance estimation (must also
be a fif-file)
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
from mne.io import Raw
from mne import pick_types
import os
if isinstance(fname_empty_room, list):
fner = fname_empty_room
else:
if isinstance(fname_empty_room, str):
fner = list([fname_empty_room])
else:
fner = list(fname_empty_room)
nfiles = len(fner)
if isinstance(fname_out, list):
fnout = fname_out
else:
if isinstance(fname_out, str):
fnout = list([fname_out])
else:
fnout = list(fname_out)
if (len(fnout) != nfiles):
print ">>> Error (create_noise_covariance_matrix):"
print " Number of in/out files do not match"
print
exit()
# loop across all filenames
for ifile in range(nfiles):
fn_in = fner[ifile]
fn_out = fnout[ifile]
print ">>> create noise covariance using file: "
path_in , name = os.path.split(fn_in)
print name
# read in data
raw_empty = Raw(fn_in, verbose=verbose)
# pick MEG channels only
picks = pick_types(raw_empty.info, meg=True, 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)
def apply_cov(fname_empty_room, filtered=True):
'''
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_covariance as cp_covariance
from mne import write_cov, pick_types
from mne.io import Raw
fner = get_files_from_list(fname_empty_room)
nfiles = len(fner)
# loop across all filenames
for ifile in range(nfiles):
fn_in = fner[ifile]
fn_fig1 = fn_in[:fn_in.rfind(ext_empty_raw)] + ',Magnetometers.tiff'
fn_fig2 = fn_in[:fn_in.rfind(ext_empty_raw)] + ',Eigenvalue_index.tiff'
#fn_out = fn_in[:fn_in.rfind(ext_empty_raw)] + ext_empty_cov
path_in, name = os.path.split(fn_in)
subject = name.split('_')[0]
# read in data
raw_empty = Raw(fn_in)
# pick MEG channels only
picks = pick_types(raw_empty.info, meg=True, exclude='bads')
# calculate noise-covariance matrix
noise_cov_mat = cp_covariance(raw_empty,
tmin=None,
tmax=None,
tstep=0.2,
picks=picks)
#noise_cov_mat = cp_covariance(raw_empty, picks=picks)
fig1, fig2 = mne.viz.plot_cov(noise_cov_mat, raw_empty.info)
# write noise-covariance matrix to disk
if filtered:
fn_out = path_in + '/%s_empty,fibp1-45' % subject + ext_empty_cov
else:
fn_out = path_in + '/%s_empty' % subject + ext_empty_cov
write_cov(fn_out, noise_cov_mat)
fig1.savefig(fn_fig1)
fig2.savefig(fn_fig2)
pl.close('all')