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_empty(fname_empty_room, require_filter=True):
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]
path_in, name = os.path.split(fn_in)
fn_empty_nr = fn_in[:fn_in.rfind(ext_empty_raw)] + ',nr-empty.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
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)
def apply_noise_reducer(raw_fname, raw=None, **cfg):
'''
apply <magic ee noise reducer> thrice to reference channels with different freq parameters
save PSD plot in subfolder /plots
!!! overwrite raw-obj, works inplace !!!
0) reset bads and check for dead channels
1) apply nr low pass filter for freq below e.g.: 5Hz <reflp>
2) apply nr high pass filter if defined <reflp>
3) apply nr notch filter to remove power line noise <refnotch>
4) save PSD plot
Parameter:
-----------
<noise_reducer> parameter used in this function :
fname_raw : input raw filename
raw : <None>\n
cfg : dict, part of config file <None>
from config file <noise_reducer> part\n
reflp : <None>\n
refhp : <None>\n
refnotch : <None>\n
plot: True
plot_show : True
plot_dir : subdir to save plots
postfix : "nr"
file_extention: ["meeg-raw.fif","rfDC-empty.fif"]
run : True
save : True
overwrite: True
ToDo add parameter extended
<noise_reducer> parameter extended
signals=[], noiseref=[], detrending=None,
tmin=None, tmax=None,
exclude_artifacts=True, checkresults=True, return_raw=False,
complementary_signal=False, fnout=None, verbose=False
Return:
--------
filename,raw-obj
'''
#--- init plot
fname_out = None
logger.info(" -> apply_noise_reducer file name: {}".format(raw_fname))
logger.debug(" -> config parameter:\n{}".format(cfg))
if not jb.check_file_extention(fname=raw_fname,
file_extention=cfg.get("file_extention")):
return
if not cfg.get("run"):
#--- return raw_fname,raw
return jb.update_and_save_raw(raw,
fin=raw_fname,
fout=None,
save=False,
postfix=cfg.get("postfix", "nr"),
overwrite=cfg.get("overwrite", True))
logger.info(
" --> preproc noise_reducer for raw file: {}".format(raw_fname))
#--- noise reduction
# apply noise reducer thrice to reference channels with different freq parameters
# !!! overwrite raw-obj !!!
save = False
raw_changed = False
jb.verbose = cfg.get("verbose")
#--- load raw, reset bads
raw, raw_fname = jb.get_raw_obj(raw_fname, raw=raw, reset_bads=True)
#--- check dead channes and mark them as bad
jb.picks.check_dead_channels(raw=raw)
#--- start plot denoising orig raw psd, avoid reloading raw data
if cfg.get("plot"):
jplt = JuMEG_PLOT_PSD(n_plots=2, name="denoising", verbose=True)
jplt.plot(raw,
title="orig: " + os.path.basename(raw_fname),
check_dead_channels=False)
#--- with redirect stdout/err
with jumeg_logger.StreamLoggerSTD(label="noise_reducer"):
#--- 1 nr low pass filter for freq below 5 hz
if cfg.get("reflp"):
raw = noise_reducer(None,
raw=raw,
reflp=cfg.get("reflp"),
return_raw=True,
verbose=cfg.get("verbose"),
exclude_artifacts=False)
raw_changed = True
#--- 2 nr high pass filter
if cfg.get("refhp"):
raw = noise_reducer(None,
raw=raw,
reflp=cfg.get("refhp"),
return_raw=True,
verbose=cfg.get("verbose"),
exclude_artifacts=False)
raw_changed = True
#--- 3 nr notch filter to remove power line noise
if cfg.get("refnotch"):
raw = noise_reducer(None,
raw=raw,
refnotch=cfg.get("refnotch"),
fnout=None,
return_raw=True,
verbose=cfg.get("verbose"),
exclude_artifacts=False)
raw_changed = True
#--- save and update filename in raw
if cfg.get("save"):
save = raw_changed
#--- update filename in raw and save if save
fname_out, raw = jb.update_and_save_raw(raw,
fin=raw_fname,
fout=None,
save=save,
update_raw_filenname=True,
postfix=cfg.get("postfix", "nr"),
overwrite=cfg.get(
"overwrite", True))
#--- plot results, avoid reloading raw data
if cfg.get("plot"):
jplt.plot(raw,
title="denoised: " + os.path.basename(fname_out),
check_dead_channels=False)
if cfg.get("plot_show"):
jplt.show()
jplt.save(fname=fname_out, plot_dir=cfg.get("plor_dir", "plots"))
if fname_out:
return fname_out, raw
else:
raise Exception("---> ERROR file name not defined !!!")
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_noise_reducer(raw_fname=None,
raw=None,
config=None,
label='noise reducer',
fname_out=None):
'''
apply <magic ee noise reducer> thrice to reference channels with different freq parameters
save PSD plot in subfolder /plots
!!! overwrite raw-obj, works inplace !!!
0) reset bads and check for dead channels
1) apply nr low pass filter for freq below e.g.: 5Hz <reflp>
2) apply nr high pass filter if defined <reflp>
3) apply nr notch filter to remove power line noise <refnotch>
4) save PSD plot
IN config
noise_reducer:
file_extention:
- meeg-raw.fif
- rfDC-empty.fif
fmax: 300
noiseref_hp:
- RFG ...
overwrite: false
plot: true
plot_dir: report
plot_show: false
postfix: nr
refhp: 0.1
reflp: 5.0
refnotch:
- 50.0
- 100.0
- 150.0
- 200.0
- 250.0
- 300.0
- 350.0
- 400.0
run: true
save: true
Parameter:
-----------
<noise_reducer> parameter used in this function :
fname_raw : input raw filename
raw : <None>\n
cfg : dict, part of config file <None>
from config file <noise_reducer> part\n
reflp : <None>\n
refhp : <None>\n
refnotch : <None>\n
plot: True
plot_show : True
plot_dir : subdir to save plots
postfix : "nr"
file_extention: ["meeg-raw.fif","rfDC-empty.fif"]
run : True
save : True
overwrite: True
ToDo add parameter extended
<noise_reducer> parameter extended
signals=[], noiseref=[], detrending=None,
tmin=None, tmax=None,
exclude_artifacts=True, checkresults=True, return_raw=False,
complementary_signal=False, fnout=None, verbose=False
Return:
--------
filename,raw-obj
'''
#with JuMEG_PipelineFrame(raw_fname=raw_fname,raw=raw,name="noise reducer",config=cfg) as JPF:
RawIsChanged = False
#--- check dead channes and mark them as bad
jb.picks.check_dead_channels(raw=raw)
#--- start plot denoising orig raw psd, avoid reloading raw data
if config.get("plot"):
jplt = JuMEG_PLOT_PSD(n_plots=3, name="denoising",
verbose=True) #,pick_types=["meg","ref"])
jplt.plot(raw,
color="green",
title="REF: " + os.path.basename(raw_fname),
check_dead_channels=False,
fmax=config.get("fmax"),
picks=jb.picks.ref_nobads(raw))
jplt.plot(raw,
color="blue",
title="MEG orig: " + os.path.basename(raw_fname),
check_dead_channels=False,
fmax=config.get("fmax"),
picks=jb.picks.meg_nobads(raw))
#self.picks = jb.picks.meg_nobads(raw))
#--- with redirect stdout/err
with jumeg_logger.StreamLoggerSTD(label=label):
#--- 1 nr low pass filter for freq below 5 hz
if config.get("reflp"):
raw = noise_reducer(None,
raw=raw,
reflp=config.get("reflp"),
return_raw=True,
verbose=config.get("verbose"),
exclude_artifacts=False)
RawIsChanged = True
#--- 2 nr high pass filter
if config.get("refhp"):
raw = noise_reducer(None,
raw=raw,
reflp=config.get("refhp"),
return_raw=True,
verbose=config.get("verbose"),
exclude_artifacts=False)
RawIsChanged = True
#--- 3 nr notch filter to remove power line noise
if config.get("refnotch"):
raw = noise_reducer(None,
raw=raw,
refnotch=config.get("refnotch"),
fnout=None,
return_raw=True,
verbose=config.get("verbose"),
exclude_artifacts=False)
RawIsChanged = True
#--- plot results, avoid reloading raw data
if config.get("plot"):
jplt.plot(raw,
title="MEG denoised: " + os.path.basename(fname_out),
check_dead_channels=False,
fmax=config.get("fmax"),
picks=jb.picks.meg_nobads(raw))
if config.get("plot_show"):
jplt.show()
fout = jplt.save(fname=fname_out,
plot_dir=config.get("plot_dir", "report"))
#--- update image list in report-config for later update MNE Report
CFG = jCFG()
data = None
report_path = os.path.dirname(fout)
report_config = os.path.join(
report_path,
raw_fname.rsplit("_", 1)[0] + "-report.yaml")
if not CFG.load_cfg(fname=report_config):
data = {"noise_reducer": {"files": os.path.basename(fout)}}
else:
CFG.config["noise_reducer"] = {"files": os.path.basename(fout)}
CFG.save_cfg(fname=report_config, data=data)
return fname_out, raw, RawIsChanged, None
def apply_noise_reducer(fname,
raw=None,
do_run=True,
verbose=False,
save=True,
plot=False,
fif_postfix="nr",
fif_extention="-raw.fif",
return_raw=True,
reflp=None,
refhp=None,
refnotch=None,
parameter={}):
'''
Applies the noise reducer to raw obj data or to fif-file.
the magic ee once
fb modified for raw obj support
imports jumeg_noise_reducer_4raw_data
'''
import os
# --- import noise_reducer and plot_power_spectrum function
# from jumeg.jumeg_4raw_data_noise_reducer import noise_reducer_4raw_data, plot_denoising_4raw_data
from jumeg.jumeg_noise_reducer import noise_reducer, plot_denoising
fname_out = None
nr_done = False
if do_run:
raw, fname_raw = jumeg_base.get_raw_obj(fname, raw=raw)
fname_out = jumeg_base.get_fif_name(raw=raw,
postfix=fif_postfix,
extention=fif_extention,
update_raw_fname=False)
# --- apply noise reducer for 50 Hz (and harmonics)
'''
"signals":[],
"noiseref":[],
"detrending": null,
"tmin": null,
"tmax": null,
"exclude_artifacts":true,
"checkresults":true,
"complementary_signal":false,
"return_raw":true
'''
if (reflp or refhp):
# raw, fname_out = noise_reducer_4raw_data(fname,raw=raw,reflp=reflp,refhp=refhp,verbose=verbose,save=False,**parameter)
fin = None
raw = noise_reducer(fin,
raw=raw,
reflp=reflp,
refhp=refhp,
return_raw=True,
verbose=verbose,
**parameter)
parameter['detrending'] = None
nr_done = True
if refnotch:
for refn in refnotch:
# raw,fname_out = noise_reducer_4raw_data(None,raw=raw,refnotch=refn,verbose=verbose,save=False,**kwargs['parameter'])
raw = noise_reducer(fin,
raw=raw,
refnotch=refn,
return_raw=True,
**parameter)
parameter['detrending'] = None
nr_done = True
# raw.info['filename'] = fname_out
if not nr_done:
return fname_raw, raw
if save:
fname_out = jumeg_base.apply_save_mne_data(raw, fname=fname_out)
if plot:
print " --> noise reducer plot power spectrum"
from distutils.dir_util import mkpath
p, pdf = os.path.split(fname_raw)
plot_dir = p + '/plots/'
mkpath(plot_dir)
fn_power_spect = plot_dir + pdf[:pdf.rfind('-raw.fif'
)] + ',denoising'
# plot_denoising_4raw_data([fname_raw, fname_out], show=False, fnout=fn_power_spect)
plot_denoising([fname_raw, fname_out],
show=False,
fnout=fn_power_spect)
print "---> noise reducer plot :" + fn_power_spect
print "---> Done noise reducer: " + fname_out
return (fname_out, raw)
fir_window='hamming',
fir_design='firwin')
raw_notch.plot_psd(fmin=0.,
fmax=100.,
tmin=None,
tmax=60.,
n_fft=None,
picks=picks,
ax=ax2)
ax2.set_title('Notch filter 50Hz applied')
# powerline removal using noise_reducer
raw_nr_notch = noise_reducer(raw_fname,
raw=raw.copy(),
detrending=False,
reflp=None,
refhp=None,
refnotch=[50., 100., 150.],
return_raw=True,
verbose=False)
raw_nr_notch.plot_psd(fmin=0.,
fmax=100.,
tmin=None,
tmax=60.,
n_fft=None,
picks=picks,
ax=ax3)
ax3.set_title('Noise reducer notch filter 50Hz applied')
# remove high freq noise (>0.1Hz) from ref channels
raw_nr2 = noise_reducer(raw_fname,
raw=raw_nr_notch,
def noise_reduction(dirname, raw_fname, denoised_fname, nr_cfg,
state_space_fname):
"""
Apply the noise reducer to the raw file and save the result.
Parameters:
-----------
dirname : str
Path to the directory where the raw file is stored.
raw_fname : str
File name of the raw file.
denoised_fname : str
File name under which the denoised raw file is saved.
nr_cfg: dict
Dict containing the noise reducer specific settings from
the config file.
state_space_fname : str
Second half of the name under which the state space dict is to
be saved, e.g., subj + state_space_name.
Returns:
--------
None
"""
refnotch = nr_cfg['refnotch']
reflp = nr_cfg['reflp']
refhp = nr_cfg['refhp']
noiseref_hp = nr_cfg['noiseref_hp']
from jumeg.jumeg_noise_reducer import noise_reducer, plot_denoising
subj = op.basename(raw_fname).split('_')[0]
ss_dict_fname = op.join(op.dirname(raw_fname), subj + state_space_fname)
# read the raw file
raw = mne.io.Raw(op.join(dirname, raw_fname), preload=True)
# apply noise reducer thrice to reference channels with different freq parameters
# the nr-raw.fif are rewritten
# low pass filter for freq below 5 hz
raw_nr = noise_reducer(raw_fname, raw=raw, reflp=reflp, return_raw=True)
raw.close()
raw_nr = noise_reducer(raw_fname,
raw=raw_nr,
refhp=refhp,
noiseref=noiseref_hp,
return_raw=True)
# notch filter to remove power line noise
raw_nr = noise_reducer(raw_fname,
raw=raw_nr,
refnotch=refnotch,
fnout=op.join(dirname, denoised_fname),
return_raw=True)
raw_nr.close()
# plot final plotting
plot_name = denoised_fname.rsplit('-raw.fif')[0] + '-plot'
plot_denoising(
[op.join(dirname, raw_fname),
op.join(dirname, denoised_fname)],
n_jobs=1,
fnout=op.join(dirname, plot_name),
show=False)
# save config file
nr_dict = nr_cfg.copy()
nr_dict['input_file'] = op.join(dirname, raw_fname)
nr_dict['process'] = 'noise_reducer'
nr_dict['output_file'] = op.join(dirname, denoised_fname)
save_state_space_file(ss_dict_fname, process_config_dict=nr_dict)
data_path = op.join(get_jumeg_path(), 'data')
print data_path
# example filname
raw_fname = "/Volumes/megraid21/sripad/cau_fif_data/jumeg_test_data/" \
"109925_CAU01A_100715_0842_2_c,rfDC-raw.fif"
# load the model for artifact rejection
# the details of the model is provided in the x_validation_shuffle_v4_split_23.txt
model_name = op.join(data_path, "dcnn_model.hdf5")
model = load_model(model_name)
# noise reducer
raw_nr = noise_reducer(raw_fname, reflp=5., return_raw=True)
raw_nr = noise_reducer(raw_fname,
raw=raw_nr,
refhp=0.1,
noiseref=['RFG ...'],
return_raw=True)
# 50HZ and 60HZ notch filter to remove noise
raw = noise_reducer(raw_fname, raw=raw_nr, refnotch=refnotch, return_raw=True)
picks = mne.pick_types(raw.info,
meg=True,
eeg=False,
eog=False,
stim=False,
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)
mne.write_events(fn_eve, eog2_correct_events)
##########################################################
# Noise_reducer for noise around 50Hz, 60Hz and below 5Hz
#---------------------------------------------------------
if do_pre:
fn_list = glob.glob(subjects_dir + '*[0-9]/MEG/*rfDC,bcc-raw.fif')
perf_path = subjects_dir + 'rn_perf/'
set_directory(perf_path)
for fn_raw in fn_list:
#import noise_reducer and plot_power_spectrum function
#apply noise reducer for 50 Hz (and harmonics)
fn_raw_nr = fn_raw[:fn_raw.rfind('-raw.fif')] + ',nr-raw.fif'
subject = os.path.basename(fn_raw_nr).split('_')[0]
fn_per = perf_path + subject
noise_reducer(fn_raw, refnotch=50, detrending=False, fnout=fn_raw_nr)
noise_reducer(fn_raw_nr,
refnotch=60,
detrending=False,
fnout=fn_raw_nr)
#apply noise reducer for frequencies below 5 Hz
noise_reducer(fn_raw_nr, reflp=5,
fnout=fn_raw_nr) # plot power spectrum
fn_power_spect = fn_raw_nr[:fn_raw_nr.rfind('-raw.fif')] + ',denoising'
plot_denoising([fn_raw, fn_raw_nr],
stim_name=tri_name,
event_id=ev_id,
show=False,
fnout=fn_per,
tmin_stim=-0.4,
tmax_stim=0.4)
def apply_noise_reducer(raw_fname=None,
raw=None,
config=None,
label="noise reducer",
fname_out=None):
'''
apply <magic ee noise reducer> thrice to reference channels with different freq parameters
save PSD plot in subfolder /plots
!!! overwrite raw-obj, works inplace !!!
0) reset bads and check for dead channels
1) apply nr low pass filter for freq below e.g.: 5Hz <reflp>
2) apply nr high pass filter if defined <reflp>
3) apply nr notch filter to remove power line noise <refnotch>
4) save PSD plot
Parameter:
-----------
<noise_reducer> parameter used in this function :
fname_raw : input raw filename
raw : <None>\n
cfg : dict, part of config file <None>
from config file <noise_reducer> part\n
reflp : <None>\n
refhp : <None>\n
refnotch : <None>\n
plot: True
plot_show : True
plot_dir : subdir to save plots
postfix : "nr"
file_extention: ["meeg-raw.fif","rfDC-empty.fif"]
run : True
save : True
overwrite: True
ToDo add parameter extended
<noise_reducer> parameter extended
signals=[], noiseref=[], detrending=None,
tmin=None, tmax=None,
exclude_artifacts=True, checkresults=True, return_raw=False,
complementary_signal=False, fnout=None, verbose=False
Return:
--------
filename,raw-obj
'''
#with JuMEG_PipelineFrame(raw_fname=raw_fname,raw=raw,name="noise reducer",config=cfg) as JPF:
RawIsChanged = False
#--- check dead channes and mark them as bad
jb.picks.check_dead_channels(raw=raw)
#--- start plot denoising orig raw psd, avoid reloading raw data
if config.get("plot"):
jplt = JuMEG_PLOT_PSD(n_plots=2, name="denoising", verbose=True)
jplt.plot(raw,
title="orig: " + os.path.basename(raw_fname),
check_dead_channels=False,
fmax=config.get("fmax"))
#--- with redirect stdout/err
with jumeg_logger.StreamLoggerSTD(label=label):
#--- 1 nr low pass filter for freq below 5 hz
if config.get("reflp"):
raw = noise_reducer(None,
raw=raw,
reflp=config.get("reflp"),
return_raw=True,
verbose=config.get("verbose"),
exclude_artifacts=False)
RawIsChanged = True
#--- 2 nr high pass filter
if config.get("refhp"):
raw = noise_reducer(None,
raw=raw,
reflp=config.get("refhp"),
return_raw=True,
verbose=config.get("verbose"),
exclude_artifacts=False)
RawIsChanged = True
#--- 3 nr notch filter to remove power line noise
if config.get("refnotch"):
raw = noise_reducer(None,
raw=raw,
refnotch=config.get("refnotch"),
fnout=None,
return_raw=True,
verbose=config.get("verbose"),
exclude_artifacts=False)
RawIsChanged = True
#--- plot results, avoid reloading raw data
if config.get("plot"):
jplt.plot(raw,
title="denoised: " + os.path.basename(fname_out),
check_dead_channels=False,
fmax=config.get("fmax"))
if config.get("plot_show"):
jplt.show()
jplt.save(fname=fname_out, plot_dir=config.get("plor_dir", "plots"))
return fname_out, raw, RawIsChanged
