def nr_plot_denoising(f1,f2,fnout):
"""
call orig plotting function
jumeg.jumeg_noise_reducer import plot_denoising
:param f1:
:param f2:
:param fnout:
:return:
"""
from jumeg.jumeg_noise_reducer import plot_denoising
plot_denoising([f1,f2],n_jobs=1,fnout=fnout,show=False)
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)
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)
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)
##########################################################
# Remove ECG and EOG artifacts
#---------------------------------------------------------
if do_rej_HE:
#from apply_chopICA import chop_and_apply_ica
ecg_ch = 'ECG 001'
eog1_ch = 'EOG 001'
eog2_ch = 'EOG 002'
# use 8Hz to account for slower T waves (ecg)
flow_ecg, fhigh_ecg = 8., 20.