def test_event():
"""Test that on_process event gets fired"""
handler = Mock()
params = mnefun.Params()
setdefaults(params)
params.on_process = handler
with patch('mnefun._mnefun.save_epochs') as func:
mnefun.do_processing(
params,
write_epochs=True,
)
handler.assert_called_with('Doing epoch EQ/DQ', func, func(), params)
def params(tmpdir):
"""Param fixture."""
params = mnefun.Params()
params.work_dir = str(tmpdir)
params.score = Mock()
params.subject_indices = []
params.subjects = [None]
params.structurals = [None]
params.dates = [None]
params.in_names = []
params.in_numbers = []
params.analyses = []
params.out_names = []
params.out_numbers = []
params.must_match = []
params.decim = 5
params.plot_drop_logs = False
return params
##ECG_channel = 'MEG1531'/NO EOG channel:
# 110, 114, 117, 118, 135 + 131, 145
##ECG_channel = 'MEG0143'/NO EOG channel
# 130
##No heart artifact in sss data/NO EOG channel
# 133, 134
## 120 Only 8/1076 good ECG epochs found (bad coils)
## 135 Only 12/1076 good ECG epochs found (bad coils)
params = mnefun.Params(tmin=-0.1,
tmax=1.2,
n_jobs=18,
decim=2,
proj_sfreq=200,
n_jobs_fir='cuda',
filter_length='5s',
lp_cut=80.,
n_jobs_resample='cuda',
bmin=-0.1,
bem_type='5120',
ecg_channel='MEG1531')
params.subjects = [
'sasi_110', 'sasi_114', 'sasi_117', 'sasi_118', 'sasi_120', 'sasi_121',
'sasi_129', 'sasi_130', 'sasi_131', 'sasi_133', 'sasi_134', 'sasi_135',
'sasi_137', 'sasi_141', 'sasi_143', 'sasi_144', 'sasi_145', 'sasi_147'
]
params.structurals = params.subjects
params.dates = [(2013, 0, 00)] * len(params.subjects)
params.subject_indices = np.setdiff1d(np.arange(len(params.subjects)), [])
# to 5 by default, which makes the negative stopping frequency (0.03-5).
# It appears that currently data are acquired (online) using bandpass filter
# (0.03 - 326.4 Hz), so it might be okay not doing offline highpass filtering.
# It's worth checking later though. However, I think we should do baseline
# correction by setting bmin and bmax. I found that mnefun does baseline
# correction by default.
# sjjoo_20160809: Commented
params = mnefun.Params(
tmin=-0.1,
tmax=0.9,
n_jobs=18, # t_adjust was -39e-3
decim=2,
n_jobs_mkl=1,
proj_sfreq=250,
n_jobs_fir='cuda',
n_jobs_resample='cuda',
filter_length='5s',
epochs_type='fif',
lp_cut=40.,
# hp_cut=0.15,hp_trans=0.1,
bmin=-0.1,
auto_bad=20.,
plot_raw=False,
bem_type='5120-5120-5120')
# This sets the position of the head relative to the sensors. These values a
# A typical head position. So now in sensor space everyone is aligned. However
# We should also note that for source analysis it is better to leave this as
# the mne-fun default
params.trans_to = (0., 0., .035)
# tmin, tmax: sets the epoch
# bmin, bmax: sets the prestim duration for baseline correction. baseline is set
# as individual as default. Refer to _mnefun.py bmax is 0.0 by default
# hp_cut, lp_cut: set cutoff frequencies for highpass and lowpass
# I found that hp_cut of 0.03 is problematic because the tansition band is set
# to 5 by default, which makes the negative stopping frequency (0.03-5).
# It appears that currently data are acquired (online) using bandpass filter
# (0.03 - 326.4 Hz), so it might be okay not doing offline highpass filtering.
# It's worth checking later though. However, I think we should do baseline
# correction by setting bmin and bmax. I found that mnefun does baseline
# correction by default.
# sjjoo_20160809: Commented
params = mnefun.Params(tmin=-0.2, tmax=1.0, t_adjust=-39e-3, n_jobs=18,
decim=2, n_jobs_mkl=1, proj_sfreq=250,
n_jobs_fir='cuda', n_jobs_resample='cuda',
filter_length='5s', epochs_type='fif', lp_cut=40,
bmin=-0.2, auto_bad=20., plot_raw=False,
bem_type = '5120')
<<<<<<< HEAD
# This sets the position of the head relative to the sensors. These values a
# A typical head position. So now in sensor space everyone is aligned. However
# We should also note that for source analysis it is better to leave this as
# the mne-fun default
params.trans_to = (0., 0., .04)
=======
params.trans_to = (0., 0., .04) # Align each subject to a common reference frame
>>>>>>> a4cd458d621a2dab85571599080677eb96297c75
params.sss_type = 'python'
params.sss_regularize = 'svd' # 'in' by default
params.tsss_dur = 10. # 60 for adults with not much head movements. This was set to 6.
"both",
subjects_dir=defaults.subjects_dir)
rois = [roi for roi in all_rois if not roi.name.startswith("unknown")]
roi_nms = [rr.name for rr in rois]
n = len(roi_nms)
laplacian = np.zeros((len(df), len(defaults.bands), n))
degree = np.zeros_like(laplacian)
A_lst = list()
reject = dict(grad=2000e-13, mag=6000e-15) # same as GenZ repo
src_fs = mne.read_source_spaces(
op.join(defaults.subjects_dir, "fsaverage", "bem",
"fsaverage-ico-5-src.fif"))
#TODO mv to YAML
state = "task" # task/rest
if state == "task":
p = mnefun.Params()
p.work_dir = defaults.megdata
p.sss_fif_tag = "_raw_sss.fif"
p.run_names = [
"%s_faces_learn_01",
"%s_thumbs_learn_01",
"%s_emojis_learn_01",
"%s_faces_test_01",
"%s_thumbs_test_01",
"%s_emojis_test_01",
]
p.lp_cut = 80
# Subject loop
for si, ss in enumerate(df.id.values):
subject = f"genz{ss}"
pitchacc_07_02 has 52 speech trials - not sure if they are usable
@author: blau
"""
import mnefun
import numpy as np
from score import score
params = mnefun.Params(tmin=-0.2,
tmax=2,
n_jobs=8,
decim=2,
proj_sfreq=200,
n_jobs_fir='cuda',
filter_length='auto',
lp_cut=80.,
n_jobs_resample='cuda',
cov_method='shrunk',
bmin=-0.1,
bem_type='5120')
params.subjects_dir = 'subjects'
params.subjects = ['pitchacc_108']
params.structurals = params.subjects
params.dates = [None] * len(params.subjects) # None used to fully anonymize
params.score = score # scoring function used to slice data into trials
# define which subjects to run
params.subject_indices = np.arange(len(params.subjects))
params.plot_drop_logs = True
# Set parameters for remotely connecting to acquisition computer
from mne.stats import ttest_1samp_no_p
from mne.minimum_norm import (read_inverse_operator, apply_inverse)
import mnefun
from mnefun import anova_time
from mnefun import get_fsaverage_medial_vertices
__copyright__ = "Copyright 2015, ILABS"
__status__ = "Development"
log(verbose='Warning')
# cd to meg directory
os.chdir('/media/ALAYA/data/ilabs/nlr/')
work_dir = os.getcwd()
# set up mnefun parameters of interest
p = mnefun.Params(lp_cut=40.)
p.analyses = ['Words_noise']
p.subjects = ['nlr01', 'nlr02', 'nlr04', 'nlr05', 'nlr06', 'nlr07', 'nlr08']
p.structurals = ['nlr01', 'nlr02', 'nlr04', 'nlr05', 'nlr06', 'nlr07', 'nlr08']
do_plots = False
reload_data = True
do_contrasts = True
do_anova = False
# Local variables
lambda2 = 1. / 9.
n_smooth = 15
fs_verts = [np.arange(10242), np.arange(10242)]
fs_medial = get_fsaverage_medial_vertices()
inv_type = 'meg-fixed' # can be meg-eeg, meg-fixed, meg, eeg-fixed, or eeg
"""
Created on Fri May 6 7:11:32 2016
@author: mdclarke
mnefun processing script for face to face
"""
import mnefun
import numpy as np
from score import score
n_cores = 4
params = mnefun.Params(tmin=-0.1,
tmax=7.0,
n_jobs=n_cores,
n_jobs_mkl=1,
lp_cut=80.,
lp_trans=3.,
n_jobs_fir=n_cores,
n_jobs_resample=n_cores,
bmin=-0.1,
decim=10,
proj_sfreq=200,
filter_length='auto')
# Notes
# 108 has 19s for 13s
params.subjects = [
'f2f_009_01_OTP', 'f2f_010_01_OTP', 'f2f_011_01_OTP', 'f2f_012_01_OTP',
'f2f_013_01_OTP', 'f2f_014_01_OTP', 'f2f_015_01_OTP', 'f2f_018_01_OTP',
'f2f_021_01_OTP', 'f2f_022_01_OTP', 'f2f_023_01_OTP', 'f2f_024_01_OTP',
'f2f_025_01_OTP', 'f2f_026_01_OTP', 'f2f_027_01_OTP', 'f2f_028_01_OTP',
'f2f_029_01_OTP', 'f2f_034_01_OTP', 'f2f_036_01_OTP', 'f2f_037_01_OTP',
'f2f_038_01_OTP', 'f2f_102_OTP', 'f2f_103_OTP', 'f2f_104_OTP',
for ch in ecg_chs:
subjects = \
df[(df['SR(Hz)'] == sr) & (df['ECG'] == ch)]['Subject_ID'].tolist()
if len(subjects) == 0:
continue
# noinspection PyTypeChecker
print(' \nUsing %d Hz as sampling rate and\n'
' %s as ECG surrogate...' % (sr, ch))
print(' %d ' % len(subjects), 'Subjects: ', subjects)
params = mnefun.Params(tmin=-0.1,
tmax=0.6,
n_jobs=18,
n_jobs_fir='cuda',
n_jobs_resample='cuda',
proj_sfreq=200,
decim=decim,
filter_length='30s',
hp_cut=.1,
hp_trans='auto',
lp_cut=30.,
lp_trans='auto',
bmin=-0.1,
ecg_channel=ch)
params.subjects = ['bad_%s' % ss for ss in subjects]
# write prebad
for si, subj in enumerate(subjects):
bad_channels = df[df['Subject_ID'] == subj]['BAD'].tolist()
if op.exists(op.join(work_dir, params.subjects[si], 'raw_fif')):
prebad_file = op.join(work_dir, params.subjects[si], 'raw_fif',
'%s_prebad.txt' % params.subjects[si])
if not op.exists(prebad_file):
if bad_channels[0] == 'None':
# License: MIT
"""Raw MEG data preprocessing script for resting state component of Adolescent project. Pipeline does..
1. write MNEFUN prebads for ACQ file
2. process ACQ file as per MNEFUN:PARAMS"""
import os.path as op
import mnefun
import numpy as np
import pandas as pd
from genz import defaults
# TO METHODS
params = mnefun.Params(n_jobs=18,
decim=1, proj_sfreq=500,
n_jobs_fir='cuda', n_jobs_resample='cuda',
filter_length='auto', lp_cut=100.,
lp_trans='auto', bem_type='5120',
tmin=-.2, tmax=.2)
## TODO:refactor to YAML ##
dfs = []
for ag in [9, 11, 13, 15, 17]:
fi = op.join(defaults.static, "GenZ_subject_information - %da group.tsv" % ag)
dfs.append(pd.read_csv(fi, sep="\t", usecols=["Subject Number", "Sex"]))
df = pd.concat(dfs)
df.columns = ["id", "sex"]
df.sort_values(by="id")
dups = df[df.duplicated("id")].index.values.tolist()
df.drop(df.index[dups], inplace=True)
df.drop(df[df.id.isin(defaults.exclude)].index, inplace=True)
df = df.dropna(how="all")
picks = [sid.lower() for sid in df.id.values]
""" GenZ pilot analysis script. @author: Kambiz Tavabi @contact: [email protected] @license: MIT @date: 04/21/2018 """ import os.path as op import mnefun import numpy as np from picks import names, bad_channels params = mnefun.Params(n_jobs=18, decim=5, proj_sfreq=200, n_jobs_fir='cuda', n_jobs_resample='cuda', filter_length='auto', lp_cut=80., lp_trans='auto', bem_type='5120') params.subjects = names params.subject_indices = np.setdiff1d(np.arange(len(params.subjects)), np.array([3])) # write prebads for si, subj in enumerate(params.subjects): if op.exists(op.join(params.work_dir, subj, 'raw_fif')): prebad_file = op.join(params.work_dir, subj, 'raw_fif', '%s_prebad.txt' % subj) if not op.exists(prebad_file): assert len(bad_channels) == len(params.subjects) if bad_channels[si] is None:
for lst in sfreqs:
sr = int(lst[2:6])
assert sr in [1200, 1800]
if sr == 1200:
decim = 2
else:
decim = 3
ecg_channel = lst[-4:]
subjects = pickedSubjects[lst]
nsubjects = len(subjects)
# noinspection PyTypeChecker
params = mnefun.Params(n_jobs=18,
decim=decim,
proj_sfreq=200,
n_jobs_fir='cuda',
n_jobs_resample='cuda',
filter_length='30s',
lp_cut=80.,
ecg_channel='MEG%s' % ecg_channel)
params.subjects = ['bad_%s' % s for s in subjects]
params.structurals = [None] * nsubjects # None means use sphere
params.score = None # defaults to passing events through
params.dates = [None] * nsubjects # None means more fully anonymize
params.subject_indices = np.arange(
nsubjects) # Define which subjects to run
# Set parameters for remotely connecting to acquisition computer
params.acq_ssh = '*****@*****.**'
params.acq_dir = '/data101/bad_baby'
# Set parameters for remotely connecting to SSS workstation ('sws')
params.sws_ssh = '*****@*****.**'
decim = 2
else:
decim = 3
ecg_channel = lst[-4:]
subjects = pickedSubjects[lst]
nsubjects = len(subjects)
# noinspection PyTypeChecker
params = mnefun.Params(tmin=-0.1,
tmax=1.,
n_jobs=18,
n_jobs_fir='cuda',
n_jobs_resample='cuda',
decim=decim,
proj_sfreq=200,
filter_length='10s',
hp_cut=32.,
hp_trans='auto',
lp_trans='auto',
lp_cut=48.,
bmin=-0.1,
auto_bad=20.,
ecg_channel='MEG%s' % ecg_channel)
params.subjects = ['bad_%s' % s for s in subjects]
params.structurals = [None] * nsubjects # None means use sphere
params.on_process = handler
params.score = score
params.dates = [(2013, 0, 00)] * len(params.subjects)
params.subject_indices = np.setdiff1d(np.arange(len(params.subjects)), [])
params.plot_drop_logs = False
params.acq_ssh = '[email protected]' # minea
import mnefun
from score import score
import numpy as np
try:
# Use niprov as handler for events, or if it's not installed, ignore
from niprov.mnefunsupport import handler
except ImportError:
handler = None
params = mnefun.Params(tmin=-0.2,
tmax=0.5,
t_adjust=-4e-3,
n_jobs=6,
n_jobs_mkl=1,
n_jobs_fir='cuda',
n_jobs_resample='cuda',
decim=5,
proj_sfreq=200,
filter_length='5s')
params.subjects = ['subj_01', 'subj_02']
params.structurals = [None, 'AKCLEE_110_slim'] # None means use sphere
params.dates = [(2014, 2, 14), None] # use "None" to more fully anonymize
params.score = score # scoring function to use
params.subject_indices = np.arange(2) # which subjects to run
params.plot_drop_logs = False # turn off for demo or plots will block
params.acq_ssh = 'minea' # can also be e.g., "*****@*****.**"
params.acq_dir = '/sinuhe/data02/eric_non_space'
params.sws_ssh = 'kasga'
params.sws_dir = '/data06/larsoner'
