Configuration parameters for the study. This should be in a folder called
library/
inside the processing/
directory.
from distutils.version import LooseVersion
import os
import numpy as np
Let’s set the path where the data is downloaded and stored.
user = os.environ['USER']
if user == 'gramfort':
study_path = '/tsi/doctorants/data_gramfort/dgw_faces'
N_JOBS = 8
elif user == 'mjas':
study_path = '/tsi/doctorants/data_gramfort/dgw_faces_reproduce'
N_JOBS = 1
elif user == 'jleppakangas':
study_path = '/tsi/doctorants/data_gramfort/dgw_faces'
N_JOBS = 8
elif user == 'alex':
study_path = '/Users/alex/work/data/mne-biomag-group-demo/'
N_JOBS = 1
elif user == 'larsoner':
study_path = os.path.join(os.path.dirname(__file__), '..', '..', '..')
N_JOBS = 4
else:
study_path = os.path.join(os.path.dirname(__file__), '..', '..', '..')
N_JOBS = 1
The subjects_dir
and meg_dir
for reading anatomical and MEG files.
subjects_dir = os.path.join(study_path, 'subjects')
meg_dir = os.path.join(study_path, 'MEG')
os.environ["SUBJECTS_DIR"] = subjects_dir
spacing = 'oct6'
mindist = 5
Some mapping betwen filenames for bad sensors and subjects
map_subjects = {1: 'subject_01', 2: 'subject_02', 3: 'subject_03',
4: 'subject_05', 5: 'subject_06', 6: 'subject_08',
7: 'subject_09', 8: 'subject_10', 9: 'subject_11',
10: 'subject_12', 11: 'subject_14', 12: 'subject_15',
13: 'subject_16', 14: 'subject_17', 15: 'subject_18',
16: 'subject_19', 17: 'subject_23', 18: 'subject_24',
19: 'subject_25'}
if not os.path.isdir(study_path):
os.mkdir(study_path)
if not os.path.isdir(subjects_dir):
os.mkdir(subjects_dir)
Subjects that are known to be bad from the publication
exclude_subjects = [1, 5, 16] # Excluded subjects
The cross talk file and calibration file are placed in the same folder.
ctc = os.path.join(os.path.dirname(__file__), 'ct_sparse.fif')
cal = os.path.join(os.path.dirname(__file__), 'sss_cal.dat')
ylim = {'eeg': [-10, 10], 'mag': [-300, 300], 'grad': [-80, 80]}
def set_matplotlib_defaults():
import matplotlib.pyplot as plt
fontsize = 8
params = {'axes.labelsize': fontsize,
'legend.fontsize': fontsize,
'xtick.labelsize': fontsize,
'ytick.labelsize': fontsize,
'axes.titlesize': fontsize + 2,
'figure.max_open_warning': 200,
'axes.spines.top': False,
'axes.spines.right': False,
'axes.grid': True,
'lines.linewidth': 1,
}
import matplotlib
if LooseVersion(matplotlib.__version__) >= '2':
params['font.size'] = fontsize
else:
params['text.fontsize'] = fontsize
plt.rcParams.update(params)
annot_kwargs = dict(fontsize=12, fontweight='bold',
xycoords="axes fraction", ha='right', va='center')
l_freq = None
tmin = -0.2
tmax = 2.9 # min duration between onsets: (400 fix + 800 stim + 1700 ISI) ms
reject_tmax = 0.8 # duration we really care about
random_state = 42
smooth = 10
fsaverage_vertices = [np.arange(10242), np.arange(10242)]
Total running time of the script: ( 0 minutes 0.000 seconds)