wmvanvliet

import numpy as np
import scipy.signal
import mne
from scipy.stats import kurtosis
from mne.preprocessing import find_outliers
from mne.fixes import nanmean
from mne.utils import logger
#from mne.preprocessing.eog import _get_eog_channel_index

wmvanvliet

import matplotlib.pyplot as plt
from matplotlib.patches import Circle
import numpy as np

plt.figure()
s = [ 50000.,10478.2, 4733.4,3185.3,2484.7,2310.9]
for s_ in s:
    radius = np.sqrt(s_)/np.pi
    plt.gca().add_patch(Circle((1, radius), radius))
plt.ylim(0, 150)

wmvanvliet

import numpy as np
from numpy.testing import assert_allclose

nchannels, nsamples = 10, 100
data = np.random.rand(nchannels, nsamples)

# Average reference
data_avg = data - np.mean(data, axis=0)

# Referenced to channel 0

wmvanvliet

# Python
import mne
import numpy as np
from matplotlib import pyplot as plt

# Channel positions
ch_names = ['A1', 'A2', 'A3', 'A4', 'A5', 'A6', 'A7', 'A8', 'A9', 'A10', 'A11', 'A12', 'A13', 'A14', 'A15', 'A16', 'A17', 'A18', 'A19', 'A20', 'A21', 'A22', 'A23', 'A24', 'A25', 'A26', 'A27', 'A28', 'A29', 'A30', 'A31', 'A32', 'B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B9', 'B10', 'B11', 'B12', 'B13', 'B14', 'B15', 'B16', 'B17', 'B18', 'B19', 'B20', 'B21', 'B22', 'B23', 'B24', 'B25', 'B26', 'B27', 'B28', 'B29', 'B30', 'B31', 'B32', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10', 'C11', 'C12', 'C13', 'C14', 'C15', 'C16', 'C17', 'C18', 'C19', 'C20', 'C21', 'C22', 'C23', 'C24', 'C25', 'C26', 'C27', 'C28', 'C29', 'C30', 'C31', 'C32', 'D1', 'D2', 'D3', 'D4', 'D5', 'D6', 'D7', 'D8', 'D9', 'D10', 'D11', 'D12', 'D13', 'D14', 'D15', 'D16', 'D17', 'D18', 'D19', 'D20', 'D21', 'D22', 'D23', 'D24', 'D25', 'D26', 'D27', 'D28', 'D29', 'D30', 'D31', 'D32']
montage = mne.channels.read_montage('biosemi128')
info 

wmvanvliet

import mne
import numpy as np
from scipy.stats import norm
from matplotlib import pyplot as plt

# Load required sample data
data_path = mne.datasets.sample.data_path()
subjects_dir = data_path + '/subjects'
evoked = mne.read_evokeds(data_path + '/MEG/sample/sample_audvis-ave.fif')[0]
fwd = mne.read_forward_solution(data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif', surf_ori=False, force_fixed=True)

wmvanvliet

from __future__ import print_function

import os.path as op
import inspect
import distance
import numpy as np
from scipy.misc import comb
import progressbar as pb
import progressbar.widgets as pw

wmvanvliet

from __future__ import print_function
import mne

data_path = mne.datasets.sample.data_path()
subjects_dir = data_path + '/subjects'
subject = 'sample'
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'

# Read the source space. Normally, you would use mne.read_source_spaces for
# this, but the sample data does not include a '-src.fif' file. Therefore, in

wmvanvliet

wmvanvliet

"""
==============================
Plotting the full MNE solution
==============================

The source space that is used for the inverse computation defines a set of
dipoles, distributed across the cortex. When visualizing a source estimate, it
is sometimes useful to show the dipole directions, as well as their estimated
magnitude.
"""

wmvanvliet

def plot_source_estimates(stc, subject=None, surface=None, hemi='lh',
                          colormap='auto', time_label='auto',
                          smoothing_steps=10, transparent=None,
                          brain_alpha=None, alpha=None, vector_alpha=1.,
                          scale_factor=None, time_viewer=False,
                          subjects_dir=None, figure=None, views='lat',
                          colorbar=True, clim='auto', cortex="classic",
                          size=800, background="black", foreground="white",
                          initial_time=None, time_unit='s', backend='auto',
                          spacing='oct6'):