def test_apply_mne_inverse_operator(): """Test MNE inverse computation """ setno = 0 snr = 3.0 lambda2 = 1.0 / snr ** 2 dSPM = True evoked = mne.fiff.Evoked(fname_data, setno=setno, baseline=(None, 0)) inverse_operator = mne.read_inverse_operator(fname_inv) res = mne.apply_inverse(evoked, inverse_operator, lambda2, dSPM) assert np.all(res['sol'] > 0) assert np.all(res['sol'] < 35)
""" =========================== Reading an inverse operator =========================== """ # Author: Alexandre Gramfort <*****@*****.**> # # License: BSD (3-clause) print __doc__ import mne fname = 'MNE-sample-data/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif' inv = mne.read_inverse_operator(fname) print "Method: %s" % inv['methods'] print "fMRI prior: %s" % inv['fmri_prior'] print "Number of sources: %s" % inv['nsource'] print "Number of channels: %s" % inv['nchan'] ############################################################################### # Show result # 3D source space import numpy as np lh_points = inv['src'][0]['rr'] lh_faces = inv['src'][0]['use_tris'] rh_points = inv['src'][1]['rr'] rh_faces = inv['src'][1]['use_tris']
def test_io_inverse(): """Test IO for inverse operator """ fwd = mne.read_inverse_operator(fname_inv)
import mne from mne.datasets import sample from mne.fiff import Evoked data_path = sample.data_path('.') fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif' fname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif' setno = 0 snr = 3.0 lambda2 = 1.0 / snr ** 2 dSPM = True # Load data evoked = Evoked(fname_evoked, setno=setno, baseline=(None, 0)) inverse_operator = mne.read_inverse_operator(fname_inv) # Compute inverse solution res = mne.apply_inverse(evoked, inverse_operator, lambda2, dSPM) # Save result in stc files lh_vertices = res['inv']['src'][0]['vertno'] rh_vertices = res['inv']['src'][1]['vertno'] lh_data = res['sol'][:len(lh_vertices)] rh_data = res['sol'][-len(rh_vertices):] mne.write_stc('mne_dSPM_inverse-lh.stc', tmin=res['tmin'], tstep=res['tstep'], vertices=lh_vertices, data=lh_data) mne.write_stc('mne_dSPM_inverse-rh.stc', tmin=res['tmin'], tstep=res['tstep'], vertices=rh_vertices, data=rh_data)
======================================================= """ # Author: Alexandre Gramfort <*****@*****.**> # # License: BSD (3-clause) print __doc__ import mne from mne.datasets import sample data_path = sample.data_path('.') fname = data_path fname += '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif' inv = mne.read_inverse_operator(fname) print "Method: %s" % inv['methods'] print "fMRI prior: %s" % inv['fmri_prior'] print "Number of sources: %s" % inv['nsource'] print "Number of channels: %s" % inv['nchan'] ############################################################################### # Show result # 3D source space lh_points = inv['src'][0]['rr'] lh_faces = inv['src'][0]['use_tris'] rh_points = inv['src'][1]['rr'] rh_faces = inv['src'][1]['use_tris'] from enthought.mayavi import mlab