def main(argv):
"""Compute the tdcs."""
# create a dir for leadfields and tmp
if not op.exists("tmp"):
import os
os.mkdir('tmp')
if not op.exists("leadfields"):
import os
os.mkdir('leadfields')
filename = 'leadfields/HDTDCS_' + argv + '.vtp'
filename_HMi = op.join('tmp', argv + '_HMi.mat')
if recompute:
model = load_headmodel(argv)
if recompute_HMi or not op.exists(filename_HMi):
hm = om.HeadMat(model['geometry'])
hm.invert()
hm.save(filename_HMi)
else:
print("Loading %s" % filename_HMi)
hm = om.SymMatrix(filename_HMi)
sm = om.EITSourceMat(model['geometry'], model['tdcssources'])
# set here the input currents (actually a current density [I/L])
activation = om.fromarray(
np.array([[-4., 1.], [1., -4.], [1., 1.], [1., 1.], [1., 1.]]))
# each column must have a zero mean
# now apply the currents and get the result
X = hm * (sm * activation)
# concatenate X with input currents (to see the what was injected)
Xt = np.append(om.asarray(X),
np.zeros((model['geometry'].size() - X.nlin(),
X.ncol())),
0)
currents = om.asarray(activation)
for s in range(model['tdcssources'].getNumberOfSensors()):
# get the triangles supporting this sensor
tris = model['tdcssources'].getInjectionTriangles(s)
for it in tris:
Xt[it.getindex(), :] = (currents[s, :] *
model['tdcssources'].getWeights()(s))
X = om.fromarray(Xt)
model['geometry'].write_vtp(filename, X)
display_vtp(filename)
def meg_gain(self, meg_file=None):
"""
Call OpenMEEG's GainMEG method to calculate the final projection matrix.
Optionaly saving the matrix for later use. The OpenMEEG matrix is
converted to a Numpy array before return.
"""
LOG.info("Computing GainMEG...")
meg_gain = om.GainMEG(self.om_inverse_head, self.om_source_matrix,
self.om_head2sensor, self.om_source2sensor)
LOG.info("meg_gain: %d x %d" % (meg_gain.nlin(), meg_gain.ncol()))
if meg_file is not None:
LOG.info("Saving meg_gain as %s..." % meg_file)
meg_gain.save(
os.path.join(OM_STORAGE_DIR, meg_file + OM_SAVE_SUFFIX))
return om.asarray(meg_gain)
def meg_gain(self, meg_file=None):
"""
Call OpenMEEG's GainMEG method to calculate the final projection matrix.
Optionaly saving the matrix for later use. The OpenMEEG matrix is
converted to a Numpy array before return.
"""
LOG.info("Computing GainMEG...")
meg_gain = om.GainMEG(self.om_inverse_head, self.om_source_matrix,
self.om_head2sensor, self.om_source2sensor)
LOG.info("meg_gain: %d x %d" % (meg_gain.nlin(), meg_gain.ncol()))
if meg_file is not None:
LOG.info("Saving meg_gain as %s..." % meg_file)
meg_gain.save(os.path.join(OM_STORAGE_DIR,
meg_file + OM_SAVE_SUFFIX))
return om.asarray(meg_gain)
#print m1.ncol() m2 = om.Matrix() m2.load(ssm_file) #m2.setvalue(2,3,-0.2) # m2(2,3)=-0.2 #print m2(2,3) #print m2(0, 0) #print m2.nlin() #print m2.ncol() ############################################################################### # Numpy interface # For a Vector v = hm(1, 10, 1, 1).getcol(0) vec = om.asarray(v) m = om.fromarray(vec) assert ((v - m.getcol(0)).norm() < 1e-15) # For a Matrix mat = om.asarray(m2) assert ((m2 - om.fromarray(mat)).frobenius_norm() < 1e-15) #print mat.shape #print mat.sum() #mat[0:2, 1:3] = 0 #print mat[0:5, 0:5] #remove useless files os.remove(hm_file) os.remove(ssm_file)
import numpy as np
import openmeeg as om
from scipy import linalg
from mayavi import mlab
from mesh import Mesh
cortex = Mesh("data/model/cortex.tri")
electrodes = np.loadtxt('data/model/eeg_channels_locations.txt')
squids = np.loadtxt('data/model/meg_channels_locations.squids')
M = om.Matrix('leadfields/eeg_leadfield.mat')
G_eeg = om.asarray(M)
chan_idx = 28
n_dipoles = G_eeg.shape[1]
###############################################################################
# Generate activation map with only one dipole active
x_simu = np.zeros(n_dipoles)
dipole_idx = 1000
x_simu[dipole_idx] = 1
# compute forward model
m = np.dot(G_eeg, x_simu)
# add measurement noise
m += 1e-8 * np.random.randn(*m.shape)
# show topography
electrodes_mesh = Mesh("data/model/eeg_channels_mesh.tri")
m1 = om.SymMatrix() m1.load(hm_file) print m1(0, 0) print m1.nlin() print m1.ncol() m2 = om.Matrix() m2.load(ssm_file) print m2(0, 0) print m2.nlin() print m2.ncol() ############################################################################### # Numpy interface # For a Vector vec = om.asarray(ai_vector) print vec.size print vec[0:5] print vec # For a Matrix mat = om.asarray(m2) print mat.shape print mat.sum() mat[0:2, 1:3] = 0 print mat[0:5, 0:5]
#print m1.ncol() m2 = om.Matrix() m2.load(ssm_file) #m2.setvalue(2,3,-0.2) # m2(2,3)=-0.2 #print m2(2,3) #print m2(0, 0) #print m2.nlin() #print m2.ncol() ############################################################################### # Numpy interface # For a Vector v=hm(1,10,1,1).getcol(0) vec = om.asarray(v) m=om.fromarray(vec) assert((v-m.getcol(0)).norm() < 1e-15) # For a Matrix mat = om.asarray(m2) assert((m2-om.fromarray(mat)).frobenius_norm() < 1e-15) #print mat.shape #print mat.sum() #mat[0:2, 1:3] = 0 #print mat[0:5, 0:5] #remove useless files os.remove(hm_file) os.remove(ssm_file)