est_meg_adjoint = om.Forward(gain_adjoint_meg_dip, sources, noise_level)
print "est_meg_adjoint : %d x %d" % (est_meg_adjoint.nlin(),
est_meg_adjoint.ncol())
est_eeg = om.Forward(gain_eeg_dip, sources, noise_level)
print "est_eeg : %d x %d" % (est_eeg.nlin(), est_eeg.ncol())
est_eeg_adjoint = om.Forward(gain_adjoint_eeg_dip, sources, noise_level)
print "est_eeg_adjoint : %d x %d" % (est_eeg_adjoint.nlin(),
est_eeg_adjoint.ncol())
###############################################################################
# Example of basic manipulations
v1 = om.Vertex(1., 0., 0., 0)
v2 = om.Vertex(0., 1., 0., 1)
v3 = om.Vertex(0., 0., 1., 2)
#print v1.norm()
#print (v1 + v2).norm()
normal = om.Vect3(1., 0., 0.)
t = om.Triangle(v1, v2, v3)
hm_file = subject + '.hm'
hm.save(hm_file)
ssm_file = subject + '.ssm'
ssm.save(ssm_file)
est_meg_adjoint = om.Forward(gain_adjoint_meg_dip, sources, noise_level)
print("est_meg_adjoint : %d x %d" %
(est_meg_adjoint.nlin(), est_meg_adjoint.ncol()))
est_eeg = om.Forward(gain_eeg_dip, sources, noise_level)
print("est_eeg : %d x %d" % (est_eeg.nlin(), est_eeg.ncol()))
est_eeg_adjoint = om.Forward(gain_adjoint_eeg_dip, sources, noise_level)
print("est_eeg_adjoint : %d x %d" %
(est_eeg_adjoint.nlin(), est_eeg_adjoint.ncol()))
# Example of basic manipulations
# TODO: the same with numpy
v1 = om.Vertex(1.0, 0.0, 0.0, 0)
v2 = om.Vertex(0.0, 1.0, 0.0, 1)
v3 = om.Vertex(0.0, 0.0, 1.0, 2)
# TODO: v4 = om.Vertex( [double] , int )
# print(v1.norm()
# print((v1 + v2).norm()
normal = om.Vect3(1.0, 0.0, 0.0)
t = om.Triangle(v1, v2, v3)
hm_file = subject + ".hm"
hm.save(hm_file)
ssm_file = subject + ".ssm"
ssm.save(ssm_file)