def main(argv):
#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_O = 'leadfields/Original_' + argv + '.vtp'
filename_R = 'leadfields/Reconstructed_' + argv + '.vtp'
if recompute:
# set matrices filenames
filename_Xo = op.join('tmp', argv + '_Xo.mat')
filename_CM = op.join('tmp', argv + '_CM.mat')
model = load_headmodel(argv)
# Compute the projector onto the sensors
M = om.Head2EEGMat(model['geometry'], model['sensors'])
# 'Brain' is the name of the domain containing the sources (a-priori)
if recompute_CM or not op.exists(filename_CM):
# CM, a matrix N_unknown X N_sensors
#CM = om.CorticalMat(model['geometry'], M, 'Brain',3, alphas[argv], betas[argv], op.join('tmp',argv + '_P.mat'))
CM = om.CorticalMat2(model['geometry'], M, 'Brain',3, gammas[argv], op.join('tmp',argv + '_H.mat'))
CM.save(str(filename_CM))
else:
CM = om.Matrix(str(filename_CM))
if model.has_key('dipsources'):
# for testing: lets compute a forward solution with a few dipoles
# and then display both the reconstruction through the CorticalMapping
# and the original
if recompute_Xo or not op.exists(filename_Xo):
X_original = forward_problem(model)
X_original.save(str(filename_Xo))
else:
X_original = om.Matrix(str(filename_Xo))
V_s = M * X_original # get the potentials at sensors
elif model.has_key('potentials'):
V_s = model['potentials']
else:
print("Error: either specify input potentials or dipsources to\
simulate them.")
X_reconstructed = CM * V_s
print "Error norm = ", (V_s-M * X_reconstructed).frobenius_norm()
# write the geometry and the solution as a VTK file (viewable in pavaview)
if model.has_key('dipsources'):
model['geometry'].write_vtp(str(filename_O), X_original)
model['geometry'].write_vtp(str(filename_R), X_reconstructed)
if model.has_key('dipsources'):
display_vtp(filename_O)
compare_vtp(filename_O,filename_R)
display_vtp(filename_R)
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)
