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)
def main(argv):
filename_O = "leadfields/Original_" + argv + ".vtp"
filename_R = "leadfields/Reconstructed_" + argv + ".vtp"
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
# ax.xaxis.set_scale('log'); ax.yaxis.set_scale('log'); ax.zaxis.set_scale('log')
N1 = 5 # choose sampling here
N2 = 1 # choose sampling here
xs = np.random.rand(N1, N2)
ys = np.random.rand(N1, N2)
zs = np.random.rand(N1, N2)
alphas = np.logspace(0.3, 1.5, N1)
betas = np.logspace(0.3, -0.3, N2)
for alph in range(0, N1):
for bet in range(0, N2):
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):
alpha = alphas[alph]
beta = betas[bet]
# CM, a matrix N_unknown X N_sensors
# CM = om.CorticalMat(model['geometry'], M, 'Brain',3,alpha,beta,op.join('tmp',argv + '_P.mat'))
CM = om.CorticalMat2(model["geometry"], M, "Brain", 3, alpha, op.join("tmp", argv + "_H.mat"))
CM.save(str(filename_CM))
else:
CM = om.Matrix(str(filename_CM))
# 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
X_reconstructed = CM * (V_s)
# write the geometry and the solution as a VTK file (viewable in pavaview)
model["geometry"].write_vtp(str(filename_R), X_reconstructed)
norm = (V_s - M * X_reconstructed).getcol(0).norm()
rdm, mag = compare_vtp(filename_O, filename_R)
sys.stderr.write(
"||="
+ str(norm)
+ "\talpha="
+ str(alpha)
+ "\tbeta="
+ str(beta)
+ "\t\tRDM="
+ str(rdm)
+ "\trMAG="
+ str(mag)
+ "\t"
+ str(mag + rdm)
+ "\n"
)
sys.stdout.write(
"||="
+ str(norm)
+ "\talpha="
+ str(alpha)
+ "\tbeta="
+ str(beta)
+ "\t\tRDM="
+ str(rdm)
+ "\trMAG="
+ str(mag)
+ "\t"
+ str(mag + rdm)
+ "\n"
)
xs[alph, bet] = alpha
ys[alph, bet] = beta
zs[alph, bet] = rdm + mag
ax.plot_wireframe(np.log(xs), np.log(ys), np.log(zs))
ax.set_xlabel("alpha")
ax.set_ylabel("beta")
ax.set_zlabel("RDM + MAG")
i = np.nonzero(zs == np.min(zs))
sys.stderr.write("xs = " + str(xs[i]) + " ys = " + str(ys[i]) + " rdm+mag= " + str(np.min(zs)) + "\n")
sys.stdout.write("xs = " + str(xs[i]) + " ys = " + str(ys[i]) + " rdm+mag= " + str(np.min(zs)) + "\n")
plt.show()
def main(argv):
"""Search parameters for the cortical mapping."""
filename_O = 'leadfields/Original_' + argv + '.vtp'
filename_R = 'leadfields/Reconstructed_' + argv + '.vtp'
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# ax.xaxis.set_scale('log')
# ax.yaxis.set_scale('log')
# ax.zaxis.set_scale('log')
N1 = 5 # choose sampling here
N2 = 1 # choose sampling here
xs = np.random.rand(N1, N2)
ys = np.random.rand(N1, N2)
zs = np.random.rand(N1, N2)
alphas = np.logspace(0.3, 1.5, N1)
betas = np.logspace(0.3, -0.3, N2)
for alph in range(0, N1):
for bet in range(0, N2):
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):
alpha = alphas[alph]
beta = betas[bet]
# CM, a matrix N_unknown X N_sensors
# CM = om.CorticalMat(model['geometry'], M, 'Brain', 3,
# alpha, beta, op.join('tmp', argv + '_P.mat'))
CM = om.CorticalMat2(model['geometry'], M, 'Brain', 3,
alpha,
op.join('tmp', argv + '_H.mat'))
CM.save(str(filename_CM))
else:
CM = om.Matrix(str(filename_CM))
# 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
X_reconstructed = CM * (V_s)
# write the geometry and the solution as a VTK file
# (viewable in pavaview)
model['geometry'].write_vtp(str(filename_R), X_reconstructed)
norm = (V_s - M * X_reconstructed).getcol(0).norm()
rdm, mag = compare_vtp(filename_O, filename_R)
print("||=%f" % norm, "\talpha=%f" % alpha, "\tbeta=%f" % beta,
"\t\tRDM=%f" % rdm, "\trMAG=%f" % mag, "\t", str(mag + rdm),
"\n", file=sys.stderr)
print("||=%f" % norm, "\talpha=%f" % alpha, "\tbeta=%f" % beta,
"\t\tRDM=%f" % rdm, "\trMAG=%f" % mag, "\t", str(mag + rdm),
"\n")
xs[alph, bet] = alpha
ys[alph, bet] = beta
zs[alph, bet] = rdm + mag
ax.plot_wireframe(np.log(xs), np.log(ys), np.log(zs))
ax.set_xlabel('alpha')
ax.set_ylabel('beta')
ax.set_zlabel('RDM + MAG')
i = np.nonzero(zs == np.min(zs))
print('xs = %f' % xs[i], ' ys = %f' % ys[i], ' rdm+mag=%f' % np.min(zs),
"\n", file=sys.stderr)
print('xs = %f' % xs[i], ' ys = %f' % ys[i], ' rdm+mag=%f' % np.min(zs),
"\n")
plt.show()