x1 = np.zeros((NumTri))
y1 = np.zeros((NumTri))
v2_v1temp = V2 - V1
x2 = np.linalg.norm(v2_v1temp, axis=1)
y2 = np.zeros((NumTri))
x3 = np.einsum('ij,ij->i', (V3 - V1), (v2_v1temp / np.column_stack(
(x2, x2, x2))))
mynorm = np.cross((v2_v1temp), V3 - V1, axis=1)
yunit = np.cross(mynorm, v2_v1temp, axis=1)
y3 = np.einsum('ij,ij->i', yunit, (V3 - V1)) / np.linalg.norm(yunit, axis=1)
sqrt_DT = (np.abs((x1 * y2 - y1 * x2) + (x2 * y3 - y2 * x3) +
(x3 * y1 - y3 * x1)))
Ar = 0.5 * (np.abs((x1 * y2 - y1 * x2) + (x2 * y3 - y2 * x3) +
(x3 * y1 - y3 * x1)))
TC = face_v_conn(surf1)
Wt = (1.0 / 3.0) * (TC)
# Wt = sp.sparse.spdiags(Wt*Ar, (0), NumTri, NumTri)
surf_weight = Wt * Ar
surf1.attributes = surf_weight
surf_weight = surf_weight[:, None]
# smooth_surf_function(dfs_left_sm, Wt*Ar*0.1, a1=0, a2=1)
surf1.attributes = ind_rois
surf1 = patch_color_attrib(surf1)
view_patch_vtk(surf1, show=1)
# sub = '110411'
# p_dir = '/home/ajoshi/data/HCP_data'
lst = os.listdir('/big_disk/ajoshi/HCP5')
rho1 = 0
# skull = readdfs(skull_name)
# skull = reducepatch(skull,.9,0)
brain = add_normals(brain)
# brain.normals=brain.vertices/5.0
skull = add_normals(skull)
# view_patch(brain)
# view_patch(skull)
writedfs('brain.dfs', brain)
writedfs('skull.dfs', skull)
print "# vertices in Brain: " + str(brain.vertices.shape[0])
print "# vertices in Skull: " + str(skull.vertices.shape[0])
m = mean_curvature(brain)
# m = smooth_surf_function(brain, m, 10, 100)
br = view_patch(brain, attrib=m, opacity=1, show=0)
view_patch(skull, opacity=.1, fig=br, show=1)
Tri = face_v_conn(brain)
Q = 1.0 + sp.zeros((brain.faces.shape[0], 1))
Q = (1.0 / 3.0) * Tri * Q
Q = Q[:, 0]
br_face_area = face_areas(brain)
Q = 1.0 + sp.zeros(brain.vertices.shape[0])
# Q=0.0*Q
# Q[1800]=1
# Q=smooth_surf_function(brain,Q,10,10)
area_v = (1.0 / 3.0) * Tri * br_face_area
v = sp.zeros(skull.vertices.shape[0]) # eq 8 from Sarvas
v_aaj = sp.zeros(skull.vertices.shape[0]) # joshi
view_patch(brain, attrib=Q, opacity=1)
for i in range(skull.vertices.shape[0]):
r0 = brain.vertices