def plot_ODF(grid_density=100):
theta_grid = np.linspace(0, np.pi, grid_density)
phi_grid = np.linspace(0, 2 * np.pi, grid_density)
phi_vec, theta_vec = np.meshgrid(phi_grid, theta_grid)
phi_vec, theta_vec = phi_vec.ravel(), theta_vec.ravel()
xyz = np.column_stack(coord.sph2car(theta_vec, phi_vec))
ODF = w[0] * single_tensor_ODF(xyz, rotation=R0)
ODF += w[1] * single_tensor_ODF(xyz, rotation=R1)
ODF = ODF.reshape((grid_density, grid_density))
plot.surf_grid_3D(ODF, theta_grid, phi_grid, scale_radius=True)
# sphere.
E = w[0] * single_tensor(gradients=xyz, bvals=b, S0=1, rotation=R0, SNR=SNR)
E += w[1] * single_tensor(gradients=xyz, bvals=b, S0=1, rotation=R1, SNR=SNR)
print "Signal mean:", E.mean()
if visualize_signal:
from dipy.core.triangle_subdivide import create_unit_sphere
sphere = create_unit_sphere(6)
bb = np.ones(len(sphere.vertices)) * b.mean()
E_ = w[0] * single_tensor(gradients=sphere.vertices, bvals=bb, S0=1, rotation=R0, SNR=SNR)
E_ += w[1] * single_tensor(gradients=sphere.vertices, bvals=bb, S0=1, rotation=R1, SNR=SNR)
ODF = w[0] * single_tensor_ODF(sphere.vertices, rotation=R0)
ODF += w[1] * single_tensor_ODF(sphere.vertices, rotation=R1)
from dipy.viz import show_odfs
show_odfs([[[E_, ODF]]], (sphere.vertices, sphere.faces))
if visualize_odf:
plot_ODF(grid_density=D)
mlab = plot.get_mlab()
mlab.show()
# ===========================-=====
# ODF-domain: Sparse reconstruction
# =================================
