def test_dni_eit():
btable = np.loadtxt(get_data('dsi515btable'))
bvals = btable[:, 0]
bvecs = btable[:, 1:]
data, descr = sim_data(bvals, bvecs)
#load odf sphere
vertices, faces = sphere_vf_from('symmetric724')
edges = unique_edges(faces)
half_vertices, half_edges, half_faces = reduce_antipodal(vertices, faces)
#create the sphere
odf_sphere = (vertices, faces)
dn = DiffusionNablaModel(bvals, bvecs, odf_sphere)
dn.relative_peak_threshold = 0.5
dn.angular_distance_threshold = 20
dnfit = dn.fit(data)
print('DiffusionNablaModel')
for i, d in enumerate(data):
print descr[i], np.sum(dnfit.peak_values[i] > 0)
ei = EquatorialInversionModel(bvals, bvecs, odf_sphere)
ei.relative_peak_threshold = 0.3
ei.angular_distance_threshold = 15
ei.set_operator('laplacian')
eifit = ei.fit(data, return_odf=True)
print('EquatorialInversionModel')
for i, d in enumerate(data):
print descr[i], np.sum(eifit.peak_values[i] > 0)
assert_equal(descr[i][1], np.sum(eifit.peak_values[i] > 0))
from dipy.viz import show_odfs
show_odfs(eifit.odf[None, None, :, :], (vertices, faces), scale=2)
def test_dni_eit():
btable=np.loadtxt(get_data('dsi515btable'))
bvals=btable[:,0]
bvecs=btable[:,1:]
data,descr=sim_data(bvals,bvecs)
#load odf sphere
vertices,faces = sphere_vf_from('symmetric724')
edges = unique_edges(faces)
half_vertices,half_edges,half_faces=reduce_antipodal(vertices,faces)
#create the sphere
odf_sphere=(vertices,faces)
dn=DiffusionNablaModel(bvals,bvecs,odf_sphere)
dn.relative_peak_threshold = 0.5
dn.angular_distance_threshold = 20
dnfit=dn.fit(data)
print('DiffusionNablaModel')
for i,d in enumerate(data):
print descr[i], np.sum(dnfit.peak_values[i]>0)
ei=EquatorialInversionModel(bvals,bvecs,odf_sphere)
ei.relative_peak_threshold = 0.3
ei.angular_distance_threshold = 15
ei.set_operator('laplacian')
eifit = ei.fit(data,return_odf=True)
print('EquatorialInversionModel')
for i,d in enumerate(data):
print descr[i], np.sum(eifit.peak_values[i]>0)
assert_equal(descr[i][1], np.sum(eifit.peak_values[i]>0))
from dipy.viz import show_odfs
show_odfs(eifit.odf[None,None,:,:], (vertices,faces), scale=2)
'odf_coeffs_*.npz')))):
data = sph_io.load(os.path.basename(fn))
print "Dipy model..."
odf = m.evaluate_odf(data['signal'])
ODFs.append(odf)
separation_from_odf(odf)
print "Quadrature model..."
odf = kernel_reconstruct(coords['odf_theta'], coords['odf_phi'], data['beta'],
theta, phi,
kernel=even_kernel, N=data['kernel_N'])
odf[odf < 0] = 0
ODFs.append(odf)
print "Actual angle:", np.rad2deg(data['separation'])
separation_from_odf(odf)
ODFs.append(multi_tensor_odf(verts, data['weights'],
mevals=[[1700e-6, 300e-6, 300e-6]] * len(data['weights']),
mevecs=data['mevecs']))
N = 3
ODFs = np.array([[ODFs]]).reshape(len(ODFs) // N, 1, N, -1)
from dipy.viz import show_odfs
show_odfs(ODFs, (verts, faces), scale=2, radial_scale=True)
"""Visualize kernel in ODF and signal space. """ from sphdif.kernel import even_kernel, inv_funk_radon_even_kernel from dipy.viz import show_odfs from dipy.core.subdivide_octahedron import create_unit_sphere sphere = create_unit_sphere(6) # sphere.z = np.dot([0, 0, 1], [x, y, z]) = cos(mu) kernel_odf = even_kernel(sphere.z, N=8) kernel_signal = inv_funk_radon_even_kernel(sphere.z, N=8) show_odfs([[[kernel_odf, kernel_signal]]], (sphere.vertices, sphere.faces))
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
# =================================
# Minimising the L1 penalized system
#
# ||Xb - y||_2^2 + lambda ||x||_1 subject to x_i >= 0.
#
"""Visualize kernel in ODF and signal space. """ from sphdif.kernel import even_kernel, inv_funk_radon_even_kernel from dipy.viz import show_odfs from dipy.core.subdivide_octahedron import create_unit_sphere sphere = create_unit_sphere(6) # sphere.z = np.dot([0, 0, 1], [x, y, z]) = cos(mu) kernel_odf = even_kernel(sphere.z, N=8) kernel_signal = inv_funk_radon_even_kernel(sphere.z, N=8) # show_odfs([[[kernel_odf, kernel_signal]]], (sphere.vertices, sphere.faces)) show_odfs([[[kernel_odf]]], (sphere.vertices, sphere.faces))
