ddsfit = dds.fit(SS)
ddsodf = ddsfit.odf(sphere)
ddsdir, _, _ = peak_directions(ddsodf, sphere, .35, 15)
print angular_similarity(sticks, ddsdir)
grid_size = 35
ds = DiffusionSpectrumModel(gtab_full,
qgrid_size=grid_size,
r_start=0.2 * (grid_size // 2),
r_end=0.7 * (grid_size // 2),
r_step=0.02 * (grid_size // 2),
filter_width=np.inf,
normalize_peaks=False)
dsfit = ds.fit(SS)
dsodf = dsfit.odf(sphere)
dsdir, _, _ = peak_directions(dsodf, sphere, .35, 15)
print angular_similarity(sticks, dsdir)
show_odfs(gqodf[None, None, None, :], sphere)
show_odfs(dsodf[None, None, None, :], sphere)
show_odfs(ddsodf[None, None, None, :], sphere)
bvals, bvecs = read_bvals_bvecs(fdsi_bvals, fdsi_bvecs)
gtab = gradient_table(bvals, bvecs)
#sb = sticks_and_ball_dummies(gtab)
S, sticks = SticksAndBall(gtab, d=0.0015, S0=100,
angles=[(0, 0), (30, 0)],
fractions=[50, 50], snr=30.0)
sphere = get_sphere('symmetric724')
sphere = sphere.subdivide(n=1)
r=np.zeros(sphere.vertices.shape[0])
sampling_lengths=[1.25,1.5,2.,2.5,3.,3.5,4.]
for ss in sampling_lengths:
gqi_model = GeneralizedQSamplingModel(gtab,
method='gqi2',
sampling_length=ss,
normalize_peaks=False)
gqi_fit = gqi_model.fit(S)
gqi_odf = gqi_fit.odf(sphere)
r=np.vstack((r,gqi_odf))
show_odfs(r[:,None,None], sphere)
