def test_odfdeconv():
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
S, sticks = multi_tensor(gtab,
mevals,
S0,
angles=[(0, 0), (90, 0)],
fractions=[50, 50],
snr=SNR)
sphere = get_sphere('symmetric724')
mevecs = [all_tensor_evecs(sticks[0]).T, all_tensor_evecs(sticks[1]).T]
odf_gt = multi_tensor_odf(sphere.vertices, [0.5, 0.5], mevals, mevecs)
e1 = 15.0
e2 = 3.0
ratio = e2 / e1
csd = ConstrainedSDTModel(gtab, ratio, None)
csd_fit = csd.fit(S)
fodf = csd_fit.odf(sphere)
directions, _, _ = peak_directions(odf_gt, sphere)
directions2, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions, directions2)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
assert_equal(directions2.shape[0], 2)
with warnings.catch_warnings(record=True) as w:
ConstrainedSDTModel(gtab, ratio, sh_order=10)
assert_equal(len(w) > 0, True)
with warnings.catch_warnings(record=True) as w:
ConstrainedSDTModel(gtab, ratio, sh_order=8)
assert_equal(len(w) > 0, False)
def test_odfdeconv():
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
S, sticks = multi_tensor(gtab, mevals, S0, angles=[(0, 0), (90, 0)],
fractions=[50, 50], snr=SNR)
sphere = get_sphere('symmetric724')
mevecs = [all_tensor_evecs(sticks[0]).T,
all_tensor_evecs(sticks[1]).T]
odf_gt = multi_tensor_odf(sphere.vertices, [0.5, 0.5], mevals, mevecs)
e1 = 15.0
e2 = 3.0
ratio = e2 / e1
csd = ConstrainedSDTModel(gtab, ratio, None)
csd_fit = csd.fit(S)
fodf = csd_fit.odf(sphere)
directions, _, _ = peak_directions(odf_gt, sphere)
directions2, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions, directions2)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
assert_equal(directions2.shape[0], 2)
with warnings.catch_warnings(record=True) as w:
ConstrainedSDTModel(gtab, ratio, sh_order=10)
assert_equal(len(w) > 0, True)
with warnings.catch_warnings(record=True) as w:
ConstrainedSDTModel(gtab, ratio, sh_order=8)
assert_equal(len(w) > 0, False)
def test_mvoxel_gqi():
data, gtab = dsi_voxels()
sphere = get_sphere('symmetric724')
gq = GeneralizedQSamplingModel(gtab, 'standard')
gqfit = gq.fit(data)
all_odfs = gqfit.odf(sphere)
# Check that the first and last voxels each have 2 peaks
odf = all_odfs[0, 0, 0]
directions, values, indices = peak_directions(odf, sphere, .35, 25)
assert_equal(directions.shape[0], 2)
odf = all_odfs[-1, -1, -1]
directions, values, indices = peak_directions(odf, sphere, .35, 25)
assert_equal(directions.shape[0], 2)
def test_mvoxel_gqi():
data, gtab = dsi_voxels()
sphere = get_sphere('symmetric724')
gq = GeneralizedQSamplingModel(gtab, 'standard')
gqfit = gq.fit(data)
all_odfs = gqfit.odf(sphere)
# Check that the first and last voxels each have 2 peaks
odf = all_odfs[0, 0, 0]
directions, values, indices = peak_directions(odf, sphere, .35, 25)
assert_equal(directions.shape[0], 2)
odf = all_odfs[-1, -1, -1]
directions, values, indices = peak_directions(odf, sphere, .35, 25)
assert_equal(directions.shape[0], 2)
def dirs_from_odf(odfs, sphere, relative_peak_threshold=.35,
min_separation_angle=25.,
peak_normalize=True,
max_peak_number=5):
# or directions from odf
num_peak_coeffs = max_peak_number * 3
peaks = np.zeros(odfs.shape[:-1] + (num_peak_coeffs,))
for index in ndindex(odfs.shape[:-1]):
vox_peaks, values, _ = peak_directions(odfs[index], sphere,
float(relative_peak_threshold),
float(min_separation_angle))
if peak_normalize is True:
values /= values[0]
vox_peaks = vox_peaks * values[:, None]
vox_peaks = vox_peaks.ravel()
m = vox_peaks.shape[0]
if m > num_peak_coeffs:
m = num_peak_coeffs
peaks[index][:m] = vox_peaks[:m]
peaks = peaks.reshape(odfs.shape[:3] + (5, 3))
return peaks
def peak_extraction(odfs_file, sphere_vertices_file, out_file, relative_peak_threshold=.5,
peak_normalize=1, min_separation_angle=45, max_peak_number=5):
in_nifti = nib.load(odfs_file)
refaff = in_nifti.get_affine()
odfs = in_nifti.get_data()
vertices = np.loadtxt(sphere_vertices_file)
sphere = Sphere(xyz=vertices)
num_peak_coeffs = max_peak_number * 3
peaks = np.zeros(odfs.shape[:-1] + (num_peak_coeffs,))
for index in ndindex(odfs.shape[:-1]):
vox_peaks, values, _ = peak_directions(odfs[index], sphere,
float(relative_peak_threshold),
float(min_separation_angle))
if peak_normalize == 1:
values /= values[0]
vox_peaks = vox_peaks * values[:, None]
vox_peaks = vox_peaks.ravel()
m = vox_peaks.shape[0]
if m > num_peak_coeffs:
m = num_peak_coeffs
peaks[index][:m] = vox_peaks[:m]
peaks_img = nib.Nifti1Image(peaks.astype(np.float32), refaff)
nib.save(peaks_img, out_file)
def peaks_extract(out_file, odf, affine, sphere,
relative_peak_threshold=.5,
peak_normalize=1,
min_separation_angle=45,
max_peak_number=5):
num_peak_coeffs = max_peak_number * 3
peaks = np.zeros(odf.shape[:-1] + (num_peak_coeffs,))
for index in ndindex(odf.shape[:-1]):
vox_peaks, values, _ = peak_directions(odf[index], sphere,
float(relative_peak_threshold),
float(min_separation_angle))
if peak_normalize == 1:
values /= values[0]
vox_peaks = vox_peaks * values[:, None]
vox_peaks = vox_peaks.ravel()
m = vox_peaks.shape[0]
if m > num_peak_coeffs:
m = num_peak_coeffs
peaks[index][:m] = vox_peaks[:m]
peaks_img = nib.Nifti1Image(peaks.astype(np.float32), affine)
nib.save(peaks_img, out_file)
def test_dsi():
# load symmetric 724 sphere
sphere = get_sphere('symmetric724')
# load icosahedron sphere
sphere2 = create_unit_sphere(5)
btable = np.loadtxt(get_data('dsi515btable'))
gtab = gradient_table(btable[:, 0], btable[:, 1:])
data, golden_directions = SticksAndBall(gtab,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0)],
fractions=[50, 50],
snr=None)
ds = DiffusionSpectrumDeconvModel(gtab)
# symmetric724
dsfit = ds.fit(data)
odf = dsfit.odf(sphere)
directions, _, _ = peak_directions(odf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions), 2,
1)
# 5 subdivisions
dsfit = ds.fit(data)
odf2 = dsfit.odf(sphere2)
directions, _, _ = peak_directions(odf2, sphere2, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions), 2,
1)
assert_equal(dsfit.pdf().shape, 3 * (ds.qgrid_size, ))
sb_dummies = sticks_and_ball_dummies(gtab)
for sbd in sb_dummies:
data, golden_directions = sb_dummies[sbd]
odf = ds.fit(data).odf(sphere2)
directions, _, _ = peak_directions(odf, sphere2, .35, 25)
if len(directions) <= 3:
assert_equal(len(directions), len(golden_directions))
if len(directions) > 3:
assert_equal(gfa(odf) < 0.1, True)
assert_raises(ValueError,
DiffusionSpectrumDeconvModel,
gtab,
qgrid_size=16)
def test_gqi():
#load symmetric 724 sphere
sphere = get_sphere('symmetric724')
#load icosahedron sphere
sphere2 = create_unit_sphere(5)
btable = np.loadtxt(get_data('dsi515btable'))
bvals = btable[:, 0]
bvecs = btable[:, 1:]
gtab = gradient_table(bvals, bvecs)
data, golden_directions = SticksAndBall(gtab,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0)],
fractions=[50, 50],
snr=None)
gq = GeneralizedQSamplingModel(gtab, method='gqi2', sampling_length=1.4)
#symmetric724
gqfit = gq.fit(data)
odf = gqfit.odf(sphere)
directions, values, indices = peak_directions(odf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions), 2,
1)
#5 subdivisions
gqfit = gq.fit(data)
odf2 = gqfit.odf(sphere2)
directions, values, indices = peak_directions(odf2, sphere2, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions), 2,
1)
sb_dummies = sticks_and_ball_dummies(gtab)
for sbd in sb_dummies:
data, golden_directions = sb_dummies[sbd]
odf = gq.fit(data).odf(sphere2)
directions, values, indices = peak_directions(odf, sphere2, .35, 25)
if len(directions) <= 3:
assert_equal(len(directions), len(golden_directions))
if len(directions) > 3:
assert_equal(gfa(odf) < 0.1, True)
def test_dsi():
#load symmetric 724 sphere
sphere = get_sphere('symmetric724')
#load icosahedron sphere
sphere2 = create_unit_sphere(5)
btable = np.loadtxt(get_data('dsi515btable'))
gtab = gradient_table(btable[:,0], btable[:,1:])
data, golden_directions = SticksAndBall(gtab, d=0.0015,
S0=100, angles=[(0, 0), (90, 0)],
fractions=[50, 50], snr=None)
ds = DiffusionSpectrumModel(gtab)
#symmetric724
dsfit = ds.fit(data)
odf = dsfit.odf(sphere)
directions, _, _ = peak_directions(odf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions),
2, 1)
#5 subdivisions
dsfit = ds.fit(data)
odf2 = dsfit.odf(sphere2)
directions, _, _ = peak_directions(odf2, sphere2, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions),
2, 1)
assert_equal(dsfit.pdf().shape, 3 * (ds.qgrid_size, ))
sb_dummies=sticks_and_ball_dummies(gtab)
for sbd in sb_dummies:
data, golden_directions = sb_dummies[sbd]
odf = ds.fit(data).odf(sphere2)
directions, _, _ = peak_directions(odf, sphere2, .35, 25)
if len(directions) <= 3:
assert_equal(len(directions), len(golden_directions))
if len(directions) > 3:
assert_equal(gfa(odf) < 0.1, True)
assert_raises(ValueError, DiffusionSpectrumModel, gtab, qgrid_size=16)
def test_peak_directions():
model = SimpleOdfModel()
fit = model.fit(None)
odf = fit.odf()
argmax = odf.argmax()
mx = odf.max()
sphere = fit.model.sphere
# Only one peak
dir, val, ind = peak_directions(odf, sphere, .5, 45)
dir_e = sphere.vertices[[argmax]]
assert_array_equal(ind, [argmax])
assert_array_equal(val, odf[ind])
assert_array_equal(dir, dir_e)
odf[0] = mx * .9
# Two peaks, relative_threshold
dir, val, ind = peak_directions(odf, sphere, 1., 0)
dir_e = sphere.vertices[[argmax]]
assert_array_equal(dir, dir_e)
assert_array_equal(ind, [argmax])
assert_array_equal(val, odf[ind])
dir, val, ind = peak_directions(odf, sphere, .8, 0)
dir_e = sphere.vertices[[argmax, 0]]
assert_array_equal(dir, dir_e)
assert_array_equal(ind, [argmax, 0])
assert_array_equal(val, odf[ind])
# Two peaks, angle_sep
dir, val, ind = peak_directions(odf, sphere, 0., 90)
dir_e = sphere.vertices[[argmax]]
assert_array_equal(dir, dir_e)
assert_array_equal(ind, [argmax])
assert_array_equal(val, odf[ind])
dir, val, ind = peak_directions(odf, sphere, 0., 0)
dir_e = sphere.vertices[[argmax, 0]]
assert_array_equal(dir, dir_e)
assert_array_equal(ind, [argmax, 0])
assert_array_equal(val, odf[ind])
def test_peak_directions():
model = SimpleOdfModel()
fit = model.fit(None)
odf = fit.odf()
argmax = odf.argmax()
mx = odf.max()
sphere = fit.model.sphere
# Only one peak
dir, val, ind = peak_directions(odf, sphere, .5, 45)
dir_e = sphere.vertices[[argmax]]
assert_array_equal(ind, [argmax])
assert_array_equal(val, odf[ind])
assert_array_equal(dir, dir_e)
odf[0] = mx * .9
# Two peaks, relative_threshold
dir, val, ind = peak_directions(odf, sphere, 1., 0)
dir_e = sphere.vertices[[argmax]]
assert_array_equal(dir, dir_e)
assert_array_equal(ind, [argmax])
assert_array_equal(val, odf[ind])
dir, val, ind = peak_directions(odf, sphere, .8, 0)
dir_e = sphere.vertices[[argmax, 0]]
assert_array_equal(dir, dir_e)
assert_array_equal(ind, [argmax, 0])
assert_array_equal(val, odf[ind])
# Two peaks, angle_sep
dir, val, ind = peak_directions(odf, sphere, 0., 90)
dir_e = sphere.vertices[[argmax]]
assert_array_equal(dir, dir_e)
assert_array_equal(ind, [argmax])
assert_array_equal(val, odf[ind])
dir, val, ind = peak_directions(odf, sphere, 0., 0)
dir_e = sphere.vertices[[argmax, 0]]
assert_array_equal(dir, dir_e)
assert_array_equal(ind, [argmax, 0])
assert_array_equal(val, odf[ind])
def test_gqi():
#load symmetric 724 sphere
sphere = get_sphere('symmetric724')
#load icosahedron sphere
sphere2 = create_unit_sphere(5)
btable = np.loadtxt(get_data('dsi515btable'))
bvals = btable[:,0]
bvecs = btable[:,1:]
gtab = gradient_table(bvals, bvecs)
data, golden_directions = SticksAndBall(gtab, d=0.0015,
S0=100, angles=[(0, 0), (90, 0)],
fractions=[50, 50], snr=None)
gq = GeneralizedQSamplingModel(gtab, method='gqi2', sampling_length=1.4)
#symmetric724
gqfit = gq.fit(data)
odf = gqfit.odf(sphere)
directions, values, indices = peak_directions(odf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions), 2, 1)
#5 subdivisions
gqfit = gq.fit(data)
odf2 = gqfit.odf(sphere2)
directions, values, indices = peak_directions(odf2, sphere2, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions), 2, 1)
sb_dummies=sticks_and_ball_dummies(gtab)
for sbd in sb_dummies:
data, golden_directions = sb_dummies[sbd]
odf = gq.fit(data).odf(sphere2)
directions, values, indices = peak_directions(odf, sphere2, .35, 25)
if len(directions) <= 3:
assert_equal(len(directions), len(golden_directions))
if len(directions) > 3:
assert_equal(gfa(odf) < 0.1, True)
def test_odf_sh_to_sharp():
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
S, sticks = multi_tensor(gtab,
mevals,
S0,
angles=[(10, 0), (100, 0)],
fractions=[50, 50],
snr=SNR)
sphere = get_sphere('symmetric724')
qb = QballModel(gtab, sh_order=8, assume_normed=True)
qbfit = qb.fit(S)
odf_gt = qbfit.odf(sphere)
Z = np.linalg.norm(odf_gt)
odfs_gt = np.zeros((3, 1, 1, odf_gt.shape[0]))
odfs_gt[:, :, :] = odf_gt[:]
odfs_sh = sf_to_sh(odfs_gt, sphere, sh_order=8, basis_type=None)
odfs_sh /= Z
fodf_sh = odf_sh_to_sharp(odfs_sh,
sphere,
basis=None,
ratio=3 / 15.,
sh_order=8,
lambda_=1.,
tau=1.)
fodf = sh_to_sf(fodf_sh, sphere, sh_order=8, basis_type=None)
directions2, _, _ = peak_directions(fodf[0, 0, 0], sphere)
assert_equal(directions2.shape[0], 2)
def test_odf_sh_to_sharp():
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
S, sticks = multi_tensor(gtab, mevals, S0, angles=[(10, 0), (100, 0)],
fractions=[50, 50], snr=SNR)
sphere = get_sphere('symmetric724')
qb = QballModel(gtab, sh_order=8, assume_normed=True)
qbfit = qb.fit(S)
odf_gt = qbfit.odf(sphere)
Z = np.linalg.norm(odf_gt)
odfs_gt = np.zeros((3, 1, 1, odf_gt.shape[0]))
odfs_gt[:,:,:] = odf_gt[:]
odfs_sh = sf_to_sh(odfs_gt, sphere, sh_order=8, basis_type=None)
odfs_sh /= Z
fodf_sh = odf_sh_to_sharp(odfs_sh, sphere, basis=None, ratio=3 / 15.,
sh_order=8, lambda_=1., tau=1.)
fodf = sh_to_sf(fodf_sh, sphere, sh_order=8, basis_type=None)
directions2, _, _ = peak_directions(fodf[0, 0, 0], sphere)
assert_equal(directions2.shape[0], 2)
# from pylab import plot, show
# plot(S, 'b')
# plot(S2, 'r')
# show()
# # plot(np.abs(S - S2))
# plot((S - S2))
# show()
gq = GeneralizedQSamplingModel(gtab_full, sampling_length=3.5)
gqfit = gq.fit(SS)
gqodf = gqfit.odf(sphere)
gqdir, _, _ = peak_directions(gqodf, sphere, .35, 15)
print angular_similarity(sticks, gqdir)
grid_size = 35
dds = DiffusionSpectrumDeconvModel(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)
ddsfit = dds.fit(SS)
ddsodf = ddsfit.odf(sphere)