def test_mapmri_odf(radial_order=6):
gtab = get_gtab_taiwan_dsi()
# load repulsion 724 sphere
sphere = default_sphere
# load icosahedron sphere
l1, l2, l3 = [0.0015, 0.0003, 0.0003]
data, golden_directions = generate_signal_crossing(gtab,
l1,
l2,
l3,
angle2=90)
mapmod = MapmriModel(gtab,
radial_order=radial_order,
laplacian_regularization=True,
laplacian_weighting=0.01)
# repulsion724
sphere2 = create_unit_sphere(5)
mapfit = mapmod.fit(data)
odf = mapfit.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
odf = mapfit.odf(sphere2)
directions, _, _ = peak_directions(odf, 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]
asmfit = mapmod.fit(data)
odf = asmfit.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)
# for the isotropic implementation check if the odf spherical harmonics
# actually represent the discrete sphere function.
mapmod = MapmriModel(gtab,
radial_order=radial_order,
laplacian_regularization=True,
laplacian_weighting=0.01,
anisotropic_scaling=False)
mapfit = mapmod.fit(data)
odf = mapfit.odf(sphere)
odf_sh = mapfit.odf_sh()
odf_from_sh = sh_to_sf(odf_sh, sphere, radial_order, basis_type=None)
assert_almost_equal(odf, odf_from_sh, 10)
def test_shore_odf():
gtab = get_isbi2013_2shell_gtab()
# load repulsion 724 sphere
sphere = default_sphere
# load icosahedron sphere
sphere2 = create_unit_sphere(5)
data, golden_directions = sticks_and_ball(gtab,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0)],
fractions=[50, 50],
snr=None)
asm = ShoreModel(gtab,
radial_order=6,
zeta=700,
lambdaN=1e-8,
lambdaL=1e-8)
# repulsion724
asmfit = asm.fit(data)
odf = asmfit.odf(sphere)
odf_sh = asmfit.odf_sh()
odf_from_sh = sh_to_sf(odf_sh, sphere, 6, basis_type=None, legacy=True)
npt.assert_almost_equal(odf, odf_from_sh, 10)
expected_phi = shore_matrix(radial_order=6, zeta=700, gtab=gtab)
npt.assert_array_almost_equal(np.dot(expected_phi, asmfit.shore_coeff),
asmfit.fitted_signal())
directions, _, _ = peak_directions(odf, sphere, .35, 25)
npt.assert_equal(len(directions), 2)
npt.assert_almost_equal(angular_similarity(directions, golden_directions),
2, 1)
# 5 subdivisions
odf = asmfit.odf(sphere2)
directions, _, _ = peak_directions(odf, sphere2, .35, 25)
npt.assert_equal(len(directions), 2)
npt.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]
asmfit = asm.fit(data)
odf = asmfit.odf(sphere2)
directions, _, _ = peak_directions(odf, sphere2, .35, 25)
if len(directions) <= 3:
npt.assert_equal(len(directions), len(golden_directions))
if len(directions) > 3:
npt.assert_equal(gfa(odf) < 0.1, True)
def test_mapmri_odf(radial_order=6):
gtab = get_gtab_taiwan_dsi()
# load symmetric 724 sphere
sphere = get_sphere('symmetric724')
# load icosahedron sphere
l1, l2, l3 = [0.0015, 0.0003, 0.0003]
data, golden_directions = generate_signal_crossing(gtab, l1, l2, l3,
angle2=90)
mapmod = MapmriModel(gtab, radial_order=radial_order,
laplacian_regularization=True,
laplacian_weighting=0.01)
# symmetric724
sphere2 = create_unit_sphere(5)
mapfit = mapmod.fit(data)
odf = mapfit.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
odf = mapfit.odf(sphere2)
directions, _, _ = peak_directions(odf, 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]
asmfit = mapmod.fit(data)
odf = asmfit.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)
# for the isotropic implementation check if the odf spherical harmonics
# actually represent the discrete sphere function.
mapmod = MapmriModel(gtab, radial_order=radial_order,
laplacian_regularization=True,
laplacian_weighting=0.01,
anisotropic_scaling=False)
mapfit = mapmod.fit(data)
odf = mapfit.odf(sphere)
odf_sh = mapfit.odf_sh()
odf_from_sh = sh_to_sf(odf_sh, sphere, radial_order, basis_type=None)
assert_almost_equal(odf, odf_from_sh, 10)
def test_shore_odf():
gtab = get_isbi2013_2shell_gtab()
# load symmetric 724 sphere
sphere = get_sphere('symmetric724')
# load icosahedron sphere
sphere2 = create_unit_sphere(5)
data, golden_directions = SticksAndBall(gtab,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0)],
fractions=[50, 50],
snr=None)
asm = ShoreModel(gtab,
radial_order=6,
zeta=700,
lambdaN=1e-8,
lambdaL=1e-8)
# symmetric724
asmfit = asm.fit(data)
odf = asmfit.odf(sphere)
odf_sh = asmfit.odf_sh()
odf_from_sh = sh_to_sf(odf_sh, sphere, 6, basis_type=None)
assert_almost_equal(odf, odf_from_sh, 10)
directions, _, _ = peak_directions(odf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions), 2,
1)
# 5 subdivisions
odf = asmfit.odf(sphere2)
directions, _, _ = peak_directions(odf, 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]
asmfit = asm.fit(data)
odf = asmfit.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)
def test_dsi():
# load repulsion 724 sphere
sphere = default_sphere
# load icosahedron sphere
sphere2 = create_unit_sphere(5)
btable = np.loadtxt(get_fnames('dsi515btable'))
gtab = gradient_table(btable[:, 0], btable[:, 1:])
data, golden_directions = sticks_and_ball(gtab,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0)],
fractions=[50, 50],
snr=None)
ds = DiffusionSpectrumDeconvModel(gtab)
# repulsion724
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_shore_odf():
gtab = get_isbi2013_2shell_gtab()
# load symmetric 724 sphere
sphere = get_sphere('symmetric724')
# load icosahedron sphere
sphere2 = create_unit_sphere(5)
data, golden_directions = SticksAndBall(gtab, d=0.0015,
S0=100, angles=[(0, 0), (90, 0)],
fractions=[50, 50], snr=None)
asm = ShoreModel(gtab, radial_order=6,
zeta=700, lambdaN=1e-8, lambdaL=1e-8)
# symmetric724
asmfit = asm.fit(data)
odf = asmfit.odf(sphere)
odf_sh = asmfit.odf_sh()
odf_from_sh = sh_to_sf(odf_sh, sphere, 6, basis_type=None)
assert_almost_equal(odf, odf_from_sh, 10)
directions, _ , _ = peak_directions(odf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(
angular_similarity(directions, golden_directions), 2, 1)
# 5 subdivisions
odf = asmfit.odf(sphere2)
directions, _ , _ = peak_directions(odf, 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]
asmfit = asm.fit(data)
odf = asmfit.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)
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_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:]
data, golden_directions = SticksAndBall(bvals, bvecs, d=0.0015,
S0=100, angles=[(0, 0), (90, 0)],
fractions=[50, 50], snr=None)
gtab = gradient_table(bvals, bvecs)
gq = GeneralizedQSamplingModel(gtab, method='gqi2', sampling_length=1.4)
#symmetric724
gq.direction_finder.config(sphere=sphere, min_separation_angle=25,
relative_peak_threshold=.35)
gqfit = gq.fit(data)
odf = gqfit.odf(sphere)
#from dipy.viz._show_odfs import show_odfs
#show_odfs(odf[None,None,None,:], (sphere.vertices, sphere.faces))
directions = gqfit.directions
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions), 2, 1)
#5 subdivisions
gq.direction_finder.config(sphere=sphere2, min_separation_angle=25,
relative_peak_threshold=.35)
gqfit = gq.fit(data)
odf2 = gqfit.odf(sphere2)
directions = gqfit.directions
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions), 2, 1)
#show_odfs(odf[None,None,None,:], (sphere.vertices, sphere.faces))
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 = gq.fit(data).directions
#show_odfs(odf[None, None, None, :], (sphere2.vertices, sphere2.faces))
if len(directions) <= 3:
assert_equal(len(gq.fit(data).directions), len(golden_directions))
if len(directions) > 3:
assert_equal(gfa(gq.fit(data).odf(sphere2)) < 0.1, True)
def test_mapmri_odf():
gtab = get_3shell_gtab()
# load symmetric 724 sphere
sphere = get_sphere('symmetric724')
# load icosahedron sphere
sphere2 = create_unit_sphere(5)
evals = np.array(([0.0017, 0.0003, 0.0003], [0.0017, 0.0003, 0.0003]))
data, golden_directions = MultiTensor(gtab,
evals,
S0=1.0,
angles=[(0, 0), (90, 0)],
fractions=[50, 50],
snr=None)
map_model = MapmriModel(gtab, radial_order=4)
# symmetric724
mapfit = map_model.fit(data)
odf = mapfit.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
odf = mapfit.odf(sphere2)
directions, _, _ = peak_directions(odf, 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]
mapfit = map_model.fit(data)
odf = mapfit.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)
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_fnames('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_mapmri_odf():
gtab = get_3shell_gtab()
# load symmetric 724 sphere
sphere = get_sphere('symmetric724')
# load icosahedron sphere
sphere2 = create_unit_sphere(5)
evals = np.array(([0.0017, 0.0003, 0.0003],
[0.0017, 0.0003, 0.0003]))
data, golden_directions = MultiTensor(
gtab, evals, S0=1.0, angles=[(0, 0), (90, 0)], fractions=[50, 50],
snr=None)
map_model = MapmriModel(gtab, radial_order=4)
# symmetric724
mapfit = map_model.fit(data)
odf = mapfit.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
odf = mapfit.odf(sphere2)
directions, _, _ = peak_directions(odf, 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]
mapfit = map_model.fit(data)
odf = mapfit.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)