def test_adc():
"""
Test the implementation of the calculation of apparent diffusion coefficient
"""
data, gtab = dsi_voxels()
dm = dti.TensorModel(gtab, 'LS')
mask = np.zeros(data.shape[:-1], dtype=bool)
mask[0, 0, 0] = True
dtifit = dm.fit(data)
sphere = create_unit_sphere(4)
# The ADC in the principal diffusion direction should be equal to the AD in
# each voxel:
pdd0 = dtifit.evecs[0,0,0,0]
sphere_pdd0 = dps.Sphere(x=pdd0[0], y=pdd0[1], z=pdd0[2])
assert_array_almost_equal(dtifit.adc(sphere_pdd0)[0,0,0],
dtifit.ad[0,0,0], decimal=5)
# Test that it works for cases in which the data is 1D
dtifit = dm.fit(data[0,0,0])
sphere_pdd0 = dps.Sphere(x=pdd0[0], y=pdd0[1], z=pdd0[2])
assert_array_almost_equal(dtifit.adc(sphere_pdd0),
dtifit.ad, decimal=5)
def test_TensorModel():
data, gtab = dsi_voxels()
dm = dti.TensorModel(gtab, 'LS')
dtifit = dm.fit(data[0, 0, 0])
assert_equal(dtifit.fa < 0.5, True)
dm = dti.TensorModel(gtab, 'WLS')
dtifit = dm.fit(data[0, 0, 0])
assert_equal(dtifit.fa < 0.5, True)
sphere = create_unit_sphere(4)
assert_equal(len(dtifit.odf(sphere)), len(sphere.vertices))
assert_almost_equal(dtifit.fa, gfa(dtifit.odf(sphere)), 1)
# Check that the multivoxel case works:
dtifit = dm.fit(data)
assert_equal(dtifit.fa.shape, data.shape[:3])
# Make some synthetic data
b0 = 1000.
bvecs, bvals = read_bvec_file(get_data('55dir_grad.bvec'))
gtab = grad.gradient_table_from_bvals_bvecs(bvals, bvecs.T)
# The first b value is 0., so we take the second one:
B = bvals[1]
#Scale the eigenvalues and tensor by the B value so the units match
D = np.array([1., 1., 1., 0., 0., 1., -np.log(b0) * B]) / B
evals = np.array([2., 1., 0.]) / B
md = evals.mean()
tensor = from_lower_triangular(D)
evecs = np.linalg.eigh(tensor)[1]
#Design Matrix
X = dti.design_matrix(bvecs, bvals)
#Signals
Y = np.exp(np.dot(X,D))
assert_almost_equal(Y[0], b0)
Y.shape = (-1,) + Y.shape
# Test fitting with different methods: #XXX Add NNLS methods!
for fit_method in ['OLS', 'WLS']:
tensor_model = dti.TensorModel(gtab,
fit_method=fit_method)
tensor_fit = tensor_model.fit(Y)
assert_true(tensor_fit.model is tensor_model)
assert_equal(tensor_fit.shape, Y.shape[:-1])
assert_array_almost_equal(tensor_fit.evals[0], evals)
assert_array_almost_equal(tensor_fit.quadratic_form[0], tensor,
err_msg =\
"Calculation of tensor from Y does not compare to analytical solution")
assert_almost_equal(tensor_fit.md[0], md)
assert_equal(tensor_fit.directions.shape[-2], 1)
assert_equal(tensor_fit.directions.shape[-1], 3)
# Test error-handling:
assert_raises(ValueError,
dti.TensorModel,
gtab,
fit_method='crazy_method')
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_odf_with_zeros():
fdata, fbval, fbvec = get_data('small_25')
gtab = grad.gradient_table(fbval, fbvec)
data = nib.load(fdata).get_data()
dm = dti.TensorModel(gtab)
df = dm.fit(data)
df.evals[0, 0, 0] = np.array([0, 0, 0])
sphere = create_unit_sphere(4)
odf = df.odf(sphere)
npt.assert_equal(odf[0, 0, 0], np.zeros(sphere.vertices.shape[0]))
def test_odf_with_zeros():
fdata, fbval, fbvec = get_data('small_25')
gtab = grad.gradient_table(fbval, fbvec)
data = nib.load(fdata).get_data()
dm = dti.TensorModel(gtab)
df = dm.fit(data)
df.evals[0, 0, 0] = np.array([0, 0, 0])
sphere = create_unit_sphere(4)
odf = df.odf(sphere)
npt.assert_equal(odf[0, 0, 0], np.zeros(sphere.vertices.shape[0]))
def test_interp_rbf():
def data_func(s, a, b):
return a * np.cos(s.theta) + b * np.sin(s.phi)
s0 = create_unit_sphere(3)
s1 = create_unit_sphere(4)
for a, b in zip([1, 2, 0.5], [1, 0.5, 2]):
data = data_func(s0, a, b)
expected = data_func(s1, a, b)
interp_data_a = interp_rbf(data, s0, s1, norm="angle")
npt.assert_(np.mean(np.abs(interp_data_a - expected)) < 0.1)
# Test that using the euclidean norm raises a warning
# (following
# https://docs.python.org/2/library/warnings.html#testing-warnings)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
interp_rbf(data, s0, s1, norm="euclidean_norm")
npt.assert_(len(w) == 1)
npt.assert_(issubclass(w[-1].category, PendingDeprecationWarning))
npt.assert_("deprecated" in str(w[-1].message))
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_interp_rbf():
def data_func(s, a, b):
return a * np.cos(s.theta) + b * np.sin(s.phi)
from dipy.core.sphere import Sphere, interp_rbf
import numpy as np
s0 = create_unit_sphere(3)
s1 = create_unit_sphere(4)
for a, b in zip([1, 2, 0.5], [1, 0.5, 2]):
data = data_func(s0, a, b)
expected = data_func(s1, a, b)
interp_data_a = interp_rbf(data, s0, s1, norm="angle")
nt.assert_(np.mean(np.abs(interp_data_a - expected)) < 0.1)
# Test that using the euclidean norm raises a warning
# (following https://docs.python.org/2/library/warnings.html#testing-warnings)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
interp_data_en = interp_rbf(data, s0, s1, norm ="euclidean_norm")
nt.assert_(len(w) == 1)
nt.assert_(issubclass(w[-1].category, DeprecationWarning))
nt.assert_("deprecated" in str(w[-1].message))
def plot_tensor_3d(Tensor, cmap='jet', mode='ADC', file_name=None,
origin=[0,0,0], colorbar=False, figure=None, vmin=None,
vmax=None, offset=0, azimuth=60, elevation=90, roll=0,
scale_factor=1.0, rgb_pdd=False):
"""
mode: either "ADC", "ellipse" or "pred_sig"
"""
Q = Tensor.Q
sphere = create_unit_sphere(5)
vertices = sphere.vertices
faces = sphere.faces
x,y,z = vertices.T
new_bvecs = np.vstack([x.ravel(), y.ravel(), z.ravel()])
Tensor = ozt.Tensor(Q, new_bvecs,
Tensor.bvals[0] * np.ones(new_bvecs.shape[-1]))
if mode == 'ADC':
v = Tensor.ADC * scale_factor
elif mode == 'ellipse':
v = Tensor.diffusion_distance * scale_factor
elif mode == 'pred_sig':
v = Tensor.predicted_signal(1) * scale_factor
else:
raise ValueError("Mode not recognized")
r, phi, theta = geo.cart2sphere(x,y,z)
x_plot, y_plot, z_plot = geo.sphere2cart(v, phi, theta)
if rgb_pdd:
evals, evecs = Tensor.decompose
xyz = evecs[0]
r = np.abs(xyz[0])/np.sum(np.abs(xyz))
g = np.abs(xyz[1])/np.sum(np.abs(xyz))
b = np.abs(xyz[2])/np.sum(np.abs(xyz))
color = (r, g, b)
else:
color = None
# Call and return straightaway:
return _display_maya_voxel(x_plot, y_plot, z_plot, faces, v, origin,
cmap=cmap, colorbar=colorbar, color=color,
figure=figure,
vmin=vmin, vmax=vmax, file_name=file_name,
azimuth=azimuth, elevation=elevation)
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_dsi():
#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)
ds = DiffusionSpectrumModel(gtab)
#symmetric724
ds.direction_finder.config(sphere=sphere, min_separation_angle=25,
relative_peak_threshold=.35)
dsfit = ds.fit(data)
odf = dsfit.odf(sphere)
directions = dsfit.directions
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions),
2, 1)
#5 subdivisions
ds.direction_finder.config(sphere=sphere2, min_separation_angle=25,
relative_peak_threshold=.35)
dsfit = ds.fit(data)
odf2 = dsfit.odf(sphere2)
directions = dsfit.directions
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions),
2, 1)
#from dipy.viz._show_odfs import show_odfs
#show_odfs(odf[None,None,None,:], (sphere.vertices, sphere.faces))
#show_odfs(odf2[None,None,None,:], (sphere2.vertices, sphere2.faces))
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 = ds.fit(data).directions
#show_odfs(odf[None, None, None, :], (sphere2.vertices, sphere2.faces))
if len(directions) <= 3:
assert_equal(len(ds.fit(data).directions), len(golden_directions))
if len(directions) > 3:
assert_equal(gfa(ds.fit(data).odf(sphere2)) < 0.1, True)
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 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)
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)
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)
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_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_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_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_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)
def test_adc():
"""
Test the implementation of the calculation of apparent diffusion coefficient
"""
data, gtab = dsi_voxels()
dm = dti.TensorModel(gtab, 'LS')
mask = np.zeros(data.shape[:-1], dtype=bool)
mask[0, 0, 0] = True
dtifit = dm.fit(data)
sphere = create_unit_sphere(4)
# The ADC in the principal diffusion direction should be equal to the AD in
# each voxel:
pdd0 = dtifit.evecs[0, 0, 0, 0]
sphere_pdd0 = dps.Sphere(x=pdd0[0], y=pdd0[1], z=pdd0[2])
assert_array_almost_equal(dtifit.adc(sphere_pdd0)[0, 0, 0],
dtifit.ad[0, 0, 0],
decimal=5)
# Test that it works for cases in which the data is 1D
dtifit = dm.fit(data[0, 0, 0])
sphere_pdd0 = dps.Sphere(x=pdd0[0], y=pdd0[1], z=pdd0[2])
assert_array_almost_equal(dtifit.adc(sphere_pdd0), dtifit.ad, decimal=5)
import sys sys.path.insert(0, '..') import numpy as np from sphdif import plot, coord from dipy.core.subdivide_octahedron import create_unit_sphere s = create_unit_sphere(3) from mayavi import mlab mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0)) mask = s.theta <= np.pi/2. plot.scatter_3D(s.theta[mask], s.phi[mask], color=(0, 0, 1)) #ef = s.edges.ravel() #for e in s.edges: # mlab.plot3d(s.x[e], s.y[e], s.z[e], tube_radius=None) N = 20 mlab.quiver3d([s.x[N]], [s.y[N]], [s.z[N]], color=(1, 0, 0), mode='2darrow') plot.show()
def plot_signal_interp(bvecs,
signal,
origin=[0, 0, 0],
maya=True,
cmap='jet',
file_name=None,
colorbar=False,
figure=None,
vmin=None,
vmax=None,
offset=0,
azimuth=60,
elevation=90,
roll=0,
points=False,
cmap_points=None,
scale_points=False,
non_neg=False,
interp_kwargs=dict(function='thin_plate', smooth=0)):
"""
Interpolate a measured signal, using RBF interpolation.
Parameters
----------
signal:
bvecs: array (3,n)
the x,y,z locations where the signal was measured
offset : float
where to place the plotted voxel (on the z axis)
points : bool
whether to show the sampling points on the interpolated
surface. Default: False.
"""
bvecs_new = np.hstack([bvecs, -bvecs])
new_signal = np.hstack([signal, signal])
s0 = Sphere(xyz=bvecs_new.T)
s1 = create_unit_sphere(7)
signal[np.isnan(signal)] = 0
interp_signal = interp_rbf(new_signal, s0, s1, **interp_kwargs)
vertices = s1.vertices
if non_neg:
interp_signal[interp_signal < 0] = 0
faces = s1.faces
x, y, z = vertices.T
r, phi, theta = geo.cart2sphere(x, y, z)
x_plot, y_plot, z_plot = geo.sphere2cart(interp_signal, phi, theta)
if points:
r, phi, theta = geo.cart2sphere(s0.x, s0.y, s0.z)
x_p, y_p, z_p = geo.sphere2cart(signal, phi, theta)
figure = _display_maya_voxel(x_p,
y_p,
z_p,
faces,
signal,
origin,
cmap=cmap,
colorbar=colorbar,
figure=figure,
vmin=vmin,
vmax=vmax,
file_name=file_name,
azimuth=azimuth,
elevation=elevation,
points=True,
cmap_points=cmap_points,
scale_points=scale_points)
# Call and return straightaway:
return _display_maya_voxel(x_plot,
y_plot,
z_plot,
faces,
interp_signal,
origin,
cmap=cmap,
colorbar=colorbar,
figure=figure,
vmin=vmin,
vmax=vmax,
file_name=file_name,
azimuth=azimuth,
elevation=elevation)
def test_create_unit_sphere():
sphere = create_unit_sphere(7)
v, e, f = sphere.vertices, sphere.edges, sphere.faces
assert_array_almost_equal((v * v).sum(1), 1)
"""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))
def plot_signal_interp(bvecs, signal, origin=[0,0,0], maya=True, cmap='jet',
file_name=None, colorbar=False, figure=None, vmin=None,
vmax=None, offset=0, azimuth=60, elevation=90, roll=0,
points=False, cmap_points=None, scale_points=False,
non_neg=False,
interp_kwargs=dict(function='thin_plate', smooth=0)):
"""
Interpolate a measured signal, using RBF interpolation.
Parameters
----------
signal:
bvecs: array (3,n)
the x,y,z locations where the signal was measured
offset : float
where to place the plotted voxel (on the z axis)
points : bool
whether to show the sampling points on the interpolated
surface. Default: False.
"""
bvecs_new = np.hstack([bvecs, -bvecs])
new_signal = np.hstack([signal, signal])
s0 = Sphere(xyz=bvecs_new.T)
s1 = create_unit_sphere(7)
signal[np.isnan(signal)] = 0
interp_signal = interp_rbf(new_signal, s0, s1, **interp_kwargs)
vertices = s1.vertices
if non_neg:
interp_signal[interp_signal<0] = 0
faces = s1.faces
x,y,z = vertices.T
r, phi, theta = geo.cart2sphere(x,y,z)
x_plot, y_plot, z_plot = geo.sphere2cart(interp_signal, phi, theta)
if points:
r, phi, theta = geo.cart2sphere(s0.x, s0.y, s0.z)
x_p, y_p, z_p = geo.sphere2cart(signal, phi, theta)
figure = _display_maya_voxel(x_p, y_p, z_p, faces,
signal, origin, cmap=cmap,
colorbar=colorbar, figure=figure,
vmin=vmin, vmax=vmax, file_name=file_name,
azimuth=azimuth, elevation=elevation,
points=True, cmap_points=cmap_points,
scale_points=scale_points)
# Call and return straightaway:
return _display_maya_voxel(x_plot, y_plot, z_plot, faces,
interp_signal, origin,
cmap=cmap, colorbar=colorbar, figure=figure,
vmin=vmin, vmax=vmax, file_name=file_name,
azimuth=azimuth, elevation=elevation)
def test_tensor_model():
fdata, fbval, fbvec = get_data('small_25')
data1 = nib.load(fdata).get_data()
gtab1 = grad.gradient_table(fbval, fbvec)
data2, gtab2 = dsi_voxels()
for data, gtab in zip([data1, data2], [gtab1, gtab2]):
dm = dti.TensorModel(gtab, 'LS')
dtifit = dm.fit(data[0, 0, 0])
assert_equal(dtifit.fa < 0.9, True)
dm = dti.TensorModel(gtab, 'WLS')
dtifit = dm.fit(data[0, 0, 0])
assert_equal(dtifit.fa < 0.9, True)
assert_equal(dtifit.fa > 0, True)
sphere = create_unit_sphere(4)
assert_equal(len(dtifit.odf(sphere)), len(sphere.vertices))
# Check that the multivoxel case works:
dtifit = dm.fit(data)
# Check that it works on signal that has already been normalized to S0:
dm_to_relative = dti.TensorModel(gtab)
if np.any(gtab.b0s_mask):
relative_data = (data[0, 0, 0]/np.mean(data[0, 0, 0,
gtab.b0s_mask]))
dtifit_to_relative = dm_to_relative.fit(relative_data)
npt.assert_almost_equal(dtifit.fa[0, 0, 0], dtifit_to_relative.fa,
decimal=3)
# And smoke-test that all these operations return sensibly-shaped arrays:
assert_equal(dtifit.fa.shape, data.shape[:3])
assert_equal(dtifit.ad.shape, data.shape[:3])
assert_equal(dtifit.md.shape, data.shape[:3])
assert_equal(dtifit.rd.shape, data.shape[:3])
assert_equal(dtifit.trace.shape, data.shape[:3])
assert_equal(dtifit.mode.shape, data.shape[:3])
assert_equal(dtifit.linearity.shape, data.shape[:3])
assert_equal(dtifit.planarity.shape, data.shape[:3])
assert_equal(dtifit.sphericity.shape, data.shape[:3])
# Test for the shape of the mask
assert_raises(ValueError, dm.fit, np.ones((10, 10, 3)), np.ones((3, 3)))
# Make some synthetic data
b0 = 1000.
bvecs, bvals = read_bvec_file(get_data('55dir_grad.bvec'))
gtab = grad.gradient_table_from_bvals_bvecs(bvals, bvecs.T)
# The first b value is 0., so we take the second one:
B = bvals[1]
# Scale the eigenvalues and tensor by the B value so the units match
D = np.array([1., 1., 1., 0., 0., 1., -np.log(b0) * B]) / B
evals = np.array([2., 1., 0.]) / B
md = evals.mean()
tensor = from_lower_triangular(D)
A_squiggle = tensor - (1 / 3.0) * np.trace(tensor) * np.eye(3)
mode = (3 * np.sqrt(6) * np.linalg.det(A_squiggle /
np.linalg.norm(A_squiggle)))
evals_eigh, evecs_eigh = np.linalg.eigh(tensor)
# Sort according to eigen-value from large to small:
evecs = evecs_eigh[:, np.argsort(evals_eigh)[::-1]]
# Check that eigenvalues and eigenvectors are properly sorted through
# that previous operation:
for i in range(3):
assert_array_almost_equal(np.dot(tensor, evecs[:, i]),
evals[i] * evecs[:, i])
# Design Matrix
X = dti.design_matrix(gtab)
# Signals
Y = np.exp(np.dot(X, D))
assert_almost_equal(Y[0], b0)
Y.shape = (-1,) + Y.shape
# Test fitting with different methods:
for fit_method in ['OLS', 'WLS', 'NLLS']:
tensor_model = dti.TensorModel(gtab,
fit_method=fit_method,
return_S0_hat=True)
tensor_fit = tensor_model.fit(Y)
assert_true(tensor_fit.model is tensor_model)
assert_equal(tensor_fit.shape, Y.shape[:-1])
assert_array_almost_equal(tensor_fit.evals[0], evals)
assert_array_almost_equal(tensor_fit.S0_hat, b0, decimal=3)
# Test that the eigenvectors are correct, one-by-one:
for i in range(3):
# Eigenvectors have intrinsic sign ambiguity
# (see
# http://prod.sandia.gov/techlib/access-control.cgi/2007/076422.pdf)
# so we need to allow for sign flips. One of the following should
# always be true:
assert_(
np.all(np.abs(tensor_fit.evecs[0][:, i] -
evecs[:, i]) < 10e-6) or
np.all(np.abs(-tensor_fit.evecs[0][:, i] -
evecs[:, i]) < 10e-6))
# We set a fixed tolerance of 10e-6, similar to array_almost_equal
err_msg = "Calculation of tensor from Y does not compare to "
err_msg += "analytical solution"
assert_array_almost_equal(tensor_fit.quadratic_form[0], tensor,
err_msg=err_msg)
assert_almost_equal(tensor_fit.md[0], md)
assert_array_almost_equal(tensor_fit.mode, mode, decimal=5)
assert_equal(tensor_fit.directions.shape[-2], 1)
assert_equal(tensor_fit.directions.shape[-1], 3)
# Test error-handling:
assert_raises(ValueError,
dti.TensorModel,
gtab,
fit_method='crazy_method')
# Test custom fit tensor method
try:
model = dti.TensorModel(gtab, fit_method=lambda *args, **kwargs: 42)
fit = model.fit_method()
except Exception as exc:
assert False, "TensorModel should accept custom fit methods: %s" % exc
assert fit == 42, "Custom fit method for TensorModel returned %s." % fit
# Test multi-voxel data
data = np.zeros((3, Y.shape[1]))
# Normal voxel
data[0] = Y
# High diffusion voxel, all diffusing weighted signal equal to zero
data[1, gtab.b0s_mask] = b0
data[1, ~gtab.b0s_mask] = 0
# Masked voxel, all data set to zero
data[2] = 0.
tensor_model = dti.TensorModel(gtab)
fit = tensor_model.fit(data)
assert_array_almost_equal(fit[0].evals, evals)
# Return S0_test
tensor_model = dti.TensorModel(gtab, return_S0_hat=True)
fit = tensor_model.fit(data)
assert_array_almost_equal(fit[0].evals, evals)
assert_array_almost_equal(fit[0].S0_hat, b0)
# Evals should be high for high diffusion voxel
assert_(all(fit[1].evals > evals[0] * .9))
# Evals should be zero where data is masked
assert_array_almost_equal(fit[2].evals, 0.)
def test_tensor_model():
fdata, fbval, fbvec = get_data('small_25')
data1 = nib.load(fdata).get_data()
gtab1 = grad.gradient_table(fbval, fbvec)
data2, gtab2 = dsi_voxels()
for data, gtab in zip([data1, data2], [gtab1, gtab2]):
dm = dti.TensorModel(gtab, 'LS')
dtifit = dm.fit(data[0, 0, 0])
assert_equal(dtifit.fa < 0.9, True)
dm = dti.TensorModel(gtab, 'WLS')
dtifit = dm.fit(data[0, 0, 0])
assert_equal(dtifit.fa < 0.9, True)
assert_equal(dtifit.fa > 0, True)
sphere = create_unit_sphere(4)
assert_equal(len(dtifit.odf(sphere)), len(sphere.vertices))
# Check that the multivoxel case works:
dtifit = dm.fit(data)
# Check that it works on signal that has already been normalized to S0:
dm_to_relative = dti.TensorModel(gtab)
if np.any(gtab.b0s_mask):
relative_data = (data[0, 0, 0] /
np.mean(data[0, 0, 0, gtab.b0s_mask]))
dtifit_to_relative = dm_to_relative.fit(relative_data)
npt.assert_almost_equal(dtifit.fa[0, 0, 0],
dtifit_to_relative.fa,
decimal=3)
# And smoke-test that all these operations return sensibly-shaped arrays:
assert_equal(dtifit.fa.shape, data.shape[:3])
assert_equal(dtifit.ad.shape, data.shape[:3])
assert_equal(dtifit.md.shape, data.shape[:3])
assert_equal(dtifit.rd.shape, data.shape[:3])
assert_equal(dtifit.trace.shape, data.shape[:3])
assert_equal(dtifit.mode.shape, data.shape[:3])
assert_equal(dtifit.linearity.shape, data.shape[:3])
assert_equal(dtifit.planarity.shape, data.shape[:3])
assert_equal(dtifit.sphericity.shape, data.shape[:3])
# Test for the shape of the mask
assert_raises(ValueError, dm.fit, np.ones((10, 10, 3)), np.ones((3, 3)))
# Make some synthetic data
b0 = 1000.
bvecs, bvals = read_bvec_file(get_data('55dir_grad.bvec'))
gtab = grad.gradient_table_from_bvals_bvecs(bvals, bvecs.T)
# The first b value is 0., so we take the second one:
B = bvals[1]
# Scale the eigenvalues and tensor by the B value so the units match
D = np.array([1., 1., 1., 0., 0., 1., -np.log(b0) * B]) / B
evals = np.array([2., 1., 0.]) / B
md = evals.mean()
tensor = from_lower_triangular(D)
A_squiggle = tensor - (1 / 3.0) * np.trace(tensor) * np.eye(3)
mode = 3 * np.sqrt(6) * np.linalg.det(
A_squiggle / np.linalg.norm(A_squiggle))
evecs = np.linalg.eigh(tensor)[1]
# Design Matrix
X = dti.design_matrix(gtab)
# Signals
Y = np.exp(np.dot(X, D))
assert_almost_equal(Y[0], b0)
Y.shape = (-1, ) + Y.shape
# Test fitting with different methods:
for fit_method in ['OLS', 'WLS', 'NLLS']:
tensor_model = dti.TensorModel(gtab, fit_method=fit_method)
tensor_fit = tensor_model.fit(Y)
assert_true(tensor_fit.model is tensor_model)
assert_equal(tensor_fit.shape, Y.shape[:-1])
assert_array_almost_equal(tensor_fit.evals[0], evals)
assert_array_almost_equal(tensor_fit.quadratic_form[0], tensor,
err_msg=\
"Calculation of tensor from Y does not compare to analytical solution")
assert_almost_equal(tensor_fit.md[0], md)
assert_array_almost_equal(tensor_fit.mode, mode, decimal=5)
assert_equal(tensor_fit.directions.shape[-2], 1)
assert_equal(tensor_fit.directions.shape[-1], 3)
# Test error-handling:
assert_raises(ValueError, dti.TensorModel, gtab, fit_method='crazy_method')
# Test multi-voxel data
data = np.zeros((3, Y.shape[1]))
# Normal voxel
data[0] = Y
# High diffusion voxel, all diffusing weighted signal equal to zero
data[1, gtab.b0s_mask] = b0
data[1, ~gtab.b0s_mask] = 0
# Masked voxel, all data set to zero
data[2] = 0.
tensor_model = dti.TensorModel(gtab)
fit = tensor_model.fit(data)
assert_array_almost_equal(fit[0].evals, evals)
# Evals should be high for high diffusion voxel
assert_(all(fit[1].evals > evals[0] * .9))
# Evals should be zero where data is masked
assert_array_almost_equal(fit[2].evals, 0.)
def test_TensorModel():
data, gtab = dsi_voxels()
dm = dti.TensorModel(gtab, 'LS')
dtifit = dm.fit(data[0, 0, 0])
assert_equal(dtifit.fa < 0.5, True)
dm = dti.TensorModel(gtab, 'WLS')
dtifit = dm.fit(data[0, 0, 0])
assert_equal(dtifit.fa < 0.5, True)
sphere = create_unit_sphere(4)
assert_equal(len(dtifit.odf(sphere)), len(sphere.vertices))
assert_almost_equal(dtifit.fa, gfa(dtifit.odf(sphere)), 1)
# Check that the multivoxel case works:
dtifit = dm.fit(data)
# And smoke-test that all these operations return sensibly-shaped arrays:
assert_equal(dtifit.fa.shape, data.shape[:3])
assert_equal(dtifit.ad.shape, data.shape[:3])
assert_equal(dtifit.md.shape, data.shape[:3])
assert_equal(dtifit.rd.shape, data.shape[:3])
assert_equal(dtifit.trace.shape, data.shape[:3])
assert_equal(dtifit.mode.shape, data.shape[:3])
assert_equal(dtifit.linearity.shape, data.shape[:3])
assert_equal(dtifit.planarity.shape, data.shape[:3])
assert_equal(dtifit.sphericity.shape, data.shape[:3])
# Test for the shape of the mask
assert_raises(ValueError, dm.fit, np.ones((10, 10, 3)), np.ones((3,3)))
# Make some synthetic data
b0 = 1000.
bvecs, bvals = read_bvec_file(get_data('55dir_grad.bvec'))
gtab = grad.gradient_table_from_bvals_bvecs(bvals, bvecs.T)
# The first b value is 0., so we take the second one:
B = bvals[1]
# Scale the eigenvalues and tensor by the B value so the units match
D = np.array([1., 1., 1., 0., 0., 1., -np.log(b0) * B]) / B
evals = np.array([2., 1., 0.]) / B
md = evals.mean()
tensor = from_lower_triangular(D)
A_squiggle = tensor - (1 / 3.0) * np.trace(tensor) * np.eye(3)
mode = 3 * np.sqrt(6) * np.linalg.det(A_squiggle / np.linalg.norm(A_squiggle))
evecs = np.linalg.eigh(tensor)[1]
# Design Matrix
X = dti.design_matrix(gtab)
# Signals
Y = np.exp(np.dot(X, D))
assert_almost_equal(Y[0], b0)
Y.shape = (-1,) + Y.shape
# Test fitting with different methods:
for fit_method in ['OLS', 'WLS', 'NLLS']:
tensor_model = dti.TensorModel(gtab,
fit_method=fit_method)
tensor_fit = tensor_model.fit(Y)
assert_true(tensor_fit.model is tensor_model)
assert_equal(tensor_fit.shape, Y.shape[:-1])
assert_array_almost_equal(tensor_fit.evals[0], evals)
assert_array_almost_equal(tensor_fit.quadratic_form[0], tensor,
err_msg=\
"Calculation of tensor from Y does not compare to analytical solution")
assert_almost_equal(tensor_fit.md[0], md)
assert_array_almost_equal(tensor_fit.mode, mode, decimal=5)
assert_equal(tensor_fit.directions.shape[-2], 1)
assert_equal(tensor_fit.directions.shape[-1], 3)
# Test error-handling:
assert_raises(ValueError,
dti.TensorModel,
gtab,
fit_method='crazy_method')
def test_create_unit_sphere():
sphere = create_unit_sphere(7)
v, e, f = sphere.vertices, sphere.edges, sphere.faces
assert_array_almost_equal((v*v).sum(1), 1)
def plot_tensor_3d(Tensor,
cmap='jet',
mode='ADC',
file_name=None,
origin=[0, 0, 0],
colorbar=False,
figure=None,
vmin=None,
vmax=None,
offset=0,
azimuth=60,
elevation=90,
roll=0,
scale_factor=1.0,
rgb_pdd=False):
"""
mode: either "ADC", "ellipse" or "pred_sig"
"""
Q = Tensor.Q
sphere = create_unit_sphere(5)
vertices = sphere.vertices
faces = sphere.faces
x, y, z = vertices.T
new_bvecs = np.vstack([x.ravel(), y.ravel(), z.ravel()])
Tensor = ozt.Tensor(Q, new_bvecs,
Tensor.bvals[0] * np.ones(new_bvecs.shape[-1]))
if mode == 'ADC':
v = Tensor.ADC * scale_factor
elif mode == 'ellipse':
v = Tensor.diffusion_distance * scale_factor
elif mode == 'pred_sig':
v = Tensor.predicted_signal(1) * scale_factor
else:
raise ValueError("Mode not recognized")
r, phi, theta = geo.cart2sphere(x, y, z)
x_plot, y_plot, z_plot = geo.sphere2cart(v, phi, theta)
if rgb_pdd:
evals, evecs = Tensor.decompose
xyz = evecs[0]
r = np.abs(xyz[0]) / np.sum(np.abs(xyz))
g = np.abs(xyz[1]) / np.sum(np.abs(xyz))
b = np.abs(xyz[2]) / np.sum(np.abs(xyz))
color = (r, g, b)
else:
color = None
# Call and return straightaway:
return _display_maya_voxel(x_plot,
y_plot,
z_plot,
faces,
v,
origin,
cmap=cmap,
colorbar=colorbar,
color=color,
figure=figure,
vmin=vmin,
vmax=vmax,
file_name=file_name,
azimuth=azimuth,
elevation=elevation)
def test_tensor_model():
fdata, fbval, fbvec = get_data('small_25')
data1 = nib.load(fdata).get_data()
gtab1 = grad.gradient_table(fbval, fbvec)
data2, gtab2 = dsi_voxels()
for data, gtab in zip([data1, data2], [gtab1, gtab2]):
dm = dti.TensorModel(gtab, 'LS')
dtifit = dm.fit(data[0, 0, 0])
assert_equal(dtifit.fa < 0.9, True)
dm = dti.TensorModel(gtab, 'WLS')
dtifit = dm.fit(data[0, 0, 0])
assert_equal(dtifit.fa < 0.9, True)
assert_equal(dtifit.fa > 0, True)
sphere = create_unit_sphere(4)
assert_equal(len(dtifit.odf(sphere)), len(sphere.vertices))
# Check that the multivoxel case works:
dtifit = dm.fit(data)
# Check that it works on signal that has already been normalized to S0:
dm_to_relative = dti.TensorModel(gtab)
if np.any(gtab.b0s_mask):
relative_data = (data[0, 0, 0]/np.mean(data[0, 0, 0,
gtab.b0s_mask]))
dtifit_to_relative = dm_to_relative.fit(relative_data)
npt.assert_almost_equal(dtifit.fa[0, 0, 0], dtifit_to_relative.fa,
decimal=3)
# And smoke-test that all these operations return sensibly-shaped arrays:
assert_equal(dtifit.fa.shape, data.shape[:3])
assert_equal(dtifit.ad.shape, data.shape[:3])
assert_equal(dtifit.md.shape, data.shape[:3])
assert_equal(dtifit.rd.shape, data.shape[:3])
assert_equal(dtifit.trace.shape, data.shape[:3])
assert_equal(dtifit.mode.shape, data.shape[:3])
assert_equal(dtifit.linearity.shape, data.shape[:3])
assert_equal(dtifit.planarity.shape, data.shape[:3])
assert_equal(dtifit.sphericity.shape, data.shape[:3])
# Test for the shape of the mask
assert_raises(ValueError, dm.fit, np.ones((10, 10, 3)), np.ones((3, 3)))
# Make some synthetic data
b0 = 1000.
bvecs, bvals = read_bvec_file(get_data('55dir_grad.bvec'))
gtab = grad.gradient_table_from_bvals_bvecs(bvals, bvecs.T)
# The first b value is 0., so we take the second one:
B = bvals[1]
# Scale the eigenvalues and tensor by the B value so the units match
D = np.array([1., 1., 1., 0., 0., 1., -np.log(b0) * B]) / B
evals = np.array([2., 1., 0.]) / B
md = evals.mean()
tensor = from_lower_triangular(D)
A_squiggle = tensor - (1 / 3.0) * np.trace(tensor) * np.eye(3)
mode = (3 * np.sqrt(6) * np.linalg.det(A_squiggle /
np.linalg.norm(A_squiggle)))
evals_eigh, evecs_eigh = np.linalg.eigh(tensor)
# Sort according to eigen-value from large to small:
evecs = evecs_eigh[:, np.argsort(evals_eigh)[::-1]]
# Check that eigenvalues and eigenvectors are properly sorted through
# that previous operation:
for i in range(3):
assert_array_almost_equal(np.dot(tensor, evecs[:, i]),
evals[i] * evecs[:, i])
# Design Matrix
X = dti.design_matrix(gtab)
# Signals
Y = np.exp(np.dot(X, D))
assert_almost_equal(Y[0], b0)
Y.shape = (-1,) + Y.shape
# Test fitting with different methods:
for fit_method in ['OLS', 'WLS', 'NLLS']:
tensor_model = dti.TensorModel(gtab,
fit_method=fit_method,
return_S0_hat=True)
tensor_fit = tensor_model.fit(Y)
assert_true(tensor_fit.model is tensor_model)
assert_equal(tensor_fit.shape, Y.shape[:-1])
assert_array_almost_equal(tensor_fit.evals[0], evals)
assert_array_almost_equal(tensor_fit.S0_hat, b0, decimal=3)
# Test that the eigenvectors are correct, one-by-one:
for i in range(3):
# Eigenvectors have intrinsic sign ambiguity
# (see
# http://prod.sandia.gov/techlib/access-control.cgi/2007/076422.pdf)
# so we need to allow for sign flips. One of the following should
# always be true:
assert_(
np.all(np.abs(tensor_fit.evecs[0][:, i] -
evecs[:, i]) < 10e-6) or
np.all(np.abs(-tensor_fit.evecs[0][:, i] -
evecs[:, i]) < 10e-6))
# We set a fixed tolerance of 10e-6, similar to array_almost_equal
err_msg = "Calculation of tensor from Y does not compare to "
err_msg += "analytical solution"
assert_array_almost_equal(tensor_fit.quadratic_form[0], tensor,
err_msg=err_msg)
assert_almost_equal(tensor_fit.md[0], md)
assert_array_almost_equal(tensor_fit.mode, mode, decimal=5)
assert_equal(tensor_fit.directions.shape[-2], 1)
assert_equal(tensor_fit.directions.shape[-1], 3)
# Test error-handling:
assert_raises(ValueError,
dti.TensorModel,
gtab,
fit_method='crazy_method')
# Test custom fit tensor method
try:
model = dti.TensorModel(gtab, fit_method=lambda *args, **kwargs: 42)
fit = model.fit_method()
except Exception as exc:
assert False, "TensorModel should accept custom fit methods: %s" % exc
assert fit == 42, "Custom fit method for TensorModel returned %s." % fit
# Test multi-voxel data
data = np.zeros((3, Y.shape[1]))
# Normal voxel
data[0] = Y
# High diffusion voxel, all diffusing weighted signal equal to zero
data[1, gtab.b0s_mask] = b0
data[1, ~gtab.b0s_mask] = 0
# Masked voxel, all data set to zero
data[2] = 0.
tensor_model = dti.TensorModel(gtab)
fit = tensor_model.fit(data)
assert_array_almost_equal(fit[0].evals, evals)
# Return S0_test
tensor_model = dti.TensorModel(gtab, return_S0_hat=True)
fit = tensor_model.fit(data)
assert_array_almost_equal(fit[0].evals, evals)
assert_array_almost_equal(fit[0].S0_hat, b0)
# Evals should be high for high diffusion voxel
assert_(all(fit[1].evals > evals[0] * .9))
# Evals should be zero where data is masked
assert_array_almost_equal(fit[2].evals, 0.)
def test_tensor_model():
fdata, fbval, fbvec = get_data('small_25')
data = nib.load(fdata).get_data()
gtab = grad.gradient_table(fbval, fbvec)
dm = dti.TensorModel(gtab, 'LS')
dtifit = dm.fit(data[0, 0, 0])
assert_equal(dtifit.fa < 0.9, True)
dm = dti.TensorModel(gtab, 'WLS')
dtifit = dm.fit(data[0, 0, 0])
assert_equal(dtifit.fa < 0.9, True)
assert_equal(dtifit.fa > 0, True)
sphere = create_unit_sphere(4)
assert_equal(len(dtifit.odf(sphere)), len(sphere.vertices))
# Check that the multivoxel case works:
dtifit = dm.fit(data)
# Check that it works on signal that has already been normalized to S0:
dm_to_relative = dti.TensorModel(gtab)
relative_data = (data[0, 0, 0]/np.mean(data[0, 0, 0, gtab.b0s_mask]))
dtifit_to_relative = dm_to_relative.fit(relative_data)
npt.assert_almost_equal(dtifit.fa[0,0,0], dtifit_to_relative.fa, decimal=3)
# And smoke-test that all these operations return sensibly-shaped arrays:
assert_equal(dtifit.fa.shape, data.shape[:3])
assert_equal(dtifit.ad.shape, data.shape[:3])
assert_equal(dtifit.md.shape, data.shape[:3])
assert_equal(dtifit.rd.shape, data.shape[:3])
assert_equal(dtifit.trace.shape, data.shape[:3])
assert_equal(dtifit.mode.shape, data.shape[:3])
assert_equal(dtifit.linearity.shape, data.shape[:3])
assert_equal(dtifit.planarity.shape, data.shape[:3])
assert_equal(dtifit.sphericity.shape, data.shape[:3])
# Test for the shape of the mask
assert_raises(ValueError, dm.fit, np.ones((10, 10, 3)), np.ones((3,3)))
# Make some synthetic data
b0 = 1000.
bvecs, bvals = read_bvec_file(get_data('55dir_grad.bvec'))
gtab = grad.gradient_table_from_bvals_bvecs(bvals, bvecs.T)
# The first b value is 0., so we take the second one:
B = bvals[1]
# Scale the eigenvalues and tensor by the B value so the units match
D = np.array([1., 1., 1., 0., 0., 1., -np.log(b0) * B]) / B
evals = np.array([2., 1., 0.]) / B
md = evals.mean()
tensor = from_lower_triangular(D)
A_squiggle = tensor - (1 / 3.0) * np.trace(tensor) * np.eye(3)
mode = 3 * np.sqrt(6) * np.linalg.det(A_squiggle / np.linalg.norm(A_squiggle))
evecs = np.linalg.eigh(tensor)[1]
# Design Matrix
X = dti.design_matrix(gtab)
# Signals
Y = np.exp(np.dot(X, D))
assert_almost_equal(Y[0], b0)
Y.shape = (-1,) + Y.shape
# Test fitting with different methods:
for fit_method in ['OLS', 'WLS', 'NLLS']:
tensor_model = dti.TensorModel(gtab,
fit_method=fit_method)
tensor_fit = tensor_model.fit(Y)
assert_true(tensor_fit.model is tensor_model)
assert_equal(tensor_fit.shape, Y.shape[:-1])
assert_array_almost_equal(tensor_fit.evals[0], evals)
assert_array_almost_equal(tensor_fit.quadratic_form[0], tensor,
err_msg=\
"Calculation of tensor from Y does not compare to analytical solution")
assert_almost_equal(tensor_fit.md[0], md)
assert_array_almost_equal(tensor_fit.mode, mode, decimal=5)
assert_equal(tensor_fit.directions.shape[-2], 1)
assert_equal(tensor_fit.directions.shape[-1], 3)
# Test error-handling:
assert_raises(ValueError,
dti.TensorModel,
gtab,
fit_method='crazy_method')
# Test multi-voxel data
data = np.zeros((3, Y.shape[1]))
# Normal voxel
data[0] = Y
# High diffusion voxel, all diffusing weighted signal equal to zero
data[1, gtab.b0s_mask] = b0
data[1, ~gtab.b0s_mask] = 0
# Masked voxel, all data set to zero
data[2] = 0.
tensor_model = dti.TensorModel(gtab)
fit = tensor_model.fit(data)
assert_array_almost_equal(fit[0].evals, evals)
# Evals should be high for high diffusion voxel
assert_(all(fit[1].evals > evals[0] * .9))
# Evals should be zero where data is masked
assert_array_almost_equal(fit[2].evals, 0.)
def test_TensorModel():
data, gtab = dsi_voxels()
dm = dti.TensorModel(gtab, 'LS')
dtifit = dm.fit(data[0, 0, 0])
assert_equal(dtifit.fa < 0.5, True)
dm = dti.TensorModel(gtab, 'WLS')
dtifit = dm.fit(data[0, 0, 0])
assert_equal(dtifit.fa < 0.5, True)
sphere = create_unit_sphere(4)
assert_equal(len(dtifit.odf(sphere)), len(sphere.vertices))
assert_almost_equal(dtifit.fa, gfa(dtifit.odf(sphere)), 1)
# Check that the multivoxel case works:
dtifit = dm.fit(data)
# And smoke-test that all these operations return sensibly-shaped arrays:
assert_equal(dtifit.fa.shape, data.shape[:3])
assert_equal(dtifit.ad.shape, data.shape[:3])
assert_equal(dtifit.md.shape, data.shape[:3])
assert_equal(dtifit.rd.shape, data.shape[:3])
assert_equal(dtifit.trace.shape, data.shape[:3])
assert_equal(dtifit.mode.shape, data.shape[:3])
assert_equal(dtifit.linearity.shape, data.shape[:3])
assert_equal(dtifit.planarity.shape, data.shape[:3])
assert_equal(dtifit.sphericity.shape, data.shape[:3])
# Test for the shape of the mask
assert_raises(ValueError, dm.fit, np.ones((10, 10, 3)), np.ones((3, 3)))
# Make some synthetic data
b0 = 1000.
bvecs, bvals = read_bvec_file(get_data('55dir_grad.bvec'))
gtab = grad.gradient_table_from_bvals_bvecs(bvals, bvecs.T)
# The first b value is 0., so we take the second one:
B = bvals[1]
# Scale the eigenvalues and tensor by the B value so the units match
D = np.array([1., 1., 1., 0., 0., 1., -np.log(b0) * B]) / B
evals = np.array([2., 1., 0.]) / B
md = evals.mean()
tensor = from_lower_triangular(D)
A_squiggle = tensor - (1 / 3.0) * np.trace(tensor) * np.eye(3)
mode = 3 * np.sqrt(6) * np.linalg.det(
A_squiggle / np.linalg.norm(A_squiggle))
evecs = np.linalg.eigh(tensor)[1]
# Design Matrix
X = dti.design_matrix(gtab)
# Signals
Y = np.exp(np.dot(X, D))
assert_almost_equal(Y[0], b0)
Y.shape = (-1, ) + Y.shape
# Test fitting with different methods:
for fit_method in ['OLS', 'WLS', 'NLLS']:
tensor_model = dti.TensorModel(gtab, fit_method=fit_method)
tensor_fit = tensor_model.fit(Y)
assert_true(tensor_fit.model is tensor_model)
assert_equal(tensor_fit.shape, Y.shape[:-1])
assert_array_almost_equal(tensor_fit.evals[0], evals)
assert_array_almost_equal(tensor_fit.quadratic_form[0], tensor,
err_msg=\
"Calculation of tensor from Y does not compare to analytical solution")
assert_almost_equal(tensor_fit.md[0], md)
assert_array_almost_equal(tensor_fit.mode, mode, decimal=5)
assert_equal(tensor_fit.directions.shape[-2], 1)
assert_equal(tensor_fit.directions.shape[-1], 3)
# Test error-handling:
assert_raises(ValueError, dti.TensorModel, gtab, fit_method='crazy_method')
