def sticks_and_ball_dummies(gtab):
sb_dummies = {}
S, sticks = SticksAndBall(gtab,
d=0.0015,
S0=100,
angles=[(0, 0)],
fractions=[100],
snr=None)
sb_dummies['1fiber'] = (S, sticks)
S, sticks = SticksAndBall(gtab,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0)],
fractions=[50, 50],
snr=None)
sb_dummies['2fiber'] = (S, sticks)
S, sticks = SticksAndBall(gtab,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[33, 33, 33],
snr=None)
sb_dummies['3fiber'] = (S, sticks)
S, sticks = SticksAndBall(gtab,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[0, 0, 0],
snr=None)
sb_dummies['isotropic'] = (S, sticks)
return sb_dummies
def test_dsi_metrics():
btable = np.loadtxt(get_fnames('dsi4169btable'))
gtab = gradient_table(btable[:, 0], btable[:, 1:])
data, golden_directions = SticksAndBall(gtab,
d=0.0015,
S0=100,
angles=[(0, 0), (60, 0)],
fractions=[50, 50],
snr=None)
dsmodel = DiffusionSpectrumModel(gtab, qgrid_size=21, filter_width=4500)
rtop_signal_norm = dsmodel.fit(data).rtop_signal()
dsmodel.fit(data).rtop_pdf()
rtop_pdf = dsmodel.fit(data).rtop_pdf(normalized=False)
assert_almost_equal(rtop_signal_norm, rtop_pdf, 6)
dsmodel = DiffusionSpectrumModel(gtab, qgrid_size=21, filter_width=4500)
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
S_0, sticks_0 = MultiTensor(gtab,
mevals,
S0=100,
angles=[(0, 0), (60, 0)],
fractions=[50, 50],
snr=None)
S_1, sticks_0 = MultiTensor(gtab,
mevals * 2.0,
S0=100,
angles=[(0, 0), (60, 0)],
fractions=[50, 50],
snr=None)
MSD_norm_0 = dsmodel.fit(S_0).msd_discrete(normalized=True)
MSD_norm_1 = dsmodel.fit(S_1).msd_discrete(normalized=True)
assert_almost_equal(MSD_norm_0, 0.5 * MSD_norm_1, 4)
def dsi_deconv_voxels():
gtab = gradient_table(np.loadtxt(get_data('dsi515btable')))
data = np.zeros((2, 2, 2, 515))
for ix in range(2):
for iy in range(2):
for iz in range(2):
data[ix, iy, iz], dirs = SticksAndBall(gtab,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0)],
fractions=[50, 50],
snr=None)
return data, gtab
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 = 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 sim_data(bvals, bvecs, d=0.0015, S0=100, snr=None):
descr = np.zeros(13).tolist()
data = np.zeros((13, len(bvals)))
descr[0] = ('isotropic', 0)
S, stics = SticksAndBall(bvals,
bvecs,
d,
S0,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[0, 0, 0],
snr=snr)
data[0] = S.copy()
descr[1] = ('one fiber', 1)
S, stics = SticksAndBall(bvals,
bvecs,
d,
S0,
angles=[(30, 0), (90, 0), (90, 90)],
fractions=[100, 0, 0],
snr=snr)
data[1] = S.copy()
descr[2] = ('two fibers', 2)
S, stics = SticksAndBall(bvals,
bvecs,
d,
S0,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[50, 50, 0],
snr=snr)
data[2] = S.copy()
descr[3] = ('three fibers', 3)
S, stics = SticksAndBall(bvals,
bvecs,
d,
S0,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[33, 33, 33],
snr=snr)
data[3] = S.copy()
descr[4] = ('three fibers iso', 3)
S, stics = SticksAndBall(bvals,
bvecs,
d,
S0,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[23, 23, 23],
snr=snr)
data[4] = S.copy()
descr[5] = ('three fibers more iso', 3)
S, stics = SticksAndBall(bvals,
bvecs,
d,
S0,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[13, 13, 13],
snr=snr)
data[5] = S.copy()
descr[6] = ('three fibers one at 60', 3)
S, stics = SticksAndBall(bvals,
bvecs,
d,
S0,
angles=[(0, 0), (60, 0), (90, 90)],
fractions=[33, 33, 33],
snr=snr)
data[6] = S.copy()
descr[7] = ('three fibers one at 90,90 one smaller', 3)
S, stics = SticksAndBall(bvals,
bvecs,
d,
S0,
angles=[(0, 0), (60, 0), (90, 90)],
fractions=[33, 33, 23],
snr=snr)
data[7] = S.copy()
descr[8] = ('three fibers one at 60, one at 90 and one smaller', 3)
S, stics = SticksAndBall(bvals,
bvecs,
d,
S0,
angles=[(0, 0), (60, 0), (90, 90)],
fractions=[33, 33, 13],
snr=snr)
data[8] = S.copy()
descr[9] = ('two fibers at 60 one smaller', 2)
S, stics = SticksAndBall(bvals,
bvecs,
d,
S0,
angles=[(0, 0), (60, 0), (90, 90)],
fractions=[50, 30, 0],
snr=snr)
data[9] = S.copy()
descr[10] = ('two fibers one at 30', 2)
S, stics = SticksAndBall(bvals,
bvecs,
d,
S0,
angles=[(0, 0), (45, 0)],
fractions=[50, 50],
snr=snr)
data[10] = S.copy()
descr[11] = ('one fiber one at 30 but small iso', 1)
S, stics = SticksAndBall(bvals,
bvecs,
d,
S0,
angles=[(30, 0), (60, 0), (90, 90)],
fractions=[60, 0, 0],
snr=snr)
data[11] = S.copy()
descr[12] = ('one fiber one at 30 but even smaller iso', 1)
S, stics = SticksAndBall(bvals,
bvecs,
d,
S0,
angles=[(0, 0), (60, 0), (90, 90)],
fractions=[30, 0, 0],
snr=snr)
data[12] = S.copy()
return data, descr
def test_dsi():
btable = np.loadtxt(get_data('dsi515btable'))
bvals = btable[:, 0]
bvecs = btable[:, 1:]
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[50, 50, 0],
snr=None)
pdf0, odf0, peaks0 = standard_dsi_algorithm(S, bvals, bvecs)
S2 = S.copy()
S2 = S2.reshape(1, len(S))
ds = DiffusionSpectrum(S2, bvals, bvecs)
assert_almost_equal(np.sum(ds.pdf(S) - pdf0), 0)
assert_almost_equal(np.sum(ds.odf(ds.pdf(S)) - odf0), 0)
#compare gfa
psi = odf0 / odf0.max()
numer = len(psi) * np.sum((psi - np.mean(psi))**2)
denom = (len(psi) - 1) * np.sum(psi**2)
GFA = np.sqrt(numer / denom)
assert_almost_equal(ds.gfa()[0], GFA)
#compare indices
#print ds.ind()
#print peak_finding(odf0,odf_faces)
#print peaks0
data = np.zeros((3, 3, 3, 515))
data[:, :, :] = S
ds = DiffusionSpectrum(data, bvals, bvecs)
ds2 = DiffusionSpectrum(data, bvals, bvecs, auto=False)
r = np.sqrt(ds2.qtable[:, 0]**2 + ds2.qtable[:, 1]**2 +
ds2.qtable[:, 2]**2)
ds2.filter = .5 * np.cos(2 * np.pi * r / 32)
ds2.fit()
assert_almost_equal(np.sum(ds2.qa() - ds.qa()), 0)
#1 fiber
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[100, 0, 0],
snr=None)
ds = DiffusionSpectrum(S.reshape(1, len(S)), bvals, bvecs)
QA = ds.qa()
assert_equal(np.sum(QA > 0), 1)
#2 fibers
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[50, 50, 0],
snr=None)
ds = DiffusionSpectrum(S.reshape(1, len(S)), bvals, bvecs)
QA = ds.qa()
assert_equal(np.sum(QA > 0), 2)
#3 fibers
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[33, 33, 33],
snr=None)
ds = DiffusionSpectrum(S.reshape(1, len(S)), bvals, bvecs)
QA = ds.qa()
assert_equal(np.sum(QA > 0), 3)
#isotropic
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[0, 0, 0],
snr=None)
ds = DiffusionSpectrum(S.reshape(1, len(S)), bvals, bvecs)
QA = ds.qa()
assert_equal(np.sum(QA > 0), 0)
def sim_data(bvals,bvecs,d=0.0015,S0=100,snr=None):
data=np.zeros((14,len(bvals)))
#isotropic
S,stics=SticksAndBall(bvals, bvecs, d, S0,
angles=[(0, 0),(90,0),(90,90)],
fractions=[0,0,0], snr=snr)
data[0]=S.copy()
#one fiber
S,stics=SticksAndBall(bvals, bvecs, d, S0,
angles=[(30, 0),(90,0),(90,90)],
fractions=[100,0,0], snr=snr)
data[1]=S.copy()
#two fibers
S,stics=SticksAndBall(bvals, bvecs, d, S0,
angles=[(0, 0),(90,0),(90,90)],
fractions=[50,50,0], snr=snr)
data[2]=S.copy()
#three fibers
S,stics=SticksAndBall(bvals, bvecs, d, S0,
angles=[(0, 0),(90,0),(90,90)],
fractions=[33,33,33], snr=snr)
data[3]=S.copy()
#three fibers iso
S,stics=SticksAndBall(bvals, bvecs, d, S0,
angles=[(0, 0),(90,0),(90,90)],
fractions=[23,23,23], snr=snr)
data[4]=S.copy()
#three fibers more iso
S,stics=SticksAndBall(bvals, bvecs, d, S0,
angles=[(0, 0),(90,0),(90,90)],
fractions=[13,13,13], snr=snr)
data[5]=S.copy()
#three fibers one at 60
S,stics=SticksAndBall(bvals, bvecs, d, S0,
angles=[(0, 0),(60,0),(90,90)],
fractions=[33,33,33], snr=snr)
data[6]=S.copy()
#three fibers one at 90,90 one smaller
S,stics=SticksAndBall(bvals, bvecs, d, S0,
angles=[(0, 0),(60,0),(90,90)],
fractions=[33,33,23], snr=snr)
data[7]=S.copy()
#three fibers one at 90,90 one even smaller
S,stics=SticksAndBall(bvals, bvecs, d, S0,
angles=[(0, 0),(60,0),(90,90)],
fractions=[33,33,13], snr=snr)
data[8]=S.copy()
#two fibers one at 60 one even smaller
S,stics=SticksAndBall(bvals, bvecs, d, S0,
angles=[(0, 0),(60,0),(90,90)],
fractions=[50,30,0], snr=snr)
data[9]=S.copy()
#two fibers one at 30 one at 60 even smaller
S,stics=SticksAndBall(bvals, bvecs, d, S0,
angles=[(0, 0),(20,0),(90,90)],
fractions=[50,50,0], snr=snr)
data[10]=S.copy()
#one fiber one at 30 but small
S,stics=SticksAndBall(bvals, bvecs, d, S0,
angles=[(30, 0),(60,0),(90,90)],
fractions=[60,0,0], snr=snr)
data[11]=S.copy()
#one fiber one at 30 but even smaller
S,stics=SticksAndBall(bvals, bvecs, d, S0,
angles=[(0, 0),(60,0),(90,90)],
fractions=[30,0,0], snr=snr)
data[12]=S.copy()
S,stics=SticksAndBall(bvals, bvecs, d, S0,
angles=[(30, 0),(60,0),(90,90)],
fractions=[0,0,0], snr=snr)
data[13]=S.copy()
return data
def test_gqi():
#load odf sphere
vertices, faces = sphere_vf_from('symmetric724')
edges = unique_edges(faces)
half_vertices, half_edges, half_faces = reduce_antipodal(vertices, faces)
#load bvals and gradients
btable = np.loadtxt(get_data('dsi515btable'))
bvals = btable[:, 0]
bvecs = btable[:, 1:]
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[50, 50, 0],
snr=None)
#pdf0,odf0,peaks0=standard_dsi_algorithm(S,bvals,bvecs)
S2 = S.copy()
S2 = S2.reshape(1, len(S))
odf_sphere = (vertices, faces)
ds = GeneralizedQSamplingModel(bvals, bvecs, odf_sphere)
dsfit = ds.fit(S)
assert_equal((dsfit.peak_values > 0).sum(), 3)
#change thresholds
ds.relative_peak_threshold = 0.5
ds.angular_distance_threshold = 30
dsfit = ds.fit(S)
assert_equal((dsfit.peak_values > 0).sum(), 2)
#1 fiber
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[100, 0, 0],
snr=None)
ds = GeneralizedQSamplingModel(bvals, bvecs, odf_sphere)
ds.relative_peak_threshold = 0.5
ds.angular_distance_threshold = 20
dsfit = ds.fit(S)
QA = dsfit.qa
#1/0
assert_equal(np.sum(QA > 0), 1)
#2 fibers
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[50, 50, 0],
snr=None)
ds = GeneralizedQSamplingModel(bvals, bvecs, odf_sphere)
ds.relative_peak_threshold = 0.5
ds.angular_distance_threshold = 20
dsfit = ds.fit(S)
QA = dsfit.qa
assert_equal(np.sum(QA > 0), 2)
#3 fibers
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[33, 33, 33],
snr=None)
ds = GeneralizedQSamplingModel(bvals, bvecs, odf_sphere)
ds.relative_peak_threshold = 0.5
dsfit = ds.fit(S)
QA = dsfit.qa
assert_equal(np.sum(QA > 0), 3)
#isotropic
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[0, 0, 0],
snr=None)
ds = GeneralizedQSamplingModel(bvals, bvecs, odf_sphere)
dsfit = ds.fit(S)
QA = dsfit.qa
assert_equal(np.sum(QA > 0), 0)
#3 fibers DSI2
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[33, 33, 33],
snr=None)
ds = GeneralizedQSamplingModel(bvals, bvecs, odf_sphere, squared=True)
ds.relative_peak_threshold = 0.5
dsfit = ds.fit(S, gfa_thr=0.05)
QA = dsfit.qa
#3 fibers DSI2 with a 3D volume
data = np.zeros((3, 3, 3, len(S)))
data[..., :] = S.copy()
dsfit = ds.fit(data, gfa_thr=0.05)
#1/0
assert_array_almost_equal(np.sum(dsfit.peak_values > 0, axis=-1),
3 * np.ones((3, 3, 3)))
def test_dsi():
#load odf sphere
vertices, faces = sphere_vf_from('symmetric724')
edges = unique_edges(faces)
half_vertices, half_edges, half_faces = reduce_antipodal(vertices, faces)
#load bvals and gradients
btable = np.loadtxt(get_data('dsi515btable'))
bvals = btable[:, 0]
bvecs = btable[:, 1:]
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[50, 50, 0],
snr=None)
#pdf0,odf0,peaks0=standard_dsi_algorithm(S,bvals,bvecs)
S2 = S.copy()
S2 = S2.reshape(1, len(S))
odf_sphere = (vertices, faces)
ds = DiffusionSpectrumModel(bvals, bvecs, odf_sphere)
dsfit = ds.fit(S)
assert_equal((dsfit.peak_values > 0).sum(), 3)
#change thresholds
ds.relative_peak_threshold = 0.5
ds.angular_distance_threshold = 30
dsfit = ds.fit(S)
assert_equal((dsfit.peak_values > 0).sum(), 2)
#assert_almost_equal(np.sum(ds.pdf(S)-pdf0),0)
#assert_almost_equal(np.sum(ds.odf(ds.pdf(S))-odf0),0)
assert_almost_equal(dsfit.gfa, np.array([0.5749720469955439]))
#1 fiber
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[100, 0, 0],
snr=None)
ds = DiffusionSpectrumModel(bvals, bvecs, odf_sphere)
dsfit = ds.fit(S)
QA = dsfit.qa
assert_equal(np.sum(QA > 0), 1)
#2 fibers
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[50, 50, 0],
snr=None)
ds = DiffusionSpectrumModel(bvals, bvecs, odf_sphere)
ds.relative_peak_threshold = 0.5
ds.angular_distance_threshold = 20
dsfit = ds.fit(S)
QA = dsfit.qa
assert_equal(np.sum(QA > 0), 2)
#Give me 2 directions
assert_equal(len(dsfit.get_directions()), 2)
#3 fibers
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[33, 33, 33],
snr=None)
ds = DiffusionSpectrumModel(bvals, bvecs, odf_sphere)
ds.relative_peak_threshold = 0.5
dsfit = ds.fit(S, return_odf=True)
QA = dsfit.qa
assert_equal(np.sum(QA > 0), 3)
#Give me 3 directions
assert_equal(len(dsfit.get_directions()), 3)
#Recalculate the odf with a different sphere.
vertices, faces = sphere_vf_from('symmetric724')
odf1 = dsfit.odf()
print len(odf1)
odf2 = dsfit.odf((vertices, faces))
print len(odf2)
assert_array_almost_equal(odf1, odf2)
#isotropic
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[0, 0, 0],
snr=None)
ds = DiffusionSpectrumModel(bvals, bvecs, odf_sphere)
dsfit = ds.fit(S)
QA = dsfit.qa
assert_equal(np.sum(QA > 0), 0)
