def test_peak_finding():
vertices, faces=get_sphere('symmetric724')
odf=np.zeros(len(vertices))
odf = np.abs(vertices.sum(-1))
odf[1] = 10.
odf[505] = 505.
odf[143] = 143.
peaks, inds=peak_finding(odf.astype('f8'), faces.astype('uint16'))
print peaks, inds
edges = unique_edges(faces)
peaks, inds = local_maxima(odf, edges)
print peaks, inds
vertices_half, edges_half, faces_half = reduce_antipodal(vertices, faces)
n = len(vertices_half)
peaks, inds = local_maxima(odf[:n], edges_half)
print peaks, inds
mevals=np.array(([0.0015,0.0003,0.0003],
[0.0015,0.0003,0.0003]))
e0=np.array([1,0,0.])
e1=np.array([0.,1,0])
mevecs=[all_tensor_evecs(e0),all_tensor_evecs(e1)]
odf = multi_tensor_odf(vertices, [0.5,0.5], mevals, mevecs)
peaks, inds=peak_finding(odf, faces)
print peaks, inds
peaks2, inds2 = local_maxima(odf[:n], edges_half)
print peaks2, inds2
assert_equal(len(peaks), 2)
assert_equal(len(peaks2), 2)
def test_dni_eit():
btable = np.loadtxt(get_data('dsi515btable'))
bvals = btable[:, 0]
bvecs = btable[:, 1:]
data, descr = sim_data(bvals, bvecs)
#load odf sphere
vertices, faces = sphere_vf_from('symmetric724')
edges = unique_edges(faces)
half_vertices, half_edges, half_faces = reduce_antipodal(vertices, faces)
#create the sphere
odf_sphere = (vertices, faces)
dn = DiffusionNablaModel(bvals, bvecs, odf_sphere)
dn.relative_peak_threshold = 0.5
dn.angular_distance_threshold = 20
dnfit = dn.fit(data)
print('DiffusionNablaModel')
for i, d in enumerate(data):
print descr[i], np.sum(dnfit.peak_values[i] > 0)
ei = EquatorialInversionModel(bvals, bvecs, odf_sphere)
ei.relative_peak_threshold = 0.3
ei.angular_distance_threshold = 15
ei.set_operator('laplacian')
eifit = ei.fit(data, return_odf=True)
print('EquatorialInversionModel')
for i, d in enumerate(data):
print descr[i], np.sum(eifit.peak_values[i] > 0)
assert_equal(descr[i][1], np.sum(eifit.peak_values[i] > 0))
from dipy.viz import show_odfs
show_odfs(eifit.odf[None, None, :, :], (vertices, faces), scale=2)
def test_dni_eit():
btable=np.loadtxt(get_data('dsi515btable'))
bvals=btable[:,0]
bvecs=btable[:,1:]
data,descr=sim_data(bvals,bvecs)
#load odf sphere
vertices,faces = sphere_vf_from('symmetric724')
edges = unique_edges(faces)
half_vertices,half_edges,half_faces=reduce_antipodal(vertices,faces)
#create the sphere
odf_sphere=(vertices,faces)
dn=DiffusionNablaModel(bvals,bvecs,odf_sphere)
dn.relative_peak_threshold = 0.5
dn.angular_distance_threshold = 20
dnfit=dn.fit(data)
print('DiffusionNablaModel')
for i,d in enumerate(data):
print descr[i], np.sum(dnfit.peak_values[i]>0)
ei=EquatorialInversionModel(bvals,bvecs,odf_sphere)
ei.relative_peak_threshold = 0.3
ei.angular_distance_threshold = 15
ei.set_operator('laplacian')
eifit = ei.fit(data,return_odf=True)
print('EquatorialInversionModel')
for i,d in enumerate(data):
print descr[i], np.sum(eifit.peak_values[i]>0)
assert_equal(descr[i][1], np.sum(eifit.peak_values[i]>0))
from dipy.viz import show_odfs
show_odfs(eifit.odf[None,None,:,:], (vertices,faces), scale=2)
def test_local_maxima():
vertices, faces=get_sphere('symmetric724')
edges = unique_edges(faces)
odf = abs(vertices.sum(-1))
odf[1] = 10.
odf[143] = 143.
odf[505] = 505
peak_values, peak_index = local_maxima(odf, edges)
assert_array_equal(peak_values, [505, 143, 10])
assert_array_equal(peak_index, [505, 143, 1])
vertices_half, edges_half, faces_half = reduce_antipodal(vertices, faces)
odf = abs(vertices_half.sum(-1))
odf[1] = 10.
odf[143] = 143.
peak_value, peak_index = local_maxima(odf, edges_half)
assert_array_equal(peak_value, [143, 10])
assert_array_equal(peak_index, [143, 1])
odf[20] = np.nan
assert_raises(ValueError, local_maxima, odf, edges_half)
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)
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)
