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():
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 create_data_2fibers(bvals,bvecs,d=0.0015,S0=100,angles=np.arange(0,92,5),snr=None):
data=np.zeros((len(angles),len(bvals)))
for (i,a) in enumerate(angles):
#2 two fibers
S,stics=SticksAndBall(bvals, bvecs, d, S0,
angles=[(0, 0),(a,0),(0,0)],
fractions=[50,50,0], snr=snr)
data[i]=S.copy()
return data
def multi_d_isotropic_data(bvals,bvecs,dvals=[0.0015],S0=100,snr=None):
print dvals, len(dvals)
#data=np.zeros((19,len(bvals)))
data=np.zeros((len(dvals),len(bvals)))
for i, d in enumerate(dvals):
#0 isotropic
S,stics=SticksAndBall(bvals, bvecs, d, S0,
angles=[(0, 0),(90,0),(90,90)],
fractions=[0,0,0], snr=snr)
data[i]=S.copy()
return data
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 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 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 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 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():
#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 create_data(d=0.0015, S0=100, snr=None):
data = np.zeros((16, 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 0, second 30
S, stics = SticksAndBall(bvals, bvecs, d, S0, angles=[(0, 0), (30, 0), (90, 90)], fractions=[50, 50, 0], snr=snr)
data[9] = S.copy()
# two fibers one at 0, second 30
S, stics = SticksAndBall(bvals, bvecs, d, S0, angles=[(0, 0), (30, 0), (90, 90)], fractions=[50, 30, 0], snr=snr)
data[10] = S.copy()
# 1 fiber with 80% isotropic
S, stics = SticksAndBall(bvals, bvecs, d, S0, angles=[(0, 0), (30, 0), (90, 90)], fractions=[20, 0, 0], snr=snr)
data[11] = S.copy()
# 1 fiber with tensor
evals = np.array([1.4, 0.35, 0.35]) * 10 ** (-3)
# evals=np.array([1.4,.2,.2])*10**(-3)
S = SingleTensor(bvals, bvecs, S0, evals=evals, evecs=np.eye(3), snr=snr)
data[12] = S.copy()
# 2 fibers with two tensors
evals = np.array([1.4, 0.35, 0.35]) * 10 ** (-3)
S = SingleTensor(bvals, bvecs, S0, evals=evals, evecs=np.eye(3), snr=None)
evals = np.array([0.35, 1.4, 0.35]) * 10 ** (-3)
S = SingleTensor(bvals, bvecs, S0, evals=evals, evecs=np.eye(3), snr=None) + S
# std=2*S0/snr
# S=S+np.random.randn(len(S))*std
data[13] = S.copy()
# 3 fibers with two tensors
evals = np.array([1.4, 0.35, 0.35]) * 10 ** (-3)
S = SingleTensor(bvals, bvecs, S0, evals=evals, evecs=np.eye(3), snr=None)
evals = np.array([0.35, 1.4, 0.35]) * 10 ** (-3)
S = SingleTensor(bvals, bvecs, S0, evals=evals, evecs=np.eye(3), snr=None) + S
evals = np.array([0.35, 0.35, 1.4]) * 10 ** (-3)
S = SingleTensor(bvals, bvecs, S0, evals=evals, evecs=np.eye(3), snr=None) + S
# std=2*S0/snr
# S=S+np.random.randn(len(S))*std
data[14] = S.copy()
evals = np.array([0.35, 0.35, 0.35]) * 10 ** (-3)
S = SingleTensor(bvals, bvecs, S0, evals=evals, evecs=np.eye(3), snr=snr)
data[15] = S.copy()
return data
def test():
# img=nib.load('/home/eg309/Data/project01_dsi/connectome_0001/tp1/RAWDATA/OUT/mr000001.nii.gz')
btable = np.loadtxt(get_data("dsi515btable"))
# volume size
sz = 16
# shifting
origin = 8
# hanning width
filter_width = 32.0
# number of signal sampling points
n = 515
# odf radius
radius = np.arange(2.1, 6, 0.2)
# create q-table
bv = btable[:, 0]
bmin = np.sort(bv)[1]
bv = np.sqrt(bv / bmin)
qtable = np.vstack((bv, bv, bv)).T * btable[:, 1:]
qtable = np.floor(qtable + 0.5)
# copy bvals and bvecs
bvals = btable[:, 0]
bvecs = btable[:, 1:]
# S=img.get_data()[38,50,20]#[96/2,96/2,20]
S, stics = SticksAndBall(
bvals, bvecs, d=0.0015, S0=100, angles=[(0, 0), (60, 0), (90, 90)], fractions=[0, 0, 0], snr=None
)
S2 = S.copy()
S2 = S2.reshape(1, len(S))
dn = DiffusionNabla(S2, bvals, bvecs, auto=False)
pR = dn.equators
odf = dn.odf(S)
# Xs=dn.precompute_interp_coords()
peaks, inds = peak_finding(odf.astype("f8"), dn.odf_faces.astype("uint16"))
print peaks
print peaks / peaks.min()
# print dn.PK
dn.fit()
print dn.PK
# """
ren = fvtk.ren()
colors = fvtk.colors(odf, "jet")
fvtk.add(ren, fvtk.point(dn.odf_vertices, colors, point_radius=0.05, theta=8, phi=8))
fvtk.show(ren)
# """
stop
# ds=DiffusionSpectrum(S2,bvals,bvecs)
# tpr=ds.pdf(S)
# todf=ds.odf(tpr)
"""
#show projected signal
Bvecs=np.concatenate([bvecs[1:],-bvecs[1:]])
X0=np.dot(np.diag(np.concatenate([S[1:],S[1:]])),Bvecs)
ren=fvtk.ren()
fvtk.add(ren,fvtk.point(X0,fvtk.yellow,1,2,16,16))
fvtk.show(ren)
"""
# qtable=5*matrix[:,1:]
# calculate radius for the hanning filter
r = np.sqrt(qtable[:, 0] ** 2 + qtable[:, 1] ** 2 + qtable[:, 2] ** 2)
# setting hanning filter width and hanning
hanning = 0.5 * np.cos(2 * np.pi * r / filter_width)
# center and index in q space volume
q = qtable + origin
q = q.astype("i8")
# apply the hanning filter
values = S * hanning
"""
#plot q-table
ren=fvtk.ren()
colors=fvtk.colors(values,'jet')
fvtk.add(ren,fvtk.point(q,colors,1,0.1,6,6))
fvtk.show(ren)
"""
# create the signal volume
Sq = np.zeros((sz, sz, sz))
for i in range(n):
Sq[q[i][0], q[i][1], q[i][2]] += values[i]
# apply fourier transform
Pr = fftshift(np.abs(np.real(fftn(fftshift(Sq), (sz, sz, sz)))))
# """
ren = fvtk.ren()
vol = fvtk.volume(Pr)
fvtk.add(ren, vol)
fvtk.show(ren)
# """
"""
from enthought.mayavi import mlab
mlab.pipeline.volume(mlab.pipeline.scalar_field(Sq))
mlab.show()
"""
# vertices, edges, faces = create_unit_sphere(5)
vertices, faces = sphere_vf_from("symmetric362")
odf = np.zeros(len(vertices))
for m in range(len(vertices)):
xi = origin + radius * vertices[m, 0]
yi = origin + radius * vertices[m, 1]
zi = origin + radius * vertices[m, 2]
PrI = map_coordinates(Pr, np.vstack((xi, yi, zi)), order=1)
for i in range(len(radius)):
odf[m] = odf[m] + PrI[i] * radius[i] ** 2
"""
ren=fvtk.ren()
colors=fvtk.colors(odf,'jet')
fvtk.add(ren,fvtk.point(vertices,colors,point_radius=.05,theta=8,phi=8))
fvtk.show(ren)
"""
"""
#Pr[Pr<500]=0
ren=fvtk.ren()
#ren.SetBackground(1,1,1)
fvtk.add(ren,fvtk.volume(Pr))
fvtk.show(ren)
"""
peaks, inds = peak_finding(odf.astype("f8"), faces.astype("uint16"))
Eq = np.zeros((sz, sz, sz))
for i in range(n):
Eq[q[i][0], q[i][1], q[i][2]] += S[i] / S[0]
LEq = laplace(Eq)
# Pr[Pr<500]=0
ren = fvtk.ren()
# ren.SetBackground(1,1,1)
fvtk.add(ren, fvtk.volume(Eq))
fvtk.show(ren)
phis = np.linspace(0, 2 * np.pi, 100)
planars = []
for phi in phis:
planars.append(sphere2cart(1, np.pi / 2, phi))
planars = np.array(planars)
planarsR = []
for v in vertices:
R = vec2vec_rotmat(np.array([0, 0, 1]), v)
planarsR.append(np.dot(R, planars.T).T)
"""
ren=fvtk.ren()
fvtk.add(ren,fvtk.point(planarsR[0],fvtk.green,1,0.1,8,8))
fvtk.add(ren,fvtk.point(2*planarsR[1],fvtk.red,1,0.1,8,8))
fvtk.show(ren)
"""
azimsums = []
for disk in planarsR:
diskshift = 4 * disk + origin
# Sq0=map_coordinates(Sq,diskshift.T,order=1)
# azimsums.append(np.sum(Sq0))
# Eq0=map_coordinates(Eq,diskshift.T,order=1)
# azimsums.append(np.sum(Eq0))
LEq0 = map_coordinates(LEq, diskshift.T, order=1)
azimsums.append(np.sum(LEq0))
azimsums = np.array(azimsums)
# """
ren = fvtk.ren()
colors = fvtk.colors(azimsums, "jet")
fvtk.add(ren, fvtk.point(vertices, colors, point_radius=0.05, theta=8, phi=8))
fvtk.show(ren)
# """
# for p in planarsR[0]:
"""
def create_data(bvals,bvecs,d=0.0015,S0=100,snr=None):
#data=np.zeros((19,len(bvals)))
data=np.zeros((2,len(bvals)))
#0 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()
'''
#1 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()
#2 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()
#3 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()
#4 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()
#5 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()
#6 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()
#7 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()
#8 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()
#9 two fibers one at 0, second 30
S,stics=SticksAndBall(bvals, bvecs, d, S0,
angles=[(0, 0),(45,0),(90,90)],
fractions=[50,50,0], snr=snr)
data[9]=S.copy()
#10 two fibers one at 0, second 30
S,stics=SticksAndBall(bvals, bvecs, d, S0,
angles=[(0, 0),(45,0),(90,90)],
fractions=[50,30,0], snr=snr)
data[10]=S.copy()
#11 one fiber with 80% isotropic
S,stics=SticksAndBall(bvals, bvecs, d, S0,
angles=[(0, 0),(30,0),(90,90)],
fractions=[20,0,0], snr=snr)
data[11]=S.copy()
#12 one fiber with tensor
evals=np.array([1.4,.35,.35])*10**(-3)
#evals=np.array([1.4,.2,.2])*10**(-3)
S=SingleTensor(bvals,bvecs,S0,evals=evals,evecs=np.eye(3),snr=snr)
data[12]=S.copy()
#13 three fibers with two tensors
evals=np.array([1.4,.35,.35])*10**(-3)
S=SingleTensor(bvals,bvecs,S0,evals=evals,evecs=np.eye(3),snr=None)
evals=np.array([.35,1.4,.35])*10**(-3)
S=SingleTensor(bvals,bvecs,S0,evals=evals,evecs=np.eye(3),snr=None)+S
#std=2*S0/snr
#S=S+np.random.randn(len(S))*std
data[13]=S.copy()
#14 three fibers with three tensors
evals=np.array([1.4,.35,.35])*10**(-3)
S=SingleTensor(bvals,bvecs,S0,evals=evals,evecs=np.eye(3),snr=None)
evals=np.array([.35,1.4,.35])*10**(-3)
S=SingleTensor(bvals,bvecs,S0,evals=evals,evecs=np.eye(3),snr=None)+S
evals=np.array([.35,.35,1.4])*10**(-3)
S=SingleTensor(bvals,bvecs,S0,evals=evals,evecs=np.eye(3),snr=None)+S
#std=2*S0/snr
#S=S+np.random.randn(len(S))*std
data[14]=S.copy()
'''
#15 isotropic with spherical tensor
#evals=np.array([.15,.15,.15])*10**(-2)
evals=np.array([d,d,d])
#evals=np.array([.35,.35,.35])*10**(-3)
#evals=np.array([.15,.15,.15])*10**(-2)
S=SingleTensor(bvals,bvecs,S0,evals=evals,evecs=np.eye(3),snr=snr)
#data[15]=S.copy()
data[1]=S.copy()
'''
#16 angles
angles=[(0,0),(60,0),(90,90)]
R=[sphere2cart(1,np.deg2rad(pair[0]),np.deg2rad(pair[1])) for pair in angles]
R=np.array(R)
R=R.T
#print R
evals=np.array([1.4,.35,.35])*10**(-3)
S=SingleTensor(bvals,bvecs,S0,evals=evals,evecs=R,snr=None)
evals=np.array([.35,1.4,.35])*10**(-3)
S=SingleTensor(bvals,bvecs,S0,evals=evals,evecs=R,snr=None)+S
evals=np.array([.35,.35,1.4])*10**(-3)
S=SingleTensor(bvals,bvecs,S0,evals=evals,evecs=R,snr=None)+S
data[16]=S.copy()
#17 angles
angles=[(0,0),(30,0),(90,90)]
R=[sphere2cart(1,np.deg2rad(pair[0]),np.deg2rad(pair[1])) for pair in angles]
R=np.array(R)
R=R.T
#print R
evals=np.array([1.4,.05,.05])*10**(-3)
S=SingleTensor(bvals,bvecs,S0,evals=evals,evecs=R,snr=None)
evals=np.array([.05,1.4,.05])*10**(-3)
S=SingleTensor(bvals,bvecs,S0,evals=evals,evecs=R,snr=None)+S
evals=np.array([.05,.05,1.4])*10**(-3)
S=SingleTensor(bvals,bvecs,S0,evals=evals,evecs=R,snr=None)+S
data[17]=S.copy()
#18 angles
angles=[(0,0),(20,0),(90,90)]
R=[sphere2cart(1,np.deg2rad(pair[0]),np.deg2rad(pair[1])) for pair in angles]
R=np.array(R)
R=R.T
#print R
evals=np.array([1.4,.05,.05])*10**(-3)
S=SingleTensor(bvals,bvecs,S0,evals=evals,evecs=R,snr=None)
evals=np.array([.05,1.4,.05])*10**(-3)
S=SingleTensor(bvals,bvecs,S0,evals=evals,evecs=R,snr=None)+S
evals=np.array([.05,.05,1.4])*10**(-3)
S=SingleTensor(bvals,bvecs,S0,evals=evals,evecs=R,snr=None)+S
data[18]=S.copy()
'''
return data
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_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)
