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)
bvals = btable[:, 0]
bvecs = btable[:, 1:]
img = nib.load(fname)
data = img.get_data()
print data.shape
mask = data[:, :, :, 0] > 50
#D=data[20:90,20:90,18:22]
#D=data[40:44,40:44,18:22]
#del data
D = data
from time import time
t0 = time()
ds = DiffusionSpectrum(D, bvals, bvecs, mask=mask)
t1 = time()
print t1 - t0, ' secs'
GFA = ds.gfa()
t2 = time()
ten = Tensor(D, bvals, bvecs, mask=mask)
t3 = time()
print t3 - t2, ' secs'
FA = ten.fa()
from dipy.tracking.propagation import EuDX
IN = ds.ind()
