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()