def test_fit_data():
fdata, fbval, fbvec = dpd.get_fnames('small_25')
fstreamlines = dpd.get_fnames('small_25_streamlines')
gtab = grad.gradient_table(fbval, fbvec)
ni_data = nib.load(fdata)
data = ni_data.get_data()
tensor_streamlines = nib.streamlines.load(fstreamlines).streamlines
tensor_streamlines = move_streamlines(tensor_streamlines, np.eye(4),
ni_data.affine)
life_model = life.FiberModel(gtab)
life_fit = life_model.fit(data, tensor_streamlines)
model_error = life_fit.predict() - life_fit.data
model_rmse = np.sqrt(np.mean(model_error**2, -1))
matlab_rmse, matlab_weights = dpd.matlab_life_results()
# Lower error than the matlab implementation for these data:
npt.assert_(np.median(model_rmse) < np.median(matlab_rmse))
# And a moderate correlation with the Matlab implementation weights:
npt.assert_(np.corrcoef(matlab_weights, life_fit.beta)[0, 1] > 0.6)
def test_fit_data():
fdata, fbval, fbvec = dpd.get_data('small_25')
gtab = grad.gradient_table(fbval, fbvec)
ni_data = nib.load(fdata)
data = ni_data.get_data()
dtmodel = dti.TensorModel(gtab)
dtfit = dtmodel.fit(data)
sphere = dpd.get_sphere()
peak_idx = dti.quantize_evecs(dtfit.evecs, sphere.vertices)
eu = edx.EuDX(dtfit.fa.astype('f8'), peak_idx,
seeds=list(nd.ndindex(data.shape[:-1])),
odf_vertices=sphere.vertices, a_low=0)
tensor_streamlines = [streamline for streamline in eu]
life_model = life.FiberModel(gtab)
life_fit = life_model.fit(data, tensor_streamlines)
model_error = life_fit.predict() - life_fit.data
model_rmse = np.sqrt(np.mean(model_error ** 2, -1))
matlab_rmse, matlab_weights = dpd.matlab_life_results()
# Lower error than the matlab implementation for these data:
npt.assert_(np.median(model_rmse) < np.median(matlab_rmse))
# And a moderate correlation with the Matlab implementation weights:
npt.assert_(np.corrcoef(matlab_weights, life_fit.beta)[0, 1] > 0.6)
def test_fit_data():
fdata, fbval, fbvec = dpd.get_data('small_25')
gtab = grad.gradient_table(fbval, fbvec)
ni_data = nib.load(fdata)
data = ni_data.get_data()
dtmodel = dti.TensorModel(gtab)
dtfit = dtmodel.fit(data)
sphere = dpd.get_sphere()
peak_idx = dti.quantize_evecs(dtfit.evecs, sphere.vertices)
eu = edx.EuDX(dtfit.fa.astype('f8'),
peak_idx,
seeds=list(nd.ndindex(data.shape[:-1])),
odf_vertices=sphere.vertices,
a_low=0)
tensor_streamlines = [streamline for streamline in eu]
life_model = life.FiberModel(gtab)
life_fit = life_model.fit(data, tensor_streamlines)
model_error = life_fit.predict() - life_fit.data
model_rmse = np.sqrt(np.mean(model_error**2, -1))
matlab_rmse, matlab_weights = dpd.matlab_life_results()
# Lower error than the matlab implementation for these data:
npt.assert_(np.median(model_rmse) < np.median(matlab_rmse))
# And a moderate correlation with the Matlab implementation weights:
npt.assert_(np.corrcoef(matlab_weights, life_fit.beta)[0, 1] > 0.68)
