def test_predict_dki():
with nbtmp.InTemporaryDirectory() as tmpdir:
fbval = op.join(tmpdir, 'dki.bval')
fbvec = op.join(tmpdir, 'dki.bvec')
fdata = op.join(tmpdir, 'dki.nii.gz')
make_dki_data(fbval, fbvec, fdata)
file_dict = dki.fit_dki(fdata, fbval, fbvec, out_dir=tmpdir)
params_file = file_dict['params']
gtab = dpg.gradient_table(fbval, fbvec)
predict_fname = dki.predict(params_file, gtab, S0_file=fdata,
out_dir=tmpdir)
prediction = nib.load(predict_fname).get_data()
npt.assert_almost_equal(prediction, nib.load(fdata).get_data())
def test_fit_dki():
fdata, fbval, fbvec = dpd.get_data('small_101D')
with nbtmp.InTemporaryDirectory() as tmpdir:
file_dict = dki.fit_dki(fdata, fbval, fbvec, out_dir=tmpdir)
for f in file_dict.values():
op.exists(f)