def _run_interface(self, runtime):
self._time_series = img_to_signals(
self.inputs.in_file,
self.inputs.atlas_file,
mask_file=self.inputs.mask_file,
background_label=self.inputs.background_label,
min_n_voxels=self.inputs.min_n_voxels,
)
df = pd.DataFrame(self._time_series)
self._cov_mat = np.asarray(df.cov())
self._corr_mat = np.asarray(df.corr())
try:
self._pcorr_mat = prec_to_partial(np.linalg.inv(self._cov_mat))
except np.linalg.LinAlgError as e:
logging.getLogger("pipeline").warning("LinAlgError %s", e)
self._pcorr_mat = np.full_like(self._cov_mat, np.nan)
return runtime
def test_prec_to_partial():
prec = np.array([[2., -1., 1.], [-1., 2., -1.], [1., -1., 1.]])
partial = np.array([[1., .5, -sqrt(2.) / 2.], [.5, 1., sqrt(2.) / 2.],
[-sqrt(2.) / 2., sqrt(2.) / 2., 1.]])
assert_array_almost_equal(prec_to_partial(prec), partial)
def test_prec_to_partial():
prec = np.array([[2., -1., 1.], [-1., 2., -1.], [1., -1., 1.]])
partial = np.array([[1., .5, -sqrt(2.) / 2.], [.5, 1., sqrt(2.) / 2.],
[-sqrt(2.) / 2., sqrt(2.) / 2., 1.]])
assert_array_almost_equal(prec_to_partial(prec), partial)