def _apply_ad(self, cellwise_field) -> Ad_array:
""" Compute transmissibility via harmonic averaging over faces."""
# References to private variables
data = self._data
tpfa = self._tpfa
if data.get("Aavatsmark_transmissibilities", False):
raise RuntimeError(
"AD version of Aavatsmark_transmissibilities not implemented.")
# Get connectivity and grid based data
ci = tpfa.ci
ci_periodic = tpfa.ci_periodic
fc_cc = tpfa.fc_cc
dist_face_cell = np.power(np.power(fc_cc, 2).sum(axis=0), 0.5)
# Consider two cases: scalar and tensor valued fields.
# assert (cellwise_field.val, np.ndarray)
# Case 1: Scalar valued fields.
if len(cellwise_field.val.shape) == 1:
t_cf_val = cellwise_field.val[ci]
t_cf_jac = cellwise_field.jac[ci]
t_cf_val /= dist_face_cell
t_cf_jac /= dist_face_cell
# Case 2: Tensor valued fields.
elif len(cellwise_field.val.shape) == 3 and all(
[cellwise_field.val.shape[i] == 3 for i in range(0, 2)]):
t_cf_tensor_val = cellwise_field.val[::, ::, ci]
t_cf_tensor_jac = cellwise_field.jac[::, ::, ci]
tn_cf_val = (t_cf_tensor_val * fc_cc).sum(axis=1)
tn_cf_jac = (t_cf_tensor_jac * fc_cc).sum(axis=1)
ntn_cf_val = (tn_cf_val * fc_cc).sum(axis=0)
ntn_cf_jac = (tn_cf_jac * fc_cc).sum(axis=0)
dist_face_cell_3 = np.power(dist_face_cell, 3)
t_cf_val = np.divide(ntn_cf_val, dist_face_cell_3)
t_cf_jac = np.divide(ntn_cf_jac, dist_face_cell_3)
else:
raise RuntimeError("Type of cell-wise field not supported.")
# Continue with AD representation and utilize chain rule.
t_face = Ad_array(t_cf_val, sps.diags(t_cf_jac).tocsc())
# The final harmonic averaging using a linear operator representation of bincount.
# TODO test!
t_face = (self.bincount_fi_periodic * dist_face_cell) * (
(self.bincount_fi_periodic * t_face**(-1))**(-1))
# Project column space of t.jac onto the actual cell
# TODO is there not a better way to create the projection matrix? By correct indexing?
c = np.arange(len(ci_periodic))
proj = sps.coo_matrix((np.ones_like(c), (c, ci_periodic))).tocsr()
t_face.jac = t_face.jac * proj
return t_face
def test_copy_scalar(self):
a = Ad_array(1, 0)
b = a.copy()
self.assertTrue(a.val == b.val)
self.assertTrue(a.jac == b.jac)
a.val = 2
a.jac = 3
self.assertTrue(b.val == 1)
self.assertTrue(b.jac == 0)