def setup(self):
g = CartGrid([5, 5])
g.compute_geometry()
perm = PermTensor(g.dim, np.ones(g.num_cells))
bnd = bc.BoundaryCondition(g)
flux, bound_flux, _, _ = mpfa.mpfa(g, perm, bnd, inverter="python")
return g, perm, bnd, flux, bound_flux
def test_one_cell_a_time_node_keyword(self):
# Update one and one cell, and verify that the result is the same as
# with a single computation.
# The test is similar to what will happen with a memory-constrained
# splitting.
g = CartGrid([3, 3])
g.compute_geometry()
# Assign random permeabilities, for good measure
np.random.seed(42)
kxx = np.random.random(g.num_cells)
kyy = np.random.random(g.num_cells)
# Ensure positive definiteness
kxy = np.random.random(g.num_cells) * kxx * kyy
perm = PermTensor(2, kxx=kxx, kyy=kyy, kxy=kxy)
flux = sps.csr_matrix((g.num_faces, g.num_cells))
bound_flux = sps.csr_matrix((g.num_faces, g.num_faces))
faces_covered = np.zeros(g.num_faces, np.bool)
bnd = bc.BoundaryCondition(g)
cn = g.cell_nodes()
for ci in range(g.num_cells):
ind = np.zeros(g.num_cells)
ind[ci] = 1
nodes = np.squeeze(np.where(cn * ind > 0))
partial_flux, partial_bound, _, _, active_faces = mpfa.mpfa_partial(
g, perm, bnd, nodes=nodes, inverter="python")
if np.any(faces_covered):
partial_flux[faces_covered, :] *= 0
partial_bound[faces_covered, :] *= 0
faces_covered[active_faces] = True
flux += partial_flux
bound_flux += partial_bound
flux_full, bound_flux_full, _, _ = mpfa.mpfa(g,
perm,
bnd,
inverter="python")
self.assertTrue((flux_full - flux).max() < 1e-8)
self.assertTrue((flux_full - flux).min() > -1e-8)
self.assertTrue((bound_flux - bound_flux_full).max() < 1e-8)
self.assertTrue((bound_flux - bound_flux_full).min() > -1e-8)