def solve(self, h, v):
# Update and solve full problem
p = self.full.solve()
# Obtain the reduced solution matrix and solve. First discretize
# the eliminated problem (without intersections) to obtain flux
# discretizations for backcalculation. These could in principle have
# been obtained from the full discretization.
self.el.solve()
# Then get the Schur complement eliminated lhs and rhs from the
# existing full discretization
perform_condensation(self.full, self.el, 1)
# And solve
self.el.x = sps.linalg.spsolve(self.el.lhs, self.el.rhs)
# Evaluate condition numbers
self.full.cond = SC.sparse_condition_number(self.full.lhs)
self.el.cond = SC.sparse_condition_number(self.el.lhs)
return p, self.el.x
)
e = [
global_error(gb, p1, p2[:p1.size]),
global_error(gb, t[-1], t_mp[-1][:p1.size]),
]
return e
if __name__ == "__main__":
main_folder = "results"
gb = define_grid()
assign_data(gb, FlowData, "problem")
edge_params(gb)
gb_full = gb
eldim = 0
gb_el, el_data = gb.duplicate_without_dimension(eldim)
Problems = BothProblems(gb)
other = Problems.flow
Problems_el = BothProblems(gb_el, "_el")
p, t = Problems.solve_and_save()
p_el, t_el = Problems_el.solve_and_save()
cond_full = SC.sparse_condition_number(Problems.flow.lhs)
cond_el = SC.sparse_condition_number(Problems_el.flow.lhs)
print("Condition numbers", cond_full, cond_el, cond_full / cond_el)
diff = t[-1][:p_el.size] - t_el[-1]
print(np.amax(np.absolute(diff)), np.sum(np.absolute(diff)))
errors = evaluate_errors(p_el, p, t_el, t, gb_el)
print(errors)