Cleanup

Author: pbrubeck

demos/goal_based_adaptivity_bvp/goal_based_adaptivity_bvp.py.rst

@@ -71,7 +71,7 @@ and :math:`p+1`), and gives a provably efficient and reliable error estimator un
 It is possible to employ cheaper and more practical approximations by setting the parameters for the :code:`GoalAdaptiveNonlinearVariationalSolver`
 appropriately, as discussed below. ::
 
-    dwr_parameters = {
+    goal_adaptive_options = {
         "max_iterations": 100,
         "use_adjoint_residual": True,
         "dual_low_method": "solve",
@@ -88,9 +88,9 @@ the DWR automation can compute effectivity indices, but this is not generally re
     tolerance = 1e-4
     problem = NonlinearVariationalProblem(F, u, bcs)
 
-    adaptive_solver = GoalAdaptiveNonlinearVariationalSolver(problem, J, tolerance, dwr_parameters,
-                                                             exact_solution=u_exact,
-                                                             primal_solver_parameters=solver_parameters)
+    adaptive_solver = GoalAdaptiveNonlinearVariationalSolver(problem, J, tolerance, goal_adaptive_options,
+                                                             primal_solver_parameters=solver_parameters,
+                                                             exact_solution=u_exact)
     adaptive_solver.solve()
 
 The initial error in the goal functional is :math:`-3.5 \times 10^{-2}`. The solver terminates with the goal functional computed to :math:`10^{-4}` after 4 refinements. Each nonlinear solve only required one Newton iteration. The error estimates :math:`\eta` are very accurate: their effectivity indices