cpp_disc = ElasticityExample(refine_steps=7)
param = {"lambda": [1.], "mu": [1.]}
# u = cpp_disc.solve(param)
parameter_type = Parameter(param).parameter_type
# cpp_disc.visualize(u, "highdim_solution_cpp.vtk")
lambda_fn, mu_fn = [GenericParameterFunctional(lambda mu: mu[n], Parameter({n: [1.]}).parameter_type) for n in ['lambda', 'mu']]
LOW, HIGH = 1, 10
ops = [DealIIMatrixOperator(getattr(cpp_disc, name)()) for name in ['lambda_mat', 'mu_mat']]
op = LincombOperator(ops, (lambda_fn, mu_fn))
rhs = VectorFunctional(ListVectorArray([DealIIVector(cpp_disc.rhs())]))
viz = PyVis(cpp_disc)
h1_op = DealIIMatrixOperator(cpp_disc.h1_mat(), "h1_0_semi")
energy_op = DealIIMatrixOperator(cpp_disc.mu_mat(), "energy")
py_disc = StationaryDiscretization(op, rhs, products={"energy": energy_op},
visualizer=viz,
parameter_space=CubicParameterSpace(parameter_type, LOW, HIGH))
coercivity_estimator = ExpressionParameterFunctional("max(mu)", parameter_type)
reductor = partial(reduce_stationary_coercive,
error_product=energy_op, coercivity_estimator=coercivity_estimator)
greedy_data = greedy(py_disc, reductor, py_disc.parameter_space.sample_uniformly(3),
use_estimator=True,
extension_algorithm=gram_schmidt_basis_extension, max_extensions=3)
rb_disc, reconstructor = greedy_data['reduced_discretization'], greedy_data['reconstructor']
half = (HIGH - LOW) / 2.
values = itertools.product((LOW, HIGH, half), (LOW, HIGH, half))
