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"""Unit tests for error control"""
# Copyright (C) 2011 Marie E. Rognes
#
# This file is part of DOLFIN.
#
# DOLFIN is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# DOLFIN is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with DOLFIN. If not, see <http://www.gnu.org/licenses/>.
import pytest
from ufl.algorithms import replace
from dolfin import *
from dolfin.fem.adaptivesolving import *
from dolfin_utils.test import skip_in_parallel
# FIXME: Move this to dolfin for user access?
def reconstruct_refined_form(form, functions, mesh):
function_mapping = {}
for u in functions:
w = Function(u.leaf_node().function_space())
w.assign(u.leaf_node())
function_mapping[u] = w
domain = mesh.leaf_node().ufl_domain()
newform = replace_integral_domains(replace(form, function_mapping), domain)
return newform, function_mapping
# This must be scope function, because the tests will modify some of the objects,
# including the mesh which gets its hierarchial adapted submeshes attached.
fixt = pytest.fixture(scope="function")
@fixt
def mesh():
return UnitSquareMesh(8, 8)
@fixt
def V(mesh):
return FunctionSpace(mesh, "Lagrange", 1)
@fixt
def u(V):
return Function(V)
@fixt
def a(V):
v = TestFunction(V)
u = TrialFunction(V)
return inner(grad(u), grad(v))*dx()
@fixt
def L(V):
v = TestFunction(V)
f = Expression("10*exp(-(pow(x[0] - 0.5, 2) + pow(x[1] - 0.5, 2)) / 0.02)", degree=1)
g = Expression("sin(5*x[0])", degree=1)
return f*v*dx() + g*v*ds()
@fixt
def problem(a, L, u, V):
bc = [DirichletBC(V, 0.0, "x[0] < DOLFIN_EPS || x[0] > 1.0 - DOLFIN_EPS")]
return LinearVariationalProblem(a, L, u, bc)
@fixt
def goal(u):
return u*dx()
@fixt
def ec(problem, goal):
return generate_error_control(problem, goal)
@skip_in_parallel
def test_check_domains(goal, mesh, a, L):
# Asserting that domains are ok before trying error control generation
msg = "Expecting only the domain from the mesh to get here through u."
assert len(goal.ufl_domains()) == 1, msg
assert goal.ufl_domains()[0] == mesh.ufl_domain(), msg
assert len(a.ufl_domains()) == 1, msg
assert a.ufl_domains()[0] == mesh.ufl_domain(), msg
assert len(L.ufl_domains()) == 1, msg
assert L.ufl_domains()[0] == mesh.ufl_domain(), msg
@skip_in_parallel
def test_error_estimation(problem, u, ec):
# Solve variational problem once
solver = LinearVariationalSolver(problem)
solver.solve()
# Compute error estimate
error_estimate = ec.estimate_error(u, problem.bcs())
# Compare estimate with defined reference
reference = 0.0011789985750808342
assert round(error_estimate - reference, 7) == 0
@skip_in_parallel
def test_error_indicators(problem, u, mesh):
# Solve variational problem once
solver = LinearVariationalSolver(problem)
solver.solve()
# Compute error indicators
indicators = Vector(mesh.mpi_comm(), u.function_space().mesh().num_cells())
indicators[0] = 1.0
#ec.compute_indicators(indicators, u) #
reference = 1.0 # FIXME
assert round(indicators.sum() - reference, 7) == 0
@skip_in_parallel
def _test_adaptive_solve(problem, goal, u, mesh):
# Solve problem adaptively
solver = AdaptiveLinearVariationalSolver(problem, goal)
tol = 0.00087
solver.solve(tol)
# Note: This old approach is now broken, as it doesn't change the integration domain:
#M = replace(goal, {u: w})
# This new approach handles the integration domain properly:
M, fm = reconstruct_refined_form(goal, [u], mesh)
# Compare computed goal with reference
reference = 0.12583303389560166
assert round(assemble(M) - reference, 7) == 0
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