def test_to_matrix_Concatenation():
np.random.seed(0)
A = np.random.randn(2, 3)
B = np.random.randn(3, 4)
C = A.dot(B)
Aop = NumpyMatrixOperator(A)
Bop = NumpyMatrixOperator(B)
Cop = Concatenation([Aop, Bop])
assert_type_and_allclose(C, Cop, 'dense')
Aop = NumpyMatrixOperator(sps.csc_matrix(A))
Bop = NumpyMatrixOperator(B)
Cop = Concatenation([Aop, Bop])
assert_type_and_allclose(C, Cop, 'dense')
Aop = NumpyMatrixOperator(A)
Bop = NumpyMatrixOperator(sps.csc_matrix(B))
Cop = Concatenation([Aop, Bop])
assert_type_and_allclose(C, Cop, 'dense')
Aop = NumpyMatrixOperator(sps.csc_matrix(A))
Bop = NumpyMatrixOperator(sps.csc_matrix(B))
Cop = Concatenation([Aop, Bop])
assert_type_and_allclose(A, Aop, 'sparse')
def test_to_matrix_LincombOperator():
np.random.seed(0)
A = np.random.randn(3, 3)
B = np.random.randn(3, 2)
a = np.random.randn()
b = np.random.randn()
C = a * A + b * B.dot(B.T)
Aop = NumpyMatrixOperator(A)
Bop = NumpyMatrixOperator(B)
Cop = LincombOperator([Aop, Concatenation([Bop, Bop.T])], [a, b])
assert_type_and_allclose(C, Cop, 'dense')
Aop = NumpyMatrixOperator(sps.csc_matrix(A))
Bop = NumpyMatrixOperator(B)
Cop = LincombOperator([Aop, Concatenation([Bop, Bop.T])], [a, b])
assert_type_and_allclose(C, Cop, 'dense')
Aop = NumpyMatrixOperator(A)
Bop = NumpyMatrixOperator(sps.csc_matrix(B))
Cop = LincombOperator([Aop, Concatenation([Bop, Bop.T])], [a, b])
assert_type_and_allclose(C, Cop, 'dense')
Aop = NumpyMatrixOperator(sps.csc_matrix(A))
Bop = NumpyMatrixOperator(sps.csc_matrix(B))
Cop = LincombOperator([Aop, Concatenation([Bop, Bop.T])], [a, b])
assert_type_and_allclose(C, Cop, 'sparse')
def test_to_matrix():
np.random.seed(0)
A = np.random.randn(2, 2)
B = np.random.randn(3, 3)
C = np.random.randn(3, 3)
X = np.bmat([[np.eye(2) + A, np.zeros((2, 3))],
[np.zeros((3, 2)), B.dot(C.T)]])
C = sps.csc_matrix(C)
Aop = NumpyMatrixOperator(A)
Bop = NumpyMatrixOperator(B)
Cop = NumpyMatrixOperator(C)
Xop = BlockDiagonalOperator([
LincombOperator([IdentityOperator(NumpyVectorSpace(2)), Aop], [1, 1]),
Concatenation(Bop, AdjointOperator(Cop))
])
assert np.allclose(X, to_matrix(Xop))
assert np.allclose(X, to_matrix(Xop, format='csr').toarray())
np.random.seed(0)
V = np.random.randn(10, 2)
Vva = NumpyVectorArray(V.T)
Vop = VectorArrayOperator(Vva)
assert np.allclose(V, to_matrix(Vop))
Vop = VectorArrayOperator(Vva, transposed=True)
assert np.allclose(V, to_matrix(Vop).T)
def action_Concatenation(self,
op,
range_basis,
source_basis,
product=None):
if source_basis is not None and op.first.linear and not op.first.parametric:
V = op.first.apply(source_basis)
return self.apply(op.second, range_basis, V, product=product)
elif range_basis is not None and op.second.linear and not op.second.parametric:
if product:
range_basis = product.apply(range_basis)
V = op.second.apply_transpose(range_basis)
return self.apply(op.first, V, source_basis)
else:
projected_first = self.apply(op.first,
None,
source_basis,
product=None)
projected_second = self.apply(op.second,
range_basis,
None,
product=product)
return Concatenation(projected_second,
projected_first,
name=op.name)
def action_Concatenation(self, op):
if len(op.operators) == 1:
return self.apply(op.operators[0])
range_basis, source_basis, product = self.range_basis, self.source_basis, self.product
last, first = op.operators[0], op.operators[-1]
if source_basis is not None and first.linear and not first.parametric:
V = first.apply(source_basis)
return type(self)(range_basis, V, product).apply(
op.with_(operators=op.operators[:-1]))
elif range_basis is not None and last.linear and not last.parametric:
if product:
range_basis = product.apply(range_basis)
V = last.apply_adjoint(range_basis)
return type(self)(V, source_basis,
None).apply(op.with_(operators=op.operators[1:]))
else:
projected_first = type(self)(None, source_basis,
product=None).apply(first)
projected_last = type(self)(range_basis, None,
product=product).apply(last)
return Concatenation(
(projected_last, ) + op.operators[1:-1] + (projected_first, ),
name=op.name)
def project_operators(self):
fom = self.fom
RB = self.bases['RB']
product = self.products['RB']
if self.initial_data_product != product:
# TODO there should be functionality for this somewhere else
projection_matrix = RB.gramian(self.initial_data_product)
projection_op = NumpyMatrixOperator(projection_matrix)
inverse_projection_op = InverseOperator(projection_op, 'inverse_projection_op')
pid = project(fom.initial_data, range_basis=RB, source_basis=None, product=self.initial_data_product)
projected_initial_data = Concatenation([inverse_projection_op, pid])
else:
projected_initial_data = project(fom.initial_data, range_basis=RB, source_basis=None,
product=product)
projected_operators = {
'mass': None if fom.mass is None or self.product_is_mass else project(fom.mass, RB, RB),
'operator': project(fom.operator, RB, RB),
'rhs': project(fom.rhs, RB, None) if fom.rhs is not None else None,
'initial_data': projected_initial_data,
'products': {k: project(v, RB, RB) for k, v in fom.products.items()},
'output_functional': project(fom.output_functional, None, RB) if fom.output_functional else None
}
return projected_operators
def project_operators_to_subbasis(self, dims):
rom = self._last_rom
dim = dims['RB']
product = self.products['RB']
if self.initial_data_product != product:
# TODO there should be functionality for this somewhere else
pop = project_to_subbasis(rom.initial_data.operators[1], dim_range=dim, dim_source=None)
inverse_projection_op = InverseOperator(
project_to_subbasis(rom.initial_data.operators[0].operator, dim_range=dim, dim_source=dim),
name='inverse_projection_op'
)
projected_initial_data = Concatenation([inverse_projection_op, pop])
else:
projected_initial_data = project_to_subbasis(rom.initial_data, dim_range=dim, dim_source=None)
projected_operators = {
'mass': (None if rom.mass is None or self.product_is_mass else
project_to_subbasis(rom.mass, dim, dim)),
'operator': project_to_subbasis(rom.operator, dim, dim),
'rhs': project_to_subbasis(rom.rhs, dim, None) if rom.rhs is not None else None,
'initial_data': projected_initial_data,
'products': {k: project_to_subbasis(v, dim, dim) for k, v in rom.products.items()},
'output_functional': project_to_subbasis(rom.output_functional, None, dim) if rom.output_functional else None
}
return projected_operators
def jacobian(self, U, mu=None):
mu = self.parse_parameter(mu)
if len(self.interpolation_dofs) == 0:
if self.source.type == self.range.type == NumpyVectorArray:
return NumpyMatrixOperator(np.zeros((0, self.source.dim)), name=self.name + '_jacobian')
else:
return ZeroOperator(self.source, self.range, name=self.name + '_jacobian')
elif hasattr(self, 'operator'):
return EmpiricalInterpolatedOperator(self.operator.jacobian(U, mu=mu), self.interpolation_dofs,
self.collateral_basis, self.triangular, self.name + '_jacobian')
else:
U_components = NumpyVectorArray(U.components(self.source_dofs), copy=False)
JU = self.restricted_operator.jacobian(U_components, mu=mu) \
.apply(NumpyVectorArray(np.eye(len(self.source_dofs)), copy=False))
try:
if self.triangular:
interpolation_coefficients = solve_triangular(self.interpolation_matrix, JU.data.T,
lower=True, unit_diagonal=True).T
else:
interpolation_coefficients = np.linalg.solve(self.interpolation_matrix, JU._array.T).T
except ValueError: # this exception occurs when AU contains NaNs ...
interpolation_coefficients = np.empty((len(JU), len(self.collateral_basis))) + np.nan
J = self.collateral_basis.lincomb(interpolation_coefficients)
if isinstance(J, NumpyVectorArray):
J = NumpyMatrixOperator(J.data.T)
else:
J = VectorArrayOperator(J, copy=False)
return Concatenation(J, ComponentProjection(self.source_dofs, self.source), name=self.name + '_jacobian')
def jacobian(self, U, mu=None):
mu = self.parse_parameter(mu)
options = self.solver_options.get('jacobian') if self.solver_options else None
if len(self.interpolation_dofs) == 0:
if isinstance(self.source, NumpyVectorSpace) and isinstance(self.range, NumpyVectorSpace):
return NumpyMatrixOperator(np.zeros((self.range.dim, self.source.dim)), solver_options=options,
source_id=self.source.id, range_id=self.range.id,
name=self.name + '_jacobian')
else:
return ZeroOperator(self.range, self.source, name=self.name + '_jacobian')
elif hasattr(self, 'operator'):
return EmpiricalInterpolatedOperator(self.operator.jacobian(U, mu=mu), self.interpolation_dofs,
self.collateral_basis, self.triangular,
solver_options=options, name=self.name + '_jacobian')
else:
restricted_source = self.restricted_operator.source
U_dofs = restricted_source.make_array(U.dofs(self.source_dofs))
JU = self.restricted_operator.jacobian(U_dofs, mu=mu) \
.apply(restricted_source.make_array(np.eye(len(self.source_dofs))))
try:
if self.triangular:
interpolation_coefficients = solve_triangular(self.interpolation_matrix, JU.to_numpy().T,
lower=True, unit_diagonal=True).T
else:
interpolation_coefficients = solve(self.interpolation_matrix, JU.to_numpy().T).T
except ValueError: # this exception occurs when AU contains NaNs ...
interpolation_coefficients = np.empty((len(JU), len(self.collateral_basis))) + np.nan
J = self.collateral_basis.lincomb(interpolation_coefficients)
if isinstance(J.space, NumpyVectorSpace):
J = NumpyMatrixOperator(J.to_numpy().T, range_id=self.range.id)
else:
J = VectorArrayOperator(J)
return Concatenation([J, ComponentProjection(self.source_dofs, self.source)],
solver_options=options, name=self.name + '_jacobian')
def __matmul__(self, other):
if not isinstance(other, OperatorInterface):
return NotImplemented
from pymor.operators.constructions import Concatenation
if isinstance(other, Concatenation):
return NotImplemented
else:
return Concatenation((self, other))
def restricted(self, dofs):
source_dofs = np.setdiff1d(np.union1d(self.grid.neighbours(0, 0)[dofs].ravel(), dofs),
np.array([-1], dtype=np.int32),
assume_unique=True)
sub_grid = SubGrid(self.grid, entities=source_dofs)
sub_boundary_info = SubGridBoundaryInfo(sub_grid, self.grid, self.boundary_info)
op = self.with_(grid=sub_grid, boundary_info=sub_boundary_info, name='{}_restricted'.format(self.name))
sub_grid_indices = sub_grid.indices_from_parent_indices(dofs, codim=0)
proj = ComponentProjection(sub_grid_indices, op.range)
return Concatenation(proj, op), sub_grid.parent_indices(0)
def collect_functional_ranges(op, image):
if isinstance(op, (LincombOperator, SelectionOperator)):
for o in op.operators:
collect_functional_ranges(o, image)
elif isinstance(op, AdjointOperator):
operator = Concatenation(
op.range_product,
op.operator) if op.range_product else op.operator
collect_operator_ranges(operator, NumpyVectorArray(np.ones(1)),
image)
elif op.linear and not op.parametric:
image.append(op.as_vector())
else:
raise ImageCollectionError(op)
def action_Concatenation(self, op):
if len(op.operators) == 1:
return self.apply(op.operators[0])
range_basis, source_basis = self.range_basis, self.source_basis
last, first = op.operators[0], op.operators[-1]
if source_basis is not None and first.linear and not first.parametric:
V = first.apply(source_basis)
return project(op.with_(operators=op.operators[:-1]), range_basis, V)
elif range_basis is not None and last.linear and not last.parametric:
V = last.apply_adjoint(range_basis)
return project(op.with_(operators=op.operators[1:]), V, source_basis)
else:
projected_first = project(first, None, source_basis)
projected_last = project(last, range_basis, None)
return Concatenation((projected_last,) + op.operators[1:-1] + (projected_first,), name=op.name)
def assemble_estimator_diffusive_flux_bb(grid, ii, subdomain_rt_spaces,
lambda_hat, kappa,
local_rt_projection):
diffusive_flux_bb_product = make_diffusive_flux_bb_product(
grid,
ii,
subdomain_rt_spaces[ii],
lambda_hat,
kappa=kappa,
over_integrate=2)
subdomain_walker = make_subdomain_walker(grid, ii)
subdomain_walker.append(diffusive_flux_bb_product)
subdomain_walker.walk()
# subdomain_rt_spaces[ii].dof_communicator,
matrix = DuneXTMatrixOperator(diffusive_flux_bb_product.matrix(),
range_id='LOCALRT_{}'.format(ii),
source_id='LOCALRT_{}'.format(ii))
return Concatenation([local_rt_projection.T, matrix, local_rt_projection],
name='diffusive_flux_bb_{}'.format(ii))
def thermalblock_concatenation_factory(xblocks, yblocks, diameter, seed):
from pymor.operators.constructions import Concatenation
op, mu, U, V, sp, rp = thermalblock_factory(xblocks, yblocks, diameter,
seed)
op = Concatenation(sp, op)
return op, mu, U, V, sp, rp
def discretize(grid_and_problem_data, solver_options, mpi_comm):
################ Setup
logger = getLogger('discretize_elliptic_block_swipdg.discretize')
logger.info('discretizing ... ')
grid, boundary_info = grid_and_problem_data['grid'], grid_and_problem_data[
'boundary_info']
local_all_dirichlet_boundary_info = make_subdomain_boundary_info(
grid, {'type': 'xt.grid.boundaryinfo.alldirichlet'})
local_subdomains, num_local_subdomains, num_global_subdomains = _get_subdomains(
grid)
local_all_neumann_boundary_info = make_subdomain_boundary_info(
grid, {'type': 'xt.grid.boundaryinfo.allneumann'})
block_space = make_block_dg_space(grid)
global_rt_space = make_rt_space(grid)
subdomain_rt_spaces = [
global_rt_space.restrict_to_dd_subdomain_view(grid, ii)
for ii in range(num_global_subdomains)
]
local_patterns = [
block_space.local_space(ii).compute_pattern('face_and_volume')
for ii in range(block_space.num_blocks)
]
coupling_patterns = {
'in_in': {},
'out_out': {},
'in_out': {},
'out_in': {}
}
coupling_matrices = {
'in_in': {},
'out_out': {},
'in_out': {},
'out_in': {}
}
for ii in range(num_global_subdomains):
ii_size = block_space.local_space(ii).size()
for jj in grid.neighboring_subdomains(ii):
jj_size = block_space.local_space(jj).size()
if ii < jj: # Assemble primally (visit each coupling only once).
coupling_patterns['in_in'][(ii, jj)] = block_space.local_space(
ii).compute_pattern('face_and_volume')
coupling_patterns['out_out'][(
ii, jj)] = block_space.local_space(jj).compute_pattern(
'face_and_volume')
coupling_patterns['in_out'][(
ii, jj)] = block_space.compute_coupling_pattern(
ii, jj, 'face')
coupling_patterns['out_in'][(
ii, jj)] = block_space.compute_coupling_pattern(
jj, ii, 'face')
coupling_matrices['in_in'][(ii, jj)] = Matrix(
ii_size, ii_size, coupling_patterns['in_in'][(ii, jj)])
coupling_matrices['out_out'][(ii, jj)] = Matrix(
jj_size, jj_size, coupling_patterns['out_out'][(ii, jj)])
coupling_matrices['in_out'][(ii, jj)] = Matrix(
ii_size, jj_size, coupling_patterns['in_out'][(ii, jj)])
coupling_matrices['out_in'][(ii, jj)] = Matrix(
jj_size, ii_size, coupling_patterns['out_in'][(ii, jj)])
boundary_patterns = {}
for ii in grid.boundary_subdomains():
boundary_patterns[ii] = block_space.local_space(ii).compute_pattern(
'face_and_volume')
################ Assemble LHS and RHS
lambda_, kappa = grid_and_problem_data['lambda'], grid_and_problem_data[
'kappa']
if isinstance(lambda_, dict):
lambda_funcs = lambda_['functions']
lambda_coeffs = lambda_['coefficients']
else:
lambda_funcs = [
lambda_,
]
lambda_coeffs = [
1,
]
logger.debug('block op ... ')
ops, block_ops = zip(*(discretize_lhs(
lf, grid, block_space, local_patterns, boundary_patterns,
coupling_matrices, kappa, local_all_neumann_boundary_info,
boundary_info, coupling_patterns, solver_options)
for lf in lambda_funcs))
global_operator = LincombOperator(ops,
lambda_coeffs,
solver_options=solver_options,
name='GlobalOperator')
logger.debug('block op global done ')
block_op = LincombOperator(block_ops,
lambda_coeffs,
name='lhs',
solver_options=solver_options)
logger.debug('block op done ')
f = grid_and_problem_data['f']
if isinstance(f, dict):
f_funcs = f['functions']
f_coeffs = f['coefficients']
else:
f_funcs = [
f,
]
f_coeffs = [
1,
]
rhss, block_rhss = zip(*(discretize_rhs(
ff, grid, block_space, global_operator, block_ops, block_op)
for ff in f_funcs))
global_rhs = LincombOperator(rhss, f_coeffs)
block_rhs = LincombOperator(block_rhss, f_coeffs)
solution_space = block_op.source
################ Assemble interpolation and reconstruction operators
logger.info('discretizing interpolation ')
# Oswald interpolation error operator
oi_op = BlockDiagonalOperator([
OswaldInterpolationErrorOperator(ii, block_op.source, grid,
block_space)
for ii in range(num_global_subdomains)
],
name='oswald_interpolation_error')
# Flux reconstruction operator
fr_op = LincombOperator([
BlockDiagonalOperator([
FluxReconstructionOperator(ii, block_op.source, grid, block_space,
global_rt_space, subdomain_rt_spaces,
lambda_xi, kappa)
for ii in range(num_global_subdomains)
]) for lambda_xi in lambda_funcs
],
lambda_coeffs,
name='flux_reconstruction')
################ Assemble inner products and error estimator operators
logger.info('discretizing inner products ')
lambda_bar, lambda_hat = grid_and_problem_data[
'lambda_bar'], grid_and_problem_data['lambda_hat']
mu_bar, mu_hat = grid_and_problem_data['mu_bar'], grid_and_problem_data[
'mu_hat']
operators = {}
local_projections = []
local_rt_projections = []
local_oi_projections = []
local_div_ops = []
local_l2_products = []
data = dict(grid=grid,
block_space=block_space,
local_projections=local_projections,
local_rt_projections=local_rt_projections,
local_oi_projections=local_oi_projections,
local_div_ops=local_div_ops,
local_l2_products=local_l2_products)
for ii in range(num_global_subdomains):
neighborhood = grid.neighborhood_of(ii)
################ Assemble local inner products
local_dg_space = block_space.local_space(ii)
# we want a larger pattern to allow for axpy with other matrices
tmp_local_matrix = Matrix(
local_dg_space.size(), local_dg_space.size(),
local_dg_space.compute_pattern('face_and_volume'))
local_energy_product_ops = []
local_energy_product_coeffs = []
for func, coeff in zip(lambda_funcs, lambda_coeffs):
local_energy_product_ops.append(
make_elliptic_matrix_operator(func,
kappa,
tmp_local_matrix.copy(),
local_dg_space,
over_integrate=0))
local_energy_product_coeffs.append(coeff)
local_energy_product_ops.append(
make_penalty_product_matrix_operator(
grid,
ii,
local_all_dirichlet_boundary_info,
local_dg_space,
func,
kappa,
over_integrate=0))
local_energy_product_coeffs.append(coeff)
local_l2_product = make_l2_matrix_operator(tmp_local_matrix.copy(),
local_dg_space)
del tmp_local_matrix
local_assembler = make_system_assembler(local_dg_space)
for local_product_op in local_energy_product_ops:
local_assembler.append(local_product_op)
local_assembler.append(local_l2_product)
local_assembler.assemble()
local_energy_product_name = 'local_energy_dg_product_{}'.format(ii)
local_energy_product = LincombOperator([
DuneXTMatrixOperator(op.matrix(),
source_id='domain_{}'.format(ii),
range_id='domain_{}'.format(ii))
for op in local_energy_product_ops
],
local_energy_product_coeffs,
name=local_energy_product_name)
operators[local_energy_product_name] = \
local_energy_product.assemble(mu_bar).with_(name=local_energy_product_name)
local_l2_product = DuneXTMatrixOperator(
local_l2_product.matrix(),
source_id='domain_{}'.format(ii),
range_id='domain_{}'.format(ii))
local_l2_products.append(local_l2_product)
# assemble local elliptic product
matrix = make_local_elliptic_matrix_operator(grid, ii, local_dg_space,
lambda_bar, kappa)
matrix.assemble()
local_elliptic_product = DuneXTMatrixOperator(
matrix.matrix(),
range_id='domain_{}'.format(ii),
source_id='domain_{}'.format(ii))
################ Assemble local to global projections
# assemble projection (solution space) -> (ii space)
local_projection = BlockProjectionOperator(block_op.source, ii)
local_projections.append(local_projection)
# assemble projection (RT spaces on neighborhoods of subdomains) -> (local RT space on ii)
ops = np.full(num_global_subdomains, None)
for kk in neighborhood:
component = grid.neighborhood_of(kk).index(ii)
assert fr_op.range.subspaces[kk].subspaces[
component].id == 'LOCALRT_{}'.format(ii)
ops[kk] = BlockProjectionOperator(fr_op.range.subspaces[kk],
component)
local_rt_projection = BlockRowOperator(
ops,
source_spaces=fr_op.range.subspaces,
name='local_rt_projection_{}'.format(ii))
local_rt_projections.append(local_rt_projection)
# assemble projection (OI spaces on neighborhoods of subdomains) -> (ii space)
ops = np.full(num_global_subdomains, None)
for kk in neighborhood:
component = grid.neighborhood_of(kk).index(ii)
assert oi_op.range.subspaces[kk].subspaces[
component].id == 'domain_{}'.format(ii)
ops[kk] = BlockProjectionOperator(oi_op.range.subspaces[kk],
component)
local_oi_projection = BlockRowOperator(
ops,
source_spaces=oi_op.range.subspaces,
name='local_oi_projection_{}'.format(ii))
local_oi_projections.append(local_oi_projection)
################ Assemble additional operators for error estimation
# assemble local divergence operator
local_rt_space = global_rt_space.restrict_to_dd_subdomain_view(
grid, ii)
local_div_op = make_divergence_matrix_operator_on_subdomain(
grid, ii, local_dg_space, local_rt_space)
local_div_op.assemble()
local_div_op = DuneXTMatrixOperator(
local_div_op.matrix(),
source_id='LOCALRT_{}'.format(ii),
range_id='domain_{}'.format(ii),
name='local_divergence_{}'.format(ii))
local_div_ops.append(local_div_op)
################ Assemble error estimator operators -- Nonconformity
operators['nc_{}'.format(ii)] = \
Concatenation([local_oi_projection.T, local_elliptic_product, local_oi_projection],
name='nonconformity_{}'.format(ii))
################ Assemble error estimator operators -- Residual
if len(f_funcs) == 1:
assert f_coeffs[0] == 1
local_div = Concatenation([local_div_op, local_rt_projection])
local_rhs = VectorFunctional(
block_rhs.operators[0]._array._blocks[ii])
operators['r_fd_{}'.format(ii)] = \
Concatenation([local_rhs, local_div], name='r1_{}'.format(ii))
operators['r_dd_{}'.format(ii)] = \
Concatenation([local_div.T, local_l2_product, local_div], name='r2_{}'.format(ii))
################ Assemble error estimator operators -- Diffusive flux
operators['df_aa_{}'.format(ii)] = LincombOperator(
[
assemble_estimator_diffusive_flux_aa(
lambda_xi, lambda_xi_prime, grid, ii, block_space,
lambda_hat, kappa, solution_space)
for lambda_xi in lambda_funcs
for lambda_xi_prime in lambda_funcs
], [
ProductParameterFunctional([c1, c2]) for c1 in lambda_coeffs
for c2 in lambda_coeffs
],
name='diffusive_flux_aa_{}'.format(ii))
operators['df_bb_{}'.format(
ii)] = assemble_estimator_diffusive_flux_bb(
grid, ii, subdomain_rt_spaces, lambda_hat, kappa,
local_rt_projection)
operators['df_ab_{}'.format(ii)] = LincombOperator(
[
assemble_estimator_diffusive_flux_ab(
lambda_xi, grid, ii, block_space, subdomain_rt_spaces,
lambda_hat, kappa, local_rt_projection, local_projection)
for lambda_xi in lambda_funcs
],
lambda_coeffs,
name='diffusive_flux_ab_{}'.format(ii))
################ Final assembly
logger.info('final assembly ')
# instantiate error estimator
min_diffusion_evs = np.array([
min_diffusion_eigenvalue(grid, ii, lambda_hat, kappa)
for ii in range(num_global_subdomains)
])
subdomain_diameters = np.array(
[subdomain_diameter(grid, ii) for ii in range(num_global_subdomains)])
if len(f_funcs) == 1:
assert f_coeffs[0] == 1
local_eta_rf_squared = np.array([
apply_l2_product(grid,
ii,
f_funcs[0],
f_funcs[0],
over_integrate=2)
for ii in range(num_global_subdomains)
])
else:
local_eta_rf_squared = None
estimator = EllipticEstimator(grid,
min_diffusion_evs,
subdomain_diameters,
local_eta_rf_squared,
lambda_coeffs,
mu_bar,
mu_hat,
fr_op,
oswald_interpolation_error=oi_op,
mpi_comm=mpi_comm)
l2_product = BlockDiagonalOperator(local_l2_products)
# instantiate discretization
neighborhoods = [
grid.neighborhood_of(ii) for ii in range(num_global_subdomains)
]
local_boundary_info = make_subdomain_boundary_info(
grid_and_problem_data['grid'],
{'type': 'xt.grid.boundaryinfo.alldirichlet'})
d = DuneDiscretization(global_operator=global_operator,
global_rhs=global_rhs,
neighborhoods=neighborhoods,
enrichment_data=(grid, local_boundary_info, lambda_,
kappa, f, block_space),
operator=block_op,
rhs=block_rhs,
visualizer=DuneGDTVisualizer(block_space),
operators=operators,
products={'l2': l2_product},
estimator=estimator,
data=data)
parameter_range = grid_and_problem_data['parameter_range']
logger.info('final assembly B')
d = d.with_(parameter_space=CubicParameterSpace(
d.parameter_type, parameter_range[0], parameter_range[1]))
logger.info('final assembly C')
return d, data
def discretize(grid_and_problem_data, T, nt):
d, d_data = discretize_ell(grid_and_problem_data)
assert isinstance(d.parameter_space, CubicParameterSpace)
parameter_range = grid_and_problem_data['parameter_range']
block_space = d_data['block_space']
# assemble global L2 product
l2_mat = d.global_operator.operators[0].matrix.copy(
) # to ensure matching pattern
l2_mat.scal(0.)
for ii in range(block_space.num_blocks):
local_l2_product = d.l2_product._blocks[ii, ii]
block_space.mapper.copy_local_to_global(
local_l2_product.matrix, local_l2_product.matrix.pattern(), ii,
l2_mat)
mass = d.l2_product
operators = {
k: v
for k, v in d.operators.items() if k not in d.special_operators
}
global_mass = DuneXTMatrixOperator(l2_mat)
local_div_ops, local_l2_products, local_projections, local_rt_projections = \
d_data['local_div_ops'], d_data['local_l2_products'], d_data['local_projections'], d_data['local_rt_projections']
for ii in range(d_data['grid'].num_subdomains):
local_div = Concatenation(
[local_div_ops[ii], local_rt_projections[ii]])
operators['r_ud_{}'.format(ii)] = \
Concatenation([local_projections[ii].T, local_l2_products[ii], local_div], name='r_ud_{}'.format(ii))
operators['r_l2_{}'.format(ii)] = \
Concatenation([local_projections[ii].T, local_l2_products[ii], local_projections[ii]],
name='r_l2_{}'.format(ii))
e = d.estimator
estimator = ParabolicEstimator(e.min_diffusion_evs, e.subdomain_diameters,
e.local_eta_rf_squared, e.lambda_coeffs,
e.mu_bar, e.mu_hat, e.flux_reconstruction,
e.oswald_interpolation_error)
d = InstationaryDuneDiscretization(
d.global_operator,
d.global_rhs,
global_mass,
T,
d.operator.source.zeros(1),
d.operator,
d.rhs,
mass=mass,
time_stepper=ImplicitEulerTimeStepper(nt=nt,
solver_options='operator'),
products=d.products,
operators=operators,
estimator=estimator,
visualizer=DuneGDTVisualizer(block_space))
d = d.with_(parameter_space=CubicParameterSpace(
d.parameter_type, parameter_range[0], parameter_range[1]))
return d, d_data
