def _AffineExpansionStorage(args: (
tuple_of(Form),
tuple_of(Matrix.Type()),
tuple_of(Vector.Type()),
tuple_of((Form, Matrix.Type())),
tuple_of((Form, Vector.Type()))
)):
return AffineExpansionStorage_Form(args)
def _product(thetas: ThetaType, operators: tuple_of(Matrix.Type())):
output = tensor_copy(operators[0])
output.zero()
for (theta, operator) in zip(thetas, operators):
theta = float(theta)
output += theta * operator
return ProductOutput(output)
def _evaluate(expression: (Matrix.Type(), Vector.Type(), Function.Type(),
TensorsList, FunctionsList,
ParametrizedTensorFactory,
ParametrizedExpressionFactory),
at: (ReducedMesh, ReducedVertices, None) = None,
**kwargs):
return evaluate_base(expression, at, **kwargs)
def _init_lhs(self, lhs: Matrix.Type(),
bcs: (list_of(DirichletBC), ProductOutputDirichletBC,
dict_of(str, list_of(DirichletBC)),
dict_of(str, ProductOutputDirichletBC))):
# Create a copy of lhs, in order not to change
# the original references when applying bcs
self.lhs = lhs.copy()
def __init__(self, args):
AffineExpansionStorage_Base.__init__(self, args)
content = list()
for arg in args:
if isinstance(arg, Form):
content.append(assemble(arg, keep_diagonal=True))
elif isinstance(arg, (Matrix.Type(), Vector.Type())):
content.append(arg)
else:
raise RuntimeError("Invalid argument to AffineExpansionStorage")
self._content = tuple(content)
def _abs(matrix: Matrix.Type()):
# Note: PETSc offers a method MatGetRowMaxAbs, but it is not wrapped in petsc4py. We do the same by hand
mat = to_petsc4py(matrix)
row_start, row_end = mat.getOwnershipRange()
i_max, j_max = None, None
value_max = None
for i in range(row_start, row_end):
cols, vals = mat.getRow(i)
for (c, v) in zip(cols, vals):
if value_max is None or fabs(v) > fabs(value_max):
i_max = i
j_max = c
value_max = v
assert i_max is not None
assert j_max is not None
assert value_max is not None
#
mpi_comm = mat.comm.tompi4py()
(global_value_max, global_ij_max) = parallel_max(value_max, (i_max, j_max), fabs, mpi_comm)
return AbsOutput(global_value_max, global_ij_max)
def _export(
solution: (
Function.Type(),
Matrix.Type(),
Vector.Type()
),
directory: (
Folders.Folder,
str
),
filename: str,
suffix: (
int,
None
) = None,
component: (
int,
str,
None
) = None
):
export_base(solution, directory, filename, suffix, component)
# along with RBniCS. If not, see <http://www.gnu.org/licenses/>.
#
from petsc4py import PETSc
from ufl import Form
from dolfin import __version__ as dolfin_version, as_backend_type, assemble, compile_cpp_code, DirichletBC, Function, FunctionSpace, PETScMatrix, PETScVector, SLEPcEigenSolver
from rbnics.backends.dolfin.evaluate import evaluate
from rbnics.backends.dolfin.matrix import Matrix
from rbnics.backends.dolfin.parametrized_tensor_factory import ParametrizedTensorFactory
from rbnics.backends.dolfin.wrapping.dirichlet_bc import ProductOutputDirichletBC
from rbnics.backends.abstract import EigenSolver as AbstractEigenSolver
from rbnics.utils.decorators import BackendFor, dict_of, list_of, overload
@BackendFor("dolfin",
inputs=(FunctionSpace, (Form, Matrix.Type(),
ParametrizedTensorFactory),
(Form, Matrix.Type(), ParametrizedTensorFactory,
None), (list_of(DirichletBC), ProductOutputDirichletBC,
dict_of(str, list_of(DirichletBC)),
dict_of(str, ProductOutputDirichletBC), None)))
class EigenSolver(AbstractEigenSolver):
def __init__(self, V, A, B=None, bcs=None):
self.V = V
if bcs is not None:
self._set_boundary_conditions(bcs)
A = self._assemble_if_form(A)
if B is not None:
B = self._assemble_if_form(B)
self._set_operators(A, B)
if self.B is not None:
function_extend_or_restrict, function_from_ufl_operators,
function_save, get_function_subspace, to_petsc4py)
from rbnics.backends.dolfin.wrapping.tensor_save import basic_tensor_save
from rbnics.utils.decorators import backend_for, ModuleWrapper, overload
from rbnics.utils.io import Folders
backend = ModuleWrapper(Function, Matrix, Vector)
wrapping_for_wrapping = ModuleWrapper(build_dof_map_writer_mapping, form_argument_space, to_petsc4py,
form_name=form_name)
tensor_save = basic_tensor_save(backend, wrapping_for_wrapping)
wrapping = ModuleWrapper(function_extend_or_restrict, function_save, get_function_subspace, tensor_save=tensor_save)
export_base = basic_export(backend, wrapping)
# Export a solution to file
@backend_for("dolfin", inputs=((Function.Type(), Matrix.Type(), Operator, Vector.Type()), (Folders.Folder, str),
str, (int, None), (int, str, None)))
def export(solution, directory, filename, suffix=None, component=None):
_export(solution, directory, filename, suffix, component)
@overload
def _export(
solution: (
Function.Type(),
Matrix.Type(),
Vector.Type()
),
directory: (
Folders.Folder,
str
def _evaluate(expression: (Matrix.Type(), Vector.Type(), TensorsList),
at: ReducedMesh, **kwargs):
assert len(kwargs) == 0
return evaluate_base(expression, at)
from rbnics.backends.dolfin.vector import Vector
from rbnics.backends.dolfin.wrapping import (build_dof_map_reader_mapping,
form_argument_space,
function_extend_or_restrict,
function_load, get_function_space,
get_function_subspace,
to_petsc4py)
from rbnics.backends.dolfin.wrapping.tensor_load import basic_tensor_load
from rbnics.utils.decorators import backend_for, ModuleWrapper
from rbnics.utils.io import Folders
backend = ModuleWrapper(Function, Matrix, Vector)
wrapping_for_wrapping = ModuleWrapper(build_dof_map_reader_mapping,
form_argument_space, to_petsc4py)
tensor_load = basic_tensor_load(backend, wrapping_for_wrapping)
wrapping = ModuleWrapper(function_extend_or_restrict,
function_load,
get_function_space,
get_function_subspace,
tensor_load=tensor_load)
import_base = basic_import_(backend, wrapping)
# Import a solution from file
@backend_for("dolfin",
inputs=((Function.Type(), Matrix.Type(), Vector.Type()),
(Folders.Folder, str), str, (int, None), (int, str, None))
)
def import_(solution, directory, filename, suffix=None, component=None):
import_base(solution, directory, filename, suffix, component)
# Copyright (C) 2015-2021 by the RBniCS authors
#
# This file is part of RBniCS.
#
# SPDX-License-Identifier: LGPL-3.0-or-later
from rbnics.backends.basic import copy as basic_copy
from rbnics.backends.dolfin.function import Function
from rbnics.backends.dolfin.matrix import Matrix
from rbnics.backends.dolfin.vector import Vector
from rbnics.backends.dolfin.wrapping import function_copy, tensor_copy
from rbnics.utils.decorators import backend_for, list_of, ModuleWrapper
backend = ModuleWrapper(Function, Matrix, Vector)
wrapping = ModuleWrapper(function_copy, tensor_copy)
copy_base = basic_copy(backend, wrapping)
@backend_for("dolfin",
inputs=((Function.Type(), list_of(Function.Type()), Matrix.Type(),
Vector.Type()), ))
def copy(arg):
return copy_base(arg)
# Copyright (C) 2015-2021 by the RBniCS authors
#
# This file is part of RBniCS.
#
# SPDX-License-Identifier: LGPL-3.0-or-later
from ufl.core.operator import Operator
from dolfin import assign as dolfin_assign
from rbnics.backends.dolfin.function import Function
from rbnics.backends.dolfin.matrix import Matrix
from rbnics.backends.dolfin.vector import Vector
from rbnics.backends.dolfin.wrapping import function_from_ufl_operators, to_petsc4py
from rbnics.utils.decorators import backend_for, list_of, overload
@backend_for("dolfin", inputs=((Function.Type(), list_of(Function.Type()), Matrix.Type(), Vector.Type()),
(Function.Type(), list_of(Function.Type()), Matrix.Type(), Operator, Vector.Type())))
def assign(object_to, object_from):
_assign(object_to, object_from)
@overload
def _assign(object_to: Function.Type(), object_from: Function.Type()):
if object_from is not object_to:
dolfin_assign(object_to, object_from)
@overload
def _assign(object_to: Function.Type(), object_from: Operator):
dolfin_assign(object_to, function_from_ufl_operators(object_from))
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with RBniCS. If not, see <http://www.gnu.org/licenses/>.
#
from ufl import Form
from dolfin import assemble, DirichletBC
from rbnics.backends.abstract import AffineExpansionStorage as AbstractAffineExpansionStorage
from rbnics.backends.dolfin.matrix import Matrix
from rbnics.backends.dolfin.vector import Vector
from rbnics.backends.dolfin.function import Function
from rbnics.utils.decorators import backend_for, list_of, overload, tuple_of
# Generic backend
@backend_for("dolfin", inputs=((tuple_of(list_of(DirichletBC)), tuple_of(Form), tuple_of(Function.Type()), tuple_of(Matrix.Type()), tuple_of(Vector.Type()), tuple_of((Form, Matrix.Type())), tuple_of((Form, Vector.Type()))), ))
def AffineExpansionStorage(args):
return _AffineExpansionStorage(args)
# Base implementation
class AffineExpansionStorage_Base(AbstractAffineExpansionStorage):
def __init__(self, args):
self._content = None
def __getitem__(self, key):
return self._content[key]
def __iter__(self):
return self._content.__iter__()
def __len__(self):
def _init_lhs(self, lhs: Matrix.Type(), bcs: None):
self.lhs = lhs
#
from ufl import Form
from dolfin import assemble, DirichletBC, PETScLUSolver
from rbnics.backends.abstract import LinearSolver as AbstractLinearSolver, LinearProblemWrapper
from rbnics.backends.dolfin.evaluate import evaluate
from rbnics.backends.dolfin.function import Function
from rbnics.backends.dolfin.matrix import Matrix
from rbnics.backends.dolfin.parametrized_tensor_factory import ParametrizedTensorFactory
from rbnics.backends.dolfin.vector import Vector
from rbnics.backends.dolfin.wrapping.dirichlet_bc import ProductOutputDirichletBC
from rbnics.utils.decorators import BackendFor, dict_of, list_of, overload
@BackendFor("dolfin",
inputs=((Form, Matrix.Type(), ParametrizedTensorFactory,
LinearProblemWrapper), Function.Type(),
(Form, ParametrizedTensorFactory, Vector.Type(),
None), (list_of(DirichletBC), ProductOutputDirichletBC,
dict_of(str, list_of(DirichletBC)),
dict_of(str, ProductOutputDirichletBC), None)))
class LinearSolver(AbstractLinearSolver):
@overload((Form, Matrix.Type(), ParametrizedTensorFactory),
Function.Type(),
(Form, ParametrizedTensorFactory, Vector.Type()),
(list_of(DirichletBC), ProductOutputDirichletBC,
dict_of(str, list_of(DirichletBC)),
dict_of(str, ProductOutputDirichletBC), None))
def __init__(self, lhs, solution, rhs, bcs=None):
self.solution = solution
self._init_lhs(lhs, bcs)
from ufl.core.operator import Operator
from rbnics.backends.basic import transpose as basic_transpose
from rbnics.backends.dolfin.basis_functions_matrix import BasisFunctionsMatrix
from rbnics.backends.dolfin.evaluate import evaluate
from rbnics.backends.dolfin.function import Function
from rbnics.backends.dolfin.functions_list import FunctionsList
from rbnics.backends.dolfin.matrix import Matrix
from rbnics.backends.dolfin.non_affine_expansion_storage import NonAffineExpansionStorage
from rbnics.backends.dolfin.parametrized_tensor_factory import ParametrizedTensorFactory
from rbnics.backends.dolfin.tensors_list import TensorsList
from rbnics.backends.dolfin.vector import Vector
from rbnics.backends.dolfin.wrapping import function_from_ufl_operators, function_to_vector, matrix_mul_vector, vector_mul_vector, vectorized_matrix_inner_vectorized_matrix
from rbnics.backends.online import OnlineMatrix, OnlineVector
from rbnics.utils.decorators import backend_for, ModuleWrapper
def AdditionalIsFunction(arg):
return isinstance(arg, Operator)
def ConvertAdditionalFunctionTypes(arg):
assert isinstance(arg, Operator)
return function_from_ufl_operators(arg)
backend = ModuleWrapper(BasisFunctionsMatrix, evaluate, Function, FunctionsList, Matrix, NonAffineExpansionStorage, ParametrizedTensorFactory, TensorsList, Vector)
wrapping = ModuleWrapper(function_to_vector, matrix_mul_vector, vector_mul_vector, vectorized_matrix_inner_vectorized_matrix)
online_backend = ModuleWrapper(OnlineMatrix=OnlineMatrix, OnlineVector=OnlineVector)
online_wrapping = ModuleWrapper()
transpose_base = basic_transpose(backend, wrapping, online_backend, online_wrapping, AdditionalIsFunction, ConvertAdditionalFunctionTypes)
@backend_for("dolfin", inputs=((BasisFunctionsMatrix, Function.Type(), FunctionsList, Matrix.Type(), Operator, ParametrizedTensorFactory, TensorsList, Vector.Type()), ))
def transpose(arg):
return transpose_base(arg)
def _assign(object_to: Matrix.Type(), object_from: Matrix.Type()):
if object_from is not object_to:
to_petsc4py(object_from).copy(to_petsc4py(object_to), to_petsc4py(object_to).Structure.SAME_NONZERO_PATTERN)
def _assemble_if_form(mat: Matrix.Type()):
return mat
# Copyright (C) 2015-2021 by the RBniCS authors
#
# This file is part of RBniCS.
#
# SPDX-License-Identifier: LGPL-3.0-or-later
from ufl import Form
from ufl.core.operator import Operator
from dolfin import FunctionSpace
from rbnics.backends.basic import GramSchmidt as BasicGramSchmidt
from rbnics.backends.dolfin.function import Function
from rbnics.backends.dolfin.matrix import Matrix
from rbnics.backends.dolfin.transpose import transpose
from rbnics.backends.dolfin.wrapping import (function_extend_or_restrict, function_from_ufl_operators,
get_function_subspace, gram_schmidt_projection_step)
from rbnics.utils.decorators import BackendFor, dict_of, ModuleWrapper, overload
backend = ModuleWrapper(Function, transpose)
wrapping = ModuleWrapper(function_extend_or_restrict, get_function_subspace, gram_schmidt_projection_step)
GramSchmidt_Base = BasicGramSchmidt(backend, wrapping)
@BackendFor("dolfin", inputs=(FunctionSpace, (Form, Matrix.Type()), (str, None)))
class GramSchmidt(GramSchmidt_Base):
@overload(Operator, (None, str, dict_of(str, str)))
def _extend_or_restrict_if_needed(self, function, component):
function = function_from_ufl_operators(function)
return GramSchmidt_Base._extend_or_restrict_if_needed(self, function, component)
#
# SPDX-License-Identifier: LGPL-3.0-or-later
from ufl import Form
from dolfin import assemble, DirichletBC, PETScLUSolver
from rbnics.backends.abstract import LinearSolver as AbstractLinearSolver, LinearProblemWrapper
from rbnics.backends.dolfin.evaluate import evaluate
from rbnics.backends.dolfin.function import Function
from rbnics.backends.dolfin.matrix import Matrix
from rbnics.backends.dolfin.parametrized_tensor_factory import ParametrizedTensorFactory
from rbnics.backends.dolfin.vector import Vector
from rbnics.backends.dolfin.wrapping.dirichlet_bc import ProductOutputDirichletBC
from rbnics.utils.decorators import BackendFor, dict_of, list_of, overload
@BackendFor("dolfin", inputs=((Form, Matrix.Type(), ParametrizedTensorFactory, LinearProblemWrapper),
Function.Type(), (Form, ParametrizedTensorFactory, Vector.Type(), None),
(list_of(DirichletBC), ProductOutputDirichletBC, dict_of(str, list_of(DirichletBC)),
dict_of(str, ProductOutputDirichletBC), None)))
class LinearSolver(AbstractLinearSolver):
@overload((Form, Matrix.Type(), ParametrizedTensorFactory),
Function.Type(), (Form, ParametrizedTensorFactory, Vector.Type()),
(list_of(DirichletBC), ProductOutputDirichletBC, dict_of(str, list_of(DirichletBC)),
dict_of(str, ProductOutputDirichletBC), None))
def __init__(self, lhs, solution, rhs, bcs=None):
self.solution = solution
self._init_lhs(lhs, bcs)
self._init_rhs(rhs, bcs)
self._apply_bcs(bcs)
self._linear_solver = "default"
self.monitor = None
# You should have received a copy of the GNU Lesser General Public License
# along with RBniCS. If not, see <http://www.gnu.org/licenses/>.
#
from math import fabs
from ufl.core.operator import Operator
from rbnics.backends.dolfin.matrix import Matrix
from rbnics.backends.dolfin.vector import Vector
from rbnics.backends.dolfin.function import Function
from rbnics.backends.dolfin.wrapping import function_from_ufl_operators, get_global_dof_coordinates, get_global_dof_component, to_petsc4py
from rbnics.utils.decorators import backend_for, overload
from rbnics.utils.mpi import parallel_max
# abs function to compute maximum absolute value of an expression, matrix or vector (for EIM). To be used in combination with max
# even though here we actually carry out both the max and the abs!
@backend_for("dolfin", inputs=((Matrix.Type(), Vector.Type(), Function.Type(), Operator), ))
def abs(expression):
return _abs(expression)
@overload
def _abs(matrix: Matrix.Type()):
# Note: PETSc offers a method MatGetRowMaxAbs, but it is not wrapped in petsc4py. We do the same by hand
mat = to_petsc4py(matrix)
row_start, row_end = mat.getOwnershipRange()
i_max, j_max = None, None
value_max = None
for i in range(row_start, row_end):
cols, vals = mat.getRow(i)
for (c, v) in zip(cols, vals):
if value_max is None or fabs(v) > fabs(value_max):
i_max = i
class LinearSolver(AbstractLinearSolver):
@overload((Form, Matrix.Type(), ParametrizedTensorFactory),
Function.Type(), (Form, ParametrizedTensorFactory, Vector.Type()),
(list_of(DirichletBC), ProductOutputDirichletBC, dict_of(str, list_of(DirichletBC)),
dict_of(str, ProductOutputDirichletBC), None))
def __init__(self, lhs, solution, rhs, bcs=None):
self.solution = solution
self._init_lhs(lhs, bcs)
self._init_rhs(rhs, bcs)
self._apply_bcs(bcs)
self._linear_solver = "default"
self.monitor = None
@overload(LinearProblemWrapper, Function.Type())
def __init__(self, problem_wrapper, solution):
self.__init__(problem_wrapper.matrix_eval(), solution, problem_wrapper.vector_eval(), problem_wrapper.bc_eval())
self.monitor = problem_wrapper.monitor
@overload(Form, (list_of(DirichletBC), ProductOutputDirichletBC, dict_of(str, list_of(DirichletBC)),
dict_of(str, ProductOutputDirichletBC), None))
def _init_lhs(self, lhs, bcs):
self.lhs = assemble(lhs, keep_diagonal=True)
@overload(ParametrizedTensorFactory, (list_of(DirichletBC), ProductOutputDirichletBC,
dict_of(str, list_of(DirichletBC)), dict_of(str, ProductOutputDirichletBC),
None))
def _init_lhs(self, lhs, bcs):
self.lhs = evaluate(lhs)
@overload(Matrix.Type(), None)
def _init_lhs(self, lhs, bcs):
self.lhs = lhs
@overload(Matrix.Type(), (list_of(DirichletBC), ProductOutputDirichletBC, dict_of(str, list_of(DirichletBC)),
dict_of(str, ProductOutputDirichletBC)))
def _init_lhs(self, lhs, bcs):
# Create a copy of lhs, in order not to change
# the original references when applying bcs
self.lhs = lhs.copy()
@overload(Form, (list_of(DirichletBC), ProductOutputDirichletBC, dict_of(str, list_of(DirichletBC)),
dict_of(str, ProductOutputDirichletBC), None))
def _init_rhs(self, rhs, bcs):
self.rhs = assemble(rhs)
@overload(ParametrizedTensorFactory, (list_of(DirichletBC), ProductOutputDirichletBC,
dict_of(str, list_of(DirichletBC)), dict_of(str, ProductOutputDirichletBC),
None))
def _init_rhs(self, rhs, bcs):
self.rhs = evaluate(rhs)
@overload(Vector.Type(), None)
def _init_rhs(self, rhs, bcs):
self.rhs = rhs
@overload(Vector.Type(), (list_of(DirichletBC), ProductOutputDirichletBC, dict_of(str, list_of(DirichletBC)),
dict_of(str, ProductOutputDirichletBC)))
def _init_rhs(self, rhs, bcs):
# Create a copy of rhs, in order not to change
# the original references when applying bcs
self.rhs = rhs.copy()
@overload(None)
def _apply_bcs(self, bcs):
pass
@overload((list_of(DirichletBC), ProductOutputDirichletBC))
def _apply_bcs(self, bcs):
for bc in bcs:
bc.apply(self.lhs, self.rhs)
@overload((dict_of(str, list_of(DirichletBC)), dict_of(str, ProductOutputDirichletBC)))
def _apply_bcs(self, bcs):
for key in bcs:
for bc in bcs[key]:
bc.apply(self.lhs, self.rhs)
def set_parameters(self, parameters):
assert len(parameters) in (0, 1)
if len(parameters) == 1:
assert "linear_solver" in parameters
self._linear_solver = parameters.get("linear_solver", "default")
def solve(self):
solver = PETScLUSolver(self._linear_solver)
solver.solve(self.lhs, self.solution.vector(), self.rhs)
if self.monitor is not None:
self.monitor(self.solution)
evaluate_sparse_vector_at_dofs,
expression_on_reduced_mesh=expression_on_reduced_mesh,
expression_on_truth_mesh=expression_on_truth_mesh,
form_on_reduced_function_space=form_on_reduced_function_space,
form_on_truth_function_space=form_on_truth_function_space)
online_backend = ModuleWrapper(OnlineFunction=OnlineFunction,
OnlineMatrix=OnlineMatrix,
OnlineVector=OnlineVector)
online_wrapping = ModuleWrapper()
evaluate_base = basic_evaluate(backend, wrapping, online_backend,
online_wrapping)
# Evaluate a parametrized expression, possibly at a specific location
@backend_for("dolfin",
inputs=((Matrix.Type(), Vector.Type(), Function.Type(), Operator,
TensorsList, FunctionsList, ParametrizedTensorFactory,
ParametrizedExpressionFactory), (ReducedMesh,
ReducedVertices, None)))
def evaluate(expression, at=None, **kwargs):
return _evaluate(expression, at, **kwargs)
@overload
def _evaluate(expression: (Matrix.Type(), Vector.Type(), TensorsList),
at: ReducedMesh, **kwargs):
assert len(kwargs) == 0
return evaluate_base(expression, at)
@overload
def AdditionalIsFunction(arg):
return isinstance(arg, Operator)
def ConvertAdditionalFunctionTypes(arg):
assert isinstance(arg, Operator)
return function_from_ufl_operators(arg)
backend = ModuleWrapper(BasisFunctionsMatrix, evaluate, Function,
FunctionsList, Matrix, NonAffineExpansionStorage,
ParametrizedTensorFactory, TensorsList, Vector)
wrapping = ModuleWrapper(function_to_vector, matrix_mul_vector,
vector_mul_vector,
vectorized_matrix_inner_vectorized_matrix)
online_backend = ModuleWrapper(OnlineMatrix=OnlineMatrix,
OnlineVector=OnlineVector)
online_wrapping = ModuleWrapper()
transpose_base = basic_transpose(backend, wrapping, online_backend,
online_wrapping, AdditionalIsFunction,
ConvertAdditionalFunctionTypes)
@backend_for("dolfin",
inputs=((BasisFunctionsMatrix, Function.Type(), FunctionsList,
Matrix.Type(), Operator, ParametrizedTensorFactory,
TensorsList, Vector.Type()), ))
def transpose(arg):
return transpose_base(arg)
#
# SPDX-License-Identifier: LGPL-3.0-or-later
from ufl import Form
from ufl.core.operator import Operator
from dolfin import FunctionSpace
from rbnics.backends.basic import GramSchmidt as BasicGramSchmidt
from rbnics.backends.dolfin.function import Function
from rbnics.backends.dolfin.matrix import Matrix
from rbnics.backends.dolfin.transpose import transpose
from rbnics.backends.dolfin.wrapping import (function_extend_or_restrict,
function_from_ufl_operators,
get_function_subspace,
gram_schmidt_projection_step)
from rbnics.utils.decorators import BackendFor, dict_of, ModuleWrapper, overload
backend = ModuleWrapper(Function, transpose)
wrapping = ModuleWrapper(function_extend_or_restrict, get_function_subspace,
gram_schmidt_projection_step)
GramSchmidt_Base = BasicGramSchmidt(backend, wrapping)
@BackendFor("dolfin",
inputs=(FunctionSpace, (Form, Matrix.Type()), (str, None)))
class GramSchmidt(GramSchmidt_Base):
@overload(Operator, (None, str, dict_of(str, str)))
def _extend_or_restrict_if_needed(self, function, component):
function = function_from_ufl_operators(function)
return GramSchmidt_Base._extend_or_restrict_if_needed(
self, function, component)
# This file is part of RBniCS.
#
# RBniCS 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.
#
# RBniCS 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 RBniCS. If not, see <http://www.gnu.org/licenses/>.
#
from ufl import Form
from rbnics.backends.basic import GramSchmidt as BasicGramSchmidt
from rbnics.backends.dolfin.matrix import Matrix
from rbnics.backends.dolfin.transpose import transpose
from rbnics.backends.dolfin.wrapping import gram_schmidt_projection_step
from rbnics.utils.decorators import BackendFor, ModuleWrapper
backend = ModuleWrapper(transpose)
wrapping = ModuleWrapper(gram_schmidt_projection_step)
GramSchmidt_Base = BasicGramSchmidt(backend, wrapping)
@BackendFor("dolfin", inputs=((Form, Matrix.Type()), ))
class GramSchmidt(GramSchmidt_Base):
pass