def transpose(backend,
wrapping,
online_backend,
online_wrapping,
AdditionalIsFunction=None,
ConvertAdditionalFunctionTypes=None,
AdditionalIsVector=None,
ConvertAdditionalVectorTypes=None,
AdditionalIsMatrix=None,
ConvertAdditionalMatrixTypes=None):
basic_transpose_instance = basic_transpose(
backend, wrapping, online_backend, online_wrapping,
AdditionalIsFunction, ConvertAdditionalFunctionTypes,
AdditionalIsVector, ConvertAdditionalVectorTypes, AdditionalIsMatrix,
ConvertAdditionalMatrixTypes)
# Define a functor first, for symmetry with basic implementation
class _Transpose_Functor(object):
def __call__(self, arg):
return _Transpose_Class(arg)
# Define the actual class which will carry out the multiplication
class _Transpose_Class(object):
@overload(
wrapping.DelayedTransposeWithArithmetic, )
def __init__(self, arg):
self.basic_transpose_instance_call = basic_transpose_instance(
arg.evaluate())
@overload(
object, )
def __init__(self, arg):
self.basic_transpose_instance_call = basic_transpose_instance(arg)
@overload(
wrapping.DelayedTransposeWithArithmetic, )
def __mul__(self, other):
return self.basic_transpose_instance_call * other.evaluate()
@overload(
object, )
def __mul__(self, other):
return self.basic_transpose_instance_call * other
return _Transpose_Functor()
#
# 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 rbnics.backends.basic import transpose as basic_transpose
from rbnics.backends.online.numpy.basis_functions_matrix import BasisFunctionsMatrix
from rbnics.backends.online.numpy.function import Function
from rbnics.backends.online.numpy.functions_list import FunctionsList
from rbnics.backends.online.numpy.matrix import Matrix
from rbnics.backends.online.numpy.non_affine_expansion_storage import NonAffineExpansionStorage
from rbnics.backends.online.numpy.tensors_list import TensorsList
from rbnics.backends.online.numpy.vector import Vector
from rbnics.backends.online.numpy.wrapping import function_to_vector, matrix_mul_vector, vector_mul_vector, vectorized_matrix_inner_vectorized_matrix
from rbnics.utils.decorators import backend_for, ModuleWrapper
backend = ModuleWrapper(BasisFunctionsMatrix, Function, FunctionsList, Matrix, NonAffineExpansionStorage, TensorsList, Vector)
wrapping = ModuleWrapper(function_to_vector, matrix_mul_vector, vector_mul_vector, vectorized_matrix_inner_vectorized_matrix)
online_backend = ModuleWrapper(OnlineMatrix=Matrix, OnlineVector=Vector)
online_wrapping = ModuleWrapper()
transpose_base = basic_transpose(backend, wrapping, online_backend, online_wrapping)
@backend_for("numpy", inputs=((BasisFunctionsMatrix, Function.Type(), FunctionsList, TensorsList, Vector.Type()), ))
def transpose(arg):
return transpose_base(arg)
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)