This knowledge extends to the DoFHandler object built on such triangulations,
which can then identify which degrees of freedom are locally owned
(see @ref GlossLocallyOwnedDofs) via calls such as
-DoFHandler::n_locally_owned_dofs_per_processor() and
+DoFHandler::compute_n_locally_owned_dofs_per_processor() and
DoFTools::extract_locally_relevant_dofs(). Finally, the DataOut class
also knows how to deal with such triangulations and will simply skip
generating graphical output on cells not locally owned.
n_local_cells = GridTools::count_cells_with_subdomain_association(
triangulation, triangulation.locally_owned_subdomain());
- local_dofs_per_process = dof_handler.n_locally_owned_dofs_per_processor();
+ local_dofs_per_process =
+ dof_handler.compute_n_locally_owned_dofs_per_processor();
// The next step is to set up constraints due to hanging nodes. This has
// been handled many times before:
DoFTools::make_sparsity_pattern(dof_handler, dsp, constraints, false);
SparsityTools::distribute_sparsity_pattern(
dsp,
- dof_handler.n_locally_owned_dofs_per_processor(),
+ dof_handler.compute_n_locally_owned_dofs_per_processor(),
mpi_communicator,
locally_relevant_dofs);
SparsityTools::distribute_sparsity_pattern(
dsp,
- dof_handler.locally_owned_dofs_per_processor(),
+ dof_handler.compute_locally_owned_dofs_per_processor(),
mpi_communicator,
locally_relevant_dofs);
dof_handler, coupling, dsp, constraints, false);
SparsityTools::distribute_sparsity_pattern(
dsp,
- dof_handler.locally_owned_dofs_per_processor(),
+ dof_handler.compute_locally_owned_dofs_per_processor(),
mpi_communicator,
locally_relevant_dofs);
preconditioner_matrix.reinit(owned_partitioning,
DoFTools::make_sparsity_pattern(dof_handler, dsp, constraints, false);
SparsityTools::distribute_sparsity_pattern(
dsp,
- dof_handler.n_locally_owned_dofs_per_processor(),
+ dof_handler.compute_n_locally_owned_dofs_per_processor(),
mpi_communicator,
locally_relevant_dofs);