dolfinx.mesh

Creation, refining and marking of meshes

Functions

compute_incident_entities(topology, ...)

compute_midpoints(mesh, dim, entities)

create_box(comm, points, n[, cell_type, ...])

Create a box mesh.

create_interval(comm, nx, points[, dtype, ...])

Create an interval mesh.

create_mesh(comm, cells, x, domain[, ...])

Create a mesh from topology and geometry arrays.

create_rectangle(comm, points, n[, ...])

Create a rectangle mesh.

create_submesh(msh, dim, entities)

create_unit_cube(comm, nx, ny, nz[, ...])

Create a mesh of a unit cube.

create_unit_interval(comm, nx[, dtype, ...])

Create a mesh on the unit interval.

create_unit_square(comm, nx, ny[, ...])

Create a mesh of a unit square.

locate_entities(mesh, dim, marker)

Compute mesh entities satisfying a geometric marking function.

locate_entities_boundary(mesh, dim, marker)

Compute mesh entities that are connected to an owned boundary facet and satisfy a geometric marking function.

meshtags(mesh, dim, entities, values)

Create a MeshTags object that associates data with a subset of mesh entities.

meshtags_from_entities(mesh, dim, entities, ...)

Create a :class:dolfinx.mesh.MeshTags` object that associates data with a subset of mesh entities, where the entities are defined by their vertices.

refine(mesh[, edges, redistribute])

Refine a mesh.

refine_plaza(mesh[, edges, redistribute, option])

Refine a mesh.

transfer_meshtag(meshtag, mesh1, parent_cell)

Generate cell mesh tags on a refined mesh from the mesh tags on the coarse parent mesh.

Classes

Mesh(mesh, domain)

A class for representing meshes.

MeshTags(meshtags)

Mesh tags associate data (markers) with a subset of mesh entities of a given dimension.
class dolfinx.mesh.CellType(self: dolfinx.cpp.mesh.CellType, value: int)

Bases: pybind11_object

Members:

point

interval

triangle

quadrilateral

tetrahedron

pyramid

prism

hexahedron

hexahedron = <CellType.hexahedron: -8>
interval = <CellType.interval: 2>
property name
point = <CellType.point: 1>
prism = <CellType.prism: -6>
pyramid = <CellType.pyramid: -5>
quadrilateral = <CellType.quadrilateral: -4>
tetrahedron = <CellType.tetrahedron: 4>
triangle = <CellType.triangle: 3>
property value
class dolfinx.mesh.GhostMode(self: dolfinx.cpp.mesh.GhostMode, value: int)

Bases: pybind11_object

Members:

none

shared_facet

shared_vertex

property name
none = <GhostMode.none: 0>
shared_facet = <GhostMode.shared_facet: 1>
shared_vertex = <GhostMode.shared_vertex: 2>
property value
class dolfinx.mesh.Mesh(mesh, domain: Mesh)[source]

Bases: object

A class for representing meshes.

Initialize mesh from a C++ mesh.

Parameters:

  • mesh – The C++ mesh object.

  • domain – The UFL domain.

Note

Mesh objects should not usually be created using this class directly.

basix_cell() → Cell[source]

Return the Basix cell type.

property comm
property geometry

Mesh geometry.

h(dim: int, entities: ndarray[Any, dtype[int32]]) → ndarray[Any, dtype[float64]][source]

Geometric size measure of cell entities.

Parameters:

  • dim – Topological dimension of the entities to compute the size measure of.

  • entities – Indices of entities of dimension dim to compute size measure of.

Returns:

Size measure for each requested entity.

property name
property topology

Mesh topology.

ufl_cell() → Cell[source]

Return the UFL cell type.

Note: This method is required for UFL compatibility.

ufl_domain() → Mesh[source]

Return the ufl domain corresponding to the mesh.

Note: This method is required for UFL compatibility.

class dolfinx.mesh.MeshTags(meshtags)[source]

Bases: object

Mesh tags associate data (markers) with a subset of mesh entities of a given dimension.

Initialize tags from a C++ MeshTags object.

Parameters:

meshtags – C++ mesh tags object.

Note

MeshTags objects should not usually be created using this initializer directly.

A Python mesh is passed to the initializer as it may have UFL data attached that is not attached the C + + Mesh that is associated with the C + + meshtags object. If mesh is passed, mesh and meshtags must share the same C + + mesh.

property dim: int

Topological dimension of the tagged entities.

find(value) → ndarray[Any, dtype[int32]][source]

Get a list of all entity indices with a given value.

Parameters:

value – Tag value to search for.

Returns:

Indices of entities with tag value.

property indices: ndarray[Any, dtype[int32]]

Indices of tagged mesh entities.

property name: str

Name of the mesh tags object.

property topology: Topology

Mesh topology with which the the tags are associated.

ufl_id() → int[source]

Identiftying integer used by UFL.

property values

Values associated with tagged mesh entities.

dolfinx.mesh.build_dual_graph(comm: MPICommWrapper, cells: dolfinx::graph::AdjacencyList<long>, tdim: int) → dolfinx::graph::AdjacencyList<long>

Build dual graph for cells

dolfinx.mesh.cell_dim(type: dolfinx.cpp.mesh.CellType) → int
dolfinx.mesh.compute_incident_entities(topology, entities: ndarray[Any, dtype[int32]], d0: int, d1: int)[source]
dolfinx.mesh.compute_midpoints(mesh: Mesh, dim: int, entities: ndarray[Any, dtype[int32]])[source]
dolfinx.mesh.create_box(comm: ~mpi4py.MPI.Comm, points: ~typing.List[~typing.Union[~numpy._typing._array_like._SupportsArray[~numpy.dtype], ~numpy._typing._nested_sequence._NestedSequence[~numpy._typing._array_like._SupportsArray[~numpy.dtype]], bool, int, float, complex, str, bytes, ~numpy._typing._nested_sequence._NestedSequence[~typing.Union[bool, int, float, complex, str, bytes]]]], n: list, cell_type=<CellType.tetrahedron: 4>, dtype: ~typing.Union[~numpy.dtype[~typing.Any], None, ~typing.Type[~typing.Any], ~numpy._typing._dtype_like._SupportsDType[~numpy.dtype[~typing.Any]], str, ~typing.Tuple[~typing.Any, int], ~typing.Tuple[~typing.Any, ~typing.Union[~typing.SupportsIndex, ~typing.Sequence[~typing.SupportsIndex]]], ~typing.List[~typing.Any], ~numpy._typing._dtype_like._DTypeDict, ~typing.Tuple[~typing.Any, ~typing.Any]] = <class 'numpy.float64'>, ghost_mode=<GhostMode.shared_facet: 1>, partitioner=None) → Mesh[source]

Create a box mesh.

Parameters:

  • comm – MPI communicator.

  • points – Coordinates of the ‘lower-left’ and ‘upper-right’ corners of the box.

  • n – List of cells in each direction

  • cell_type – The cell type.

  • dtype – Float type for the mesh geometry(numpy.float32 or numpy.float64).

  • ghost_mode – The ghost mode used in the mesh partitioning.

  • partitioner – Function that computes the parallel distribution of cells across MPI ranks.

Returns:

A mesh of a box domain.

dolfinx.mesh.create_cell_partitioner(*args, **kwargs)

Overloaded function.

  1. create_cell_partitioner(arg0: dolfinx.cpp.mesh.GhostMode) -> Callable[[MPICommWrapper, int, int, dolfinx::graph::AdjacencyList<long>], dolfinx::graph::AdjacencyList<int>]

Create default cell partitioner.

  1. create_cell_partitioner(part: Callable[[MPICommWrapper, int, dolfinx::graph::AdjacencyList<long>, bool], dolfinx::graph::AdjacencyList<int>], ghost_mode: dolfinx.cpp.mesh.GhostMode = <GhostMode.none: 0>) -> Callable[[MPICommWrapper, int, int, dolfinx::graph::AdjacencyList<long>], dolfinx::graph::AdjacencyList<int>]

Create a cell partitioner from a graph partitioning function.

dolfinx.mesh.create_interval(comm: ~mpi4py.MPI.Comm, nx: int, points: ~typing.Union[~numpy._typing._array_like._SupportsArray[~numpy.dtype], ~numpy._typing._nested_sequence._NestedSequence[~numpy._typing._array_like._SupportsArray[~numpy.dtype]], bool, int, float, complex, str, bytes, ~numpy._typing._nested_sequence._NestedSequence[~typing.Union[bool, int, float, complex, str, bytes]]], dtype: ~typing.Union[~numpy.dtype[~typing.Any], None, ~typing.Type[~typing.Any], ~numpy._typing._dtype_like._SupportsDType[~numpy.dtype[~typing.Any]], str, ~typing.Tuple[~typing.Any, int], ~typing.Tuple[~typing.Any, ~typing.Union[~typing.SupportsIndex, ~typing.Sequence[~typing.SupportsIndex]]], ~typing.List[~typing.Any], ~numpy._typing._dtype_like._DTypeDict, ~typing.Tuple[~typing.Any, ~typing.Any]] = <class 'numpy.float64'>, ghost_mode=<GhostMode.shared_facet: 1>, partitioner=None) → Mesh[source]

Create an interval mesh.

Parameters:

  • comm – MPI communicator.

  • nx – Number of cells.

  • points – Coordinates of the end points.

  • dtype – Float type for the mesh geometry(numpy.float32 or numpy.float64).

  • ghost_mode – Ghost mode used in the mesh partitioning. Options are GhostMode.none and GhostMode.shared_facet.

  • partitioner – Partitioning function to use for determining the parallel distribution of cells across MPI ranks.

Returns:

An interval mesh.

dolfinx.mesh.create_mesh(comm: Comm, cells: Union[ndarray, AdjacencyList_int64], x: ndarray, domain: Mesh, partitioner=None) → Mesh[source]

Create a mesh from topology and geometry arrays.

Parameters:

  • comm – MPI communicator to define the mesh on.

  • cells – Cells of the mesh. cells[i] is the ‘nodes’ of cell i.

  • x – Mesh geometry (‘node’ coordinates), with shape (num_nodes, gdim).

  • domain – UFL mesh.

  • partitioner – Function that computes the parallel distribution of cells across MPI ranks.

Returns:

A mesh.

dolfinx.mesh.create_rectangle(comm: ~mpi4py.MPI.Comm, points: ~typing.Union[~numpy._typing._array_like._SupportsArray[~numpy.dtype], ~numpy._typing._nested_sequence._NestedSequence[~numpy._typing._array_like._SupportsArray[~numpy.dtype]], bool, int, float, complex, str, bytes, ~numpy._typing._nested_sequence._NestedSequence[~typing.Union[bool, int, float, complex, str, bytes]]], n: ~typing.Union[~numpy._typing._array_like._SupportsArray[~numpy.dtype], ~numpy._typing._nested_sequence._NestedSequence[~numpy._typing._array_like._SupportsArray[~numpy.dtype]], bool, int, float, complex, str, bytes, ~numpy._typing._nested_sequence._NestedSequence[~typing.Union[bool, int, float, complex, str, bytes]]], cell_type=<CellType.triangle: 3>, dtype: ~typing.Union[~numpy.dtype[~typing.Any], None, ~typing.Type[~typing.Any], ~numpy._typing._dtype_like._SupportsDType[~numpy.dtype[~typing.Any]], str, ~typing.Tuple[~typing.Any, int], ~typing.Tuple[~typing.Any, ~typing.Union[~typing.SupportsIndex, ~typing.Sequence[~typing.SupportsIndex]]], ~typing.List[~typing.Any], ~numpy._typing._dtype_like._DTypeDict, ~typing.Tuple[~typing.Any, ~typing.Any]] = <class 'numpy.float64'>, ghost_mode=<GhostMode.shared_facet: 1>, partitioner=None, diagonal: ~dolfinx.cpp.mesh.DiagonalType = <DiagonalType.right: 1>) → Mesh[source]

Create a rectangle mesh.

Parameters:

  • comm – MPI communicator.

  • points – Coordinates of the lower - left and upper - right corners of the rectangle.

  • n – Number of cells in each direction.

  • cell_type – Mesh cell type.

  • dtype – Float type for the mesh geometry(numpy.float32 or numpy.float64)

  • ghost_mode – Ghost mode used in the mesh partitioning.

  • partitioner – Function that computes the parallel distribution of cells across MPI ranks.

  • diagonal – Direction of diagonal of triangular meshes. The options are left, right, crossed, left / right, right / left.

Returns:

A mesh of a rectangle.

dolfinx.mesh.create_unit_cube(comm: ~mpi4py.MPI.Comm, nx: int, ny: int, nz: int, cell_type=<CellType.tetrahedron: 4>, dtype: ~typing.Union[~numpy.dtype[~typing.Any], None, ~typing.Type[~typing.Any], ~numpy._typing._dtype_like._SupportsDType[~numpy.dtype[~typing.Any]], str, ~typing.Tuple[~typing.Any, int], ~typing.Tuple[~typing.Any, ~typing.Union[~typing.SupportsIndex, ~typing.Sequence[~typing.SupportsIndex]]], ~typing.List[~typing.Any], ~numpy._typing._dtype_like._DTypeDict, ~typing.Tuple[~typing.Any, ~typing.Any]] = <class 'numpy.float64'>, ghost_mode=<GhostMode.shared_facet: 1>, partitioner=None) → Mesh[source]

Create a mesh of a unit cube.

Parameters:

  • comm – MPI communicator.

  • nx – Number of cells in “x” direction.

  • ny – Number of cells in “y” direction.

  • nz – Number of cells in “z” direction.

  • cell_type – Mesh cell type

  • dtype – Float type for the mesh geometry(numpy.float32 or numpy.float64).

  • ghost_mode – Ghost mode used in the mesh partitioning.

  • partitioner – Function that computes the parallel distribution of cells across MPI ranks.

Returns:

A mesh of an axis-aligned unit cube with corners at (0, 0, 0)

and (1, 1, 1).

dolfinx.mesh.create_unit_interval(comm: ~mpi4py.MPI.Comm, nx: int, dtype: ~typing.Union[~numpy.dtype[~typing.Any], None, ~typing.Type[~typing.Any], ~numpy._typing._dtype_like._SupportsDType[~numpy.dtype[~typing.Any]], str, ~typing.Tuple[~typing.Any, int], ~typing.Tuple[~typing.Any, ~typing.Union[~typing.SupportsIndex, ~typing.Sequence[~typing.SupportsIndex]]], ~typing.List[~typing.Any], ~numpy._typing._dtype_like._DTypeDict, ~typing.Tuple[~typing.Any, ~typing.Any]] = <class 'numpy.float64'>, ghost_mode=<GhostMode.shared_facet: 1>, partitioner=None) → Mesh[source]

Create a mesh on the unit interval.

Parameters:

  • comm – MPI communicator.

  • nx – Number of cells.

  • points – Coordinates of the end points.

  • dtype – Float type for the mesh geometry(numpy.float32 or numpy.float64).

  • ghost_mode – Ghost mode used in the mesh partitioning. Options are GhostMode.none and GhostMode.shared_facet.

  • partitioner – Partitioning function to use for determining the parallel distribution of cells across MPI ranks.

Returns:

A unit interval mesh with end points at 0 and 1.

dolfinx.mesh.create_unit_square(comm: ~mpi4py.MPI.Comm, nx: int, ny: int, cell_type=<CellType.triangle: 3>, dtype: ~typing.Union[~numpy.dtype[~typing.Any], None, ~typing.Type[~typing.Any], ~numpy._typing._dtype_like._SupportsDType[~numpy.dtype[~typing.Any]], str, ~typing.Tuple[~typing.Any, int], ~typing.Tuple[~typing.Any, ~typing.Union[~typing.SupportsIndex, ~typing.Sequence[~typing.SupportsIndex]]], ~typing.List[~typing.Any], ~numpy._typing._dtype_like._DTypeDict, ~typing.Tuple[~typing.Any, ~typing.Any]] = <class 'numpy.float64'>, ghost_mode=<GhostMode.shared_facet: 1>, partitioner=None, diagonal: ~dolfinx.cpp.mesh.DiagonalType = <DiagonalType.right: 1>) → Mesh[source]

Create a mesh of a unit square.

Parameters:

  • comm – MPI communicator.

  • nx – Number of cells in the “x” direction.

  • ny – Number of cells in the “y” direction.

  • cell_type – Mesh cell type.

  • dtype – Float type for the mesh geometry(numpy.float32 or numpy.float64).

  • ghost_mode – Ghost mode used in the mesh partitioning.

  • partitioner – Function that computes the parallel distribution of cells across MPI ranks.

  • diagonal – Direction of diagonal.

Returns:

A mesh of a square with corners at (0, 0) and (1, 1).

dolfinx.mesh.exterior_facet_indices(topology: dolfinx.cpp.mesh.Topology) → numpy.ndarray[numpy.int32]
dolfinx.mesh.locate_entities(mesh: Mesh, dim: int, marker: Callable) → ndarray[source]

Compute mesh entities satisfying a geometric marking function.

Parameters:

  • mesh – Mesh to locate entities on.

  • dim – Topological dimension of the mesh entities to consider.

  • marker – A function that takes an array of points x with shape (gdim, num_points) and returns an array of booleans of length num_points, evaluating to True for entities to be located.

Returns:

Indices (local to the process) of marked mesh entities.

dolfinx.mesh.locate_entities_boundary(mesh: Mesh, dim: int, marker: Callable) → ndarray[source]

Compute mesh entities that are connected to an owned boundary facet and satisfy a geometric marking function.

For vertices and edges, in parallel this function will not necessarily mark all entities that are on the exterior boundary. For example, it is possible for a process to have a vertex that lies on the boundary without any of the attached facets being a boundary facet. When used to find degrees-of-freedom, e.g. using dolfinx.fem.locate_dofs_topological(), the function that uses the data returned by this function must typically perform some parallel communication.

Parameters:

  • mesh – Mesh to locate boundary entities on.

  • dim – Topological dimension of the mesh entities to consider

  • marker – Function that takes an array of points x with shape (gdim, num_points) and returns an array of booleans of length num_points, evaluating to True for entities to be located.

Returns:

Indices (local to the process) of marked mesh entities.

dolfinx.mesh.meshtags(mesh: Mesh, dim: int, entities: ndarray[Any, dtype[int32]], values: Union[ndarray, int, float]) → MeshTags[source]

Create a MeshTags object that associates data with a subset of mesh entities.

Parameters:

  • mesh – The mesh.

  • dim – Topological dimension of the mesh entity.

  • entities – Indices(local to process) of entities to associate values with . The array must be sorted and must not contain duplicates.

  • values – The corresponding value for each entity.

Returns:

A mesh tags object.

Note

The type of the returned MeshTags is inferred from the type of values.

dolfinx.mesh.meshtags_from_entities(mesh: Mesh, dim: int, entities: AdjacencyList_int32, values: ndarray[Any, dtype[Any]])[source]

Create a :class:dolfinx.mesh.MeshTags` object that associates data with a subset of mesh entities, where the entities are defined by their vertices.

Parameters:

  • mesh – The mesh.

  • dim – Topological dimension of the mesh entity.

  • entities – Entities to associated values with, with entities defined by their vertices.

  • values – The corresponding value for each entity.

Returns:

A mesh tags object.

Note

The type of the returned MeshTags is inferred from the type of values.

dolfinx.mesh.refine(mesh: Mesh, edges: Optional[ndarray] = None, redistribute: bool = True) → Mesh[source]

Refine a mesh.

Parameters:

  • mesh – Mesh from which to create the refined mesh.

  • edges – Indices of edges to split during refinement. If None, mesh refinement is uniform.

  • redistribute – Refined mesh is re-partitioned if True.

Returns:

Refined mesh.

dolfinx.mesh.refine_plaza(mesh: ~dolfinx.mesh.Mesh, edges: ~typing.Optional[~numpy.ndarray] = None, redistribute: bool = True, option: ~dolfinx.cpp.refinement.RefinementOption = <RefinementOption.none: 0>) → tuple[dolfinx.mesh.Mesh, numpy.ndarray[Any, numpy.dtype[numpy.int32]], numpy.ndarray[Any, numpy.dtype[numpy.int32]]][source]

Refine a mesh.

Parameters:

  • mesh – Mesh from which to create the refined mesh.

  • edges – Indices of edges to split during refinement. If None, mesh refinement is uniform.

  • redistribute – Refined mesh is re-partitioned if True.

  • option – Control computation of the parent-refined mesh data.

Returns:

Refined mesh, list of parent cell for each refine cell, and list of parent facets.

dolfinx.mesh.to_string(type: dolfinx.cpp.mesh.CellType) → str
dolfinx.mesh.to_type(cell: str) → dolfinx.cpp.mesh.CellType
dolfinx.mesh.transfer_meshtag(meshtag: MeshTags, mesh1: Mesh, parent_cell: ndarray[Any, dtype[int32]], parent_facet: Optional[ndarray[Any, dtype[int8]]] = None) → MeshTags[source]

Generate cell mesh tags on a refined mesh from the mesh tags on the coarse parent mesh.

Parameters:

  • meshtag – Mesh tags on the coarse, parent mesh.

  • mesh1 – The refined mesh.

  • parent_cell – Index of the parent cell for each cell in the refined mesh.

  • parent_facet – Index of the local parent facet for each cell in the refined mesh. Only required for transfer tags on facets.

Returns:

Mesh tags on the refined mesh.