dolfinx.la

Linear algebra functionality

Functions

is_orthonormal(basis[, eps])	Check that list of vectors are orthonormal.
matrix_csr(sp[, block_mode, dtype])	Create a distributed sparse matrix.
norm(x[, type])	Compute a norm of the vector.
orthonormalize(basis)	Orthogonalise set of vectors in-place.
vector(map[, bs, dtype])	Create a distributed vector.

Classes

InsertMode(*values)
MatrixCSR(A)	A distributed sparse matrix that uses compressed sparse row storage.
Norm(*values)
Vector(x)	A distributed vector object.

class dolfinx.la.InsertMode(*values)

Bases: Enum

add = 0

insert = 1

class dolfinx.la.MatrixCSR(A: MatrixCSR_float32 | MatrixCSR_float64 | MatrixCSR_complex64 | MatrixCSR_complex128)

Bases: object

A distributed sparse matrix that uses compressed sparse row storage.

Note

Objects of this type should be created using matrix_csr() and not created using this initialiser.

Parameters:

A – The C++/nanobind matrix object.

add(x: ndarray[Any, dtype[floating]], rows: ndarray[Any, dtype[int32]], cols: ndarray[Any, dtype[int32]], bs: int = 1) → None

Add a block of values in the matrix.

property block_size: list

Block sizes for the matrix.

property data: ndarray[Any, dtype[floating]]

Underlying matrix entry data.

index_map(i: int) → IndexMap

Index map for row/column.

Parameters:

i – 0 for row map, 1 for column map.

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

Local column indices.

property indptr: ndarray[Any, dtype[int64]]

Local row pointers.

mult(x: Vector, y: Vector) → None

Compute y += Ax.

Parameters:

• x – Input Vector

• y – Output Vector

scatter_reverse() → None

Scatter and accumulate ghost values.

set(x: ndarray[Any, dtype[floating]], rows: ndarray[Any, dtype[int32]], cols: ndarray[Any, dtype[int32]], bs: int = 1) → None

Set a block of values in the matrix.

set_value(x: floating) → None

Set all non-zero entries to a value.

Parameters:

x – The value to set all non-zero entries to.

squared_norm() → floating

Compute the squared Frobenius norm.

Note

This operation is collective and requires communication.

to_dense() → ndarray[Any, dtype[floating]]

Copy to a dense 2D array.

Note

Typically used for debugging.

to_scipy(ghosted: bool = False)

Convert to a SciPy CSR/BSR matrix. Data is shared.

Note

SciPy must be available.

Parameters:

ghosted – If True rows that are ghosted in parallel are included in the returned SciPy matrix, otherwise ghost rows are not included.

Returns:

SciPy compressed sparse row (both block sizes equal to one) or a SciPy block compressed sparse row matrix.

class dolfinx.la.Norm(*values)

Bases: Enum

frobenius = 3

l1 = 0

l2 = 1

linf = 2

class dolfinx.la.Vector(x: Vector_float32 | Vector_float64 | Vector_complex64 | Vector_complex128 | Vector_int8 | Vector_int32 | Vector_int64)

Bases: object

A distributed vector object.

Parameters:

x – C++ Vector object.

Note

This initialiser is intended for internal library use only. User code should call vector() to create a vector object.

property array: ndarray

Local representation of the vector.

property block_size: int

Block size for the vector.

property index_map: IndexMap

Index map that describes size and parallel distribution.

property petsc_vec

PETSc vector holding the entries of the vector.

Upon first call, this function creates a PETSc Vec object that wraps the degree-of-freedom data. The Vec object is cached and the cached Vec is returned upon subsequent calls.

Note

When the object is destroyed it will destroy the underlying petsc4py vector automatically.

scatter_forward() → None

Update ghost entries.

scatter_reverse(mode: InsertMode) → None

Scatter ghost entries to owner.

Parameters:

mode – Control how scattered values are set/accumulated by owner.

dolfinx.la.is_orthonormal(basis: list[Vector], eps: float = 1e-12) → bool

Check that list of vectors are orthonormal.

dolfinx.la.matrix_csr(sp: ~dolfinx.cpp.la.SparsityPattern, block_mode=BlockMode.compact, dtype: ~numpy.dtype[~typing.Any] | None | type[~typing.Any] | ~numpy._typing._dtype_like._SupportsDType[~numpy.dtype[~typing.Any]] | str | tuple[~typing.Any, int] | tuple[~typing.Any, ~typing.SupportsIndex | ~collections.abc.Sequence[~typing.SupportsIndex]] | list[~typing.Any] | ~numpy._typing._dtype_like._DTypeDict | tuple[~typing.Any, ~typing.Any] = <class 'numpy.float64'>) → MatrixCSR

Create a distributed sparse matrix.

The matrix uses compressed sparse row storage.

Parameters:

• sp – The sparsity pattern that defines the nonzero structure of the matrix the parallel distribution of the matrix.

• dtype – The scalar type.

Returns:

A sparse matrix.

dolfinx.la.norm(x: Vector, type: Norm = Norm.l2) → floating

Compute a norm of the vector.

Parameters:

• x – Vector to measure.

• type – Norm type to compute.

Returns:

Computed norm.

dolfinx.la.orthonormalize(basis: list[Vector])

Orthogonalise set of vectors in-place.

dolfinx.la.vector(map, bs=1, dtype: ~numpy.dtype[~typing.Any] | None | type[~typing.Any] | ~numpy._typing._dtype_like._SupportsDType[~numpy.dtype[~typing.Any]] | str | tuple[~typing.Any, int] | tuple[~typing.Any, ~typing.SupportsIndex | ~collections.abc.Sequence[~typing.SupportsIndex]] | list[~typing.Any] | ~numpy._typing._dtype_like._DTypeDict | tuple[~typing.Any, ~typing.Any] = <class 'numpy.float64'>) → Vector

Create a distributed vector.

Parameters:

• map – Index map the describes the size and distribution of the vector.

• bs – Block size.

• dtype – The scalar type.

Returns:

A distributed vector.