dolfinx.la
Linear algebra functionality
Functions
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Orthogoalise set of PETSc vectors in-place |
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Check that list of PETSc vectors are orthonormal |
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Create a distributed sparse matrix. |
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Create a distributed vector. |
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Create a distributed PETSc vector. |
Classes
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A distributed sparse matrix that uses compressed sparse row storage. |
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A distributed vector object. |
- class dolfinx.la.InsertMode(self: dolfinx.cpp.la.InsertMode, value: int)
Bases:
pybind11_object
Members:
add
insert
- add = <InsertMode.add: 0>
- insert = <InsertMode.insert: 1>
- property name
- property value
- class dolfinx.la.MatrixCSR(A)[source]
Bases:
object
A distributed sparse matrix that uses compressed sparse row storage.
- Parameters:
A – The C++/pybind11 matrix object.
Note
Objects of this type should be created using
matrix_csr()
and not created using the class initialiser.- property block_size
Block sizes for the matrix.
- property data: ndarray[Any, dtype[floating]]
Underlying matrix entry data.
- index_map(i: int) IndexMap [source]
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.
- set_value(x: floating) None [source]
Set all non-zero entries to a value.
- Parameters:
x – The value to set all non-zero entries to.
- squared_norm() floating [source]
Compute the squared Frobenius norm.
Note
This operation is collective and requires communication.
- to_dense() ndarray[Any, dtype[floating]] [source]
Copy to a dense 2D array.
Note
Typically used for debugging.
- to_scipy(ghosted=False)[source]
Convert to a SciPy CSR/BSR matrix. Data is shared.
SciPy must be available.
- Parameters:
ghosted – If
True
rows that are ghosted in parallel are included in the return 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(self: dolfinx.cpp.la.Norm, value: int)
Bases:
pybind11_object
Members:
l1
l2
linf
frobenius
- frobenius = <Norm.frobenius: 3>
- l1 = <Norm.l1: 0>
- l2 = <Norm.l2: 1>
- linf = <Norm.linf: 2>
- property name
- property value
- class dolfinx.la.Vector(x)[source]
Bases:
object
A distributed vector object.
- Parameters:
map – Index map the describes the size and distribution of the vector
bs – Block size
Note
Objects of this type should be created using
vector()
and not created using the class initialiser.- property array: ndarray
Local representation of the vector.
- property block_size: int
Block size for the vector.
- norm(type=<Norm.l2: 1>) floating [source]
Compute a norm of the vector.
- Parameters:
type – Norm type to computed.
- Returns:
Computed norm.
- scatter_reverse(mode: InsertMode) None [source]
Scatter ghost entries to owner.
- Parameters:
mode – Control how scattered values are set/accumulated by owner.
- dolfinx.la.create_petsc_vector(map, bs: int)[source]
Create a distributed PETSc vector.
- Parameters:
map – Index map that describes the size and parallel layout of the vector to create.
bs – Block size of the vector.
- dolfinx.la.is_orthonormal(basis, eps: float = 1e-12) bool [source]
Check that list of PETSc vectors are orthonormal
- dolfinx.la.matrix_csr(sp, block_mode=<BlockMode.compact: 0>, dtype=<class 'numpy.float64'>) MatrixCSR [source]
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.