Bindings and Python interface for the MUMPS sparse solver
Project description
Python-MUMPS
Python bindings for the MUMPS: a parallel sparse direct solver.
Scope
This package targets MUMPS packaged by conda-forge using Cython bindings. It aims to provide a full wrapper of the MUMPS sequential API. Its primary target OS is Linux.
Next steps include:
- Support for Windows and OSX
- Support for distributed (MPI) MUMPS
Installation
python-mumps
works with Python 3.10 and higher on Linux, Windows and Mac.
The recommended way to install python-mumps
is using mamba
/conda
.
mamba install -c conda-forge python-mumps
python-mumps
can also be installed from PyPI, however this is a more involved procedure
that requires separately installing the MUMPS library and a C compiler.
Usage example
The following example shows how Python-MUMPS can be used to implement sparse diagonalization with Scipy.
import scipy.sparse.linalg as sla
from scipy.sparse import identity
import mumps
def sparse_diag(matrix, k, sigma, **kwargs):
"""Call sla.eigsh with mumps support.
See scipy.sparse.linalg.eigsh for documentation.
"""
class LuInv(sla.LinearOperator):
def __init__(self, A):
inst = mumps.Context()
inst.analyze(A, ordering='pord')
inst.factor(A)
self.solve = inst.solve
sla.LinearOperator.__init__(self, A.dtype, A.shape)
def _matvec(self, x):
return self.solve(x.astype(self.dtype))
opinv = LuInv(matrix - sigma * identity(matrix.shape[0]))
return sla.eigsh(matrix, k, sigma=sigma, OPinv=opinv, **kwargs)
Development
Pixi
python-mumps
recommends pixi.
After installing pixi, use
pixi run test -v # (Pytest arguments go after test)
This will also install the necessary dependencies.
pre-commit
python-mumps
uses pre-commit to enforce code style. After installing it, run
pre-commit install
or if you want to use pre-commit provided by pixi, run
pixi run pre-commit install
Project details
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