scikit-umfpack 0.4.1

Description:

scikitumfpack 0.4.1

scikit-umfpack
scikit-umfpack provides
wrapper of UMFPACK sparse direct solver to SciPy.
Usage:
>>> from scikits.umfpack import spsolve, splu
>>> lu = splu(A)
>>> x = spsolve(A, b)
Installing scikits.umfpack also enables using UMFPACK solver via some of
the scipy.sparse.linalg functions, for SciPy >= 0.14.0. Note you will
need to have installed UMFPACK before hand. UMFPACK is a part of
SuiteSparse.

References


[1]
T. A. Davis, Algorithm 832: UMFPACK - an unsymmetric-pattern
multifrontal method with a column pre-ordering strategy, ACM Trans. on
Mathematical Software, 30(2), 2004, pp. 196–199.
https://dl.acm.org/doi/abs/10.1145/992200.992206


[2]
P. Amestoy, T. A. Davis, and I. S. Duff, Algorithm 837: An approximate
minimum degree ordering algorithm, ACM Trans. on Mathematical Software,
30(3), 2004, pp. 381–388.
https://dl.acm.org/doi/abs/10.1145/1024074.1024081


[3]
T. A. Davis, J. R. Gilbert, S. Larimore, E. Ng, Algorithm 836: COLAMD,
an approximate column minimum degree ordering algorithm, ACM Trans. on
Mathematical Software, 30(3), 2004, pp. 377–380.
https://doi.org/10.1145/1024074.1024080





Installation
Releases of scikit-umfpack can be installed from source using pip, or with
a package manager like conda . To install from source, first ensure the
dependencies described in the next section are installed, then run:
pip install scikit-umfpack
To install scikit-umfpack from its source code directory, run in the root of
a clone of the Git repository:
pip install .

Dependencies
scikit-umfpack depends on NumPy, SciPy, and SuiteSparse.
To build scikit-umfpack, the following are needed:
- a C compiler
- a BLAS library with CBLAS symbols (e.g., OpenBLAS, Accelerate on macOS, or reference BLAS)
- NumPy
- SuiteSparse (which contains UMFPACK)
- SWIG
pkg-config is an optional dependency, if it’s installed it may be used to
detect a BLAS library.
SuiteSparse cannot be installed from PyPI, however it will likely be available
from your package manager of choice. E.g., installing on Ubuntu 22.04 can be
achieved with:
sudo apt-get install libsuitesparse-dev
or from Conda-forge on any supported OS with:
conda install suitesparse
SuiteSparse can also be built from source, see the instructions in the README
of the SuiteSparse repository.

Detection of UMFPACK
During the build, scikit-umfpack tries to automatically detect the UMFPACK
shared library and headers. In case SuiteSparse is installed in a non-standard
location, this autodetection may fail. If that happens, it is possible to
provide the paths to the library and include directories in a config file (a
Meson machine file). This file should contain absolute paths. For example, for
a conda env on Windows, it may look like:
[properties]
umfpack-libdir = 'C:\Users\micromamba\envs\scikit-umfpack-dev\Library\lib'
umfpack-includedir = 'C:\Users\micromamba\envs\scikit-umfpack-dev\Library\include\suitesparse'
If that file is named nativefile.ini, then the pip invocation should
look like (note that $PWD ensures an absolute path to the native file is
used):
pip install . -Csetup-args=--native-file=$PWD/nativefile.ini




Development

Code
You can check the latest sources with the command:
git clone https://github.com/scikit-umfpack/scikit-umfpack.git
or if you have write privileges:
git clone [email protected]:scikit-umfpack/scikit-umfpack.git


Testing
After installation, you can launch the test suite from outside the source
directory (you will need to have the pytest package installed):
pip install pytest
pytest --pyargs scikits.umfpack