# `pfapack`: Efficient numerical computation of the Pfaffian for dense and banded skew-symmetric matrices
Code and algorithms are taken from [arXiv:1102.3440](https://arxiv.org/abs/1102.3440) which is authored by [Michael Wimmer](https://michaelwimmer.org/).
[](https://github.com/basnijholt/pfapack/blob/master/LICENSE)
[](https://github.com/basnijholt/pfapack/actions?query=workflow%3Atests)
[](https://codecov.io/gh/basnijholt/pfapack)
[](https://pfapack.readthedocs.io)
[](https://pypi.org/project/pfapack/)
[](https://pypi.org/project/pfapack/)
### Install
Recommended way (because it includes faster C/FORTRAN bindings)
```bash
conda install -c conda-forge pfapack
```
Alternatively use
```bash
pip install pfapack
```
## Usage
```python
from pfapack import pfaffian as pf
import numpy.matlib
A = numpy.matlib.rand(100, 100)
A = A - A.T
pfa1 = pf.pfaffian(A)
pfa2 = pf.pfaffian(A, method="H")
pfa3 = pf.pfaffian_schur(A)
print(pfa1, pfa2, pfa3)
```
If installed with `conda`, C/FORTRAN code is included with Python bindings, use it like:
```python
from pfapack.ctypes import pfaffian as cpf
pfa1 = cpf(A)
pfa2 = cpf(A, method="H")
print(pfa1, pfa2)
```
## Citing
If you have used `pfapack` in your research, please cite it using the following `bib` entry:
```
@article{wimmer2012algorithm,
title={Efficient numerical computation of the pfaffian for dense and banded skew-symmetric matrices},
author={Michael Wimmer},
journal={ACM Transactions on Mathematical Software (TOMS)},
volume={38},
number={4},
pages={1--17},
year={2012},
publisher={ACM New York, NY, USA}
}
```
## License
MIT License
## Contributions
- Bas Nijholt
- [Michael Wimmer (author of the algorithms)](https://arxiv.org/abs/1102.3440)
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"description": "# `pfapack`: Efficient numerical computation of the Pfaffian for dense and banded skew-symmetric matrices\n\nCode and algorithms are taken from [arXiv:1102.3440](https://arxiv.org/abs/1102.3440) which is authored by [Michael Wimmer](https://michaelwimmer.org/).\n\n[](https://github.com/basnijholt/pfapack/blob/master/LICENSE)\n[](https://github.com/basnijholt/pfapack/actions?query=workflow%3Atests)\n[](https://codecov.io/gh/basnijholt/pfapack)\n[](https://pfapack.readthedocs.io)\n[](https://pypi.org/project/pfapack/)\n[](https://pypi.org/project/pfapack/)\n\n### Install\nRecommended way (because it includes faster C/FORTRAN bindings)\n```bash\nconda install -c conda-forge pfapack\n```\n\nAlternatively use\n```bash\npip install pfapack\n```\n\n## Usage\n```python\nfrom pfapack import pfaffian as pf\nimport numpy.matlib\n\nA = numpy.matlib.rand(100, 100)\nA = A - A.T\npfa1 = pf.pfaffian(A)\npfa2 = pf.pfaffian(A, method=\"H\")\npfa3 = pf.pfaffian_schur(A)\n\nprint(pfa1, pfa2, pfa3)\n```\n\nIf installed with `conda`, C/FORTRAN code is included with Python bindings, use it like:\n```python\nfrom pfapack.ctypes import pfaffian as cpf\n\npfa1 = cpf(A)\npfa2 = cpf(A, method=\"H\")\n\nprint(pfa1, pfa2)\n```\n\n## Citing\nIf you have used `pfapack` in your research, please cite it using the following `bib` entry:\n```\n@article{wimmer2012algorithm,\n title={Efficient numerical computation of the pfaffian for dense and banded skew-symmetric matrices},\n author={Michael Wimmer},\n journal={ACM Transactions on Mathematical Software (TOMS)},\n volume={38},\n number={4},\n pages={1--17},\n year={2012},\n publisher={ACM New York, NY, USA}\n}\n```\n\n## License\nMIT License\n\n## Contributions\n- Bas Nijholt\n- [Michael Wimmer (author of the algorithms)](https://arxiv.org/abs/1102.3440)\n",
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