Metadata-Version: 2.1
Name: pfapack
Version: 0.2.0
Summary: Efficient numerical computation of the Pfaffian for dense and banded skew-symmetric matrices.
Home-page: https://github.com/basnijholt/pfapack
Author: Bas Nijholt (package) and M. Wimmer (code)
Author-email: basnijholt@gmail.com
License: MIT
Description: # `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/).
        
        [![license](https://img.shields.io/github/license/basnijholt/pfapack)](https://github.com/basnijholt/pfapack/blob/master/LICENSE)
        [![tests](https://github.com/basnijholt/pfapack/workflows/tests/badge.svg)](https://github.com/basnijholt/pfapack/actions?query=workflow%3Atests)
        [![codecov](https://img.shields.io/codecov/c/github/basnijholt/pfapack)](https://codecov.io/gh/basnijholt/pfapack)
        [![docs](https://img.shields.io/readthedocs/pfapack)](https://pfapack.readthedocs.io)
        [![version](https://img.shields.io/pypi/v/pfapack)](https://pypi.org/project/pfapack/)
        [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pfapack)](https://pypi.org/project/pfapack/)
        
        ### Install
        ```bash
        pip install pfapack
        ```
        
        ## Usage
        ```python
        from pfapack import pfaffian as pf
        import numpy.matlib
        
        # first real matrices
        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)
        ```
        
        ## 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)
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Utilities
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: docs
Provides-Extra: testing
