giotto-ph 0.1.0

High performance tool for Persistent Homology computations.

giotto-ph

giotto-ph is a high-performance implementation of Vietoris–Rips (VR) persistence on the CPU, and is distributed under the GNU AGPLv3 license. It consists of an improved reimplementation of Morozov and Nigmetov’s “lock-free Ripser” and in addition makes use of a parallel implementation of the apparent pairs optimization used in Ripser v1.2. It also contains an improved reimplementation of GUDHI’s Edge Collapse (EC) algorithm and offers support for weighted VR filtrations. See also Morozov’s Ripser fork, Nigmetov’s Oineus library, and GUDHI’s EC implementation.

giotto-ph is part of the Giotto family of open-source projects and designed for tight integration with the giotto-tda and pyflagser libraries.

Project genesis

giotto-ph is the result of a collaborative effort between L2F SA, the Laboratory for Topology and Neuroscience at EPFL, and the Institute of Reconfigurable & Embedded Digital Systems (REDS) of HEIG-VD.

License

giotto-ph is distributed under the AGPLv3 license. If you need a different distribution license, please contact the L2F team.

Documentation

Please visit https://giotto-ai.github.io/giotto-ph and navigate to the version you are interested in.

Installation

Dependencies

The latest stable version of giotto-ph requires:

• Python (>= 3.6)

• NumPy (>= 1.19.1)

• SciPy (>= 1.5.0)

• scikit-learn (>= 0.23.1)

User installation

The simplest way to install giotto-ph is using pip

python -m pip install -U giotto-ph

If necessary, this will also automatically install all the above dependencies. Note: we recommend upgrading pip to a recent version as the above may fail on very old versions.

Developer installation

Please consult the dedicated page for detailed instructions on how to build giotto-ph from sources across different platforms.

Contributing

We welcome new contributors of all experience levels. The Giotto community goals are to be helpful, welcoming, and effective. To learn more about making a contribution to giotto-ph, please consult the relevant page.

Testing

After installation, you can launch the test suite from inside the source directory

pytest

Important links

• Issue tracker: https://github.com/giotto-ai/giotto-ph/issues

Citing giotto-ph

If you use giotto-ph in a scientific publication, we would appreciate citations to the following paper:

giotto-ph: A Python Library for High-Performance Computation of Persistent Homology of Vietoris–Rips Filtrations, Burella Pérez et al, arXiv:2107.05412, 2021.

You can use the following BibTeX entry:

@misc{tauzin2020giottotda,
      title={giotto-ph: A Python Library for High-Performance Computation of Persistent Homology of Vietoris--Rips Filtrations},
      author={Julián Burella Pérez and Sydney Hauke and Umberto Lupo and Matteo Caorsi and Alberto Dassatti},
      year={2021},
      eprint={2107.05412},
      archivePrefix={arXiv},
      primaryClass={cs.CG}
}

Community

giotto-ai Slack workspace: https://slack.giotto.ai/

Contacts

[e-mail]