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Add scaling figure and section explaining scaling in README (#17)
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ulupo authored Aug 4, 2021
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Expand Up @@ -24,13 +24,15 @@ for weighted VR filtrations. See also `Morozov's Ripser fork <https://github.com
``giotto-ph`` is part of the `Giotto <https://github.com/giotto-ai>`_ family of open-source projects and designed for tight integration with
the `giotto-tda <https://github.com/giotto-ai/giotto-tda>`_ and `pyflagser <https://github.com/giotto-ai/giotto-tda>`_ libraries.


Project genesis
===============

``giotto-ph`` is the result of a collaborative effort between `L2F SA <https://www.l2f.ch/>`_,
the `Laboratory for Topology and Neuroscience <https://www.epfl.ch/labs/hessbellwald-lab/>`_ at EPFL,
and the `Institute of Reconfigurable & Embedded Digital Systems (REDS) <https://heig-vd.ch/en/research/reds>`_ of HEIG-VD.


License
=======

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``giotto-ph`` is distributed under the AGPLv3 `license <https://github.com/giotto-ai/giotto-tda/blob/master/LICENSE>`_.
If you need a different distribution license, please contact the `L2F team`_.


Parallel persistent homology backend
====================================

Computing persistence barcodes of large datasets and in high homology degrees is challenging even on modern hardware. ``giotto-ph``'s persistent homology backend
is able to distribute the key stages of the computation (namely, search for apparent pairs and coboundary matrix reduction) across an arbitrary number of available CPU threads.

On challenging datasets, the scaling is quite favourable as shown in the following figure (for more details, see our paper linked below):

.. image:: https://raw.githubusercontent.com/giotto-ai/giotto-ph/main/docs/images/multithreading_speedup.svg
:width: 500px
:align: center


Basic usage in Python
=====================

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dgm_dtm = ripser_parallel(pc, weights="DTM", n_threads=-1)
Documentation and Tutorials
===========================

Jupyter notebook tutorials can be found in the `examples folder <https://github.com/giotto-ai/giotto-ph/blob/main/examples>`_.
The API reference can be found at https://giotto-ai.github.io/giotto-ph.


Installation
============

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.. _contributing-section:


Contributing
============

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pytest gph


Important links
===============

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