Metadata-Version: 2.1
Name: elpigraph-python
Version: 0.2.2
Summary: UNKNOWN
Home-page: https://github.com/j-bac/elpigraph-python
Maintainer: Jonathan Bac
Maintainer-email: 
License: UNKNOWN
Project-URL: Bug Reports, https://github.com/j-bac/elpigraph-python/issues
Project-URL: Source, https://github.com/j-bac/elpigraph-python/
Description: [![Documentation Status](https://readthedocs.org/projects/elpigraph-python/badge/?version=latest)](https://elpigraph-python.readthedocs.io/en/latest/?badge=latest)
        [![GitHub license](https://img.shields.io/github/license/j-bac/elpigraph-python)](https://github.com/j-bac/elpigraph-python/blob/master/LICENSE)
        [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
        [![DOI:10.3390/e22030296](https://img.shields.io/badge/DOI-10.3390%2Fe22030296-blue)](https://doi.org/10.3390/e22030296)
        
        Description
        ===========
        
        This package provides a Python implementation of the ElPiGraph algorithm with cpu and gpu support. A
        self-contained description of the algorithm is available
        [here](https://github.com/auranic/Elastic-principal-graphs/blob/master/ElPiGraph_Methods.pdf)
        or in the [paper](https://www.mdpi.com/1099-4300/22/3/296)
        
        It replicates the [R implementation](https://github.com/j-bac/ElPiGraph.R),
        coded by [Luca Albergante](https://github.com/Albluca) and should return exactly the same results. Please open an issue if you do  notice different output. Differences between the two versions are detailed in [differences.md](differences.md). This package extends initial work by [Louis Faure](https://github.com/LouisFaure/ElPiGraph.P) and [Alexis Martin](https://github.com/AlexiMartin/ElPiGraph.P).
        
        A native MATLAB implementation of the algorithm (coded by [Andrei
        Zinovyev](https://github.com/auranic/) and [Evgeny
        Mirkes](https://github.com/Mirkes)) is also
        [available](https://github.com/auranic/Elastic-principal-graphs)
        
        Citation
        ========
        
        When using this package, please cite our [paper](https://www.mdpi.com/1099-4300/22/3/296):
        
        Albergante, L.  et al . Robust and Scalable Learning of Complex Intrinsic Dataset Geometry via ElPiGraph (2020)
        
        Requirements
        ============
        
        This code was tested with Python 3.7.1, and requires the following packages:
        - pandas
        - scipy
        - numba
        - numpy
        - python-igraph
        - scikit-learn
        
        In addition, to enable gpu support:
        - cupy
        https://docs-cupy.chainer.org/en/stable/install.html#
        
        The requirements.txt file provides the versions this package has been tested with
        
        Installation
        ====================
        ```bash
        git clone https://github.com/j-bac/elpigraph-python.git
        cd elpigraph
        pip install .
        ```
        or
        
        ```bash
        pip install git+https://github.com/j-bac/elpigraph-python.git
        ```
        
Keywords: machine_learning graphs dimension_reduction single_cell
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Description-Content-Type: text/markdown
Provides-Extra: tests
Provides-Extra: docs
