Metadata-Version: 2.1
Name: NEMtropy
Version: 1.0.2
Summary: NEMtropy is a Maximum-Entropy toolbox for networks, it provides the user with a state of the art solver for a range variety of Maximum Entropy Networks models derived from the ERGM family. This module allows you to solve the desired model and generate a number of randomized graphs from the original one: the so-called graphs ensemble.
Home-page: https://github.com/nicoloval/NEMtropy/
Author: Nicolo' Vallarano, Emiliano Marchese
Author-email: nicolo.vallarano@imtlucca.it, emiliano.marchese@imtlucca.it
License: GNU General Public License v3
Download-URL: https://github.com/nicoloval/NEMtropy/archive/v1.0.0.zip
Description: ![PyPI](https://img.shields.io/pypi/v/nemtropy)
        ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/nemtropy)
        [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
        
        [comment]: # insert arxiv badge
        
        NEMtropy: Network Entropy Maximization, a Toolbox Running On PYthon
        -------------------------------------------------------------------
        
        NEMtropy is a Maximum-Entropy toolbox for networks, released as a python3 module. 
        
        NEMtropy provides the user with a state of the art solver for a range variety of Maximum Entropy Networks models derived from the ERGM family.
        This module allows you to solve the desired model and generate a number of randomized graphs from the original one: the so-called _graphs ensemble_.
        
        NEMtropy builds on the current literature on the matter, improving both in speed of convergence and in the scale of the feasible networks.
        To explore Maximum-Entropy modeling on networks, checkout [Maximum Entropy Hub](https://meh.imtlucca.it/).
        
        The models implemented in NEMtropy are presented in a forthcoming [paper](arxiv).
        If you use the module for your scientific research, please consider citing us:
        
        ```
            bibtex snippet
        ```
        
        ## Currently Implemented Models
        The main feature of NEMtropy is (but not limited to) *network randomization*. 
        The specific kind of network to randomize and property to preserve defines the model you need:
        
        * **UBCM** *Undirected Binary Configuration Model* [[1]](#1)
        * **UECM** *Undirected Enhanced Configuration Model* [[1]](#1)
        * **DBCM** *Directed Binary Configuration Model* [[1]](#1)
        * **DECM** *Directed Binary Enhanced Model* [[1]](#1)
        * **CReMa** [[2]](#2)
        
        The following table may helps you identify the model that fits your needs in function of the type of network you are working with;
        for in-depth discussion please see the references.
        
        [...] | Undirected Graph | Directed Graph
        ----- | ---------------- | -------------- 
        **Binary Graph** | *UBCM* | *DBCM* 
        **Weighted Graph** | *UECM*, *CReMa*  | *DECM*, *CReMa*
        
        _References_
        
        * <a id="1">[1]</a>
            Squartini, Tiziano, Rossana Mastrandrea, and Diego Garlaschelli.
            "Unbiased sampling of network ensembles."
            New Journal of Physics 17.2 (2015): 023052.
            https://arxiv.org/abs/1406.1197
        * <a id="2">[2]</a>
            Parisi, Federica, Tiziano Squartini, and Diego Garlaschelli.
            "A faster horse on a safer trail: generalized inference for the efficient reconstruction of weighted networks."
            New Journal of Physics 22.5 (2020): 053053.
            https://arxiv.org/abs/1811.09829
        
        
        Installation
        ------------
        
        Menet can be installed via pip. You can get it from your terminal:
        
        ```
            $ pip install menet
        ```
        
        If you already install the package and wish to upgrade it,
        you can simply type from your terminal:
        
        ```
            $ pip install menet --upgrade
        ```
        
        Development
        -----------
        Please work on a feature branch and create a pull request to the development 
        branch. If necessary to merge manually do so without fast forward:
        
        ```
            $ git merge --no-ff myfeature
        ```
        
        To build a development environment run:
        
        ```
            $ python3 -m venv venv 
            $ source venv/bin/activate 
            $ pip install -e '.[dev]'
        ```
        
        Testing
        -------
        If you want to test the package integrity, you can run the following 
        bash command from the tests directory:
        
        ```
            $ bash run_all.sh
        ```
        
        __P.S.__ _at the moment there may be some problems with the DECM solver functions_
        
        Credits
        -------
        
        _Authors_:
        
        [Nicolò Vallarano](http://www.imtlucca.it/en/nicolo.vallarano/)(a.k.a. [nicoloval](https://github.com/nicoloval))
        
        [Emiliano Marchese](https://www.imtlucca.it/en/emiliano.marchese/) (a.k.a. [EmilianoMarchese](https://github.com/EmilianoMarchese))
        
        _Acknowledgements:_
        
        The module was developed under the supervision of [Tiziano Squartini](http://www.imtlucca.it/en/tiziano.squartini/) and  [Mario Zanon](http://www.imtlucca.it/it/mario.zanon/).
        It was developed at[IMT school of advanced studies Lucca] (https://www.imtlucca.it/). 
        
        _Fundings_
        
        Nicolò Vallarano aknoledges funding from _insert stuff_ 
        
Keywords: Network reconstruction,Networks Null Models,Maximum Entrophy Methods
Platform: UNKNOWN
Classifier: License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
Provides-Extra: dev
