Metadata-Version: 2.1
Name: netsci
Version: 0.0.1
Summary: Analyzing Complex Networks with Python
Home-page: https://github.com/gialdetti/netsci/
Author: Eyal Gal
Author-email: eyalgl@gmail.com
License: UNKNOWN
Description: # netsci
        Analyzing Complex Networks with Python
        
        
        |    Author    |                 Version                  |                   Demo                   |
        | :----------: | :--------------------------------------: | :--------------------------------------: |
        | Gialdetti | ![image](https://img.shields.io/pypi/v/jupyterthemes.svg) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/gialdetti/netsci/master?filepath=examples%2Fnotebooks%2Fnetwork_motifs.ipynb) |
        
        
        netsci is a python package for efficient statistical analysis of spatially-embedded networks. In addition, it offers efficient implementations of motif counting algorithms.
        For other models and metrics, we highly recommend using existing and richer tools. Noteworthy packages are the magnificent [NetworkX](https://networkx.github.io), [graph-tool](https://graph-tool.skewed.de) or [Brain Connectivity Toolbox](https://sites.google.com/site/bctnet/).
        
        ## Simple example
        Analyzing a star network (of four nodes)
        
        ```python
        >>> import numpy as np
        >>> import netsci.visualization as nsv
        >>> A = np.array([[0,1,1,1], [0,0,0,0], [0,0,0,0], [0,0,0,0]])
        >>> nsv.plot_directed_network(A, pos=[[0,0],[-1,1],[1,1],[0,-np.sqrt(2)]])
        ```
        ![Alt text](./examples/images/star4_network.png)
        
        
        ```python
        >>> import netsci.metrics.motifs as nsm
        >>> f = nsm.motifs(A, algorithm='brute-force')
        >>> print(f)
        [1 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0]
        ```
        
        ```python
        >>> nsv.bar_motifs(f)
        ```
        ![Alt text](examples/images/star4_motifs.png)
        
        
        ## Testing
        After installation, you can launch the test suite:
        ```bash
        $ pytest
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Description-Content-Type: text/markdown
