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    "info": {
        "author": "Eyal Gal",
        "author_email": "eyalgl@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
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        "description": "# netsci\nAnalyzing Complex Networks with Python\n\n\n|    Author    |                 Version                  |                   Demo                   |\n| :----------: | :--------------------------------------: | :--------------------------------------: |\n| 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) |\n\n\nnetsci is a python package for efficient statistical analysis of spatially-embedded networks. In addition, it offers efficient implementations of motif counting algorithms.\nFor 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/).\n\n## Simple example\nAnalyzing a star network (of four nodes)\n\n```python\n>>> import numpy as np\n>>> import netsci.visualization as nsv\n>>> A = np.array([[0,1,1,1], [0,0,0,0], [0,0,0,0], [0,0,0,0]])\n>>> nsv.plot_directed_network(A, pos=[[0,0],[-1,1],[1,1],[0,-np.sqrt(2)]])\n```\n![Alt text](./examples/images/star4_network.png)\n\n\n```python\n>>> import netsci.metrics.motifs as nsm\n>>> f = nsm.motifs(A, algorithm='brute-force')\n>>> print(f)\n[1 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0]\n```\n\n```python\n>>> nsv.bar_motifs(f)\n```\n![Alt text](examples/images/star4_motifs.png)\n\n\n## Testing\nAfter installation, you can launch the test suite:\n```bash\n$ pytest\n```\n\n\n",
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