https://github.com/ryu577/graphing

https://github.com/ryu577/graphing

graph network

Last synced: 7 days ago
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• Host: GitHub
• URL: https://github.com/ryu577/graphing
• Owner: ryu577
• License: mit
• Created: 2020-12-17T09:55:16.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2023-10-09T22:29:09.000Z (about 1 year ago)
• Last Synced: 2024-10-05T18:16:43.710Z (about 1 month ago)
• Topics: graph, network
• Language: Python
• Homepage:
• Size: 142 KB
• Stars: 6
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# graphing

This Python library provides several graphing-related utilities that can be used to apply graph theory concepts and graph algorithms to a variety of problems.

## Getting Started

This library is available for use on PyPI here: [https://pypi.org/project/graphing/](https://pypi.org/project/graphing/)

For local development, do the following. 

- Clone this repository.

- Set up and activate a Python3 virtual environment using `conda`. More info here: [https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-with-commands](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-with-commands)

- Navigate to the `graphing` repo.

- Run the command: `python3 setup.py install` to install the package in the conda virtual environment. 

- As development progresses, run the above command to update the build in the conda virtual environment.

## Sample Code

Try to run the following sample code:

> from graphing.special_graphs.neural_trigraph.path_cover import min_cover_trigraph

> 

> from graphing.special_graphs.neural_trigraph.rand_graph import *

> ## Generate a random neural trigraph. Here, it is two sets of edges between layers 1 and 2 (edges1) and layers 2 and 3 (edges2)

> edges1, edges2 = neur_trig_edges(7, 3, 7, shuffle_p=.05)

> ## Find the full-path cover for this neural trigraph.

> paths1 = min_cover_trigraph(edges1, edges2)

> 

> print(paths1)