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/

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
• 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)