graphworks 0.5.0

Graph theoretic classes and helper functions.

Graphworks

A Python module for efficient graph theoretic programming

Usage

See the wiki

TLDR

First, pip install graphworks

import json
from src.graphworks.graph import Graph

json_graph = {"label": "my graph", "edges": {"A": ["B"], "B": []}}
graph = Graph("my graph", input_graph=json.dumps(json_graph))
print(graph)

Development

Requirements

• Python 3.8+
• virtualenv
• numpy
• graphviz

Install the required packages

pip install virtualenv
virtualenv env

Start the virtualenv

source ./env/bin/activate

You can deactivate the virtualenv with

deactivate

Lastly, install the required libraries

pip install -r requirements.txt

Building the package

• Update the version number in graphworks.__init__.py
• Run python -m build
• Run twine check dist/*
• Upload to test PyPi: twine upload --repository-url https://test.pypi.org/legacy/ dist/*
• Upload to PyPi main: twine upload --skip-existing dist/*
• To autopublish, tag commit with git tag -a vX.Y.Z -m 'release message
• Then git push --tags

Diagnostics

• Run the unit tests: python -m unittest discover tests '*_tests.py'
• Run unit test coverage: coverage run --source=graphworks/ -m unittest discover tests '*_tests.py'
• Generate test coverage reports (either works):
  • coverage report --omit="*/test*,*/venv/*"
  • coverage html --omit="*/test*,*/venv/*"

TODO

• Create Vertex class
• https://www.python-course.eu/graphs_python.php
• Build out directed graphs algorithms
  • https://algs4.cs.princeton.edu/42digraph/
• Allow for weighted graph algorithms
  • Jarnik's algorithm
  • Dijkstra's algorithm
• C++ binaries for speeding up graph computations