Contents¶
Overview¶
Update: This is now merged into networkx package (via networkx/#3127). See networkx.algorithms.coloring.equitable_color.
| docs | |
|---|---|
| tests |   |
| package |      |
Equitable coloring for networkX graphs.
From Wikipedia:
In graph theory [..] an equitable coloring is an assignment of colors to the vertices of an undirected graph, in such a way that
- No two adjacent vertices have the same color, and
- The numbers of vertices in any two color classes differ by at most one.
Kierstead et. al. have provided a fast polynomial time algorithm for uncovering an equitable coloring using r + 1 colors for a graph with maximum degree r.
This package is an implementation of the algorithm for networkX graphs.
- Free software: MIT license
Installation¶
pip install equitable-coloring
Usage¶
To use equitable-coloring:
>>> import networkx as nx
>>> from equitable_coloring import equitable_color
>>> from equitable_coloring.utils import is_equitable
>>> G = nx.cycle_graph(4)
>>> d = equitable_color(G, num_colors=3)
>>> is_equitable(G, d)
True
Documentation¶
Development¶
To run the all tests run:
pip install pytest-cov # Needed the first time.
python setup.py test
Or, you can use tox.
Usage¶
To use equitable-coloring in a project:
>>> import networkx as nx
>>> from equitable_coloring import equitable_color
>>> from equitable_coloring.utils import is_equitable
>>> G = nx.cycle_graph(4)
>>> d = equitable_color(G, num_colors=3)
>>> is_equitable(G, d)
True
Contributing¶
Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.
Bug reports¶
When reporting a bug please include:
- Your operating system name and version.
- Any details about your local setup that might be helpful in troubleshooting.
- Detailed steps to reproduce the bug.
Documentation improvements¶
equitable-coloring could always use more documentation, whether as part of the official equitable-coloring docs, in docstrings, or even on the web in blog posts, articles, and such.
Feature requests and feedback¶
The best way to send feedback is to file an issue at https://github.com/musically-ut/equitable-coloring/issues.
If you are proposing a feature:
- Explain in detail how it would work.
- Keep the scope as narrow as possible, to make it easier to implement.
- Remember that this is a volunteer-driven project, and that code contributions are welcome :)
Development¶
To set up equitable-coloring for local development:
Fork equitable-coloring (look for the “Fork” button).
Clone your fork locally:
git clone git@github.com:your_name_here/equitable-coloring.git
Create a branch for local development:
git checkout -b name-of-your-bugfix-or-feature
Now you can make your changes locally.
When you’re done making changes, run all the checks, doc builder and spell checker with tox one command:
toxCommit your changes and push your branch to GitHub:
git add . git commit -m "Your detailed description of your changes." git push origin name-of-your-bugfix-or-feature
Submit a pull request through the GitHub website.
Pull Request Guidelines¶
If you need some code review or feedback while you’re developing the code just make the pull request.
For merging, you should:
- Include passing tests (run
tox) [1]. - Update documentation when there’s new API, functionality etc.
- Add a note to
CHANGELOG.rstabout the changes. - Add yourself to
AUTHORS.rst.
| [1] | If you don’t have all the necessary python versions available locally you can rely on Travis - it will run the tests for each change you add in the pull request. It will be slower though … |
Tips¶
To run a subset of tests:
tox -e envname -- pytest -k test_myfeature
To run all the test environments in parallel (you need to pip install detox):
detox
Authors¶
- Utkarsh Upadhyay - https://musicallyut.in