About

Tribe is a utility that will allow you to extract a network (a graph) from a communication network that we all use often - our email. Tribe is designed to read an email mbox (a native format for email in Python)and write the resulting graph to a GraphML file on disk. This utility is generally used for District Data Labs' Graph Analytics with Python and NetworkX course, but can be used for anyone interested in studying networks.

Contributing

Tribe is open source, and I'd love your help. If you would like to contribute, you can do so in the following ways:

  1. Add issues or bugs to the bug tracker: https://github.com/DistrictDataLabs/tribe/issues
  2. Work on a card on the dev board: https://waffle.io/DistrictDataLabs/tribe
  3. Create a pull request in Github: https://github.com/DistrictDataLabs/tribe/pulls

Note that labels in the Github issues are defined in the blog post: How we use labels on GitHub Issues at Mediocre Laboratories.

If you are a member of the District Data Labs Faculty group, you have direct access to the repository, which is set up in a typical production/release/development cycle as described in A Successful Git Branching Model. A typical workflow is as follows:

  1. Select a card from the dev board - preferably one that is "ready" then move it to "in-progress".

  2. Create a branch off of develop called "feature-[feature name]", work and commit into that branch.

    ~$ git checkout -b feature-myfeature develop
    
  3. Once you are done working (and everything is tested) merge your feature into develop.

    ~$ git checkout develop
    ~$ git merge --no-ff feature-myfeature
    ~$ git branch -d feature-myfeature
    ~$ git push origin develop
    
  4. Repeat. Releases will be routinely pushed into master via release branches, then deployed to the server.

Contributors

Thank you for all your help contributing to make Tribe a great project!

Maintainers

Contributors

  • Your name welcome here!

Changelog

The release versions that are sent to the Python package index (PyPI) are also tagged in Github. You can see the tags through the Github web application and download the tarball of the version you'd like.

The versioning uses a three part version system, "a.b.c" - "a" represents a major release that may not be backwards compatible. "b" is incremented on minor releases that may contain extra features, but are backwards compatible. "c" releases are bug fixes or other micro changes that developers should feel free to immediately update to.

Version 1.3

  • tag: v1.3
  • release: Wednesday, July 6, 2016
  • commit: see tag

After some feedback about the length of time it was taking to create the edges in the NetworkX graph, we modified the FreqDist object to memoize calls to N, B, and M. This means that on a per edge basis, far fewer complete traversals of the distribution are carried out. Already we have observed minutes worth of performance improvements as a result. The Graph also now carries more information including edge weights by frequency, count, and by L1 norm. The Graph itself carries email count and file size information data alongside other information.

Version 1.2

  • tag: v1.2
  • release: Wednesday, June 22, 2016
  • commit: cac3d6c

In this release we have improved some of the handling code to make things a bit more robust with students who work on a variety of operating systems. For example we have added a progress indicator so that something appears to be happening on very large mbox files (and you're not left wondering). Additionally we have added better error handling so one bad email doesn't ruin your day. We also made the library Python 2.7 and Python 3.5 compatible with a better test suite.

Version 1.1.2

  • tag: v1.1.2
  • release: Thursday, November 20, 2014
  • deployment: Friday, March 11, 2016
  • commit: 69fe3c6

This is the initial release of Tribe that has been used for teaching since the first SNA workshop in 2014. This version was cleaned up a bit, with extra dependency removal and better organization. This is also the first version that was deployed to PyPI.