BigBang

BigBang is a toolkit for studying communications data from collaborative projects. It currently supports analyzing mailing lists from Sourceforge, Mailman, or .mbox files.

Installation*

You can use Anaconda. This will also install the conda package management system, which you can use to complete installation.

Install Anaconda, with Python version 3.*.

If you choose not to use Anaconda, you may run into issues with versioning in Python. Add the Conda installation directory to your path during installation.

You also need need to have Git and Pip (for Python3) installed.

Run the following commands:

git clone https://github.com/datactive/bigbang.git
cd bigbang
bash conda-setup.sh
python3 setup.py develop --user

Usage

There are serveral Jupyter notebooks in the examples/ directory of this repository. To open them and begin exploring, run the following commands in the root directory of this repository:

source activate bigbang
ipython notebook examples/

Collecting mail archives

BigBang comes with a script for collecting files from public Mailman web archives. An example of this is the scipy-dev mailing list page. To collect the archives of the scipy-dev mailing list, run the following command from the root directory of this repository:

python3 bin/collect_mail.py -u http://mail.python.org/pipermail/scipy-dev/

You can also give this command a file with several urls, one per line. One of these is provided in the examples/ directory.

python3 bin/collect_mail.py -f examples/urls.txt

Once the data has been collected, BigBang has functions to support analysis.

Collecting IETF draft metadata

BigBang can also be used to analyze data from IETF drafts.

It does this using the Glasgow IPL group's ietfdata tool.

The script takes an argument, the working group acronym

python3 bin/collect_draft_metadata.py -w httpbis

Git

BigBang can also be used to analyze data from Git repositories.

Documentation on this feature can be found here.

Development

Unit tests

To run the automated unit tests, use: pytest tests/unit.

Our current goal is code coverage of 60%. Add new unit tests within tests/unit. Unit tests run quickly, without relying on network requests.

Documentation

Docstrings are preferred, so that auto-generated web-based documentation will be possible (#412). You can follow the Google style guide for docstrings.

Formatting

Run pre-commit install to get automated usage of black, flake8 and isort to all Python code files for consistent formatting across developers. We try to follow the PEP8 style guide.

Community

If you are interested in participating in BigBang development or would like support from the core development team, please subscribe to the bigbang-dev mailing list and let us know your suggestions, questions, requests and comments. A development chatroom is also available.

In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to make participation in our project and our community a harassment-free experience for everyone.

Troubleshooting

If the installation described above does not work, you can try to run the installation with Pip:

git clone https://github.com/datactive/bigbang.git
# optionally create a new virtualenv here
pip3 install -r requirements.txt
python3 setup.py develop --user

If you have problems installing, you might want to have a look at the video tutorial below (clicking on the image will take you to YouTube).

License

MIT, see LICENSE for its text. This license may be changed at any time according to the principles of the project Governance.

Christovis/bigbang