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README.md

BigBang

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

Gitter

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.

Run the following commands:

git clone https://github.com/datactive/bigbang.git
conda create -n bigbang
cd bigbang
bash conda-setup.sh
source activate bigbang

(If you use a different conda environment name, you'll need to modify conda-setup.sh to match.)

pip installation

Alternatively you can use pip for installation. Run the following commands:

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

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:

python 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.

python 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

python 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.

Unit tests

We use unittest for automated tests.

To run the tests from the command like, use the command pytest.

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.

License

AGPL-3.0, see LICENSE for its text. This license may be changed at any time according to the principles of the project Governance.

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