BigBang is a toolkit for studying communications data from collaborative projects. It currently supports analyzing mailing lists from Sourceforge, Mailman, or .mbox files.
You can use Anaconda. This will also install
conda package management system, which you can use to complete
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.)
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
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
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
The script takes an argument, the working group acronym
python bin/collect_draft_metadata.py -w httpbis
BigBang can also be used to analyze data from Git repositories.
Documentation on this feature can be found here.
unittest for automated tests.
To run the tests from the command like, use the command
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.
AGPL-3.0, see LICENSE for its text. This license may be changed at any time according to the principles of the project Governance.