bel-commons 0.3.1

Installation

BEL Commons can be installed easily from PyPI with the following code in your favorite shell:

$ pip install bel_commons

Get the latest code on GitHub with:

$ python3 -m pip install git+https://github.com/bel-commons/bel-commons.git

It’s also suggested to use a relational database management system like PostgreSQL and install their corresponding connectors:

$ python3 -m pip install psycopg2-binary

Run BEL Commons

A test server can be easily run with:

$ bel-commons run

A more powerful server like gunicorn can also be used like:

$ gunicorn bel_commons.wsgi:flask_app

Running with the Parser

To run the parser, you’ll need an instance of a message queue like RabbitMQ (or any other message queue supported by Celery), a results backend like Redis, and a worker. It’s best to run in docker if you want to do this.

Run with Docker

Clone this repo from GitHub

$ git clone https://github.com/bel-commons/bel-commons.git
$ cd bel-commons

Create a file called .env and generate both SECRET_KEY and SECURITY_PASSWORD_SALT.

SECRET_KEY=mypassword
SECURITY_PASSWORD_SALT=mypassword
BUTLER_NAME="BEL Commons Butler"
BUTLER_EMAIL=bel@example.com
BUTLER_PASSWORD=butlerpassword

Run docker compose:

$ docker-compose up

Ports exposed:

• 5002: BEL Commons web application

• 5432: PostgreSQL database

Reset the Database

For the times when you just have to burn it down and start over:

1. bel-commons manage drop will nuke the database and output a user list

2. bel-commons manage load will automatically add the most recently exported user list

3. bel-commons manage examples load will automatically load some example networks and data sets

Supporters

This project has been supported by several organizations:

• University of Bonn

• Bonn Aachen International Center for IT

• Fraunhofer Institute for Algorithms and Scientific Computing

• Fraunhofer Center for Machine Learning

• IMI (in the AETIONOMY project)

Logo

The BEL Commons logo was designed by Scott Colby.