FastGrow Server

Server backend of the FastGrow web application. FastGrow is a fast fragment growing approach using a shape-based algorithm. A detailed explanation of the method can be found in the accompanying paper:

Patrick Penner, Virginie Martiny, Louis Bellmann, Florian Flachsenberg, Marcus Gastreich, Christophe Meyer and Matthias Rarey (submitted) FastGrow: On-the-Fly Growing and its Application to DYRK1A

A running instance of the web application can be found at https://grow.zbh.uni-hamburg.de/

Core FastGrow functionality will be available in BioSolveIT’s SeeSAR modeling package.

Setup

Get python dependencies with conda.

conda create --name fastgrow -c anaconda -c conda-forge python=3.9 django celery psycopg2 redis redis-py vine pylint pylint-django coverage selenium
conda activate fastgrow

At this point either set the environment variables $USER, $PASSWORD and $DATABASE to the ones configured in the fast\_grow\_server/settings.py or replace them with appropriate values below.

fast_grow_server requires a postgres database and a separate database user. You can create a user with appropriate permissions with this:

psql -d postgres -c "CREATE ROLE $USER WITH ENCRYPTED PASSWORD '$PASSWORD'; ALTER ROLE $USER WITH LOGIN CREATEDB;"

To create the fast_grow_server database execute the following:

psql -d postgres -c "CREATE DATABASE $DATABASE;"

Perform the fast_grow_server migrations with:

python manage.py migrate

Ensure the bin directory exists and contains the following binaries:

• bin/Clipper
• bin/DatabaseBuilder
• bin/FastGrow
• bin/Preprocessor

The default configured backend and result system for celery is redis. Redis must be installed and available at the url configured in the fast\_grow\_server/settings.py.

To run the tests execute:

python manage.py test

Celery workers can be run with:

celery -A fast_grow_server worker --loglevel=INFO -O fair

...or in a more debug and IDE friendly way:

python /path/to/env/fastgrow/bin/celery -A fast_grow_server worker --loglevel=INFO -O fair

To start the server run:

python manage.py runserver

...which should start the server at http://localhost:8000/

Code Quality

Contributions to the project must comply to the following quality criteria to keep the code maintainable for all developers:

Criteria	Threshold
pylint	>9.0
coverage (overall)	>90%
coverage (single file)	>80%

Run pylint for static code quality check with

find fast_grow* -type f -name "*.py" | xargs pylint --load-plugins pylint_django --django-settings-module=fast_grow_server.settings

Run test coverage

coverage run --source=fast_grow manage.py test
coverage report