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atmosphere ☁️

Build Status Coverage Code Health

Atmosphere addresses the growing needs for highly configurable and customized computational resources to support research efforts in plant sciences. Atmosphere is an integrative, private, self-service cloud computing platform designed to provide easy access to preconfigured, frequently used analysis routines, relevant algorithms, and data sets in an available-on-demand environment designed to accommodate computationally and data-intensive bioinformatics tasks.

Installation

Install the required python packages

pip install -r requirements.txt

A separate environment is provided for developers

pip install -r dev_requirements.txt

The *requirements.txt files are generated using pip-tools. See REQUIREMENTS.md for instructions on using pip-tools and upgrading packages in Atmosphere.

Some Features

  • A powerful web client for management and administration of virtual machines
  • A fully RESTful API service for integrating with existing infrastructure components
  • Virtual machine images preconfigured for computational science and iPlant's infrastructure

Running scripts

There are several utility scripts in ./scripts. To run these:

cd <path to atmosphere>
export DJANGO_SETTINGS_MODULE='atmosphere.settings'
export PYTHONPATH="$PWD:$PYTHONPATH"
python scripts/<name of script>

Contributing

See HACKING.md.

Coding Style

We rely on a tool to format the codebase. If you install from dev_requirements.txt, the yapf binary will be in your environment.

yapf --in-place --parallel   -- $(git ls-files | grep '\.py$')

When master is pulled, it's helpful to know if a pip install or a manage.py migrate is necessary. To get other helpful warnings:

ln -s $(pwd)/contrib/post-merge.hook $(pwd)/.git/hooks/post-merge

Coding Conventions

Import ordering

Imports should be grouped into the sections below and in sorted order.

  1. Standard libraries
  2. Third-party libraries
  3. External project libraries
  4. Local libraries

License

See LICENSE.txt for license information

Lead

Authors

The following individuals who have help/helped make ☁️ great appear in alphabetic order, by surname.

Where the cloud lives!

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Languages

  • Python 93.2%
  • Gherkin 3.3%
  • HTML 1.7%
  • Shell 1.6%
  • Other 0.2%