Installation

Note

For the moment, ariaec Command Line Interface (CLI) is only compatible for Python 2.7 but the transition to Python 3 is planned in the near future. Installation procedure may change in the future especially after the transition to Python 3 which should be the next major update.

Warning

This package contains patches for CNS-solve which needs to be compiled with the sources after the installation. Please follow post installation instruction before using aria2 pipeline.

Dependencies

Before starting the installation procedure, be sure to check if dependencies below are actually correctly installed.

  • pip (>= 18.0)
  • git (>= 2.0)
  • cns-solve (1.21)
  • ccpnmr analysis (optional)
  • ccpn data model (optional)

CNS-solve

Academic users can obtain a free version of CNS from the CNS web site. Commercial users need to purchase a CNX license from Accelrys (MSI).

CCPNMR

In order to use the CCPN data model for import/export or data conversion, you also need to install the CCPN data model, and the CCPNMR Analysis software in case you want to export restraint lists, NOE assignments, and analyses of the generated structures, e.g. the result of the violation analysis. The complete CCPN distribution including the API, the FormatConverter, and CCPNMR Analysis can be obtained here.

The CCPN distribution provides scripts which guide you through the installation process.

Virtual environments

The usual installation process is to install the package inside a virtual environment for Python. In order to create a virtual environment, we need an environment manager like virtualenv (or venv if we use Python 3) with the package manager pip. Another possibility is to use the conda CLI from the Anaconda Python distribution which is a package and an environment manager.

Note

This step is optional but strongly recommended in order to avoid overlaps between Python packages already installed in your global/local environment.

virtualenv

To install the latest stable release, do the following steps on your terminal :

  1. Install or update pip and virtualenv in your current python environment
python -m pip install --user --upgrade pip virtualenv

2. Go to your project directory and create a virtual environment by giving the name of a directory to place the new virtual environment (e.g. venv).

python -m virtualenv venv
  1. Activate the new environment.
source venv/bin/activate

Note

With this procedure, you will need to activate the environment before any call to ariaec CLI with source venv/bin/activate

conda

If you have Anaconda distribution instead of , you can use conda can perform the same operations as virtualenv.

  1. Create an environment
conda create --name ariaec python=2.7
  1. Activate the environment
source activate ariaec

Python Package Index

Since ariaec is hosted on gitlab, we will use pip with git in order to install the package.

pip install git+https://gitlab.pasteur.fr/bis-aria/ariaec

Note

If you don’t use a virtual environment for Python, it is recommemded to use the flag --user in order to install ariaec to the Python user install directory (typically ~/.local folder)

From source

Another possibility is to download the ZIP or TAR version from Gitlab and use the following steps to install the package.

  1. Download ZIP or TAR version from Gitlab website or through command line
wget -O ariaec-master.tar.gz https://gitlab.pasteur.fr/bis-aria/ariaec/repository/master/archive.tar.gz
mkdir ariaec-master && tar xzf ariaec-master.tar.gz -C ariaec-master --strip-components 1
  1. You can then call pip locally to install the package in your environment.
cd ariaec-master
mkdir build_aria && cp -r src setup.py PKG-INFO MANIFEST.in build_aria/
cd build_aria
pip install --user .

Note

Local pip install may lead to unexpected computation time if your directory contains more than the ARIA package. In most cases, this is due to virtualenv folders within the same directory. In those situations, the pip command will also look into virtualenvs folders. A simple workaround to solve this issue is to create a empty folder for the installation as described above.

Singularity

For the sake of reproductibility, a Singularity recipe is also available allowing cross platform and legacy OS support.

sudo singularity build aria.simg aria.shub

The CLI described in [usage](usage.rst) section will then be accessible by calling the generated singularity image.

singularity run aria.simg COMMAND

Dockerfile

Developers can also build their own Docker container using the Dockerfile [1].

Post installation instructions

ARIA CNS

The only piece of Fortran code that needs to be compiled together with your CNS sources can be found in src/aria/cns/src. Only the code for floating chirality assignment is being used. The procedure has not changed since ARIA version 1.2 :

1. Please copy all the ARIA fortran files located in the subdirectory src/aria/cns/src to the directory cns_solve_1.1/source of the unpacked cns_solve distribution (this will overwrite some files which is okay).

2. Edit the file cns_solve_env in the directory cns_solve_1.1 to define the location of the CNSsolve directory.

3. Go to the directory cns_solve_1.1 and type make install. This should start the compilation process.

[1]In order to build it, you have to be root on your computer.