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INSTALL.md

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How to install NARPS Open Pipelines ?

1 - Get the code

First, fork the repository, so you have your own working copy of it.

Then, you have two options to clone the project :

Option 1: Using DataLad (recommended)

Cloning the fork using Datalad will allow you to get the code as well as "links" to the data, because the NARPS data is bundled in this repository as datalad subdatasets.

datalad install --recursive https://github.com/YOUR_GITHUB_USERNAME/narps_open_pipelines.git

Option 2: Using Git

Cloning the fork using git ; by doing this, you will only get the code.

git clone https://github.com/YOUR_GITHUB_USERNAME/narps_open_pipelines.git

2 - Get the data

Ignore this step if you used DataLad (option 1) in the previous step.

Otherwise, there are several ways to get the data.

3 - Set up the environment

The Narps Open Pipelines project is build upon several dependencies, such as Nipype but also the original software packages used by the pipelines (SPM, FSL, AFNI...).

To facilitate this step, we created a Docker container based on Neurodocker that contains the necessary Python packages and software. To install the Docker image, two options are available.

Option 1: Using Dockerhub

docker pull elodiegermani/open_pipeline:latest

The image should install itself. Once it's done you can check the image is available on your system:

docker images
   docker.io/elodiegermani/open_pipeline    latest    0f3c74d28406    9 months ago    22.7 GB

Option 2: Using a Dockerfile

The Dockerfile used to create the image stored on DockerHub is available at the root of the repository (Dockerfile). But you might want to personalize this Dockerfile. To do so, change the command below that will generate a new Dockerfile:

docker run --rm repronim/neurodocker:0.7.0 generate docker \
           --base neurodebian:stretch-non-free --pkg-manager apt \
           --install git \
           --fsl version=6.0.3 \
           --afni version=latest method=binaries install_r=true install_r_pkgs=true install_python2=true install_python3=true \
           --spm12 version=r7771 method=binaries \
           --user=neuro \
           --workdir /home \
           --miniconda create_env=neuro \
                       conda_install="python=3.8 traits jupyter nilearn graphviz nipype scikit-image" \
                       pip_install="matplotlib" \
                       activate=True \
           --env LD_LIBRARY_PATH="/opt/miniconda-latest/envs/neuro:$LD_LIBRARY_PATH" \
           --run-bash "source activate neuro" \
           --user=root \
           --run 'chmod 777 -Rf /home' \
           --run 'chown -R neuro /home' \
           --user=neuro \
           --run 'mkdir -p ~/.jupyter && echo c.NotebookApp.ip = \"0.0.0.0\" > ~/.jupyter/jupyter_notebook_config.py' > Dockerfile

When you are satisfied with your Dockerfile, just build the image:

docker build --tag [name_of_the_image] - < Dockerfile

When the image is built, follow the instructions in docs/environment.md to start the environment from it.