Integration Testing:

Note: This demo was updated at the traiing evernt and is intended to run on OS X and GNU/Linux environments.

Information queried from NITRC-IR and stored in a google spreadsheet

https://docs.google.com/spreadsheets/d/11an55u9t2TAf0EV2pHN0vOd8Ww2Gie-tHp9xGULh_dA

Within your current environment

Setup

1. Make sure FSL is available in your environment and accessible from the command line.

2. If you already have a conda environment, please follow the detailed steps below.

3. If you do not have a conda environment, make sure you have curl and unzip commands available, and the following step will download and install a Python 2 conda environment with the appropriate Python packages:

curl -Ok https://raw.githubusercontent.com/ReproNim/simple_workflow/e4063fa95cb494da496565ec27c4ffe8a4901c45/Simple_Prep.sh
source Simple_Prep.sh

Run the demo

python run_demo_workflow.py --key 11an55u9t2TAf0EV2pHN0vOd8Ww2Gie-tHp9xGULh_dA
python check_output.py

To run on one subject you can do:

python run_demo_workflow.py --key 11an55u9t2TAf0EV2pHN0vOd8Ww2Gie-tHp9xGULh_dA -n 1

Detailed steps for setting up environment

Install miniconda if you do not have it.

For Linux:

curl -o miniconda.sh  http://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh

For OS X:

curl -o miniconda.sh  http://repo.continuum.io/miniconda/Miniconda2-latest-MacOSX-x86_64.sh

Setup miniconda:

chmod +x miniconda.sh
./miniconda.sh -b
conda config --add channels conda-forge

If you did not add miniconda to your environment (.bash_profile), execute to add to your current environment:

export PATH=$HOME/miniconda2/bin:$PATH

Download the Simple Workflow repository and create the specific versioned Python environment for Nipype

curl -OsSL https://github.com/ReproNim/simple_workflow/archive/master.zip
unzip master.zip
cd simple_workflow-master
conda env create -f environment.yml
source activate bh_demo
pip install https://github.com/satra/prov/archive/enh/rdf-1.x.zip

Within Docker

Using containerization solutions, such as docker, allows to create multiple complete computation environments while varying versions of any analysis pipeline components or inputs. You could use Simple_Prep_docker script to generate environments based on previous Debian or Ubuntu releases for which NeuroDebian builds of FSL were available in the past.

N.B. ATM NeuroDebian snapshots repository is not widely open yet, so if you would like to browse it, please "knock" first by running curl -s http://neuro.debian.net/_files/knock-snapshots command in your shell.

Generate an environment

For an example we will generate an environment based on Debian jessie release with FSL 5.0.8-3 as it was available in March of 2015:

./Simple_Prep_docker jessie 20150306T060524Z

which will generate a local docker image repronim:simple_prep_USER_jessie_20150306T060524Z (USER will correspond to your user name), with all necessary for computation components installed.

Run the demo

You can normally run a demo with only one additional step necessary -- setting up environment variables (to point to FSL binaries and enable conda environment):

docker run -it --rm repronim:simple_prep_USER_jessie_20150306T060524Z /bin/bash -c '
  . setup_environment;
  cd simple_workflow-master
  && python run_demo_workflow.py --key 11an55u9t2TAf0EV2pHN0vOd8Ww2Gie-tHp9xGULh_dA
  && python check_output.py'

which would generate a new temporary container, perform analysis, run the check, and quit.