NIMS is a scientific data management system, specifically designed for neuroimaging data. It is implemented in Python, based on TurboGears. NIMS is open-source software, released under the MIT license (see below for details).
Set up a virtual python environment:
sudo apt-get install build-essential python-dev python-virtualenv python-numpy python-imaging python-h5py
virtualenv --system-site-packages tg2env
source tg2env/bin/activate
Install the TurboGears development tools:
easy_install -i http://turbogears.org/2.2/downloads/2.2.0 tg.devtools
Clone NIMS from GitHub and install its dependencies:
git clone git@github.com:cni/nims.git
cd nims
python setup.py develop
easy_install "repoze.what >= 1.0.8" "repoze.what-quickstart"
Optionally, add PostgreSQL and LDAP support:
sudo apt-get install postgresql-client libpq-dev
easy_install psycopg2
sudo apt-get install libldap2-dev libsasl2-dev
easy_install python-ldap
Generate a development database and start the paste web server:
paster setup-app development.ini --name=nimsgears
paster serve --reload development.ini
Set up a virtual python environment:
apt-get install build-essential python-dev python-virtualenv php5 libapache2-mod-wsgi libapache2-webauth
virtualenv --system-site-packages /var/local/tg2env
source /var/local/tg2env/bin/activate
Clone NIMS from GitHub and install its dependencies:
easy_install -i http://turbogears.org/2.2/downloads/2.2.0 tg.devtools
git clone git://github.com/cni/nims.git /var/local/nims
cd !$
python setup.py develop
Add PostgreSQL and LDAP support:
apt-get install postgresql-client libpq-dev python-ldap
easy_install psycopg2
Generate the production database:
# first, obtain or create production.ini
paster setup-app production.ini
Configure and restart Apache:
ln -s $PWD/apache.conf /etc/apache2/conf.d/nims.conf
mkdir /var/cache/nimsgears && chown www-data:www-data /var/cache/nimsgears
service apache2 restart
Once you have the NIMS web GUI running, you need to get your data into NIMS. We use various utilities to pull data from the MR scanner and insert it into NIMS. We use a suite of python scripts (various reapers, sorter.py, scheduler.py and processor.py, all in nimsproc) to do this. The reapers (dicomreaper.py, pfilereaper.py) copy data from the scanner and put it in the NIMS staging area. The sorter moves data from the staging area into the NIMS storage area and creates appropriate database entries. The scheduler finds new datasets in the database and schedules processing jobs for them. The processor executes the scheduled jobs. Some processing steps involve finding peripheral data files associated with the dataset, such as physio files. This is also where NIfTI creation happens and image pyramids are generated. All these tools have nice command-line help, so usage should be self-explanatory.
You also need to run a process to maintain the shadow filesystem, which is a view into the data files that provides human-friendly paths, which are based on the database (e.g., all datasets for an experiment "exp1" will appear in a directory called "exp1"). Each user gets their own shadow filesystem, and it allows them to have read-only access to the data through, e.g., scp. This shadow filesystem is rebuilt periodically to keep it in sync with the current DB structure (see nimsutil/symlinks.py).
We usually launch all these processes using screen, so that we can connect to the screen session from anywhere to check on them. We provide an example .screenrc file that launches all the processes in separate screens (run as "screen -c reaper.screenrc").
Data are automatically organized by the sorter using the PatientID field from DICOM files (or the analogous field in the raw MR data files). The format of the Patient ID string is PI_UID/experiment_name.
Copyright (c) 2014 The Board of Trustees of the Leland Stanford Junior University
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