Download and install¶
This page covers the necessary steps to install Nipype.
Install¶
The installation process is similar to other Python packages.
If you already have a Python environment setup that has the dependencies listed below, you can do:
easy_install nipype
or:
pip install nipype
Debian and Ubuntu¶
Add the NeuroDebian repository and install
the python-nipype package using apt-get or your favorite package
manager.
Mac OS X¶
The easiest way to get nipype running on Mac OS X is to install Anaconda or Canopy and then add nibabel and nipype by executing:
easy_install nibabel
easy_install nipype
From source¶
If you downloaded the source distribution named something
like nipype-x.y.tar.gz, then unpack the tarball, change into the
nipype-x.y directory and install nipype using:
python setup.py install
Note: Depending on permissions you may need to use sudo.
Testing the install¶
The best way to test the install is to run the test suite. If you have nose installed, then do the following:
python -c "import nipype; nipype.test()"
you can also test with nosetests:
nosetests --with-doctest /software/nipy-repo/masternipype/nipype
--exclude=external --exclude=testing
All tests should pass (unless you’re missing a dependency). If SUBJECTS_DIR variable is not set some FreeSurfer related tests will fail. If any tests fail, please report them on our bug tracker.
On Debian systems, set the following environment variable before running tests:
export MATLABCMD=$pathtomatlabdir/bin/$platform/MATLAB
where, $pathtomatlabdir is the path to your matlab installation and $platform is the directory referring to x86 or x64 installations (typically glnxa64 on 64-bit installations).
Avoiding any MATLAB calls from testing¶
On unix systems, set an empty environment variable:
export NIPYPE_NO_MATLAB=
This will skip any tests that require matlab.
Dependencies¶
Below is a list of required dependencies, along with additional software recommendations.
Must Have¶
Python 2.7
- Nibabel 1.0 - 1.4
- Neuroimaging file i/o library
- NetworkX 1.0 - 1.8
- Python package for working with complex networks.
NumPy 1.3 - 1.7
- SciPy 0.7 - 0.12
- Numpy and Scipy are high-level, optimized scientific computing libraries.
Enthought Traits 4.0.0 - 4.3.0
Dateutil 1.5 -
Strong Recommendations¶
- IPython 0.10.2 - 1.0.0
- Interactive python environment. This is necessary for some parallel components of the pipeline engine.
- Matplotlib 1.0 - 1.2
- Plotting library
RDFLib 4.1 RDFLibrary required for provenance export as RDF