Users who work at a facility with a site-wide install of MNE (e.g. Martinos center) are encouraged to contact their technical staff about how to access and use MNE, as the instructions might differ.
There are multiple options available for getting a suitable Python interpreter running on your system. However, for a fast and up to date scientific Python environment that resolves all dependencies, we recommend the Anaconda Python distribution.
Python has two major versions currently available, 2.7+ and 3.3+. Currently the 3D visualization dependencies Mayavi and PySurfer only run out of the box on Python 2.7, so we recommend using Python 2.7. You can get Anaconda 2.7 for Windows, OSX, and Linux download and installation instructions from the ContinuumIO site.
Once everything is set up, you should be able to check the version
python that is installed:
$ conda --version conda 4.2.14 $ which python /home/agramfort/anaconda/bin/python $ python --version Python 2.7.12 :: Continuum Analytics, Inc.
If your installation doesn’t look something like this, something went wrong and you should try to fix it. Try looking through the Anaconda documentation or Googling for Anaconda install tips (StackExchange results are often helpful).
You can then do this to resolve the MNE dependencies:
$ conda install scipy matplotlib scikit-learn mayavi ipython-notebook $ pip install PySurfer
Now that you have a working Python environment you can install MNE.
Users who would like a MATLAB-like interface should consider using Spyder,
which can easily be installed
$ conda install spyder.
There are a many options for installing MNE, but two of the most useful and common are:
Use the stable release version of MNE. It can be installed as:
$ pip install mne --upgrade
We tend to release about once every six months, and this command can be used to update the install after each release.
Use the development master version of MNE. If you want to be able to update your version between releases for bugfixes or new features, this will set you up for frequent updates:
$ git clone git://github.com/mne-tools/mne-python.git $ cd mne-python $ python setup.py develop
A feature of
python setup.py develop is that any changes made to
the files (e.g., by updating to latest
master) will be reflected in
mne as soon as you restart your Python interpreter. So to update to
the latest version of the
master development branch, you can do:
$ git pull origin master
and MNE will be updated to have the latest changes.
If you plan to contribute to MNE, please read how to Contribute to MNE.
To check that everything went fine, in ipython, type:
>>> import mne
If you get a new prompt with no error messages, you should be good to go!