Advanced setup of MNE-Python¶
When using MNE-Python within IPython or a Jupyter notebook, we strongly recommend using the Qt matplotlib backend for fast and correct rendering. On Linux, for example, Qt is the only matplotlib backend for which 3D rendering will work correctly. On macOS, certain matplotlib functions might not work as expected on backends other than Qt. Enabling Qt can be accomplished when starting IPython from a terminal:
$ ipython --matplotlib=qt
or in a Jupyter Notebook, you can use the “magic” command:
In : %matplotlib qt
This will create separate pop-up windows for each figure, and has the advantage that the 3D plots will retain rich interactivity (so, for example, you can click-and-drag to rotate cortical surface activation maps).
If you are creating a static notebook or simply prefer Jupyter’s inline plot display, MNE-Python will work with the standard “inline” magic:
In : %matplotlib inline
but some functionality will be lost. For example, mayavi scenes will still pop-up a separate window, but only one window at a time is possible, and interactivity within the scene is limited in non-blocking plot calls.
If you are using MNE-Python on Windows through IPython or Jupyter, you might
also have to use the IPython magic command
%gui qt after importing
MNE-Python, Mayavi or PySurfer (see here). For example:
In : from mayavi import mlab In : %gui qt
If you installed the
nb_conda_kernels package into your
environment (as recommended), you should be able to launch
notebooks from within the Anaconda Navigator GUI without having to explicitly
switch to the
mne environment first; look for
Python [conda env:mne]
when choosing which notebook kernel to use. Otherwise, be sure to activate the
mne environment before launching the notebook.
If you want access to the latest features and bugfixes, you can easily switch from the stable version of MNE-Python to the current development version.
In between releases, function and class APIs can change without warning.
For a one-time update to latest master, make sure you’re in the conda
environment where MNE-Python is installed (if you followed the default install
instructions, this will be
base), and use
pip to upgrade:
$ conda activate name_of_my_mne_environment $ pip install --upgrade --no-deps https://github.com/mne-tools/mne-python/archive/master.zip
If you plan to contribute to MNE-Python, or just prefer to use git rather than pip to make frequent updates, check out the contributing guide.
While the Anaconda Python distribution provides many conveniences, other
distributions of Python should also work with MNE-Python. In particular,
Miniconda is a lightweight alternative to Anaconda that is fully compatible;
like Anaconda, Miniconda includes the
conda command line tool for
installing new packages and managing environments; unlike Anaconda, Miniconda
starts off with a minimal set of around 30 packages instead of Anaconda’s
hundreds. See the installation instructions for Miniconda for more info.
It is also possible to use a system-level installation of Python (version 3.5
or higher) and use
pip to install MNE-Python and its dependencies, using
the provided requirements file:
curl --remote-name https://raw.githubusercontent.com/mne-tools/mne-python/master/requirements.txt pip install --user requirements.txt
Other configurations will probably also work, but we may be unable to offer support if you encounter difficulties related to your particular Python installation choices.
Some operations in MNE-Python can utilize NVIDIA CUDA GPU processing to speed up some operations (e.g. FIR filtering) by roughly an order of magnitude. To use CUDA, first ensure that you are running the NVIDIA proprietary drivers on your operating system, and then do:
$ conda install cupy $ MNE_USE_CUDA=true python -c "import mne; mne.cuda.init_cuda(verbose=True)" Enabling CUDA with 1.55 GB available memory
If you receive a message reporting the GPU’s available memory, CuPy is working properly. To permanently enable CUDA in MNE, you can do:
>>> mne.utils.set_config('MNE_USE_CUDA', 'true')
You can then test MNE CUDA support by running the associated test:
$ pytest mne/tests/test_filter.py -k cuda
If the tests pass, then CUDA should work in MNE. You can use CUDA in methods
that state that they allow passing
n_jobs='cuda', such as
and they should run faster than the CPU-based multithreading such as
On remote systems, it might be possible to use MESA software rendering
swr) for 3D visualization (with some tweaks).
For example, on CentOS 7.5 you might be able to use an environment variable
to force MESA to use modern OpenGL by using this before executing
$ export MESA_GL_VERSION_OVERRIDE=3.3
Also, it’s possible that different software rending backends might perform
better than others, such as using the
llvmpipe backend rather than
When upgrading MNE-Python to version 0.20, some macOS users may end up with conflicting versions of some of the 3D plotting dependencies. If you plot using the pyvista 3D backend and find that you can click-drag to rotate the brain, but cannot adjust any of the settings sliders, it is likely that your versions of VTK and/or QT are incompatible. This series of commands should fix it:
$ conda uninstall vtk $ pip uninstall -y pyvista $ conda install vtk $ pip install --no-cache pyvista
If you installed VTK using
pip rather than
conda, substitute the first
pip uninstall -y vtk.
If you run into trouble when visualizing source estimates (or anything else) using mayavi, you can try setting a couple of environment variables at the beginning of your script, session, or notebook:
>>> import os >>> os.environ['ETS_TOOLKIT'] = 'qt4' >>> os.environ['QT_API'] = 'pyqt5'
This will tell mayavi to use Qt backend with PyQt bindings, instead of the default PySide. For more information, see http://docs.enthought.com/mayavi/mayavi/building_applications.html#integrating-in-a-qt-application.