Advanced setup

Working with Jupyter Notebooks and JupyterLab

If you like using Jupyter notebooks, you should also update the “base” conda environment to include the nb_conda_kernels package; this will make it easier to use MNE-Python in Jupyter Notebooks launched from the Anaconda GUI:

$ conda install --name=base nb_conda_kernels

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 [1]: %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 [1]: %matplotlib inline

but some functionality will be lost. For example, PyVista 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.

Windows

If you are using MNE-Python on Windows through IPython or Jupyter, you might also have to use the IPython magic command %gui qt (see here). For example:

In [2]: %gui qt

If you installed the nb_conda_kernels package into your base environment (as recommended), you should be able to launch mne-capable 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 use another Python setup and you encounter some difficulties please report them on the MNE Forum or on the GitHub issues page to get assistance.

It is also possible to interact with the 3D plots without installing Qt by using the notebook 3d backend:

In [1]: import mne
In [2]: mne.viz.set_3d_backend("notebook")

The notebook 3d backend requires PyVista to be installed along with other packages, please follow Install via pip or conda.

Installing to a headless Linux server

First, follow the standard installation instructions. Next, you can choose to install the osmesa (off-screen MESA) VTK variant, which avoids the need to use Xvfb to start a virtual display (and have a sufficiently updated MESA to render properly):

$ conda install -c conda-forge "vtk>=9.2=*osmesa*" "mesalib=21.2.5"

Using the development version

See Upgrading to the development version for how to do a one-time update to the latest development version of MNE-Python. If you plan to contribute to MNE-Python, or just prefer to use git rather than pip to make frequent updates, there are instructions for installing from a git clone in the Contributing guide.

Choosing the Qt framework

The conda-forge version of MNE-Python ships with PyQt5. If you would like to use a different binding, you can instead install MNE-Python via pip:

$ pip install "mne[full]"          # uses PyQt6
$ pip install "mne[full-pyqt6]"    # same as above
$ pip install "mne[full-pyside6]"  # use PySide6
$ pip install "mne[full-no-qt]"    # don't install any Qt binding

GPU acceleration with CUDA

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 mne.io.Raw.filter() and mne.io.Raw.resample(), and they should run faster than the CPU-based multithreading such as n_jobs=8.

Off-screen rendering with MESA

On remote Linux systems, it might be possible to use MESA software rendering (such as llvmpipe or 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 spyder or python:

$ 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 swr. In newer MESA (21+), rendering can be incorrect when using MSAA, so consider setting:

$ export MNE_3D_OPTION_MULTI_SAMPLES=1

MESA also can have trouble with full-screen antialiasing, which you can disable with:

$ export MNE_3D_OPTION_ANTIALIAS=false

or by doing mne.viz.set_3d_options(antialias=False) within a given Python session.

Some hardware-accelerated graphics on linux (e.g., some Intel graphics cards) provide an insufficient implementation of OpenGL, and in those cases it can help to force software rendering instead with something like:

$ export LIBGL_ALWAYS_SOFTWARE=true

Another issue that may come up is that the MESA software itself may be out of date in certain operating systems, for example CentOS. This may lead to incomplete rendering of some 3D plots. A solution is described in this Github comment. It boils down to building a newer version (e.g., 18.3.6) locally following a variant of these instructions. If you have CentOS 7 or newer, you can also try some prebuilt binaries we made. After downloading the files, untar them and add them to the appropriate library paths using the following commands:

$ tar xzvf mesa_18.3.6_centos_lib.tgz
$ export LIBGL_DRIVERS_PATH="${PWD}/lib"
$ export LD_LIBRARY_PATH="${PWD}/lib"

To check that everything went well, type the following:

$ glxinfo | grep "OpenGL core profile version"

which should give:

OpenGL core profile version string: 3.3 (Core Profile) Mesa 18.3.6

Another way to check is to type:

$ mne sys_info

and it should show the right version of MESA:

...
pyvista:       0.27.4 {pyvistaqt=0.2.0, OpenGL 3.3 (Core Profile) Mesa 18.3.6 via llvmpipe (LLVM 3.4, 256 bits)}
...

Troubleshooting 3D plots

3D plotting trouble after upgrade on macOS

When upgrading MNE-Python from version 0.19 or lower, 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 line for pip uninstall -y vtk.