Design philosophy

Interactive versus scripted analysis

MNE-Python has some great interactive plotting abilities that can help you explore your data, and there are a few GUI-like interactive plotting commands (like browsing through the raw data and clicking to mark bad channels, or click-and-dragging to annotate bad temporal spans). But in general it is not possible to use MNE-Python to mouse-click your way to a finished, publishable analysis. MNE-Python works best when you assemble your analysis pipeline into one or more Python scripts. On the plus side, your scripts act as a record of everything you did in your analysis, making it easy to tweak your analysis later and/or share it with others (including your future self).

Integration with the scientific python stack

MNE-Python also integrates well with other standard scientific Python libraries. For example, MNE-Python objects underlyingly store their data in NumPy arrays, making it easy to apply custom algorithms or pass your data into one of scikit-learn’s machine learning pipelines. MNE-Python’s 2-D plotting functions also return matplotlib Figure objects, and the 3D plotting functions return mne.viz.Figure3D classes with a .plotter attribute pointing to pyvista.Plotter instances, so you can customize your MNE-Python plots using any of matplotlib or PyVista’s plotting commands. The intent is that MNE-Python will get most neuroscientists 90% of the way to their desired analysis goal, and other packages can get them over the finish line.

Submodule-based organization

A useful-to-know organizing principle is that MNE-Python objects and functions are separated into submodules. This can help you discover related functions if you’re using an editor that supports tab-completion. For example, you can type mne.preprocessing.<TAB> to see all the functions in the preprocessing submodule; similarly for visualization functions (mne.viz), functions for reading and writing data (mne.io), statistics (mne.stats), etc. This also helps save keystrokes — instead of:

import mne
mne.preprocessing.eog.peak_finder(...)
mne.preprocessing.eog.find_eog_events(...)
mne.preprocessing.eog.create_eog_epochs(...)

you can import submodules directly, and use just the submodule name to access its functions:

from mne.preprocessing import eog
eog.peak_finder(...)
eog.find_eog_events(...)
eog.create_eog_epochs(...)

(Mostly) unified API

Whenever possible, we’ve tried to provide a unified API for the different data classes. For example, the Raw, Epochs, Evoked, and SourceEstimate classes all have a plot() method that can typically be called with no parameters specified and still yield an informative plot of the data. Similarly, they all have the methods copy(), crop(), resample() and save() with similar or identical method signatures. The sensor-level classes also all have an info attribute containing an Info object, which keeps track of channel names and types, applied filters, projectors, etc. See The Info data structure for more info.

In-place operation

Because neuroimaging datasets can be quite large, MNE-Python tries very hard to avoid making unnecessary copies of your data behind-the-scenes. To further improve memory efficiency, many object methods operate in-place (and silently return their object to allow method chaining). In-place operation may lead you to frequent use of the copy() method during interactive, exploratory analysis — so you can try out different preprocessing approaches or parameter settings without having to re-load the data each time — but it can also be a big memory-saver when applying a finished script to dozens of subjects’ worth of data.