.. include:: ../links.inc

.. _design_philosophy:

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 <scikit-learn_>`_ machine learning pipelines.
MNE-Python's 2-D plotting functions also return `matplotlib`_
:class:`~matplotlib.figure.Figure` objects, and the 3D plotting functions
return :class:`mne.viz.Figure3D` classes with a ``.plotter`` attribute
pointing to :class:`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
:samp:`mne.preprocessing.{<TAB>}` to see all the functions in the preprocessing
submodule; similarly for visualization functions (:mod:`mne.viz`), functions
for reading and writing data (:mod:`mne.io`), statistics (:mod:`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 :class:`~mne.io.Raw`, :class:`~mne.Epochs`,
:class:`~mne.Evoked`, and :class:`~mne.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 :class:`~mne.Info` object, which keeps track of channel
names and types, applied filters, projectors, etc. See :ref:`tut-info-class`
for more info.


.. _sect-meth-chain:

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.



.. LINKS

.. _`method chaining`: https://en.wikipedia.org/wiki/Method_chaining
