.. include:: ../links.inc

.. _faq:

================================
Frequently Asked Questions (FAQ)
================================

.. contents:: Page contents
   :local:


General MNE-Python issues
=========================


Help! I can't get Python and MNE-Python working!
------------------------------------------------

Check out our section on how to get Anaconda up and running over at the
:ref:`getting started page <install_python_and_mne_python>`.


I still can't get it to work!
-----------------------------

See :ref:`help`.


I can't get Mayavi/3D plotting to work under Windows
----------------------------------------------------

If Mayavi plotting in Jupyter Notebooks doesn't work well, using the IPython
magic ``%gui qt`` after importing MNE/Mayavi/PySurfer should `help
<https://github.com/ipython/ipython/issues/10384>`_.

.. code:: ipython

   from mayavi import mlab
   %gui qt


How do I cite MNE?
------------------

See :ref:`cite`.


I'm not sure how to do *X* analysis step with my *Y* data...
------------------------------------------------------------

Knowing "the right thing" to do with EEG and MEG data is challenging. We use
the `MNE mailing list`_ to discuss analysis strategies for different kinds of
data. It's worth searching the archives to see if there have been relevant
discussions in the past, but don't hesitate to ask a new question if the answer
isn't out there already.


I think I found a bug, what do I do?
------------------------------------

If you're *confident* that you've found a bug, head over to the `GitHub issues
page`_ and do a quick search to see if it's already been reported, and if not,
`open a new issue
<https://github.com/mne-tools/mne-python/issues/new?template=bug_report.md>`__.
If you're *not sure* whether it's a bug, user error, bad data file, etc., try
asking on the `MNE mailing list`_ or the `MNE gitter channel`_ first. In either
case, you should:

- Try :ref:`using the latest master version <installing_master>` to see if the
  problem persists before reporting the bug, as it may have been fixed since
  the latest release.

- Try to replicate the problem with one of the :ref:`MNE sample datasets
  <datasets>`. If you can't, provide a link to the data file that does yield
  the error.

- Provide the *smallest possible* code sample that replicates the error you're
  seeing. Using a `GitHub Public Gist <https://gist.github.com>`_ for the code
  sample is recommended when using the mailing list; on Gitter use three
  backticks (`\`\`\``) at the beginning and end of the code block to separate
  it from your question or explanation.


Why is it dangerous to "pickle" my MNE-Python objects and data for later use?
-----------------------------------------------------------------------------

`Pickling <https://docs.python.org/3/library/pickle.html>`_ data and MNE-Python
objects for later use can be tempting due to its simplicity and generality, but
it is usually not the best option. Pickling is not designed for stable
persistence, and it is likely that you will not be able to read your data in
the not-too-distant future. For details, see:

- http://www.benfrederickson.com/dont-pickle-your-data/
- https://stackoverflow.com/questions/21752259/python-why-pickle

MNE-Python is designed to provide its own file saving formats (often based on
the FIF standard) for its objects usually via a ``save`` method or ``write_*``
method, e.g. :func:`mne.io.Raw.save`, :func:`mne.Epochs.save`,
:func:`mne.write_evokeds`, :func:`mne.SourceEstimate.save`. If you have some
data that you want to save but can't figure out how, shoot an email to the `MNE
mailing list`_ or post it to the `GitHub issues page`_.

If you want to write your own data to disk (e.g., subject behavioral scores),
we strongly recommend using `h5io <https://github.com/h5io/h5io>`_, which is
based on the `HDF5 format
<https://en.wikipedia.org/wiki/Hierarchical_Data_Format>`_ and h5py_, to save
data in a fast, future-compatible, standard format.


I downloaded a dataset once, but MNE-Python is asking to download it again. Why?
--------------------------------------------------------------------------------

The default location for the MNE-sample data is ``~/mne_data``. If you
downloaded data and an example asks you whether to download it again, make sure
the data reside in the examples directory and that you run the script from its
current directory:

.. code-block:: console

  $ cd examples/preprocessing

Then in Python you can do::

  In [1]: %run plot_find_ecg_artifacts.py

See :ref:`datasets` for a list of all available datasets and some advanced
configuration options, e.g. to specify a custom location for storing the
datasets.


.. _faq_cpu:

A function uses multiple CPU cores even though I didn't tell it to. Why?
------------------------------------------------------------------------

Ordinarily in MNE-python the ``parallel`` module is used to deploy multiple
cores via the ``n_jobs`` variable. However, functions like
:func:`mne.preprocessing.maxwell_filter` that use :mod:`scipy.linalg` do not
have an ``n_jobs`` flag but may still use multiple cores. This is because
:mod:`scipy.linalg` is built with linear algebra libraries that natively
support multithreading:

- `OpenBLAS <http://www.openblas.net/>`_
- `Intel Math Kernel Library (MKL) <https://software.intel.com/en-us/mkl>`_,
  which uses `OpenMP <https://www.openmp.org/>`_

To control how many cores are used for linear-algebra-heavy functions like
:func:`mne.preprocessing.maxwell_filter`, you can set the ``OMP_NUM_THREADS``
or ``OPENBLAS_NUM_THREADS`` environment variable to the desired number of cores
for MKL or OpenBLAS, respectively. This can be done before running Python, or
inside Python you can achieve the same effect by, e.g.::

    >>> import os
    >>> num_cpu = '4' # Set as a string
    >>> os.environ['OMP_NUM_THREADS'] = num_cpu

This must be done *before* running linear algebra functions; subsequent
changes in the same Python session will have no effect.


I have a mystery FIF file, how do I read it?
--------------------------------------------

The :func:`mne.what` function can be called on any :file:`.fif` file to
identify the kind of data contained in the file. This will help you determine
whether to use :func:`mne.read_cov`, :func:`mne.read_epochs`,
:func:`mne.read_evokeds`, etc. There is also a corresponding command line tool
:ref:`mne what <gen_mne_what>`:

.. code-block:: console

    $ mne what sample_audvis_eog-eve.fif
    events


Resampling and decimating data
==============================

What are all these options for resampling, decimating, and binning data?
------------------------------------------------------------------------

There are many functions in MNE-Python for changing the effective sampling rate
of data. We'll discuss some major ones here, with some of their implications:

- :func:`mne.io.Raw.resample` is used to resample (typically downsample) raw
  data. Resampling is the two-step process of applying a low-pass FIR filter
  and subselecting samples from the data.

  Using this function to resample data before forming :class:`mne.Epochs`
  for final analysis is generally discouraged because doing so effectively
  loses precision of (and jitters) the event timings, see
  `this gist <https://gist.github.com/larsoner/01642cb3789992fbca59>`_ as
  a demonstration. However, resampling raw data can be useful for
  (at least):

    - Computing projectors in low- or band-passed data
    - Exploring data

- :func:`mne.preprocessing.ICA.fit` decimates data without low-passing,
  but is only used for fitting a statistical model to the data.

- :func:`mne.Epochs.decimate`, which does the same thing as the
  ``decim`` parameter in the :class:`mne.Epochs` constructor, sub-selects every
  :math:`N^{th}` sample before and after each event. This should only be
  used when the raw data have been sufficiently low-passed e.g. by
  :func:`mne.io.Raw.filter` to avoid aliasing artifacts.

- :func:`mne.Epochs.resample`, :func:`mne.Evoked.resample`, and
  :func:`mne.SourceEstimate.resample` all resample data.
  This process avoids potential aliasing artifacts because the
  resampling process applies a low-pass filter. However, this filtering
  introduces edge artifacts. Edge artifacts also exist when using
  :func:`mne.io.Raw.resample`, but there the edge artifacts are constrained
  to two times: the start and end of the recording. With these three methods,
  edge artifacts are introduced to the start and end of every epoch
  of data (or the start and end of the :class:`mne.Evoked` or
  :class:`mne.SourceEstimate` data), which often has a more pronounced
  effect on the data.

- :func:`mne.SourceEstimate.bin` can be used to decimate, with or without
  "binning" (averaging across data points). This is equivalent to applying
  a moving-average (boxcar) filter to the data and decimating. A boxcar in
  time is a `sinc <https://en.wikipedia.org/wiki/Sinc_function>`_ in
  frequency, so this acts as a simplistic, non-ideal low-pass filter;
  this will reduce but not eliminate aliasing if data were not sufficiently
  low-passed. In the case where the "filter" or bin-width is a single sample
  (i.e., an impulse) this operation simplifies to decimation without filtering.


Resampling raw data is taking forever! What do I do?
----------------------------------------------------

:func:`mne.io.Raw.resample` has a parameter ``npad=='auto'``. This is the
default, but if you've changed it you could try changing it back to ``'auto'``,
it might help.

If you have an NVIDIA GPU you could also try using :ref:`CUDA`, which can
sometimes speed up filtering and resampling operations by an order of
magnitude.


Inverse Solution
================


How should I regularize the covariance matrix?
----------------------------------------------

The estimated covariance can be numerically unstable and tends to induce
correlations between estimated source amplitudes and the number of samples
available. It is thus suggested to regularize the noise covariance
matrix (see :ref:`cov_regularization_math`), especially if only few samples
are available. Unfortunately it is not easy to tell the effective number of
samples, hence, to choose the appropriate regularization. In MNE-Python,
regularization is done using advanced regularization methods described in [1]_.
For this the 'auto' option can be used. With this option cross-validation will
be used to learn the optimal regularization::

    >>> import mne
    >>> epochs = mne.read_epochs(epochs_path) # doctest: +SKIP
    >>> cov = mne.compute_covariance(epochs, tmax=0., method='auto') # doctest: +SKIP

This procedure evaluates the noise covariance quantitatively by how well it
whitens the data using the negative log-likelihood of unseen data. The final
result can also be visually inspected. Under the assumption that the baseline
does not contain a systematic signal (time-locked to the event of interest),
the whitened baseline signal should be follow a multivariate Gaussian
distribution, i.e., whitened baseline signals should be between -1.96 and 1.96
at a given time sample. Based on the same reasoning, the expected value for the
:term:`Global Field Power (GFP) <GFP>` is 1 (calculation of the :term:`GFP`
should take into account the true degrees of freedom, e.g. ``ddof=3`` with 2
active SSP vectors)::

    >>> evoked = epochs.average() # doctest: +SKIP
    >>> evoked.plot_white(cov) # doctest: +SKIP

This plot displays both, the whitened evoked signals for each channels and the
whitened :term:`GFP`. The numbers in the :term:`GFP` panel represent the
estimated rank of the data, which amounts to the effective degrees of freedom
by which the squared sum across sensors is divided when computing the whitened
:term:`GFP`. The whitened :term:`GFP` also helps detecting spurious late evoked
components which can be the consequence of over- or under-regularization.

Note that if data have been processed using signal space separation (SSS) [2]_,
gradiometers and magnetometers will be displayed jointly because both are
reconstructed from the same SSS basis vectors with the same numerical rank.
This also implies that both sensor types are not any longer linearly
independent.

These methods for evaluation can be used to assess model violations. Additional
introductory materials can be found `here
<https://speakerdeck.com/dengemann/eeg-sensor-covariance-using-cross-validation>`_.

For expert use cases or debugging the alternative estimators can also be
compared::

    >>> covs = mne.compute_covariance(epochs, tmax=0., method='auto', return_estimators=True) # doctest: +SKIP
    >>> evoked = epochs.average() # doctest: +SKIP
    >>> evoked.plot_white(covs) # doctest: +SKIP

This will plot the whitened evoked for the optimal estimator and display the
:term:`GFPs <GFP>` for all estimators as separate lines in the related panel.

References
----------

.. [1] Engemann D. and Gramfort A. (2015) Automated model selection in
       covariance estimation and spatial whitening of MEG and EEG signals,
       vol. 108, 328-342, NeuroImage.

.. [2] Taulu, S., Simola, J., Kajola, M., 2005. Applications of the signal
       space separation method. IEEE Trans. Signal Proc. 53, 3359–3372.
