Metadata-Version: 2.1
Name: mne-realtime
Version: 0.1.1
Summary: A module for real-time data analysis with MNE.
Home-page: https://github.com/mne-tools/mne-realtime
Maintainer: Teon Brooks
Maintainer-email: teon.brooks@gmail.com
License: new BSD
Download-URL: https://github.com/mne-tools/mne-realtime
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/x-rst
Provides-Extra: tests
Provides-Extra: docs
License-File: LICENSE.txt

.. -*- mode: rst -*-

|Azure|_ |CircleCI|_ |Codecov|_

.. |Azure| image:: https://dev.azure.com/mne-tools/mne-realtime/_apis/build/status/mne-tools.mne-realtime?branchName=main
.. _Azure: https://dev.azure.com/mne-tools/mne-realtime/_build/latest?definitionId=1&branchName=main

.. |CircleCI| image:: https://circleci.com/gh/mne-tools/mne-realtime.svg?style=svg
.. _CircleCI: https://circleci.com/gh/mne-tools/mne-realtime

.. |Codecov| image:: https://codecov.io/gh/mne-tools/mne-realtime/branch/main/graph/badge.svg
.. _Codecov: https://codecov.io/gh/mne-tools/mne-realtime

MNE-realtime
============

This is a repository for realtime analysis of MEG/EEG data with MNE. The documentation can be found here:

   * `Examples`_
   * `API`_

Dependencies
------------

* `numpy`_
* `MNE`_

Installation
------------

We recommend the Anaconda Python distribution. We require that you use Python 3. You may choose to install mne-realtime via pip.

Besides ``numpy`` and ``scipy`` (which are included in the standard Anaconda
installation), you will need to install the most recent version of ``MNE``
using the ``pip`` tool:

.. code-block:: bash

   $ pip install -U mne


Then install ``mne-realtime``:

.. code-block:: bash

   $ pip install https://api.github.com/repos/mne-tools/mne-realtime/zipball/main

These ``pip`` commands also work if you want to upgrade if a newer version of
``mne-realtime`` is available. If you do not have administrator privileges on the
computer, use the ``--user`` flag with ``pip``.

Quickstart
----------

.. code-block:: python

    info = mne.io.read_info(op.join(data_path, 'MEG', 'sample',
                            'sample_audvis_raw.fif'))
    with FieldTripClient(host='localhost', port=1972,
                         tmax=30, wait_max=5, info=info) as rt_client:
        rt_epochs = RtEpochs(rt_client, event_id, tmin, tmax, ...)
        rt_epochs.start()
        for ev in rt_epochs.iter_evoked():
            epoch_data = ev.data

        # or alternatively, get last n_samples
        rt_epoch = rt_client.get_data_as_epoch(n_samples=500)
        continuous_data = rt_epoch.get_data()

The ``FieldTripClient`` supports `multiple vendors through the FieldTrip buffer <http://www.fieldtriptoolbox.org/development/realtime/implementation/>`_.
It can be replaced with other clients such as ``LSLClient``. See `API`_ for a list of clients.

Bug reports
-----------

Use the `github issue tracker <https://github.com/mne-tools/mne-realtime/issues>`_
to report bugs.

 .. _Examples: https://mne.tools/mne-realtime/auto_examples/index.html
 .. _API: https://mne.tools/mne-realtime/api.html
 .. _numpy: https://numpy.org
 .. _MNE: https://mne.tools


