Metadata-Version: 2.4
Name: sesameeg
Version: 0.0.3
Summary: Sequential Monte Carlo algorithm for multi dipolar source modeling in MEEG.
Home-page: https://github.com/pybees/sesameeg/
Download-URL: https://github.com/pybees/sesameeg/
Maintainer: SESAMEEG developers
License: new BSD
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 :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Python: >=3.5
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: mne
Provides-Extra: tests
Requires-Dist: pytest; extra == "tests"
Requires-Dist: pytest-cov; extra == "tests"
Provides-Extra: docs
Requires-Dist: sphinx; extra == "docs"
Requires-Dist: sphinx-gallery; extra == "docs"
Requires-Dist: sphinx_rtd_theme; extra == "docs"
Requires-Dist: numpydoc; extra == "docs"
Requires-Dist: matplotlib; extra == "docs"
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.. -*- mode: rst -*-

SESAMEEG: SEquential Semi-Analytic Montecarlo Estimation for MEEG
=================================================================

This is a Python3 implementation of the Bayesian multi-dipole modeling method and Sequential Monte Carlo algorithm
SESAME described in [1]_.
The algorithm takes in input a forward solution and a MEEG evoked data time series, and outputs a posterior
probability map for brain activity, as well as estimates of the  number of sources, their locations and their
amplitudes.

Installation
============

To install this package, the easiest way is using ``pip``. It will install this
package and its dependencies. The ``setup.py`` depends on ``numpy``, ``scipy``  and ``mne``
for the installation so it is advised to install them beforehand. To
install this package, please run the following commands:

(Latest stable version)

.. code::

    pip install numpy scipy mne
    pip install sesameeg

If you do not have admin privileges on the computer, use the ``--user`` flag
with ``pip``. To upgrade, use the ``--upgrade`` flag provided by ``pip``.

To check if everything worked fine, you can run:

.. code::

	python -c 'import sesameeg'

and it should not give any error messages.


Bug reports
===========

Use the `github issue tracker <https://github.com/pybees/sesameeg/issues>`_ to report bugs.


Authors of the code
-------------------
| Gianvittorio Luria <luria@dima.unige.it>,
| Sara Sommariva <sommariva@dima.unige.it>,
| Alberto Sorrentino <sorrentino@dima.unige.it>.

Cite our work
=============

If you use this code in your project, please consider citing our work:

.. [1] S. Sommariva and A. Sorrentino, `Sequential Monte Carlo samplers for semi-linear inverse problems and application to Magnetoencephalography <https://doi.org/10.1088/0266-5611/30/11/114020>`_. Inverse Problems, 30 114020 (2014).
