Metadata-Version: 2.1
Name: cmne
Version: 0.0.21
Summary: Contextual Minimum Norm Estimates (CMNE)
Home-page: https://github.com/chdinh/cmne
Maintainer: Christoph Dinh
Maintainer-email: christoph.dinh@brain-link.de
License: MIT License
Download-URL: https://github.com/chdinh/cmne
Project-URL: Source, https://github.com/chdinh/cmne/
Project-URL: Tracker, https://github.com/chdinh/cmne/issues/
Description: .. -*- mode: rst -*-
        
        |PyPI|_ |GH-CI|_
        
        .. |PyPI| image:: https://badge.fury.io/py/cmne.svg?label=PyPI%20downloads
        .. _PyPI: https://pypi.org/project/cmne/
        
        .. |GH-CI| image:: https://github.com/chdinh/cmne/actions/workflows/ci.yml/badge.svg?branch=main
        .. _GH-CI: https://github.com/chdinh/cmne/actions/workflows/ci.yml
        
        
        Contextual Minimum-Norm Estimates (CMNE): A Deep Learning Method for Source Estimation in Neuronal Networks
        ===========================================================================================================
        
        For more information on CMNE, please read the following papers:
        
          Dinh C, Samuelsson JGW*, Hunold A, HÃ¤mÃ¤lÃ¤inen MS, Khan S. Contextual MEG and EEG Source Estimates Using Spatiotemporal LSTM Networks. Front. Neurosci 2021;15:119-134; doi: https://doi.org/10.3389/fnins.2021.552666
        
          Dinh C, Samuelsson JGW*, Hunold A, HÃ¤mÃ¤lÃ¤inen MS, Khan S. Contextual Minimum-Norm Estimates (CMNE): A Deep Learning Method for Source Estimation in Neuronal Networks. arXiv:1909.02636; doi: https://doi.org/10.48550/arXiv.1909.02636
        
        
        Installation
        ^^^^^^^^^^^^
        
        To install the latest stable version of CMNE, you can use pip_ in a terminal:
        
        .. code-block:: bash
        
            pip install -U cmne
        
        
        Usage of the Docker Container
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        Build the docker image with
        
        .. code-block:: bash
        
            docker build -t brain-link/cmne:v0.01 .
        
        and run it with
        
        .. code-block:: bash
        
            docker run -ti -v <YOUR DATA DIR>:/workspace/data -v <YOUR CMNE RESULTS DIR>:/workspace/results -v <YOUR CMNE GIT DIR>:/workspace/cmne --name CMNE brain-link/cmne:v0.01
        
        It is convinient to install CMNE for development directly from the local repository. Change the directory to '/workspace/cmne' in the CLI of the Docker Container and run
        
        .. code-block:: bash
        
            pip install -e .
        
        
        Licensing
        ^^^^^^^^^
        CMNE is **MIT-licensed**:
        
            Copyright (c) 2017-2022, authors of CMNE.
            All rights reserved.
        
            Permission is hereby granted, free of charge, to any person obtaining a copy
            of this software and associated documentation files (the "Software"), to deal
            in the Software without restriction, including without limitation the rights
            to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
            copies of the Software, and to permit persons to whom the Software is
            furnished to do so, subject to the following conditions:
        
            The above copyright notice and this permission notice shall be included in all
            copies or substantial portions of the Software.
        
            **THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
            IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
            FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
            AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
            LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
            OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
            SOFTWARE.**
        
        
        .. _pip: https://pip.pypa.io/en/stable/
        
Keywords: MEG EEG spatiotemporal source estimation spatial filtering grid-based Markov localization LSTM deep learning
Platform: any
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering
Classifier: Programming Language :: Python :: 3
Classifier: Development Status :: 1 - Planning
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/x-rst
