MNE python project for MEG and EEG data analysis.
Project description
mne-python
This package is designed for sensor- and source-space analysis of [M/E]EG data, including frequency-domain and time-frequency analyses, MVPA/decoding and non-parametric statistics. This package is presently evolving quickly and thanks to the adopted open development environment user contributions can be easily incorporated.
Get more information
This page only contains bare-bones instructions for installing mne-python.
If you’re familiar with MNE and you’re looking for information on using mne-python specifically, jump right to the mne-python homepage. This website includes tutorials, helpful examples, and a handy function reference, among other things.
If you’re unfamiliar with MNE, you can visit the MNE homepage for full user documentation.
Get the latest code
To get the latest code using git, simply type:
git clone git://github.com/mne-tools/mne-python.git
If you don’t have git installed, you can download a zip of the latest code: https://github.com/mne-tools/mne-python/archive/master.zip
Install mne-python
As any Python packages, to install MNE-Python, after obtaining the source code (e.g. from git), go in the mne-python source code directory and do:
python setup.py install
or if you don’t have admin access to your python setup (permission denied when install) use:
python setup.py install --user
You can also install the latest release version with easy_install:
easy_install -U mne
or with pip:
pip install mne
for an update of an already installed version use:
pip install mne --upgrade
or for the latest development version (the most up to date):
pip install -e git+https://github.com/mne-tools/mne-python#egg=mne-dev --user
Dependencies
The required dependencies to build the software are python >= 2.6, NumPy >= 1.6, SciPy >= 0.7.2 and matplotlib >= 0.98.4.
Some isolated functions require pandas >= 0.7.3. Decoding relies on scikit-learn >= 0.15.
To run the tests you will also need nose >= 0.10. and the MNE sample dataset (will be downloaded automatically when you run an example … but be patient).
To use NVIDIA CUDA for resampling and FFT FIR filtering, you will also need to install the NVIDIA CUDA SDK, pycuda, and scikits.cuda. The difficulty of this varies by platform; consider reading the following site for help getting pycuda to work (typically the most difficult to configure):
Contribute to mne-python
Please see the documentation on the mne-python homepage:
Mailing list
http://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
Running the test suite
To run the test suite, you need nosetests and the coverage modules. Run the test suite using:
nosetests
from the root of the project.
Making a release and uploading it to PyPI
This command is only run by project manager, to make a release, and upload in to PyPI:
python setup.py sdist bdist_egg register upload
Licensing
MNE-Python is BSD-licenced (3 clause):
This software is OSI Certified Open Source Software. OSI Certified is a certification mark of the Open Source Initiative.
Copyright (c) 2011, authors of MNE-Python All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
Neither the names of MNE-Python authors nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission.
This software is provided by the copyright holders and contributors “as is” and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. In no event shall the copyright owner or contributors be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software, even if advised of the possibility of such damage.
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