http://martinos.org/mne/

    4,861 commits
    15 branches
    16 releases
    33 contributors

    Python 99.3%
    Other 0.6%

PythonOther
mne-python /

Merge pull request #1175 from dgwakeman/write_trans …
latest commit 1c606eda60
Alexandre Gramfort agramfort authored 8 hours ago
	bin 	FIX: Fix examples 	3 months ago
	doc 	fix markdown VS rst 	8 days ago
	examples 	cleanup viz 	10 days ago
	mne 	COM:round 1 	a day ago
	.coveragerc 	more coveralls 	2 months ago
	.gitignore 	WIP mne_kit2fiff: from KIT SQD files to fif files 	a year ago
	.mailmap 	update mailmap 	3 months ago
	.travis.yml 	FIX: Fix coveralls 	a month ago
	AUTHORS.rst 	FIX: Add Teon 	9 months ago
	LICENSE.txt 	fix copyrights and authors 	2 years ago
	MANIFEST.in 	FIX: MANIFEST.in 	3 months ago
	Makefile 	Allow wget to continue download of a partially downloaded file 	22 days ago
	README.rst 	Update README.rst 	2 months ago
	dictionary.txt 	ENH: Use codespell 	11 months ago
	setup.cfg 	FIX: Undo HTML coverage 	2 months ago
	setup.py 	WIP: Add mapping 	3 months ago
README.rst

Travis
mne-python

This package is designed for sensor- and source-space analysis of M-EEG data, including frequency-domain and time-frequency analyses 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 a tutorial, 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 or tarball of the latest code: http://github.com/mne-tools/mne-python/archives/master
Install mne-python

As any Python packages, to install MNE-Python, 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 --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 and nitime (multitaper analysis).

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 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):

http://wiki.tiker.net/PyCuda/Installation/
Contribute to mne-python

Please see the documentation on the mne-python homepage:

http://martinos.org/mne/contributing.html
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.

http://martinos.org/mne/

http://martinos.org/mne/

    4,861 commits
    15 branches
    16 releases
    33 contributors

    Python 99.3%
    Other 0.6%

PythonOther
mne-python /

Merge pull request #1175 from dgwakeman/write_trans …
latest commit 1c606eda60
Alexandre Gramfort agramfort authored 8 hours ago
	bin 	FIX: Fix examples 	3 months ago
	doc 	fix markdown VS rst 	8 days ago
	examples 	cleanup viz 	10 days ago
	mne 	COM:round 1 	a day ago
	.coveragerc 	more coveralls 	2 months ago
	.gitignore 	WIP mne_kit2fiff: from KIT SQD files to fif files 	a year ago
	.mailmap 	update mailmap 	3 months ago
	.travis.yml 	FIX: Fix coveralls 	a month ago
	AUTHORS.rst 	FIX: Add Teon 	9 months ago
	LICENSE.txt 	fix copyrights and authors 	2 years ago
	MANIFEST.in 	FIX: MANIFEST.in 	3 months ago
	Makefile 	Allow wget to continue download of a partially downloaded file 	22 days ago
	README.rst 	Update README.rst 	2 months ago
	dictionary.txt 	ENH: Use codespell 	11 months ago
	setup.cfg 	FIX: Undo HTML coverage 	2 months ago
	setup.py 	WIP: Add mapping 	3 months ago
README.rst

Travis
mne-python

This package is designed for sensor- and source-space analysis of M-EEG data, including frequency-domain and time-frequency analyses 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 a tutorial, 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 or tarball of the latest code: http://github.com/mne-tools/mne-python/archives/master
Install mne-python

As any Python packages, to install MNE-Python, 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 --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 and nitime (multitaper analysis).

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 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):

http://wiki.tiker.net/PyCuda/Installation/
Contribute to mne-python

Please see the documentation on the mne-python homepage:

http://martinos.org/mne/contributing.html
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.