ENH: Add source links, fix CSS
This commit is contained in:
Eric Larson
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+46
-122
@@ -1,138 +1,62 @@
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body {
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font-family: 'Open Sans', 'Lucida Grande', 'Lucida Sans Unicode', 'Geneva', 'Verdana', sans-serif;
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/* Override bootstrap.min.css to avoid .. container:: padding problems */
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.nosize {
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width: auto;
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}
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.nopad {
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padding: 0px;
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}
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.halfpad {
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margin-left: 1%;
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margin-right: 1%;
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width: 48%;
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padding: 0px;
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margin: 10px;
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float: left;
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}
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/* Display content (e.g., images) in the middle center of a div */
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.midcenter {
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display: table-cell;
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vertical-align:middle;
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text-align: center;
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float: none;
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height: 300px;
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}
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/* Omit the version in the navbar */
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.navbar-version {
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display: none;
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}
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a {
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color: rgb(41, 122, 204);
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text-decoration: none;
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}
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a:hover {
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color: rgb(102, 179, 255);
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}
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h2 a {
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color: rgb(0,0,0);
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text-decoration: none;
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}
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h1 {
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font-weight: 1000;
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}
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h2 {
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background-color: rgb(255,255,255);
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font-size: 140%;
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font-weight: 800;
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}
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h3 {
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font-size: 110%;
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font-weight: 600;
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}
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/* Many Sphinx-rendered things are put in blockquotes, don't change size*/
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blockquote {
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font-size: 100% !important;
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}
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code {
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color: rgb(61, 107, 153) !important;
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background-color: rgb(230, 242, 255) !important;
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}
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/* This is our dev warning bar */
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.devbar {
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background-color: rgb(242, 80, 80);
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color: white;
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font-weight:bold;
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text-align: center;
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padding: 10px;
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min-width: 910px;
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vertical-align: middle;
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}
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.devbar a {
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color: rgb(255, 238, 0);
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text-decoration: none;
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/* Override the sphinx-gallery download ugliness */
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div.sphx-glr-download a {
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background-color: inherit;
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background-image: inherit;
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border-radius: inherit;
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border: 1px solid #000;
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color: inherit;
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display: inherit;
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/* Not valid in old browser, hence we keep the line above to override */
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display: inherit;
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font-weight: inherit;
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padding: inherit;
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text-align: inherit;
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}
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.devbar a:hover {
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color: black;
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text-decoration: underline;
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}
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#navbar a:hover {
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color: rgb(242, 80, 80);
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}
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.note a {
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color: rgb(0,0,255);
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}
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.note {
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background-color: rgb(204, 230, 255);
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color: rgb(0, 0, 0);
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border: none;
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}
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.note pre {
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background-color: rgb(242, 249, 255);
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}
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blockquote {
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padding: 0 0 0 15px;
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margin: 0 0 20px;
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border-left: 5px solid #eeeeee;
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}
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.span.box {
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width: 47%;
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height: 230px;
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float: left;
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border-radius: 20px;
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background: rgb(242, 249, 255);
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border-style: solid;
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border-color: rgb(143, 173, 204);
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margin: 5px;
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padding: 0px 0px 0px 10px;
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}
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|
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.span h2 {
|
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background: rgb(242, 249, 255);
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}
|
||||
|
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.span.box code {
|
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background-color: rgb(204, 230, 255) !important;
|
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}
|
||||
|
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/* OVERRIDES */
|
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.sphx-glr-download {
|
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background-color: rgb(204, 230, 255);
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border-radius: 10px;
|
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border-style: solid;
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border-color: rgb(143, 173, 204);
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max-width: 55ex;
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}
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.highlight pre {
|
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background-color: rgb(242, 249, 255) !important;
|
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}
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.warning {
|
||||
background-color: rgb(230, 57, 57);
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color: white;
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}
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.warning a {
|
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color: rgb(255, 238, 0);
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}
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dt:target, .highlighted {
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background-color: rgb(255, 238, 0);
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}
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dt:target code {
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color: inherit !important;
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background-color: inherit !important;
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}
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.label { /* Necessary for multiple refs, from bootstrap.min.css:7 */
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color: #2c3e50;
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}
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div.sphx-glr-download a:hover {
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box-shadow: inset 0 1px 0 rgba(255,255,255,.1), 0 1px 5px rgba(0,0,0,.25);
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text-decoration: none;
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background-image: none;
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background-color: inherit;
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}
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Vendored
+2
-2
@@ -44,8 +44,8 @@
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{% block relbar1 %}
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{% if build_dev_html|tobool %}
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<div class="devbar">
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This documentation is for the development version ({{ release }}) - <a href="http://martinos.org/mne/stable">Stable version</a>
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<div class="row devbar alert alert-danger">
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This documentation is for the <strong>development version ({{ release }})</strong> - <a href="http://martinos.org/mne/stable">switch to Stable</a>
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</div>
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{% endif %}
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@@ -1,17 +0,0 @@
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.. _cite:
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How to cite MNE
|
||||
---------------
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||||
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||||
If you use the implementations provided by the MNE software in your research, you should cite:
|
||||
|
||||
[1] A. Gramfort, M. Luessi, E. Larson, D. Engemann, D. Strohmeier, C. Brodbeck, L. Parkkonen, M. Hämäläinen, `MNE software for processing MEG and EEG data <http://www.ncbi.nlm.nih.gov/pubmed/24161808>`_, NeuroImage, Volume 86, 1 February 2014, Pages 446-460, ISSN 1053-8119, `[DOI] <http://dx.doi.org/10.1016/j.neuroimage.2013.10.027>`__
|
||||
|
||||
If you use the Python code you should cite as well:
|
||||
|
||||
[2] A. Gramfort, M. Luessi, E. Larson, D. Engemann, D. Strohmeier, C. Brodbeck, R. Goj, M. Jas, T. Brooks, L. Parkkonen, M. Hämäläinen, `MEG and EEG data analysis with MNE-Python <http://journal.frontiersin.org/article/10.3389/fnins.2013.00267/abstract>`_, Frontiers in Neuroscience, Volume 7, 2013, ISSN 1662-453X, `[DOI] <http://dx.doi.org/10.3389/fnins.2013.00267>`__
|
||||
|
||||
To cite specific versions of the software, you can use the DOIs provided by
|
||||
`Zenodo <https://zenodo.org/search?ln=en&p=mne-python>`_.
|
||||
|
||||
You should as well cite the related method papers, some of which are listed in :ref:`ch_reading`.
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+84
-19
@@ -12,11 +12,15 @@
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# All configuration values have a default; values that are commented out
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# serve to show the default.
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import sys
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import inspect
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import os
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from os.path import relpath, dirname
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import sys
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from datetime import date
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import sphinx_gallery
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import sphinx_gallery # noqa
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import sphinx_bootstrap_theme
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from numpydoc import numpydoc, docscrape # noqa
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import mne
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# If extensions (or modules to document with autodoc) are in another directory,
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# add these directories to sys.path here. If the directory is relative to the
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@@ -25,7 +29,6 @@ curdir = os.path.dirname(__file__)
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sys.path.append(os.path.abspath(os.path.join(curdir, '..', 'mne')))
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sys.path.append(os.path.abspath(os.path.join(curdir, 'sphinxext')))
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import mne
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if not os.path.isdir('_images'):
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os.mkdir('_images')
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@@ -34,26 +37,26 @@ if not os.path.isdir('_images'):
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# If your documentation needs a minimal Sphinx version, state it here.
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#needs_sphinx = '1.0'
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# XXX This hack defines what extra methods numpydoc will document
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docscrape.ClassDoc.extra_public_methods = mne.utils._doc_special_members
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# Add any Sphinx extension module names here, as strings. They can be
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# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
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from numpydoc import numpydoc, docscrape
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docscrape.ClassDoc.extra_public_methods = mne.utils._doc_special_members
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extensions = [
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'sphinx.ext.autodoc',
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'sphinx.ext.autosummary',
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||||
'sphinx.ext.coverage',
|
||||
'sphinx.ext.doctest',
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||||
'sphinx.ext.intersphinx',
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||||
'sphinx.ext.todo',
|
||||
'sphinx.ext.coverage',
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'sphinx.ext.linkcode',
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'sphinx.ext.mathjax',
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'sphinx.ext.todo',
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'sphinx_gallery.gen_gallery',
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'numpydoc',
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'gen_commands',
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]
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extensions += ['numpydoc']
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extensions += ['gen_commands'] # auto generate the doc for the python commands
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# extensions += ['flow_diagram] # generate flow chart in cookbook
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autosummary_generate = True
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autodoc_default_flags = ['inherited-members']
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@@ -118,7 +121,7 @@ exclude_patterns = ['source/generated']
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#show_authors = False
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# The name of the Pygments (syntax highlighting) style to use.
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pygments_style = 'sphinx'
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pygments_style = 'manni' # friendly, manni, murphy, tango
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# A list of ignored prefixes for module index sorting.
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modindex_common_prefix = ['mne.']
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@@ -137,17 +140,20 @@ html_theme = 'bootstrap'
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# further. For a list of options available for each theme, see the
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# documentation.
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html_theme_options = {
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'navbar_title': ' ',
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'source_link_position': "footer",
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'bootswatch_theme': "flatly",
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'navbar_sidebarrel': False,
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'bootstrap_version': "3",
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'navbar_title': ' ', # we replace this with an image
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'source_link_position': "nav", # default
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'bootswatch_theme': "cerulean", # yeti paper lumen
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'navbar_sidebarrel': False, # Render the next/prev links in navbar?
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'navbar_pagenav': False,
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'navbar_class': "navbar navbar-inverse",
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'bootstrap_version': "3", # default
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'navbar_links': [
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("Get started", "getting_started"),
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("Tutorials", "tutorials"),
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("Gallery", "auto_examples/index"),
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("API", "python_reference"),
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("Manual", "manual/index"),
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("Contribute", "contributing"),
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("FAQ", "faq"),
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],
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}
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@@ -272,6 +278,8 @@ intersphinx_mapping = {
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'python': ('http://docs.python.org/', None),
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'numpy': ('http://docs.scipy.org/doc/numpy-dev/', None),
|
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'scipy': ('http://scipy.github.io/devdocs/', None),
|
||||
'sklearn': ('http://scikit-learn.org/stable/', None),
|
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'matplotlib': ('http://matplotlib.org/', None),
|
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}
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examples_dirs = ['../examples', '../tutorials']
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@@ -291,8 +299,8 @@ sphinx_gallery_conf = {
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'reference_url': {
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'mne': None,
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'matplotlib': 'http://matplotlib.org',
|
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'numpy': 'http://docs.scipy.org/doc/numpy-1.10.1',
|
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'scipy': 'http://docs.scipy.org/doc/scipy-0.17.0/reference',
|
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'numpy': 'http://docs.scipy.org/doc/numpy/reference',
|
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'scipy': 'http://docs.scipy.org/doc/scipy/reference',
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'mayavi': 'http://docs.enthought.com/mayavi/mayavi'},
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'examples_dirs': examples_dirs,
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'gallery_dirs': gallery_dirs,
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@@ -302,3 +310,60 @@ sphinx_gallery_conf = {
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}
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numpydoc_class_members_toctree = False
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# -----------------------------------------------------------------------------
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# Source code links (adapted from SciPy (doc/source/conf.py))
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# -----------------------------------------------------------------------------
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def linkcode_resolve(domain, info):
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"""
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Determine the URL corresponding to Python object
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"""
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if domain != 'py':
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return None
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modname = info['module']
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fullname = info['fullname']
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submod = sys.modules.get(modname)
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if submod is None:
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return None
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obj = submod
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for part in fullname.split('.'):
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try:
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obj = getattr(obj, part)
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except:
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return None
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try:
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fn = inspect.getsourcefile(obj)
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except:
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fn = None
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if not fn:
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try:
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fn = inspect.getsourcefile(sys.modules[obj.__module__])
|
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except:
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fn = None
|
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if not fn:
|
||||
return None
|
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|
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try:
|
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source, lineno = inspect.getsourcelines(obj)
|
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except:
|
||||
lineno = None
|
||||
|
||||
if lineno:
|
||||
linespec = "#L%d-L%d" % (lineno, lineno + len(source) - 1)
|
||||
else:
|
||||
linespec = ""
|
||||
|
||||
fn = relpath(fn, start=dirname(mne.__file__))
|
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|
||||
if 'git' in mne.__version__:
|
||||
return "http://github.com/mne-tools/mne-python/blob/master/mne/%s%s" % ( # noqa
|
||||
fn, linespec)
|
||||
else:
|
||||
return "http://github.com/mne-tools/mne-python/blob/maint/%s/mne/%s%s" % ( # noqa
|
||||
mne.__version__, fn, linespec)
|
||||
|
||||
+46
@@ -19,6 +19,32 @@ Help! I can't get Python and MNE-Python working!
|
||||
Check out our section on how to get Anaconda up and running over at the
|
||||
:ref:`getting started page <install_interpreter>`.
|
||||
|
||||
I still can't get it to work!
|
||||
-----------------------------
|
||||
For analysis talk, join the `MNE mailing list`_. File specific feature
|
||||
requests or bug reports `on GitHub <https://github.com/mne-tools/mne-python/issues/>`_.
|
||||
You can also chat with developers `on Gitter <https://gitter.im/mne-tools/mne-python>`_.
|
||||
|
||||
How do I contribute?
|
||||
--------------------
|
||||
Check out the :ref:`contributing` page.
|
||||
|
||||
How do I cite MNE?
|
||||
------------------
|
||||
If you use the implementations provided by the MNE software in your research,
|
||||
you should cite:
|
||||
|
||||
- A. Gramfort, M. Luessi, E. Larson, D. Engemann, D. Strohmeier, C. Brodbeck, L. Parkkonen, M. Hämäläinen, `MNE software for processing MEG and EEG data <http://www.ncbi.nlm.nih.gov/pubmed/24161808>`_, NeuroImage, Volume 86, 1 February 2014, Pages 446-460, ISSN 1053-8119, `[DOI] <http://dx.doi.org/10.1016/j.neuroimage.2013.10.027>`__
|
||||
|
||||
If you use the Python code you should cite as well:
|
||||
|
||||
- A. Gramfort, M. Luessi, E. Larson, D. Engemann, D. Strohmeier, C. Brodbeck, R. Goj, M. Jas, T. Brooks, L. Parkkonen, M. Hämäläinen, `MEG and EEG data analysis with MNE-Python <http://journal.frontiersin.org/article/10.3389/fnins.2013.00267/abstract>`_, Frontiers in Neuroscience, Volume 7, 2013, ISSN 1662-453X, `[DOI] <http://dx.doi.org/10.3389/fnins.2013.00267>`__
|
||||
|
||||
To cite specific versions of the software, you can use the DOIs provided by
|
||||
`Zenodo <https://zenodo.org/search?ln=en&p=mne-python>`_.
|
||||
|
||||
You should as well cite the related method papers, some of which are listed in :ref:`ch_reading`.
|
||||
|
||||
I'm not sure how to do *X* analysis step with my *Y* data...
|
||||
------------------------------------------------------------
|
||||
|
||||
@@ -87,6 +113,26 @@ which is based on the
|
||||
h5py_, to save data in a fast, future-compatible, standard format.
|
||||
|
||||
|
||||
I downloaded a dataset once, but MNE-Python is asking to download it again. What gives?
|
||||
------------------------------------------------------------------------------------------
|
||||
The default location for the MNE-sample data is ``~/mne_data``.
|
||||
If you downloaded data and an example asks you whether to download it again,
|
||||
make sure the data reside in the examples directory and that you run the
|
||||
script from its current directory:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ cd examples/preprocessing
|
||||
|
||||
Then in Python you can do::
|
||||
|
||||
In [1]: %run plot_find_ecg_artifacts.py
|
||||
|
||||
See :ref:`datasets` for a list of all available datasets and some advanced
|
||||
configuration options, e.g. to specify a custom location for storing the
|
||||
datasets.
|
||||
|
||||
|
||||
Resampling and decimating data
|
||||
==============================
|
||||
|
||||
|
||||
+96
-78
@@ -1,32 +1,9 @@
|
||||
.. include:: links.inc
|
||||
|
||||
.. _getting_started:
|
||||
.. _get_started:
|
||||
|
||||
Getting started
|
||||
===============
|
||||
|
||||
.. _introduction_to_mne:
|
||||
|
||||
**MNE** is an academic software package that aims to provide data analysis
|
||||
pipelines encompassing all phases of M/EEG data processing.
|
||||
|
||||
MNE started as tool written in C by Matti Hämäläinen while at MGH in Boston.
|
||||
MNE was then extended with the Python programming language to implement
|
||||
nearly all MNE-C’s functionality, offer transparent scripting, and
|
||||
:ref:`extend MNE-C’s functionality considerably <what_can_you_do>`.
|
||||
|
||||
A basic :ref:`ch_matlab` is also available mostly
|
||||
to allow reading and write MNE files. The sister :ref:`mne_cpp` project
|
||||
aims to provide modular and open-source tools for acquisition,
|
||||
visualization, and analysis.
|
||||
|
||||
.. note:: This package is based on the FIF file format from Neuromag. But, it
|
||||
can read and convert CTF, BTI/4D, KIT and various EEG formats to
|
||||
FIF (see :ref:`IO functions <ch_convert>`).
|
||||
|
||||
If you have been using MNE-C, there is no need to convert your fif
|
||||
files to a new system or database -- MNE-Python works nicely with
|
||||
the historical fif files.
|
||||
Get started
|
||||
============
|
||||
|
||||
Installation
|
||||
------------
|
||||
@@ -35,67 +12,74 @@ To get started with MNE, visit the installation instructions for
|
||||
:ref:`MNE-Python <install_python_and_mne_python>` and
|
||||
:ref:`MNE-C <install_mne_c>`:
|
||||
|
||||
.. container:: span box
|
||||
.. container:: row
|
||||
|
||||
.. raw:: html
|
||||
.. container:: panel panel-default halfpad
|
||||
|
||||
<h3>MNE-Python</h3>
|
||||
.. container:: panel-heading nosize
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
MNE-Python
|
||||
|
||||
install_mne_python
|
||||
.. container:: panel-body nosize
|
||||
|
||||
.. container:: span box
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
|
||||
.. raw:: html
|
||||
install_mne_python
|
||||
|
||||
<h3>MNE-C</h3>
|
||||
.. container:: panel panel-default halfpad
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
.. container:: panel-heading nosize
|
||||
|
||||
install_mne_c
|
||||
MNE-C
|
||||
|
||||
.. container:: panel-body nosize
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
|
||||
install_mne_c
|
||||
|
||||
.. container:: row
|
||||
|
||||
.. container:: col-md-8
|
||||
|
||||
.. _what_can_you_do:
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<h2>What can you do with MNE-Python?</h2>
|
||||
|
||||
- **Raw data visualization** to visualize recordings
|
||||
(see :ref:`general_examples` for more).
|
||||
- **Epoching**: Define epochs, baseline correction, handle conditions etc.
|
||||
- **Averaging** to get Evoked data.
|
||||
- **Compute SSP projectors** to remove ECG and EOG artifacts.
|
||||
- **Compute ICA** to remove artifacts or select latent sources.
|
||||
- **Maxwell filtering** to remove environmental noise.
|
||||
- **Boundary Element Modeling**: single and three-layer BEM model
|
||||
creation and solution computation.
|
||||
- **Forward modeling**: BEM computation and mesh creation
|
||||
(see :ref:`ch_forward`).
|
||||
- **Linear inverse solvers** (dSPM, sLORETA, MNE, LCMV, DICS).
|
||||
- **Sparse inverse solvers** (L1/L2 mixed norm MxNE, Gamma Map,
|
||||
Time-Frequency MxNE, RAP-MUSIC).
|
||||
- **Connectivity estimation** in sensor and source space.
|
||||
- **Visualization of sensor and source space data**
|
||||
- **Time-frequency** analysis with Morlet wavelets (induced power,
|
||||
intertrial coherence, phase lock value) also in the source space.
|
||||
- **Spectrum estimation** using multi-taper method.
|
||||
- **Mixed Source Models** combining cortical and subcortical structures.
|
||||
- **Dipole Fitting**
|
||||
- **Decoding** multivariate pattern analysis of M/EEG topographies.
|
||||
- **Compute contrasts** between conditions, between sensors, across
|
||||
subjects etc.
|
||||
- **Non-parametric statistics** in time, space and frequency
|
||||
(including cluster-level).
|
||||
- **Scripting** (batch and parallel computing)
|
||||
|
||||
|
||||
.. _what_can_you_do:
|
||||
|
||||
What can you do with MNE using Python?
|
||||
--------------------------------------
|
||||
|
||||
- **Raw data visualization** to visualize recordings
|
||||
(see :ref:`general_examples` for more).
|
||||
- **Epoching**: Define epochs, baseline correction, handle conditions etc.
|
||||
- **Averaging** to get Evoked data.
|
||||
- **Compute SSP projectors** to remove ECG and EOG artifacts.
|
||||
- **Compute ICA** to remove artifacts or select latent sources.
|
||||
- **Maxwell filtering** to remove environmental noise.
|
||||
- **Boundary Element Modeling**: single and three-layer BEM model
|
||||
creation and solution computation.
|
||||
- **Forward modeling**: BEM computation and mesh creation
|
||||
(see :ref:`ch_forward`).
|
||||
- **Linear inverse solvers** (dSPM, sLORETA, MNE, LCMV, DICS).
|
||||
- **Sparse inverse solvers** (L1/L2 mixed norm MxNE, Gamma Map,
|
||||
Time-Frequency MxNE, RAP-MUSIC).
|
||||
- **Connectivity estimation** in sensor and source space.
|
||||
- **Visualization of sensor and source space data**
|
||||
- **Time-frequency** analysis with Morlet wavelets (induced power,
|
||||
intertrial coherence, phase lock value) also in the source space.
|
||||
- **Spectrum estimation** using multi-taper method.
|
||||
- **Mixed Source Models** combining cortical and subcortical structures.
|
||||
- **Dipole Fitting**
|
||||
- **Decoding** multivariate pattern analysis of M/EEG topographies.
|
||||
- **Compute contrasts** between conditions, between sensors, across
|
||||
subjects etc.
|
||||
- **Non-parametric statistics** in time, space and frequency
|
||||
(including cluster-level).
|
||||
- **Scripting** (batch and parallel computing)
|
||||
|
||||
|
||||
Is that all you can do with MNE-Python?
|
||||
---------------------------------------
|
||||
|
||||
Short answer is **No**! You can also do:
|
||||
There is much more beyond these general categories such as:
|
||||
|
||||
- detect heart beat QRS component
|
||||
- detect eye blinks and EOG artifacts
|
||||
@@ -116,8 +100,42 @@ Short answer is **No**! You can also do:
|
||||
- and many more things ...
|
||||
|
||||
|
||||
What you're not supposed to do with MNE-Python
|
||||
----------------------------------------------
|
||||
What you're not supposed to do with MNE-Python:
|
||||
|
||||
- **Brain and head surface segmentation** for use with BEM
|
||||
models -- use Freesurfer_.
|
||||
|
||||
.. container:: col-md-4
|
||||
|
||||
.. container:: panel panel-info nosize nopad
|
||||
|
||||
.. container:: panel-heading nosize
|
||||
|
||||
Historical notes
|
||||
|
||||
.. container:: panel-body nosize
|
||||
|
||||
MNE started as tool written in C by Matti Hämäläinen while at MGH in
|
||||
Boston.
|
||||
|
||||
- :ref:`MNE-C <c_reference>` is Matti's C code.
|
||||
|
||||
- MNE-Python was built in the Python programming language to reimplement
|
||||
all MNE-C’s functionality, offer transparent scripting, and extend
|
||||
MNE-C’s functionality considerably (see left). Thus it is the
|
||||
primary focus of this documentation.
|
||||
|
||||
- :ref:`ch_matlab` is available mostly to allow reading and writing
|
||||
FIF files.
|
||||
|
||||
- :ref:`mne_cpp` aims to provide modular and open-source tools for
|
||||
real-time acquisition, visualization, and analysis. It provides
|
||||
a :ref:`separate website <mne_cpp>` for documentation and releases.
|
||||
|
||||
The MNE tools are based on the FIF file format from Neuromag.
|
||||
However, MNE-Python can read native CTF, BTI/4D, KIT and various
|
||||
EEG formats (see :ref:`IO functions <ch_convert>`).
|
||||
|
||||
If you have been using MNE-C, there is no need to convert your fif
|
||||
files to a new system or database -- MNE-Python works nicely with
|
||||
the historical fif files.
|
||||
|
||||
+106
-123
@@ -2,151 +2,134 @@
|
||||
|
||||
.. include:: links.inc
|
||||
|
||||
.. raw:: html
|
||||
.. container:: row extra-bottom-padding
|
||||
|
||||
<div class="container"><div class="row">
|
||||
<div class="col-md-8"><div style="text-align: center; height: 270px">
|
||||
<span style="display: inline-block; height: 100%; vertical-align: middle"></span>
|
||||
<a href="index.html"><img src="_static/mne_logo.png" border="0" alt="MNE" style="vertical-align: middle"></a>
|
||||
</div></div>
|
||||
<div class="col-md-4"><div style="float: left">
|
||||
<a href="index.html"><img src="_static/institutions.png"" border="0" alt="Institutions"/></a>
|
||||
</div></div>
|
||||
</div></div>
|
||||
.. container:: col-md-8 nopad
|
||||
|
||||
.. raw:: html
|
||||
.. container:: midcenter nopad
|
||||
|
||||
<div class="container-fluid">
|
||||
<div class="row">
|
||||
<div class="col-md-8">
|
||||
<br>
|
||||
.. image:: _static/mne_logo.png
|
||||
:alt: MNE
|
||||
|
||||
MNE is a community-driven software package designed for **processing
|
||||
electroencephalography (EEG) and magnetoencephalography (MEG) data**
|
||||
providing comprehensive tools and workflows for
|
||||
(:ref:`among other things <what_can_you_do>`):
|
||||
.. container:: col-md-4 nopad
|
||||
|
||||
1. Preprocessing and denoising
|
||||
2. Source estimation
|
||||
3. Time–frequency analysis
|
||||
4. Statistical testing
|
||||
5. Estimation of functional connectivity
|
||||
6. Applying machine learning algorithms
|
||||
7. Visualization of sensor- and source-space data
|
||||
.. container:: col-md-8 midcenter nopad
|
||||
|
||||
MNE includes a comprehensive Python_ package supplemented by tools compiled
|
||||
from C code for the LINUX and Mac OSX operating systems, as well as a MATLAB toolbox.
|
||||
|
||||
**From raw data to source estimates in about 30 lines of code** (Try it :ref:`by installing it <getting_started>` or `in an experimental online demo <http://mybinder.org/repo/mne-tools/mne-binder/notebooks/plot_introduction.ipynb>`_!):
|
||||
|
||||
.. code:: python
|
||||
|
||||
>>> import mne # doctest: +SKIP
|
||||
>>> raw = mne.io.read_raw_fif('raw.fif', preload=True) # load data # doctest: +SKIP
|
||||
>>> raw.info['bads'] = ['MEG 2443', 'EEG 053'] # mark bad channels # doctest: +SKIP
|
||||
>>> raw.filter(l_freq=None, h_freq=40.0) # low-pass filter data # doctest: +SKIP
|
||||
>>> # Extract epochs and save them:
|
||||
>>> picks = mne.pick_types(raw.info, meg=True, eeg=True, eog=True, # doctest: +SKIP
|
||||
>>> exclude='bads') # doctest: +SKIP
|
||||
>>> events = mne.find_events(raw) # doctest: +SKIP
|
||||
>>> reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6) # doctest: +SKIP
|
||||
>>> epochs = mne.Epochs(raw, events, event_id=1, tmin=-0.2, tmax=0.5, # doctest: +SKIP
|
||||
>>> proj=True, picks=picks, baseline=(None, 0), # doctest: +SKIP
|
||||
>>> preload=True, reject=reject) # doctest: +SKIP
|
||||
>>> # Compute evoked response and noise covariance
|
||||
>>> evoked = epochs.average() # doctest: +SKIP
|
||||
>>> cov = mne.compute_covariance(epochs, tmax=0) # doctest: +SKIP
|
||||
>>> evoked.plot() # plot evoked # doctest: +SKIP
|
||||
>>> # Compute inverse operator:
|
||||
>>> fwd_fname = 'sample_audvis−meg−eeg−oct−6−fwd.fif' # doctest: +SKIP
|
||||
>>> fwd = mne.read_forward_solution(fwd_fname, surf_ori=True) # doctest: +SKIP
|
||||
>>> inv = mne.minimum_norm.make_inverse_operator(raw.info, fwd, # doctest: +SKIP
|
||||
>>> cov, loose=0.2) # doctest: +SKIP
|
||||
>>> # Compute inverse solution:
|
||||
>>> stc = mne.minimum_norm.apply_inverse(evoked, inv, lambda2=1./9., # doctest: +SKIP
|
||||
>>> method='dSPM') # doctest: +SKIP
|
||||
>>> # Morph it to average brain for group study and plot it
|
||||
>>> stc_avg = mne.morph_data('sample', 'fsaverage', stc, 5, smooth=5) # doctest: +SKIP
|
||||
>>> stc_avg.plot() # doctest: +SKIP
|
||||
|
||||
MNE development is driven by :ref:`extensive contributions from the community <whats_new>`.
|
||||
Direct financial support for the project has been provided by:
|
||||
|
||||
- (US) National Institute of Biomedical Imaging and Bioengineering (NIBIB)
|
||||
grants 5R01EB009048 and P41EB015896 (Center for Functional Neuroimaging
|
||||
Technologies)
|
||||
- (US) NSF awards 0958669 and 1042134.
|
||||
- (US) NCRR *Center for Functional Neuroimaging Technologies* P41RR14075-06
|
||||
- (US) NIH grants 1R01EB009048-01, R01 EB006385-A101, 1R01 HD40712-A1, 1R01
|
||||
NS44319-01, and 2R01 NS37462-05
|
||||
- (US) Department of Energy Award Number DE-FG02-99ER62764 to The MIND
|
||||
Institute.
|
||||
- (FR) IDEX Paris-Saclay, ANR-11-IDEX-0003-02, via the
|
||||
`Center for Data Science <http://www.datascience-paris-saclay.fr/>`_.
|
||||
- (FR) European Research Council (ERC) Starting Grant (ERC-YStG-263584).
|
||||
- (FR) French National Research Agency (ANR-14-NEUC-0002-01).
|
||||
- (FR) European Research Council (ERC) Starting Grant (ERC-YStG-676943).
|
||||
- Amazon Web Services - Research Grant issued to Denis A. Engemann
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<div class="col-md-8">
|
||||
<script type="text/javascript" src="http://www.ohloh.net/p/586838/widgets/project_basic_stats.js"></script>
|
||||
</div>
|
||||
.. image:: _static/institutions.png
|
||||
:alt: Institutions
|
||||
|
||||
|
||||
.. raw:: html
|
||||
.. container:: row
|
||||
|
||||
</div>
|
||||
<div class="col-md-4">
|
||||
<h2>Documentation</h2>
|
||||
.. container:: col-md-8
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
Community-driven software for **processing time-resolved neural
|
||||
signals including magneto- and electro-encephalography (MEG, EEG)**,
|
||||
offering comprehensive data analysis tools for Windows, OSX, and Linux:
|
||||
|
||||
getting_started
|
||||
tutorials
|
||||
auto_examples/index
|
||||
faq
|
||||
contributing
|
||||
- Preprocessing and denoising
|
||||
- Source estimation
|
||||
- Time–frequency analysis
|
||||
- Statistical testing
|
||||
- Functional connectivity
|
||||
- Machine learning
|
||||
- Visualization of sensor- and source-space data
|
||||
- And :ref:`much more <get_started>`
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
From raw data to source estimates **in about 20 lines of code** (try it `in an experimental online demo <http://mybinder.org/repo/mne-tools/mne-binder/notebooks/plot_introduction.ipynb>`_!)::
|
||||
|
||||
python_reference
|
||||
manual/index
|
||||
whats_new
|
||||
>>> import mne # doctest: +SKIP
|
||||
>>> from scipy.signal import lfilter
|
||||
>>> raw = mne.io.read_raw_fif('raw.fif') # load data # doctest: +SKIP
|
||||
>>> raw.info['bads'] = ['MEG 2443', 'EEG 053'] # mark bad channels # doctest: +SKIP
|
||||
>>> raw.filter(l_freq=None, h_freq=40.0) # low-pass filter # doctest: +SKIP
|
||||
>>> events = mne.find_events(raw, 'STI014') # extract events and epoch data # doctest: +SKIP
|
||||
>>> epochs = mne.Epochs(raw, events, event_id=1, tmin=-0.2, tmax=0.5, # doctest: +SKIP
|
||||
>>> reject=dict(grad=4000e-13, mag=4e-12, eog=150e-6)) # doctest: +SKIP
|
||||
>>> evoked = epochs.average() # compute evoked # doctest: +SKIP
|
||||
>>> evoked.plot() # butterfly plot the evoked data # doctest: +SKIP
|
||||
>>> cov = mne.compute_covariance(epochs, tmax=0, method='shrunk') # doctest: +SKIP
|
||||
>>> fwd = mne.read_forward_solution(fwd_fname, surf_ori=True) # doctest: +SKIP
|
||||
>>> inv = mne.minimum_norm.make_inverse_operator( # doctest: +SKIP
|
||||
>>> raw.info, fwd, cov, loose=0.2) # compute inverse operator # doctest: +SKIP
|
||||
>>> stc = mne.minimum_norm.apply_inverse(
|
||||
>>> evoked, inv, lambda2=1. / 9., method='dSPM') # apply it # doctest: +SKIP
|
||||
>>> stc_fs = stc.morph('fsaverage') # morph to fsaverage # doctest: +SKIP
|
||||
>>> stc_fs.plot() # plot source data on fsaverage's brain # doctest: +SKIP
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
Direct financial support for MNE has been provided by the United States:
|
||||
|
||||
cite
|
||||
references
|
||||
cited
|
||||
- NIH National Institute of Biomedical Imaging and Bioengineering
|
||||
*5R01EB009048* and *P41EB015896* (Center for Functional Neuroimaging
|
||||
Technologies)
|
||||
- NSF awards *0958669* and *1042134*.
|
||||
- NCRR *P41RR14075-06* (Center for Functional Neuroimaging Technologies)
|
||||
- NIH *1R01EB009048-01*, *R01EB006385-A101*, *1R01HD40712-A1*,
|
||||
*1R01NS44319-01*, *2R01NS37462-05*
|
||||
- Department of Energy Award Number *DE-FG02-99ER62764* (The MIND
|
||||
Institute)
|
||||
- Amazon Web Services - *Research Grant* issued to Denis A. Engemann
|
||||
|
||||
.. raw:: html
|
||||
And France:
|
||||
|
||||
<h2>Community</h2>
|
||||
- IDEX Paris-Saclay, *ANR-11-IDEX-0003-02*, via the
|
||||
`Center for Data Science <http://www.datascience-paris-saclay.fr/>`_.
|
||||
- European Research Council Starting Grant *ERC-YStG-263584* and
|
||||
*ERC-YStG-676943*
|
||||
- French National Research Agency *ANR-14-NEUC-0002-01*.
|
||||
|
||||
* Analysis talk: join the `MNE mailing list`_
|
||||
.. container:: col-md-8 midcenter
|
||||
|
||||
* `Feature requests and bug reports on GitHub <https://github.com/mne-tools/mne-python/issues/>`_
|
||||
.. raw:: html
|
||||
|
||||
* `Chat with developers on Gitter <https://gitter.im/mne-tools/mne-python>`_
|
||||
<script type="text/javascript" src="http://www.ohloh.net/p/586838/widgets/project_basic_stats.js"></script>
|
||||
|
||||
.. raw:: html
|
||||
.. container:: col-md-4
|
||||
|
||||
<h2>Versions</h2>
|
||||
.. container:: panel panel-default nosize nopad
|
||||
|
||||
<ul>
|
||||
<li><a href=http://martinos.org/mne/stable>Stable</a></li>
|
||||
<li><a href=http://martinos.org/mne/dev>Development</a></li>
|
||||
</ul>
|
||||
.. raw:: html
|
||||
|
||||
<div style="float: left; padding: 10px; width: 100%;">
|
||||
<a class="twitter-timeline" href="https://twitter.com/mne_python" data-widget-id="317730454184804352">Tweets by @mne_python</a>
|
||||
</div>
|
||||
<div class="panel-heading"><h3 class="panel-title">Documentation</h3></div>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
.. container:: panel-body nosize
|
||||
|
||||
- :ref:`get_started`
|
||||
- :ref:`tutorials`
|
||||
- :ref:`general_examples`
|
||||
- :ref:`API Reference <api_reference>`
|
||||
- :ref:`manual`
|
||||
- :ref:`contributing`
|
||||
- :ref:`FAQ`
|
||||
- :ref:`whats_new`
|
||||
- :ref:`cited`
|
||||
|
||||
.. container:: panel panel-default nosize nopad
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<div class="panel-heading"><h3 class="panel-title">Community</h3></div>
|
||||
|
||||
.. container:: panel-body nosize nopad
|
||||
|
||||
- `MNE mailing list`_ for analysis talk
|
||||
- `GitHub <https://github.com/mne-tools/mne-python/issues/>`_ for
|
||||
requests and bug reports
|
||||
- `Gitter <https://gitter.im/mne-tools/mne-python>`_ to chat with devs
|
||||
|
||||
.. container:: panel panel-default nosize nopad
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<div class="panel-heading"><h3 class="panel-title">Versions</h3></div>
|
||||
|
||||
.. container:: panel-body nosize nopad
|
||||
|
||||
- `Stable <http://martinos.org/mne/stable>`_
|
||||
- `Development <http://martinos.org/mne/dev>`_
|
||||
|
||||
.. container:: panel panel-default nosize nopad
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<a class="twitter-timeline" href="https://twitter.com/mne_python" data-widget-id="317730454184804352">Tweets by @mne_python</a>
|
||||
|
||||
@@ -701,7 +701,7 @@ MNE software and setup for individual users:
|
||||
- The setup scripts have changed.
|
||||
|
||||
The installation and user-level effects of the new software
|
||||
organization are discussed in :ref:`getting_started`.
|
||||
organization are discussed in :ref:`install_mne_c`.
|
||||
|
||||
In addition, several minor bugs have been fixed in the source
|
||||
code. Most relevant changes visible to the user are listed below.
|
||||
|
||||
+37
-50
@@ -11,22 +11,20 @@ and class usage information.
|
||||
|
||||
.. contents:: Contents
|
||||
:local:
|
||||
:depth: 1
|
||||
:depth: 2
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<h2>Cookbook</h2>
|
||||
A quick run-through of the basic steps involved in M/EEG source analysis.
|
||||
Cookbook
|
||||
--------
|
||||
A quick run-through of the basic steps involved in M/EEG source analysis.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
|
||||
cookbook
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<h2>Reading your data</h2>
|
||||
How to get your raw data loaded in MNE.
|
||||
Reading your data
|
||||
-----------------
|
||||
How to get your raw data loaded in MNE.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
@@ -34,10 +32,9 @@ and class usage information.
|
||||
io
|
||||
memory
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<h2>Preprocessing</h2>
|
||||
Dealing with artifacts and noise sources in data.
|
||||
Preprocessing
|
||||
-------------
|
||||
Dealing with artifacts and noise sources in data.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
@@ -47,10 +44,9 @@ and class usage information.
|
||||
preprocessing/ssp
|
||||
channel_interpolation
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<h2>Source localization</h2>
|
||||
Projecting raw data into source (brain) space.
|
||||
Source localization
|
||||
-------------------
|
||||
Projecting raw data into source (brain) space.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
@@ -59,67 +55,61 @@ and class usage information.
|
||||
source_localization/inverse
|
||||
source_localization/morph
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<h2>Time-frequency analysis</h2>
|
||||
Decomposing time-domain signals into time-frequency representations.
|
||||
Time-frequency analysis
|
||||
-----------------------
|
||||
Decomposing time-domain signals into time-frequency representations.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
|
||||
time_frequency
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<h2>Statistics</h2>
|
||||
Using parametric and non-parametric tests with M/EEG data.
|
||||
Statistics
|
||||
----------
|
||||
Using parametric and non-parametric tests with M/EEG data.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
|
||||
statistics
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<h2>Decoding</h2>
|
||||
Decoding
|
||||
--------
|
||||
How to do decoding in MNE-Python.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
|
||||
decoding
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<h2>Datasets</h2>
|
||||
How to use dataset fetchers for public data
|
||||
Datasets
|
||||
--------
|
||||
How to use dataset fetchers for public data
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
|
||||
datasets_index
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<h2>Migrating</h2>
|
||||
Migrating
|
||||
---------
|
||||
Migrating from other software packages.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
|
||||
migrating
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<h2>Pitfalls</h2>
|
||||
Pitfalls
|
||||
--------
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
|
||||
pitfalls
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<h2>C Tools</h2>
|
||||
|
||||
C Tools
|
||||
-------
|
||||
Additional information about various MNE-C tools.
|
||||
|
||||
.. toctree::
|
||||
@@ -129,20 +119,17 @@ Additional information about various MNE-C tools.
|
||||
gui/analyze
|
||||
gui/browse
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<h2>MATLAB Tools</h2>
|
||||
Information about the MATLAB toolbox.
|
||||
MNE-MATLAB
|
||||
----------
|
||||
Information about the MATLAB toolbox.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
|
||||
matlab
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<h2>Appendices</h2>
|
||||
|
||||
Appendices
|
||||
----------
|
||||
More details about our implementations and software.
|
||||
|
||||
.. toctree::
|
||||
|
||||
+1
-3
@@ -21,6 +21,4 @@ For further information please visit the MNE-CPP project pages:
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<div>
|
||||
<script type="text/javascript" src="http://www.openhub.net/p/687714/widgets/project_basic_stats.js"></script>
|
||||
</div>
|
||||
<div><script type="text/javascript" src="http://www.openhub.net/p/687714/widgets/project_basic_stats.js"></script></div>
|
||||
|
||||
+143
-114
@@ -19,174 +19,203 @@ For further reading:
|
||||
- For details about analysis steps: :ref:`manual`
|
||||
- For details about specific functions and classes: :ref:`api_reference`
|
||||
|
||||
.. note:: The default location for the MNE-sample data is
|
||||
my-path-to/mne-python/examples. If you downloaded data and an
|
||||
example asks you whether to download it again, make sure
|
||||
the data reside in the examples directory
|
||||
and that you run the script from its current directory.
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ cd examples/preprocessing
|
||||
|
||||
Then in Python you can do::
|
||||
|
||||
In [1]: %run plot_find_ecg_artifacts.py
|
||||
.. contents:: Categories
|
||||
:local:
|
||||
:depth: 1
|
||||
|
||||
|
||||
See :ref:`datasets` for a list of all available datasets and some
|
||||
advanced configuration options, e.g. to specify a custom
|
||||
location for storing the datasets.
|
||||
.. container:: row
|
||||
|
||||
.. container:: span box
|
||||
.. container:: panel panel-default halfpad
|
||||
|
||||
.. raw:: html
|
||||
.. raw:: html
|
||||
|
||||
<h2>Introduction to MNE and Python</h2>
|
||||
<div class="panel-heading"><h3 class="panel-title">Introduction to MNE and Python</h3></div>
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
.. container:: panel-body nosize
|
||||
|
||||
auto_tutorials/plot_python_intro.rst
|
||||
tutorials/seven_stories_about_mne.rst
|
||||
auto_tutorials/plot_introduction.rst
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
|
||||
.. container:: span box
|
||||
auto_tutorials/plot_python_intro.rst
|
||||
tutorials/seven_stories_about_mne.rst
|
||||
auto_tutorials/plot_introduction.rst
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<h2>Background information</h2>
|
||||
.. container:: row
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
.. container:: panel panel-default halfpad
|
||||
|
||||
auto_tutorials/plot_background_filtering.rst
|
||||
.. raw:: html
|
||||
|
||||
.. container:: span box
|
||||
<div class="panel-heading"><h3 class="panel-title">Background Information</h3></div>
|
||||
|
||||
.. raw:: html
|
||||
.. container:: panel-body nosize
|
||||
|
||||
<h2>Preprocessing</h2>
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
auto_tutorials/plot_background_filtering.rst
|
||||
|
||||
auto_tutorials/plot_artifacts_detection.rst
|
||||
auto_tutorials/plot_artifacts_correction_filtering.rst
|
||||
auto_tutorials/plot_artifacts_correction_rejection.rst
|
||||
auto_tutorials/plot_artifacts_correction_ssp.rst
|
||||
auto_tutorials/plot_artifacts_correction_ica.rst
|
||||
auto_tutorials/plot_artifacts_correction_maxwell_filtering.rst
|
||||
|
||||
.. container:: span box
|
||||
.. container:: panel panel-default halfpad
|
||||
|
||||
.. raw:: html
|
||||
.. raw:: html
|
||||
|
||||
<h2>Sensor-level analysis</h2>
|
||||
<div class="panel-heading"><h3 class="panel-title">Preprocessing</h3></div>
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
.. container:: panel-body nosize
|
||||
|
||||
auto_tutorials/plot_epoching_and_averaging.rst
|
||||
auto_tutorials/plot_eeg_erp.rst
|
||||
auto_tutorials/plot_sensors_time_frequency.rst
|
||||
auto_tutorials/plot_sensors_decoding.rst
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
|
||||
.. container:: span box
|
||||
auto_tutorials/plot_artifacts_detection.rst
|
||||
auto_tutorials/plot_artifacts_correction_filtering.rst
|
||||
auto_tutorials/plot_artifacts_correction_rejection.rst
|
||||
auto_tutorials/plot_artifacts_correction_ssp.rst
|
||||
auto_tutorials/plot_artifacts_correction_ica.rst
|
||||
auto_tutorials/plot_artifacts_correction_maxwell_filtering.rst
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<h2>Visualization and Reporting</h2>
|
||||
.. container:: row
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
.. container:: panel panel-default halfpad
|
||||
|
||||
auto_tutorials/plot_visualize_raw.rst
|
||||
auto_tutorials/plot_visualize_epochs.rst
|
||||
auto_tutorials/plot_visualize_evoked.rst
|
||||
tutorials/report.rst
|
||||
.. raw:: html
|
||||
|
||||
.. container:: span box
|
||||
<div class="panel-heading"><h3 class="panel-title">Data Structures and Containers</h3></div>
|
||||
|
||||
.. raw:: html
|
||||
.. container:: panel-body nosize
|
||||
|
||||
<h2>Manipulating Data Structures and Containers</h2>
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
auto_tutorials/plot_object_raw.rst
|
||||
auto_tutorials/plot_modifying_data_inplace.rst
|
||||
auto_tutorials/plot_object_epochs.rst
|
||||
auto_tutorials/plot_object_evoked.rst
|
||||
auto_tutorials/plot_creating_data_structures.rst
|
||||
auto_tutorials/plot_info.rst
|
||||
|
||||
auto_tutorials/plot_object_raw.rst
|
||||
auto_tutorials/plot_modifying_data_inplace.rst
|
||||
auto_tutorials/plot_object_epochs.rst
|
||||
auto_tutorials/plot_object_evoked.rst
|
||||
auto_tutorials/plot_creating_data_structures.rst
|
||||
auto_tutorials/plot_info.rst
|
||||
|
||||
.. container:: span box
|
||||
.. container:: panel panel-default halfpad
|
||||
|
||||
.. raw:: html
|
||||
.. raw:: html
|
||||
|
||||
<h2>Source-level analysis</h2>
|
||||
<div class="panel-heading"><h3 class="panel-title">Visualization and Reporting</h3></div>
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
.. container:: panel-body nosize
|
||||
|
||||
auto_tutorials/plot_forward.rst
|
||||
auto_tutorials/plot_compute_covariance.rst
|
||||
auto_tutorials/plot_mne_dspm_source_localization.rst
|
||||
auto_tutorials/plot_dipole_fit.rst
|
||||
auto_tutorials/plot_brainstorm_auditory.rst
|
||||
auto_tutorials/plot_brainstorm_phantom_ctf.rst
|
||||
auto_tutorials/plot_brainstorm_phantom_elekta.rst
|
||||
auto_tutorials/plot_point_spread.rst
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
|
||||
.. container:: span box
|
||||
auto_tutorials/plot_visualize_raw.rst
|
||||
auto_tutorials/plot_visualize_epochs.rst
|
||||
auto_tutorials/plot_visualize_evoked.rst
|
||||
tutorials/report.rst
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<h2>Sensor-space Univariate Statistics</h2>
|
||||
.. container:: row
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
.. container:: panel panel-default halfpad
|
||||
|
||||
auto_tutorials/plot_stats_cluster_methods.rst
|
||||
auto_tutorials/plot_stats_spatio_temporal_cluster_sensors.rst
|
||||
auto_tutorials/plot_stats_cluster_1samp_test_time_frequency.rst
|
||||
auto_tutorials/plot_stats_cluster_time_frequency.rst
|
||||
.. raw:: html
|
||||
|
||||
.. container:: span box
|
||||
<div class="panel-heading"><h3 class="panel-title">Sensor analysis</h3></div>
|
||||
|
||||
.. raw:: html
|
||||
.. container:: panel-body nosize
|
||||
|
||||
<h2>Source-space Univariate Statistics</h2>
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
auto_tutorials/plot_epoching_and_averaging.rst
|
||||
auto_tutorials/plot_eeg_erp.rst
|
||||
auto_tutorials/plot_sensors_time_frequency.rst
|
||||
auto_tutorials/plot_sensors_decoding.rst
|
||||
|
||||
auto_tutorials/plot_stats_cluster_time_frequency_repeated_measures_anova.rst
|
||||
auto_tutorials/plot_stats_cluster_spatio_temporal_2samp.rst
|
||||
auto_tutorials/plot_stats_cluster_spatio_temporal_repeated_measures_anova.rst
|
||||
auto_tutorials/plot_stats_cluster_spatio_temporal.rst
|
||||
.. container:: panel panel-default halfpad
|
||||
|
||||
.. container:: span box
|
||||
.. raw:: html
|
||||
|
||||
.. raw:: html
|
||||
<div class="panel-heading"><h3 class="panel-title">Source Analysis</h3></div>
|
||||
|
||||
<h2>Multivariate Statistics - Decoding</h2>
|
||||
.. container:: panel-body nosize
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
|
||||
auto_tutorials/plot_sensors_decoding.rst
|
||||
auto_tutorials/plot_forward.rst
|
||||
auto_tutorials/plot_compute_covariance.rst
|
||||
auto_tutorials/plot_mne_dspm_source_localization.rst
|
||||
auto_tutorials/plot_dipole_fit.rst
|
||||
auto_tutorials/plot_brainstorm_auditory.rst
|
||||
auto_tutorials/plot_brainstorm_phantom_ctf.rst
|
||||
auto_tutorials/plot_brainstorm_phantom_elekta.rst
|
||||
auto_tutorials/plot_point_spread.rst
|
||||
|
||||
.. container:: span box
|
||||
.. container:: row
|
||||
|
||||
.. raw:: html
|
||||
.. container:: panel panel-default halfpad
|
||||
|
||||
<h2>Command line tools</h2>
|
||||
.. raw:: html
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
<div class="panel-heading"><h3 class="panel-title">Sensor-space Statistics</h3></div>
|
||||
|
||||
tutorials/command_line.rst
|
||||
generated/commands.rst
|
||||
.. container:: panel-body nosize
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
|
||||
auto_tutorials/plot_stats_cluster_methods.rst
|
||||
auto_tutorials/plot_stats_spatio_temporal_cluster_sensors.rst
|
||||
auto_tutorials/plot_stats_cluster_1samp_test_time_frequency.rst
|
||||
auto_tutorials/plot_stats_cluster_time_frequency.rst
|
||||
|
||||
|
||||
.. container:: panel panel-default halfpad
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<div class="panel-heading"><h3 class="panel-title">Source-space Statistics</h3></div>
|
||||
|
||||
.. container:: panel-body nosize
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
|
||||
auto_tutorials/plot_stats_cluster_time_frequency_repeated_measures_anova.rst
|
||||
auto_tutorials/plot_stats_cluster_spatio_temporal_2samp.rst
|
||||
auto_tutorials/plot_stats_cluster_spatio_temporal_repeated_measures_anova.rst
|
||||
auto_tutorials/plot_stats_cluster_spatio_temporal.rst
|
||||
|
||||
|
||||
.. container:: row
|
||||
|
||||
.. container:: panel panel-default halfpad
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<div class="panel-heading"><h3 class="panel-title">Decoding</h3></div>
|
||||
|
||||
.. container:: panel-body nosize
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
|
||||
auto_tutorials/plot_sensors_decoding.rst
|
||||
|
||||
.. container:: row
|
||||
|
||||
.. container:: panel panel-default halfpad
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<div class="panel-heading"><h3 class="panel-title">Command-line Tools</h3></div>
|
||||
|
||||
.. container:: panel-body nosize
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
|
||||
generated/commands.rst
|
||||
tutorials/command_line.rst
|
||||
|
||||
+12
-10
@@ -51,7 +51,12 @@ Z1 = bivariate_normal(X, Y, 8.0, 7.0, -5.0, 0.9, 1.0)
|
||||
Z2 = bivariate_normal(X, Y, 15.0, 2.5, 2.6, -2.5, 2.5)
|
||||
Z = Z2 - 0.7 * Z1
|
||||
|
||||
# color map: field gradient (yellow-red-transparent-blue-cyan)
|
||||
# color map: field gradient (yellow-red-gray-blue-cyan)
|
||||
# yrtbc = {
|
||||
# 'red': ((0, 1, 1), (0.4, 1, 1), (0.5, 0.5, 0.5), (0.6, 0, 0), (1, 0, 0)),
|
||||
# 'blue': ((0, 0, 0), (0.4, 0, 0), (0.5, 0.5, 0.5), (0.6, 1, 1), (1, 1, 1)), # noqa
|
||||
# 'green': ((0, 1, 1), (0.4, 0, 0), (0.5, 0.5, 0.5), (0.6, 0, 0), (1, 1, 1)), # noqa
|
||||
# }
|
||||
yrtbc = {'red': ((0.0, 1.0, 1.0), (0.5, 1.0, 0.0), (1.0, 0.0, 0.0)),
|
||||
'blue': ((0.0, 0.0, 0.0), (0.5, 0.0, 1.0), (1.0, 1.0, 1.0)),
|
||||
'green': ((0.0, 1.0, 1.0), (0.5, 0.0, 0.0), (1.0, 1.0, 1.0)),
|
||||
@@ -78,6 +83,7 @@ mult = (plot_dims / dims).min()
|
||||
mult = [mult, -mult] # y axis is inverted (origin at top left)
|
||||
offset = plot_dims / 2. - center_fudge
|
||||
mne_clip = Path(offset + vert * mult, mne_path.codes)
|
||||
ax.add_patch(PathPatch(mne_clip, color='w', zorder=0, linewidth=0))
|
||||
# apply clipping mask to field gradient and lines
|
||||
im.set_clip_path(mne_clip, transform=im.get_transform())
|
||||
for coll in cs.collections:
|
||||
@@ -103,11 +109,8 @@ yy = np.max([tag_clip.vertices.max(0)[-1],
|
||||
ax.set_ylim(np.ceil(yy), yl[-1])
|
||||
|
||||
# only save actual image extent plus a bit of padding
|
||||
extent = Bbox(np.c_[ax.get_xlim(), ax.get_ylim()])
|
||||
extent = extent.transformed(ax.transData + fig.dpi_scale_trans.inverted())
|
||||
plt.draw()
|
||||
plt.savefig(op.join(static_dir, 'mne_logo.png'),
|
||||
bbox_inches=extent.expanded(1.2, 1.))
|
||||
plt.savefig(op.join(static_dir, 'mne_logo.png'), transparent=True)
|
||||
plt.close()
|
||||
|
||||
# 92x22 image
|
||||
@@ -125,7 +128,9 @@ ax = plt.Axes(fig, [0., 0., 1., 1.])
|
||||
ax.set_axis_off()
|
||||
fig.add_axes(ax)
|
||||
# plot rainbow
|
||||
im = plt.imshow(X, cmap=mne_field_grad_cols, aspect='equal')
|
||||
im = ax.imshow(X, cmap=mne_field_grad_cols, aspect='equal', zorder=1)
|
||||
im = ax.imshow(np.ones_like(X) * 0.5, cmap='Greys', aspect='equal', zorder=0,
|
||||
clim=[0, 1])
|
||||
plot_dims = np.r_[np.diff(ax.get_xbound()), np.diff(ax.get_ybound())]
|
||||
# MNE text in white
|
||||
mne_path = TextPath((0, 0), 'MNE')
|
||||
@@ -148,9 +153,6 @@ xpad = np.abs(np.diff([xmin, xl[1]])) / 20.
|
||||
ypad = np.abs(np.diff([ymax, ymin])) / 20.
|
||||
ax.set_xlim(xmin - xpad, xl[1] + xpad)
|
||||
ax.set_ylim(ymax + ypad, ymin - ypad)
|
||||
extent = Bbox(np.c_[ax.get_xlim(), ax.get_ylim()])
|
||||
extent = extent.transformed(ax.transData + fig.dpi_scale_trans.inverted())
|
||||
plt.draw()
|
||||
plt.savefig(op.join(static_dir, 'mne_logo_small.png'), transparent=True,
|
||||
bbox_inches=extent)
|
||||
plt.savefig(op.join(static_dir, 'mne_logo_small.png'), transparent=True)
|
||||
plt.close()
|
||||
|
||||
@@ -300,7 +300,7 @@ tlim = [center - 0.2, center + 0.2]
|
||||
tticks = [tlim[0], center, tlim[1]]
|
||||
flim = [20, 70]
|
||||
|
||||
x = np.zeros(int(sfreq * dur))
|
||||
x = np.zeros(int(sfreq * dur) + 1)
|
||||
blip = morlet(sfreq, [morlet_freq], n_cycles=7)[0].imag / 20.
|
||||
n_onset = int(center * sfreq) - len(blip) // 2
|
||||
x[n_onset:n_onset + len(blip)] += blip
|
||||
|
||||
@@ -50,7 +50,6 @@ What you're not supposed to do with MNE Python
|
||||
|
||||
- **Brain and head surface segmentation** for use with BEM
|
||||
models -- use Freesurfer.
|
||||
- **Raw movement compensation** -- use Elekta Maxfilter™
|
||||
|
||||
|
||||
.. note:: This package is based on the FIF file format from Neuromag. It
|
||||
@@ -61,10 +60,10 @@ What you're not supposed to do with MNE Python
|
||||
Installation of the required materials
|
||||
---------------------------------------
|
||||
|
||||
See :ref:`getting_started` with Python.
|
||||
See :ref:`install_python_and_mne_python`.
|
||||
|
||||
.. note:: The expected location for the MNE-sample data is
|
||||
my-path-to/mne-python/examples. If you downloaded data and an example asks
|
||||
``~/mne_data``. If you downloaded data and an example asks
|
||||
you whether to download it again, make sure
|
||||
the data reside in the examples directory and you run the script from its
|
||||
current directory.
|
||||
|
||||
@@ -6,24 +6,15 @@
|
||||
Permutation t-test on toy data with spatial clustering
|
||||
======================================================
|
||||
|
||||
Following the illustrative example of Ridgway et al. 2012,
|
||||
Following the illustrative example of Ridgway et al. 2012 [1]_,
|
||||
this demonstrates some basic ideas behind both the "hat"
|
||||
variance adjustment method, as well as threshold-free
|
||||
cluster enhancement (TFCE) methods in mne-python.
|
||||
cluster enhancement (TFCE) [2]_ methods in mne-python.
|
||||
|
||||
This toy dataset consists of a 40 x 40 square with a "signal"
|
||||
present in the center (at pixel [20, 20]) with white noise
|
||||
added and a 5-pixel-SD normal smoothing kernel applied.
|
||||
|
||||
For more information, see:
|
||||
Ridgway et al. 2012, "The problem of low variance voxels in
|
||||
statistical parametric mapping; a new hat avoids a 'haircut'",
|
||||
NeuroImage. 2012 Feb 1;59(3):2131-41.
|
||||
|
||||
Smith and Nichols 2009, "Threshold-free cluster enhancement:
|
||||
addressing problems of smoothing, threshold dependence, and
|
||||
localisation in cluster inference", NeuroImage 44 (2009) 83-98.
|
||||
|
||||
In the top row plot the T statistic over space, peaking toward the
|
||||
center. Note that it has peaky edges. Second, with the "hat" variance
|
||||
correction/regularization, the peak becomes correctly centered. Third,
|
||||
@@ -49,8 +40,8 @@ methods tightens the area declared significant (again FWER corrected),
|
||||
and allows for evaluation of each point independently instead of as
|
||||
a single, broad cluster.
|
||||
|
||||
Note that this example does quite a bit of processing, so even on a
|
||||
fast machine it can take a few minutes to complete.
|
||||
.. note:: This example does quite a bit of processing, so even on a
|
||||
fast machine it can take a few minutes to complete.
|
||||
"""
|
||||
# Authors: Eric Larson <larson.eric.d@gmail.com>
|
||||
# License: BSD (3-clause)
|
||||
@@ -145,7 +136,7 @@ T_obs_hat, clusters, p_values, H0 = \
|
||||
spatio_temporal_cluster_1samp_test(X, n_jobs=1, threshold=threshold,
|
||||
connectivity=connectivity,
|
||||
tail=1, n_permutations=n_permutations,
|
||||
stat_fun=stat_fun)
|
||||
stat_fun=stat_fun, buffer_size=None)
|
||||
|
||||
# Let's put the cluster data in a readable format
|
||||
ps_hat = np.zeros(width * width)
|
||||
@@ -167,7 +158,7 @@ T_obs_tfce_hat, clusters, p_values, H0 = \
|
||||
spatio_temporal_cluster_1samp_test(X, n_jobs=1, threshold=threshold_tfce,
|
||||
connectivity=connectivity,
|
||||
tail=1, n_permutations=n_permutations,
|
||||
stat_fun=stat_fun)
|
||||
stat_fun=stat_fun, buffer_size=None)
|
||||
T_obs_tfce_hat = T_obs_tfce_hat.reshape((width, width))
|
||||
ps_tfce_hat = -np.log10(p_values.reshape((width, width)))
|
||||
|
||||
@@ -210,3 +201,14 @@ for ax in axs:
|
||||
cbar.set_ticklabels(['%0.1f' % p for p in p_lims])
|
||||
|
||||
plt.show()
|
||||
|
||||
###############################################################################
|
||||
# References
|
||||
# ----------
|
||||
# .. [1] Ridgway et al. 2012, "The problem of low variance voxels in
|
||||
# statistical parametric mapping; a new hat avoids a 'haircut'",
|
||||
# NeuroImage. 2012 Feb 1;59(3):2131-41.
|
||||
#
|
||||
# .. [2] Smith and Nichols 2009, "Threshold-free cluster enhancement:
|
||||
# addressing problems of smoothing, threshold dependence, and
|
||||
# localisation in cluster inference", NeuroImage 44 (2009) 83-98.
|
||||
|
||||
Reference in New Issue
Block a user