[Neuroimaging] [ANN] MNE-Python 0.12
Alexandre Gramfort
alexandre.gramfort at telecom-paristech.fr
Wed May 11 03:59:36 EDT 2016
Hi,
We are pleased to announce the new 0.12 release of MNE-Python. This release
comes with many improvements to usability, visualization and documentation
and bug fixes.
A few highlights:
-
We entirely revamped our documentation at the MNE website with a new
easy-to-follow structure, have a look at http://martinos.org/mne and let
us know if you would like to read more on a particular topic. See eg.
http://martinos.org/mne/stable/tutorials.html
-
We introduced annotations for marking arbitrary segments of raw data.
This can be used in order to annotate M/EEG recordings with naturalistic
stimuli or for rejecting bad segments of data. See
http://martinos.org/mne/dev/auto_tutorials/plot_brainstorm_auditory.html
for an example.
-
Added the ability to create animations/movies of sensor topographies.
See Evoked.animate_topomap method.
-
We now have movement compensation for Maxwell-Filter
-
We have new short-hand plotting function for showing sensor positions
and layouts.
-
We now explicitly support ECoG data with a specific ecog channel type.
-
Evoked activity (as a butterfly time series) and corresponding topomaps
can now be shown in one plot with `Evoked.plot_joint()` for spatio-temporal
brain dynamics
-
Support for reading and estimation of fixed-position dipole time courses
(similar to Elekta ``xfit``)
-
New mne.io.read_raw_cnt function for reading Neuroscan CNT files
Notable API changes:
-
To unify in-place modification vs. copying API, the `copy` parameter was
deprecated for all MNE object methods and will be removed in a later
version; instead, `inst.copy().method()` is to be used. Also, all object
methods now return `self`, allowing reliable chaining (e.g. `raw_resampled
= raw.copy().filter(1).resample(100)`)
-
Generalization Across Time now supports custom predict functions, e.g.
predicting probabilities rather than classes, via the `predict_method`
keyword argument; and an option was added to score either across or within
folds via the `predict_mode` keyword argument.
-
We now have additional decimation parameters for time-frequency methods
-
When estimating covariance from raw data, the same regularization
methods can be used as for estimating the covariance from epoched data.
-
From now on ECG, EOG and EMG channels are shown by default in butterfly
plots
For a full list of improvements and API changes, see:
http://martinos.org/mne/stable/whats_new.html#version-0-12
To install the latest release the following command should do the job:
pip install --upgrade --user mne
As usual we welcome your bug reports, feature requests, critiques and
contributions.
Some links:
- https://github.com/mne-tools/mne-python (code + readme on how to install)
- http://martinos.org/mne/stable/ (full MNE documentation)
Follow us on Twitter: https://twitter.com/mne_python
Regards,
The MNE-Python developers
People who contributed to this release with their number of commits:
The committer list for this release is the following (preceded by
number of commits):
-
348 Eric Larson
-
347 Jaakko Leppakangas
-
157 Alexandre Gramfort
-
139 Jona Sassenhagen
-
67 Jean-Remi King
-
32 Chris Holdgraf
-
31 Denis A. Engemann
-
30 Mainak Jas
-
16 Christopher J. Bailey
-
13 Marijn van Vliet
-
10 Mark Wronkiewicz
-
9 Teon Brooks
-
9 kaichogami
-
8 Clément Moutard
-
5 Camilo Lamus
-
5 mmagnuski
-
4 Christian Brodbeck
-
4 Daniel McCloy
-
4 Yousra Bekhti
-
3 Fede Raimondo
-
1 Jussi Nurminen
-
1 MartinBaBer
-
1 Mikolaj Magnuski
-
1 Natalie Klein
-
1 Niklas Wilming
-
1 Richard Höchenberger
-
1 Sagun Pai
-
1 Sourav Singh
-
1 Tom Dupré la Tour
-
1 kambysese
-
1 pbnsilva
-
1 sviter
-
1 zuxfoucault
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