[Mne_analysis] [ANN] MNE-Python 0.12

[Alexandre Gramfort]

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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