[Neuroimaging] [ANN] MNE-Python 0.14

[Bertrand Thirion]

Congratulations ! 
B 

----- Mail original -----

> De: "Alexandre Gramfort" <alexandre.gramfort at telecom-paristech.fr>
> À: "Neuroimaging analysis in Python" <neuroimaging at python.org>,
> megcommunity at jiscmail.ac.uk, neurospin-time-group at googlegroups.com, "mne
> analysis" <mne_analysis at nmr.mgh.harvard.edu>
> Envoyé: Vendredi 24 Mars 2017 10:13:34
> Objet: [Neuroimaging] [ANN] MNE-Python 0.14

> Hi,

> We are pleased to announce the new 0.14 release of MNE-Python. As usual this
> release comes with new features, bug fixes, and many improvements to
> usability, visualization, and documentation.

> A few highlights

> ============

> *

> We have added I/O support for Artemis123 infant/toddler MEG data
> *

> We no longer require MNE-C for BEM and scalp processing steps
> *

> Interactive annotation mode is now available in raw plotting
> *

> Dipole locations can now be visualized with MRI slice overlay
> *

> Add minimum-phase filtering option in mne.io.Raw.filter()
> *

> New mne.datasets.visual_92_categories dataset with an example of
> Representational Similarity Analysis (RSA)

> Notable API changes

> ================

> *

> Fix bug with DICS and LCMV (functions mne.beamformer.lcmv and
> mne.beamformer.dics ) where regularization was done improperly. The default
> reg=0.01 has been changed to reg=0.05
> *

> The filtering functions band_pass_filter, band_stop_filter, low_pass_filter,
> and high_pass_filter have been deprecated in favor of mne.filter.filter_data
> *

> mne.decoding.Scaler now scales each channel independently using data from all
> time points (epochs and times) instead of scaling all channels for each time
> point. It also now accepts parameter scalings to determine the data scaling
> method (default is None to use static channel-type-based scaling)
> *

> The default tmax=60. In mne.io.Raw.plot_psd will change to tmax=np.inf in
> 0.15
> *

> The mne.decoding.LinearModel class will no longer support plot_filters and
> plot_patterns, use mne.EvokedArray with mne.decoding.get_coef instead
> *

> Made functions mne.time_frequency.tfr_array_multitaper ,
> mne.time_frequency.tfr_array_morlet , mne.time_frequency.tfr_array_stockwell
> , mne.time_frequency.psd_array_multitaper and
> mne.time_frequency.psd_array_welch public to allow computing TFRs and PSDs
> on numpy arrays
> *

> mne.preprocessing.ICA.fit now rejects data annotated bad by default when
> working with Raw.

> For a full list of improvements and API changes, see:

> http://martinos.org/mne/stable/whats_new.html#version-0-14

> 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 (in alphabetical order):

> * Alexander Rudiuk

> * Alexandre Gramfort

> * Annalisa Pascarella

> * Antti Rantala

> * Asish Panda

> * Burkhard Maess

> * Chris Holdgraf

> * Christian Brodbeck

> * Cristóbal Moënne-Loccoz

> * Daniel McCloy

> * Denis A. Engemann

> * Eric Larson

> * Erkka Heinila

> * Hermann Sonntag

> * Jaakko Leppakangas

> * Jakub Kaczmarzyk

> * Jean-Remi King

> * Jon Houck

> * Jona Sassenhagen

> * Jussi Nurminen

> * Keith Doelling

> * Leonardo S. Barbosa

> * Lorenz Esch

> * Lorenzo Alfine

> * Luke Bloy

> * Mainak Jas

> * Marijn van Vliet

> * Matt Boggess

> * Matteo Visconti

> * Mikolaj Magnuski

> * Niklas Wilming

> * Paul Pasler

> * Richard Höchenberger

> * Sheraz Khan

> * Stefan Repplinger

> * Teon Brooks

> * Yaroslav Halchenko

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> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
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