[Mne_analysis] [ANN] MNE-Python 0.14
Chris Holdgraf
choldgraf at berkeley.edu
Fri Mar 24 12:18:15 EDT 2017
Woohoo! Way to go MNE team! What an awesome and useful project!
--
_____________________________________
PhD Candidate in Neuroscience | UC Berkeley <http://hwni.org/>
Data Science Fellow | Berkeley Institute for Data Science
<https://bids.berkeley.edu>
Editor and Web Director | Berkeley Science Review
<http://sciencereview.berkeley.edu/>
_____________________________________
On Fri, Mar 24, 2017 at 2:14 AM Alexandre Gramfort <
alexandre.gramfort at telecom-paristech.fr> wrote:
> 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
> <http://martinos.org/mne/stable/generated/mne.io.read_raw_artemis123.html>
> infant/toddler MEG data
> -
>
> We no longer require MNE-C for BEM and scalp processing steps
> -
>
> Interactive annotation
> <http://martinos.org/mne/stable/generated/mne.Annotations.html> mode
> is now available in raw plotting
> -
>
> Dipole locations can now be visualized with MRI slice overlay
> <http://martinos.org/mne/stable/auto_tutorials/plot_dipole_fit.html>
> -
>
> Add minimum-phase filtering option in mne.io.Raw.filter()
> <http://martinos.org/mne/stable/generated/mne.io.Raw.html#mne.io.Raw.filter>
> -
>
> New mne.datasets.visual_92_categories
> <http://martinos.org/mne/stable/python_reference.html#module-mne.datasets.visual_92_categories>
> dataset with an example of Representational Similarity Analysis (RSA)
> <http://martinos.org/mne/stable/auto_examples/decoding/decoding_rsa.html#sphx-glr-auto-examples-decoding-decoding-rsa-py>
>
>
> Notable API changes
>
> ================
>
> -
>
> Fix bug with DICS and LCMV (functions mne.beamformer.lcmv
> <http://martinos.org/mne/stable/generated/mne.beamformer.lcmv.html>
> and mne.beamformer.dics
> <http://martinos.org/mne/stable/generated/mne.beamformer.dics.html>)
> 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
> <http://martinos.org/mne/stable/generated/mne.filter.filter_data.html>
> -
>
> mne.decoding.Scaler
> <http://martinos.org/mne/stable/generated/mne.decoding.Scaler.html>
> 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
> <http://martinos.org/mne/stable/generated/mne.io.Raw.html?highlight=plot_psd#mne.io.Raw.plot_psd>
> will change to tmax=np.inf in 0.15
> -
>
> The mne.decoding.LinearModel
> <http://martinos.org/mne/stable/generated/mne.decoding.LinearModel.html#mne.decoding.LinearModel>
> class will no longer support plot_filters and plot_patterns, use
> mne.EvokedArray
> <http://martinos.org/mne/stable/generated/mne.EvokedArray.html> with
> mne.decoding.get_coef
> <http://martinos.org/mne/stable/generated/mne.decoding.get_coef.html>
> instead
> -
>
> Made functions mne.time_frequency.tfr_array_multitaper
> <http://martinos.org/mne/stable/generated/mne.time_frequency.tfr_array_multitaper.html>,
> mne.time_frequency.tfr_array_morlet
> <http://martinos.org/mne/stable/generated/mne.time_frequency.tfr_array_morlet.html>,
> mne.time_frequency.tfr_array_stockwell
> <http://martinos.org/mne/stable/generated/mne.time_frequency.tfr_array_stockwell.html>,
> mne.time_frequency.psd_array_multitaper
> <http://martinos.org/mne/stable/generated/mne.time_frequency.psd_array_multitaper.html>
> and mne.time_frequency.psd_array_welch
> <http://martinos.org/mne/stable/generated/mne.time_frequency.psd_array_welch.html>
> public to allow computing TFRs and PSDs on numpy arrays
> -
>
> mne.preprocessing.ICA.fit
> <http://martinos.org/mne/stable/generated/mne.preprocessing.ICA.html#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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