[Mne_analysis] [ANN] MNE-Python 0.15 {Disarmed}

Alexandre Gramfort alexandre.gramfort at inria.fr
Fri Oct 20 03:10:39 EDT 2017
Search archives:

Hi,

We are very pleased to announce the new 0.15 release of MNE-Python. This
release comes with new features, bug fixes, and many improvements to
usability, visualization, and documentation.

A few highlights

============

   -

   We reinvented our documentation. Our website now unifies tutorials,
   examples and background information into one coherent narrative structure
   while preserving context. Check it out
   <http://martinos.org/mne/stable/documentation.html>!


   -

   Add mne.decoding.cross_val_multiscore()
   <http://mne-tools.github.io/dev/generated/mne.decoding.cross_val_multiscore.html#mne.decoding.cross_val_multiscore>
   to allow scoring of multiple tasks, typically used with the new
   mne.decoding.SlidingEstimator
   <http://mne-tools.github.io/dev/generated/mne.decoding.SlidingEstimator.html#mne.decoding.SlidingEstimator>
   -

   Add mne.decoding.ReceptiveField
   <http://mne-tools.github.io/dev/generated/mne.decoding.ReceptiveField.html#mne.decoding.ReceptiveField>
   module for modeling neural responses to continuous stimulation
   -

   Add mne.decoding.SPoC
   <http://mne-tools.github.io/dev/generated/mne.decoding.SPoC.html#mne.decoding.SPoC>
   to fit and apply spatial filters based on continuous target variables
   -

   mne.io.Raw.plot()
   <http://mne-tools.github.io/dev/generated/mne.io.Raw.html#mne.io.Raw.plot>
   butterfly mode (toggled with ‘b’ key)
   -

   IO support for EGI MFF format
   -

   mne.fit_dipole()
   <http://mne-tools.github.io/dev/generated/mne.fit_dipole.html#mne.fit_dipole>
   confidence intervals, number of free parameters, and χ²
   -

   Add mne.VectorSourceEstimate
   <http://mne-tools.github.io/dev/generated/mne.VectorSourceEstimate.html#mne.VectorSourceEstimate>
   class which enables working with both source power and dipole orientations;
   use option pick_ori='vector' to mne.minimum_norm.apply_inverse()
   -

   New high-frequency somatosensory MEG dataset
   -

   Add unit-noise-gain beamformer and neural activity index (weight
   normalization) to LCMV beamformer with weight_norm parameter
   -

   Add filtering functions mne.Epochs.filter()
   <https://mne-tools.github.io/dev/generated/mne.Epochs.html#mne.Epochs.filter>
   and mne.Evoked.filter()
   <https://mne-tools.github.io/dev/generated/mne.Evoked.html#mne.Evoked.filter>,
   as well as pad argument to mne.io.Raw.filter()
   <https://mne-tools.github.io/dev/generated/mne.io.Raw.html#mne.io.Raw.filter>
   -

   Enable morphing between hemispheres with mne.compute_morph_matrix()
   <https://mne-tools.github.io/dev/generated/mne.compute_morph_matrix.html#mne.compute_morph_matrix>
   -

   Add interactive time cursor and category/amplitude status message in
   window for evoked plot
   -

   We exposed a rank parameter in mne.viz.evoked.plot_evoked_white()
   <http://martinos.org/mne/dev/generated/mne.viz.plot_evoked_white.html#mne.viz.plot_evoked_white>
   that allows for correcting the scaling of the visualization on the spot in
   cases where the rank estimate of the covariance is not accurate (for
   certain SSS’d data)


Notable API changes

================

   -

   ICA channel names have now been reformatted to start from zero, e.g.
   "ICA000", to match indexing schemes in mne.preprocessing.ICA
   <http://mne-tools.github.io/dev/generated/mne.preprocessing.ICA.html#mne.preprocessing.ICA>
   -

   Add skip_by_annotation to mne.io.Raw.filter()
   <https://mne-tools.github.io/dev/generated/mne.io.Raw.html#mne.io.Raw.filter>
   to process data concatenated with e.g. mne.concatenate_raws()
   <https://mne-tools.github.io/dev/generated/mne.concatenate_raws.html#mne.concatenate_raws>
   separately
   -

   Add new filtering mode fir_design='firwin' (default in the next 0.16
   release) that gets improved attenuation using fewer samples compared to
   fir_design='firwin2' (default in 0.15)
   -

   Add mne.beamformer.make_lcmv()
   <http://mne-tools.github.io/dev/generated/mne.beamformer.make_lcmv.html#mne.beamformer.make_lcmv>
   and mne.beamformer.apply_lcmv()
   <http://mne-tools.github.io/dev/generated/mne.beamformer.apply_lcmv.html#mne.beamformer.apply_lcmv>,
   mne.beamformer.apply_lcmv_epochs()
   <http://mne-tools.github.io/dev/generated/mne.beamformer.apply_lcmv_epochs.html#mne.beamformer.apply_lcmv_epochs>,
   and mne.beamformer.apply_lcmv_raw()
   <http://mne-tools.github.io/dev/generated/mne.beamformer.apply_lcmv_raw.html#mne.beamformer.apply_lcmv_raw>
   to enable the separate computation and application of LCMV beamformer
   weights
   -

   mne.set_eeg_reference()
   <http://mne-tools.github.io/dev/generated/mne.set_eeg_reference.html#mne.set_eeg_reference>
   and related methods (e.g. mne.io.Raw.set_eeg_reference()
   <http://mne-tools.github.io/dev/generated/mne.io.Raw.html#mne.io.Raw.set_eeg_reference>)
   have a new argument projection, which if set to False directly applies
   an average reference instead of adding an SSP projector
   -

   mne.find_events()
   <http://mne-tools.github.io/dev/generated/mne.find_events.html#mne.find_events>
   mask_type parameter will change from 'not_and' to 'and' d
   -

   picks parameter in mne.beamformer.lcmv()
   <http://mne-tools.github.io/dev/generated/mne.beamformer.lcmv.html#mne.beamformer.lcmv>,
   mne.beamformer.lcmv_epochs()
   <http://mne-tools.github.io/dev/generated/mne.beamformer.lcmv_epochs.html#mne.beamformer.lcmv_epochs>,
   mne.beamformer.lcmv_raw()
   <http://mne-tools.github.io/dev/generated/mne.beamformer.lcmv_raw.html#mne.beamformer.lcmv_raw>,
   mne.beamformer.tf_lcmv()
   <http://mne-tools.github.io/dev/generated/mne.beamformer.tf_lcmv.html#mne.beamformer.tf_lcmv>
   and mne.beamformer.rap_music()
   <http://mne-tools.github.io/dev/generated/mne.beamformer.rap_music.html#mne.beamformer.rap_music>
   is now deprecated
   -

   The keyword argument frequencies has been deprecated in favor of freqs
   in various time-frequency functions, e.g.
   mne.time_frequency.tfr_array_morlet()
   <http://mne-tools.github.io/dev/generated/mne.time_frequency.tfr_array_morlet.html#mne.time_frequency.tfr_array_morlet>
   -

   Deprecate force_fixed and surf_ori in mne.read_forward_solution()
   <http://mne-tools.github.io/dev/generated/mne.read_forward_solution.html#mne.read_forward_solution>
   -

   The behavior of 'mean_flip' label-flipping in
   mne.extract_label_time_course()
   <https://mne-tools.github.io/dev/generated/mne.extract_label_time_course.html#mne.extract_label_time_course>
   and related functions has been changed such that the flip, instead of
   having arbitrary sign, maximally aligns in the positive direction of the
   normals of the label


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

http://martinos.org/mne/stable/whats_new.html#version-0-15

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_news

Regards,

The MNE-Python developers

People who contributed to this release (in alphabetical order):

* akshay0724

* Alejandro Weinstein

* Alexander Rudiuk

* Alexandre Barachant

* Alexandre Gramfort

* Andrew Dykstra

* Britta Westner

* Chris Bailey

* Chris Holdgraf

* Christian Brodbeck

* Christopher Holdgraf

* Clemens Brunner

* Cristóbal Moënne-Loccoz

* Daniel McCloy

* Daniel Strohmeier

* Denis A. Engemann

* Emily P. Stephen

* Eric Larson

* Fede Raimondo

* Jaakko Leppakangas

* Jean-Baptiste Schiratti

* Jean-Remi King

* Jesper Duemose Nielsen

* Joan Massich

* Jon Houck

* Jona Sassenhagen

* Jussi Nurminen

* Laetitia Grabot

* Laura Gwilliams

* Luke Bloy

* Lukáš Hejtmánek

* Mainak Jas

* Marijn van Vliet

* Mathurin Massias

* Matt Boggess

* Mikolaj Magnuski

* Nicolas Barascud

* Nicole Proulx

* Phillip Alday

* Ramonapariciog Apariciogarcia

* Robin Tibor Schirrmeister

* Rodrigo Hübner

* S. M. Gutstein

* Simon Kern

* Teon Brooks

* Yousra Bekhti
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20171020/5d2b93b0/attachment-0001.html 


More information about the Mne_analysis mailing list