[Neuroimaging] [ANN] MNE-Python 0.22

[Alexandre Gramfort]

Hello everyone,

we’re ahead of our typical release cycle and just published MNE-Python
0.22! 🎉 🎁 🎅

Please find a detailed list of changes and contributors below.

With this year coming to a close, we’d like to take this opportunity to
thank you all for your continued support, and wish you and your loved ones
Happy Holidays.

Stay healthy and take care! 😷

All the best,

Your MNE Team.

A few highlights

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


   -

   The 3D viewer of source time courses based on pyvista can now support
   picking labels from any freesurfer annotation. We highly recommend you now
   use pyvista and not pysurfer/mayavi for STC visualization.
   -

   Performing ICA is now much simpler for most users: instead of offering 3
   parameters -- n_components, n_pca_components, and max_pca_components --
   that would interact in often hard-to-understand ways, you can now simply
   pass a single parameter  -- n_components -- to mne.preprocessing.ICA and
   get what you want. The n_pca_compoents and max_pca_components parameters
   have been deprecated and will be removed in MNE-Python 0.23. Please also
   see the “Notable API changes” section for details.
   -

   When plotting ICA sources via .ICA.plot_sources(), right-clicking on a
   component name will open a properties plot (the one you previously had to
   create using ICA.plot_properties()). This makes exploration of ICA data
   more interactive.
   -

   Annotations can now be shown and hidden interactively in raw plots using
   a checkbox. Extremely useful for datasets with overlapping Annotations!
   -

   Source estimates can now be baseline-corrected using their new
   apply_baseline() method.
   -

   The new function mne.stc_near_sensors() visualizes sEEG and ECoG data.
   -

   Fiducials can now be estimated when visualizing the coregistration by
   passing mri_fiducials=’estimated’ to mne.viz.plot_alignment().
   -

   Numerous improvements of volumetric source space support.
   -

   When cropping the baseline period of baseline-corrected Epochs, the
   information about the original baseline will be preserved to retain
   provenance.
   -

   We now offer spatio-spectral decomposition (SSD) via mne.decoding.SSD.
   -

   New readers: mne.read_evokeds_mff() for averaged MFFs, and
   mne.io.read_raw_boxy()  for optical imaging data recorded using ISS
   Imgagent I/II hardware and BOXY recording software.



Notable API changes

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

We have changed a few things that will require you to adjust your code.


   -

   The n_pca_components and max_pca_components argument of
   mne.preprocessing.ICA has been deprecated, use n_components during
   initialization, and n_pca_components in ICA.apply() instead.
   -

   The trans argument of mne.extract_label_time_course() is deprecated and
   will be removed in 0.23 as it is no longer necessary.
   -

   The parameter event_colors in mne.viz.plot_epochs and mne.Epochs.plot()
   is deprecated, replaced by event_color which is consistent with
   mne.viz.plot_raw and provides greater flexibility.


Full list of API changes:

https://mne.tools/stable/whats_new.html#api-changes


Full changelog

===========

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

https://mne.tools/stable/whats_new.html#version-0-22-0

Find the full documentation at https://mne.tools/
<https://mne.tools/stable/index.html>

Installing the new release

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

Since quite a few things – including dependencies – have changed, we
recommend creating a new environment with a “fresh” installation. Please
follow the installation instructions on our website:

https://mne.tools/stable/install/mne_python.html

Feedback

========

As usual, we welcome your bug reports, feature requests, critiques, and
contributions. Development takes place on GitHub. If you would like to
contribute, star ⭐ the project, or just take a peek at the code, visit
https://github.com/mne-tools/mne-python.

You may follow us on Twitter: https://twitter.com/mne_news

We hope you will enjoy the new features and many, many small improvements
we have added, and are looking forward to receiving your feedback.

Stay safe and take care!

The MNE-Python developers


Contributors

==========

MNE-Python is a community-driven project. We are always very happy to
welcome new contributors of code and documentation! 34 people contributed
to this release – and a whopping 10 were first-timers! Thank you all so
very much for your time and effort, we truly appreciate it!

First-time contributors:


   -

   Aniket Pradhan
   -

   Austin Hurst
   -

   Eduard Ort
   -

   Evan Hathaway
   -

   Hongjiang Ye
   -

   Jeff Stout
   -

   Jonathan Kuziek
   -

   Quianliang Li
   -

   Tod Flak
   -

   Victoria Peterson


Recurring contributors:


   -

   Adam Li
   -

   Alexandre Gramfort
   -

   Christian Brodbeck
   -

   Clemens Brunner
   -

   Daniel McCloy
   -

   Denis A. Engemann
   -

   Eric Larson
   -

   Evgenii Kalenkovich
   -

   Fede Raimondo
   -

   Guillaume Favelier
   -

   Jean-Remi King
   -

   Jussi Nurminen
   -

   Keith Doelling
   -

   Kyle Mathewson
   -

   Mads Jensen
   -

   Mainak Jas
   -

   Marijn van Vliet
   -

   Mikolaj Magnuski
   -

   Olaf Hauk
   -

   Quianliang Li
   -

   Richard Höchenberger
   -

   Robert Luke
   -

   Stefan Appelhoff
   -

   Thomas Hartmann
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