[Mne_analysis] [ANN] MNE-Python 0.13

Alexandre Gramfort alexandre.gramfort at telecom-paristech.fr
Mon Nov 21 03:37:02 EST 2016
Search archives:

hi,

I just pushed a maintenance version 0.13.1 on PyPi:

https://pypi.python.org/pypi/mne/0.13.1

you're recommended to upgrade.

pip install -U mne

Alex

On Wed, Sep 28, 2016 at 10:02 PM, Alexandre Gramfort
<alexandre.gramfort at telecom-paristech.fr> wrote:
> Hi,
>
>
> We are pleased to announce the new 0.13 release of MNE-Python. As usual this
> release comes with new features, many improvements to usability,
> visualization and documentation and bug fixes.
>
>
> A couple of major API changes are being implemented, so we recommend that
> users read through the changes carefully.
>
>
> Support for Python 2.6 has been dropped, and the minimum supported
> dependencies are now NumPy 1.8, SciPy 0.12, and Matplotlib 1.3.
>
>
> A few highlights
>
> ============
>
>
> Our filtering functionality has been significantly improved:
>
> In FIR filters the  parameters filter_length, l_trans_bandwidth, and
> h_trans_bandwidth are now automatically determined. We also added a phase
> argument in e.g. in mne.io.Raw.filter(). This means that the new recommended
> defaults are l_trans_bandwidth='auto', h_trans_bandwidth='auto', and
> filter_length='auto'. This should generally reduce filter artifacts at the
> expense of slight decrease in effective filter stop-band attenuation. For
> details see Defaults in MNE-Python.
>
> An improved phase='zero' zero-phase FIR filtering has been added.
>
> We added a second-order sections (instead of (b, a) form) IIR filtering
> which commonly has less numerical error
>
> We added a generic array-filtering function mne.filter.filter_data() for
> numpy arrays.
>
> Constructing IIR filters in mne.filter.construct_iir_filter() will default
> to output='sos' in 0.14
>
>
> We extended and tuned our visualization functionality:
>
> The ordering parameters ‘selection’ and ‘position’ were added to
> mne.viz.plot_raw() to allow plotting of specific regions of the sensor
> array.
>
> mne.viz.plot_trans() now also shows head position indicators.
>
> We have new plotting functions for independent component properties, similar
> to `pop_prop` in EEGLAB.
>
> There is a new function mne.viz.plot_compare_evokeds() to show multiple
> evoked time courses at a single location, or the mean over a ROI, or the
> GFP. This is achieved by automatically averaging and calculating a
> confidence interval if multiple subjects are given.
>
> We now have an interactive colormap option in our image plotting functions.
>
> Subsets of sensors can now be interactively selected by the so called lasso
> selector. Checkout mne.viz.plot_sensors() and mne.viz.plot_raw() when using
> order=’selection’ or order=’position’.
>
> In viz.plot_bem() brain surfaces can now be plotted.
>
> mne.preprocessing.ICA.plot_components() can now be used interactively.
>
>
> We refactored and extended our multvariate statistical analysis
> functionality and made it more compatible with scikit-klearn:
>
> The mne.decoding.TimeFrequency allows to transform signals in scikit-learn
> pipelines.
>
> the mne.decoding.UnsupervisedSpatialFilter provides interface for
> scikit-learn decomposition algorithms such that they can be easily used with
> MNE data.
>
> We added support for multiclass decoding in mne.decoding.CSP.
>
>
> And as always many more good things:
>
> There is now a --filterchpi option to mne browse_raw.
>
> mne.Evoked objects can now be decimated with mne.Evoked.decimate().
>
> Functional near-infrared spectroscopy (fNIRS) data can now be processed.
>
> MaxShield (IAS) can now be read for evoked data (e.g., from the acquisition
> machine) in mne.read_evokeds()
>
> We added a single trial container for time-frequency representations
> (mne.time_frequency.EpochsTFR), an average parameter to
> mne.time_frequency.tfr_morlet() and mne.time_frequency.tfr_multitaper().
> This way time-frequency transforms can be easily computed on single trial
> epochs without averaging.
>
>
> Notable API changes
>
> ================
>
>
> Components obtained from mne.preprocessing.ICA are now sorted by explained
> variance
>
> Adding an EEG reference channel using mne.io.add_reference_channels() will
> now use its digitized location from the FIFF file if present.
>
> The add_eeg_ref argument in core functions like mne.io.read_raw_fif() and
> mne.Epochs has been deprecated in favor of using mne.set_eeg_reference() and
> equivalent instance methods like raw.set_eeg_reference().
>
> When CTF gradient compensation is applied to raw data, it is no longer
> reverted on save of mne.io.Raw.save().
>
> Weighted addition and subtraction of Evoked as ev1 + ev2 and ev1 - ev2 have
> been deprecated, use explicit mne.combine_evoked(...,weights='nave')
> instead.
>
> Deprecated support for passing a lits of filenames to mne.io.Raw
> constructor, use mne.io.read_raw_fif() and mne.concatenate_raws() instead.
>
> Now channels with units of ‘C’, ‘µS’, ‘uS’, ‘ARU’ and ‘S’ will be turned to
> misc by default in mne.io.read_raw_brainvision().
>
> Add mne.io.anonymize_info() function to anonymize measurements and add
> methods to mne.io.Raw, mne.Epochs and mne.Evoked.
>
> Deprecated the baseline parameter in mne.Evoked. Use
> mne.Epochs.apply_baseline() instead.
>
> The default dataset location has been changed from examples/ in the
> MNE-Python root directory to ~/mne_data in the user’s home directory
>
> mne.decoding.EpochsVectorizer has been deprecated in favor of
> mne.decoding.Vectorizer.
>
> Deprecated mne.time_frequency.cwt_morlet() and
> mne.time_frequency.single_trial_power() in favour of
> mne.time_frequency.tfr_morlet()with parameter average=False.
>
> Extended Infomax is now the new default in mne.preprocessing.infomax()
> (extended=True).
>
>
> For a full list of improvements and API changes, see:
>
>
> http://martinos.org/mne/stable/whats_new.html#version-0-13
>
>
> 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 Barachant
>
>    * Alexandre Gramfort
>
>    * Asish Panda
>
>    * Camilo Lamus
>
>    * Chris Holdgraf
>
>    * Christian Brodbeck
>
>    * Christopher J. Bailey
>
>    * Christopher Mullins
>
>    * Clemens Brunner
>
>    * Denis A. Engemann
>
>    * Eric Larson
>
>    * Federico Raimondo
>
>    * Félix Raimundo
>
>    * Guillaume Dumas
>
>    * Jaakko Leppakangas
>
>    * Jair Montoya
>
>    * Jean-Remi King
>
>    * Johannes Niediek
>
>    * Jona Sassenhagen
>
>    * Jussi Nurminen
>
>    * Keith Doelling
>
>    * Mainak Jas
>
>    * Marijn van Vliet
>
>    * Michael Krause
>
>    * Mikolaj Magnuski
>
>    * Nick Foti
>
>    * Phillip Alday
>
>    * Simon-Shlomo Poil
>
>    * Teon Brooks
>
>    * Yaroslav Halchenko
>
>



More information about the Mne_analysis mailing list