[Neuroimaging] [ANN] MNE-Python 0.13
bthirion
bertrand.thirion at inria.fr
Wed Sep 28 16:13:25 EDT 2016
Congratulations !
Bertrand
On 28/09/2016 22:02, Alexandre Gramfort 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 <http://www.numpy.org/>1.8, SciPy
> <http://www.scipy.org/>0.12, and Matplotlib <http://matplotlib.org/>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()
> <http://mne-tools.github.io/dev/generated/mne.io.Raw.html#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
> <http://mne-tools.github.io/dev/auto_tutorials/plot_background_filtering.html#tut-filtering-in-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()
> <http://mne-tools.github.io/dev/generated/mne.filter.filter_data.html#mne.filter.filter_data>for
> numpy arrays.
>
> *
>
> Constructing IIR filters in mne.filter.construct_iir_filter()
> <http://mne-tools.github.io/dev/generated/mne.filter.construct_iir_filter.html#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()
> <http://mne-tools.github.io/dev/generated/mne.viz.plot_raw.html#mne.viz.plot_raw>to
> allow plotting of specific regions of the sensor array.
>
> *
>
> mne.viz.plot_trans()
> <http://mne-tools.github.io/dev/generated/mne.viz.plot_trans.html#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()
> <http://mne-tools.github.io/dev/generated/mne.viz.plot_compare_evokeds.html#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()
> <http://mne-tools.github.io/dev/generated/mne.viz.plot_sensors.html#mne.viz.plot_sensors>and
> mne.viz.plot_raw()
> <http://mne-tools.github.io/dev/generated/mne.viz.plot_raw.html#mne.viz.plot_raw>when
> using order=’selection’ or order=’position’.
>
> *
>
> In viz.plot_bem()
> <http://mne-tools.github.io/dev/generated/mne.viz.plot_bem.html#mne.viz.plot_bem>brain
> surfaces can now be plotted.
>
> *
>
> mne.preprocessing.ICA.plot_components()
> <http://mne-tools.github.io/dev/generated/mne.preprocessing.ICA.html#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
> <http://mne-tools.github.io/dev/generated/mne.decoding.CSP.html#mne.decoding.CSP>.
>
>
> And as always many more good things:
>
> *
>
> There is now a --filterchpi option to mne browse_raw
> <http://mne-tools.github.io/dev/generated/commands.html#gen-mne-browse-raw>.
>
> *
>
> mne.Evoked
> <http://mne-tools.github.io/dev/generated/mne.Evoked.html#mne.Evoked>objects
> can now be decimated with mne.Evoked.decimate()
> <http://mne-tools.github.io/dev/generated/mne.Evoked.html#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()
> <http://mne-tools.github.io/dev/generated/mne.read_evokeds.html#mne.read_evokeds>
>
> *
>
> We added a single trial container for time-frequency
> representations (mne.time_frequency.EpochsTFR
> <http://mne-tools.github.io/dev/generated/mne.time_frequency.EpochsTFR.html#mne.time_frequency.EpochsTFR>),
> an average parameter to mne.time_frequency.tfr_morlet()
> <http://mne-tools.github.io/dev/generated/mne.time_frequency.tfr_morlet.html#mne.time_frequency.tfr_morlet>and
> mne.time_frequency.tfr_multitaper()
> <http://mne-tools.github.io/dev/generated/mne.time_frequency.tfr_multitaper.html#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
> <http://mne-tools.github.io/dev/generated/mne.preprocessing.ICA.html#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()
> <http://mne-tools.github.io/dev/generated/mne.io.read_raw_fif.html#mne.io.read_raw_fif>and
> mne.Epochs
> <http://mne-tools.github.io/dev/generated/mne.Epochs.html#mne.Epochs>has
> been deprecated in favor of using mne.set_eeg_reference()
> <http://mne-tools.github.io/dev/generated/mne.set_eeg_reference.html#mne.set_eeg_reference>and
> equivalent instance methods like raw.set_eeg_reference()
> <http://mne-tools.github.io/dev/generated/mne.io.Raw.html#mne.io.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()
> <http://mne-tools.github.io/dev/generated/mne.io.Raw.html#mne.io.Raw.save>.
>
> *
>
> Weighted addition and subtraction of Evoked
> <http://mne-tools.github.io/dev/generated/mne.Evoked.html#mne.Evoked>as
> ev1 + ev2 and ev1 - ev2 have been deprecated, use explicit
> mne.combine_evoked(...,weights='nave')
> <http://mne-tools.github.io/dev/generated/mne.combine_evoked.html#mne.combine_evoked>instead.
>
> *
>
> Deprecated support for passing a lits of filenames to mne.io.Raw
> <http://mne-tools.github.io/dev/generated/mne.io.Raw.html#mne.io.Raw>constructor,
> use mne.io.read_raw_fif()
> <http://mne-tools.github.io/dev/generated/mne.io.read_raw_fif.html#mne.io.read_raw_fif>and
> mne.concatenate_raws()
> <http://mne-tools.github.io/dev/generated/mne.concatenate_raws.html#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()
> <http://mne-tools.github.io/dev/generated/mne.io.read_raw_brainvision.html#mne.io.read_raw_brainvision>.
>
> *
>
> Add mne.io.anonymize_info()function to anonymize measurements and
> add methods to mne.io.Raw
> <http://mne-tools.github.io/dev/generated/mne.io.Raw.html#mne.io.Raw>,
> mne.Epochs
> <http://mne-tools.github.io/dev/generated/mne.Epochs.html#mne.Epochs>and
> mne.Evoked
> <http://mne-tools.github.io/dev/generated/mne.Evoked.html#mne.Evoked>.
>
> *
>
> Deprecated the baseline parameter in mne.Evoked
> <http://mne-tools.github.io/dev/generated/mne.Evoked.html#mne.Evoked>.
> Use mne.Epochs.apply_baseline()
> <http://mne-tools.github.io/dev/generated/mne.Epochs.html#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
> <http://mne-tools.github.io/dev/generated/mne.decoding.EpochsVectorizer.html#mne.decoding.EpochsVectorizer>has
> been deprecated in favor of mne.decoding.Vectorizer.
>
> *
>
> Deprecated mne.time_frequency.cwt_morlet()
> <http://mne-tools.github.io/dev/generated/mne.time_frequency.cwt_morlet.html#mne.time_frequency.cwt_morlet>and
> mne.time_frequency.single_trial_power()
> <http://mne-tools.github.io/dev/generated/mne.time_frequency.single_trial_power.html#mne.time_frequency.single_trial_power>in
> favour of mne.time_frequency.tfr_morlet()
> <http://mne-tools.github.io/dev/generated/mne.time_frequency.tfr_morlet.html#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
>
>
>
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/neuroimaging/attachments/20160928/8520b8e4/attachment-0001.html>
More information about the Neuroimaging
mailing list