Yasa

This is a bugfix release. The latest pre-trained classifiers for [yasa.SleepStaging](https://raphaelvallat.com/yasa/build/html/generated/yasa.SleepStaging.html#yasa.SleepStaging) were accidentally missing from the previous release. They have now been included in this release. We recommend all users to upgrade to this version with `pip install –upgrade yasa`

This is a major release with an important bugfix for the slow-waves detection as well as API-breaking changes in the automatic sleep staging module. Please read the full changelog [here](https://raphaelvallat.com/yasa/build/html/changelog.html).

We recommend all users to upgrade to this version with `pip install –upgrade yasa`.

**New functions**

Added [yasa.topoplot()](https://raphaelvallat.com/yasa/build/html/generated/yasa.topoplot.html#yasa.topoplot), a wrapper around [mne.viz.plot_topomap()](https://mne.tools/stable/generated/mne.viz.plot_topomap.html#mne.viz.plot_topomap). See [15_topoplot.ipynb](https://github.com/raphaelvallat/yasa/blob/master/notebooks/15_topoplot.ipynb).

**Enhancements**
- The default frequency range for slow-waves in [yasa.sw_detect()](https://raphaelvallat.com/yasa/build/html/generated/yasa.sw_detect.html#yasa.sw_detect) is now 0.3-1.5 Hz instead of 0.3-2 Hz. Indeed, most slow-waves have a frequency below 1Hz. This may result in slightly different coupling values when `coupling=True` so make sure to homogenize your slow-waves detection pipeline across all nights in your dataset.
- yasa.trimbothstd() now handles missing values in input array.
- [yasa.bandpower_from_psd()](https://raphaelvallat.com/yasa/build/html/generated/yasa.bandpower_from_psd.html#yasa.bandpower_from_psd) and [yasa.bandpower_from_psd_ndarray()](https://raphaelvallat.com/yasa/build/html/generated/yasa.bandpower_from_psd_ndarray.html#yasa.bandpower_from_psd_ndarray) now print a warning if the PSD contains negative values. See [issue 29](https://github.com/raphaelvallat/yasa/issues/29).
- Upon loading, YASA will now use the [outdated](https://github.com/alexmojaki/outdated) package to check and warn the user if a newer stable version is available.
- YASA now uses the [AntroPy](https://github.com/raphaelvallat/antropy) package to calculate non-linear features in the automatic sleep staging module. Previously, YASA was using [EntroPy](https://github.com/raphaelvallat/entropy), which could not be installed using pip.

This is a major release with several new functions, the biggest of which is the addition of an automatic sleep staging module (yasa.SleepStaging). This means that YASA can now automatically score the sleep stages of your raw EEG data. The classifier was trained and validated on more than 3000 nights from the National Sleep Research Resource (NSRR) website.

Briefly, the algorithm works by calculating a set of features for each 30-sec epochs from a central EEG channel (required), as well as an EOG channel (optional) and an EMG channel (optional). For best performance, users can also specify the age and the sex of the participants. Pre-trained classifiers are already included in YASA. The automatic sleep staging algorithm requires the LightGBM and entropy package.

For more details, please see: https://raphaelvallat.com/yasa/build/html/changelog.html

Major release with important changes in the output of the spindles, slow-waves and REMs detection. See full changelog at https://raphaelvallat.com/yasa/build/html/changelog.html

This is a major release with several new functions and enhancements. The full changelog can be found [here](https://raphaelvallat.com/yasa/build/html/changelog.html).