Yasa

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).

- Added [yasa.plot_spectrogram()](https://raphaelvallat.com/yasa/build/html/generated/yasa.plot_spectrogram.html#yasa.plot_spectrogram) function.
- Added [lspopt](https://github.com/hbldh/lspopt) in the dependencies
- YASA now requires MNE>0.19.

**Two new functions:**
- [yasa.sliding_window()](https://raphaelvallat.com/yasa/build/html/generated/yasa.sliding_window.html#yasa.sliding_window): calculate a sliding window of a 1D or 2D EEG signal (useful to avoid for loop when calculating epoch-by-epoch features)
- [yasa.irasa()](https://raphaelvallat.com/yasa/build/html/generated/yasa.irasa.html#yasa.irasa): separate the aperiodic (= fractal, or 1/f) and oscillatory component of the power spectra of EEG data using the IRASA method.

**Code refactoring**
- Reorganized code into several sub-files for readability (internal changes with no effect on user experience).

**Minor additions to the major v0.1.5 release**

- Added [bandpower](https://htmlpreview.github.io/?https://raw.githubusercontent.com/raphaelvallat/yasa/master/html/spectral.html#yasa.spectral.bandpower) function
- One can now directly pass a raw MNE object in several multi-channel functions of YASA, instead of manually passing `data`, `sf`, and `ch_names`. YASA will automatically convert MNE data from Volts to uV, and extract the sampling frequency and channel names. Examples of this can be found in the [Jupyter notebooks examples](https://github.com/raphaelvallat/yasa/tree/master/notebooks).

**Major update**

- Added REM detection ([rem_detect](https://htmlpreview.github.io/?https://raw.githubusercontent.com/raphaelvallat/yasa/master/html/main.html#yasa.main.rem_detect)) on LOC and ROC EOG channels + [example notebook](https://github.com/raphaelvallat/yasa/blob/master/notebooks/09_REMs_detection.ipynb)
- Added [yasa/hypno.py](https://github.com/raphaelvallat/yasa/blob/master/yasa/hypno.py) file, with several functions to [load and upsample](https://htmlpreview.github.io/?https://raw.githubusercontent.com/raphaelvallat/yasa/master/html/hypno.html) sleep stage vector (hypnogram).
- Added [yasa/spectral.py](https://github.com/raphaelvallat/yasa/blob/master/yasa/spectral.py) file, which includes the [bandpower_from_psd](https://htmlpreview.github.io/https://raw.githubusercontent.com/raphaelvallat/yasa/master/html/spectral.html#yasa.spectral.bandpower_from_psd) function to calculate the single or multi-channel spectral power in specified bands from a pre-computed PSD (see example notebook at [notebooks/10_bandpower.ipynb](https://github.com/raphaelvallat/yasa/blob/master/notebooks/10_bandpower.ipynb))

**Minor update**

- Added `get_sync_sw` function to get the synchronized timings of landmarks timepoints in slow-wave sleep. This can be used in combination with [seaborn.lineplot](https://seaborn.pydata.org/generated/seaborn.lineplot.html) to plot an average template of the detected slow-wave, per channel.