yasa.SWResults

class yasa.SWResults(events, data, sf, ch_names, hypno, data_filt)

Output class for slow-waves detection.

Attributes

_eventspandas.DataFrame

Output detection dataframe

_dataarray_like

EEG data of shape (n_chan, n_samples).

_data_filtarray_like

Slow-wave filtered EEG data of shape (n_chan, n_samples).

_sffloat

Sampling frequency of data.

_ch_nameslist

Channel names.

_hypnoarray_like or None

Sleep staging vector.

__init__(self, events, data, sf, ch_names, hypno, data_filt)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(self, events, data, sf, ch_names, …)	Initialize self.
get_mask(self)	Return a boolean array indicating for each sample in data if this sample is part of a detected event (True) or not (False).
get_sync_events(self[, center, time_before, …])	Return the raw data of each detected event after centering to a specific timepoint.
plot_average(self[, center, hue, …])	Plot the average slow-wave.
plot_detection(self)	Plot an overlay of the detected slow-waves on the EEG signal.
summary(self[, grp_chan, grp_stage, …])	Return a summary of the SW detection, optionally grouped across channels and/or stage.

get_mask(self)

Return a boolean array indicating for each sample in data if this sample is part of a detected event (True) or not (False).

get_sync_events(self, center='NegPeak', time_before=0.4, time_after=0.8, filt=None, None)

Return the raw data of each detected event after centering to a specific timepoint.

Parameters

centerstr

Landmark of the event to synchronize the timing on. Default is to use the negative peak of the slow-wave.

time_beforefloat

Time (in seconds) before center.

time_afterfloat

Time (in seconds) after center.

filttuple

Optional filtering to apply to data. For instance, filt=(1, 30) will apply a 1 to 30 Hz bandpass filter, and filt=(None, 40) will apply a 40 Hz lowpass filter. Filtering is done using default parameters in the mne.filter.filter_data() function.

Returns

df_syncpandas.DataFrame

Ouput long-format dataframe:

'Event' : Event number
'Time' : Timing of the events (in seconds)
'Amplitude' : Raw or filtered data for event
'Channel' : Channel
'IdxChannel' : Index of channel in data
'Stage': Sleep stage in which the events occured (if available)

plot_average(self, center='NegPeak', hue='Channel', time_before=0.4, time_after=0.8, filt=None, None, figsize=6, 4.5, **kwargs)

Plot the average slow-wave.

Parameters

centerstr

Landmark of the event to synchronize the timing on. Default is to use the negative peak of the slow-wave.

huestr

Grouping variable that will produce lines with different colors. Can be either ‘Channel’ or ‘Stage’.

time_beforefloat

Time (in seconds) before center.

time_afterfloat

Time (in seconds) after center.

filttuple

Optional filtering to apply to data. For instance, filt=(1, 30) will apply a 1 to 30 Hz bandpass filter, and filt=(None, 40) will apply a 40 Hz lowpass filter. Filtering is done using default parameters in the mne.filter.filter_data() function.

figsizetuple

Figure size in inches.

**kwargsdict

Optional argument that are passed to seaborn.lineplot().

plot_detection(self)

Plot an overlay of the detected slow-waves on the EEG signal.

This only works in Jupyter and it requires the ipywidgets (https://ipywidgets.readthedocs.io/en/latest/) package.

To activate the interactive mode, make sure to run:

>>> %matplotlib widget

New in version 0.4.0.

summary(self, grp_chan=False, grp_stage=False, aggfunc='mean', sort=True)

Return a summary of the SW detection, optionally grouped across channels and/or stage.

Parameters

grp_chanbool

If True, group by channel (for multi-channels detection only).

grp_stagebool

If True, group by sleep stage (provided that an hypnogram was used).

aggfuncstr or function

Averaging function (e.g. 'mean' or 'median').

sortbool

If True, sort group keys when grouping.