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__(events, data, sf, ch_names, hypno, data_filt)

Methods

__init__(events, data, sf, ch_names, hypno, ...)
compare_channels([score, max_distance_sec])	Compare detected slow-waves across channels.
compare_detection(other[, max_distance_sec, ...])	Compare the detected slow-waves against either another YASA detection or against custom annotations (e.g. ground-truth human scoring).
find_cooccurring_spindles(spindles[, lookaround])	Given a spindles detection summary dataframe, find slow-waves that co-occur with sleep spindles.
get_coincidence_matrix([scaled])	Return the (scaled) coincidence matrix.
get_mask()	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([center, time_before, ...])	Return the raw data of each detected event after centering to a specific timepoint.
plot_average([center, hue, time_before, ...])	Plot the average slow-wave.
plot_detection()	Plot an overlay of the detected slow-waves on the EEG signal.
summary([grp_chan, grp_stage, mask, ...])	Return a summary of the SW detection, optionally grouped across channels and/or stage.