yasa.SpindlesResults#

class yasa.SpindlesResults(events, data, sf, ch_names, hypno, data_filt)[source]#

Output class for spindles detection.

The parameters are stored as private attributes, e.g. self._events.

Parameters:

eventspandas.DataFrame: Output detection dataframe
dataarray_like: Original EEG data of shape (n_chan, n_samples).
data_filtarray_like: Sigma-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.

Methods

`__init__`(events, data, sf, ch_names, hypno, ...)
`compare_channels`([score, max_distance_sec, ...])	Compare detected spindles across channels.
`compare_detection`(other[, max_distance_sec, ...])	Compare the detected spindles against either another YASA detection or against custom annotations (e.g. ground-truth human scoring).
`get_coincidence_matrix`([scaled])	Return the (scaled) coincidence matrix.
`get_mask`()	Return an array indicating for each sample in data if this sample is part of a detected event (1) or not (0).
`get_sync_events`([center, time_before, ...])	Return the raw or filtered data of each detected event after centering to a specific timepoint.
`plot_average`([center, hue, time_before, ...])	Plot the average spindle.
`plot_detection`()	Plot an overlay of the detected spindles on the EEG signal.
`summary`([grp_chan, grp_stage, mask, ...])	Return a summary of the spindles detection, optionally grouped across channels and/or stage.