yasa.SpindlesResults.get_sync_events#

SpindlesResults.get_sync_events(center='Peak', time_before=1, time_after=1, filt=(None, None), mask=None, as_dataframe=True)[source]#

Return the raw or filtered 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 center peak of the spindles.
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.
maskarray_like or None: Custom boolean mask. Only the detected events for which mask is True will be included. Default is None, i.e. no masking (all events are included).
as_dataframeboolean: If True (default), returns a long-format pandas dataframe. If False, returns a list of numpy arrays. Each element of the list a unique channel, and the shape of the numpy arrays within the list is (n_events, n_times).

Returns:

df_syncpandas.DataFrame

Ouput long-format dataframe (if as_dataframe=True):

'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)