yasa.REMResults

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

Output class for REMs detection.

Attributes

_eventspandas.DataFrame

Output detection dataframe

_dataarray_like

EOG data of shape (n_chan, n_samples), where the two channels are LOC and ROC.

_data_filtarray_like

Filtered EOG data of shape (n_chan, n_samples), where the two channels are LOC and ROC.

_sffloat

Sampling frequency of data.

_ch_nameslist

Channel names (= ['LOC', 'ROC'])

_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 events across channels.
compare_detection(other[, max_distance_sec, ...])	Compare detected events between two detection methods, or against a ground-truth scoring.
get_coincidence_matrix([scaled])
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 or filtered data of each detected event after centering to a specific timepoint.
plot_average([center, time_before, ...])	Plot the average REM.
plot_detection()	Plot an overlay of the detected events on the signal.
summary([grp_stage, mask, aggfunc, sort])	Return a summary of the REM detection, optionally grouped across stage.

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='Peak', time_before=0.4, time_after=0.4, filt=(None, None), mask=None)

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 peak of the REM.

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).

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

plot_average(center='Peak', time_before=0.4, time_after=0.4, filt=(None, None), mask=None, figsize=(6, 4.5), **kwargs)

Plot the average REM.

Parameters

centerstr

Landmark of the event to synchronize the timing on. Default is to use the peak of the REM.

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).

figsizetuple

Figure size in inches.

**kwargsdict

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

summary(grp_stage=False, mask=None, aggfunc='mean', sort=True)

Return a summary of the REM detection, optionally grouped across stage.

Parameters

grp_stagebool

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

maskarray_like or None

Custom boolean mask. Only the detected events for which mask is True will be included in the summary. Default is None, i.e. no masking (all events are included).

aggfuncstr or function

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

sortbool

If True, sort group keys when grouping.