wonambi.detect.arousal module

Module to detect arousals

class wonambi.detect.arousal.DetectArousal(method='HouseDetector', duration=None)

Bases: object

Design slow wave detection on a single channel.

Parameters:

• method (str) – one of the predefined methods

• freq_band (tuple of (float or None)) – frequency band of interest in Hz

• spectrogram (dict) –

  ‘dur’: float

  window length in sec

  ’overlap’: float

  ratio of overlap between consecutive windows

  ’detrend’: str

  ’constant’, ‘linear’ or False

• det_thresh (float) – minimum factor increase of mean frequency between consecutive windows

• min_interval (float) – minimum duration between consecutive arousals, in sec

• duration (tuple of float) – min and max duration of arousals

wonambi.detect.arousal.detect_HouseDetector(dat_orig, s_freq, time, opts)

House arousal detection.

Parameters:

• dat_orig (ndarray (dtype='float')) – vector with the data for one channel

• s_freq (float) – sampling frequency

• time (ndarray (dtype='float')) – vector with the time points for each sample

• opts (instance of 'DetectSlowWave') –

  ‘duration’tuple of float

  min and max duration of arousal

Returns:

• list of dict – list of detected arousals

• float – arousal density, per 30-s epoch

wonambi.detect.arousal.make_arousals(events, time, s_freq)

Create dict for each arousal, based on events of time points.

Parameters:

• events (ndarray (dtype='int')) – N x 5 matrix with start, end samples

• data (ndarray (dtype='float')) – vector with the data

• time (ndarray (dtype='float')) – vector with time points

• s_freq (float) – sampling frequency

Returns:

list of dict – list of all the arousals, with information about start, end, duration (s),

wonambi.detect.arousal.splitpoint(a, sf)