baseline_envelope#

pypillometry.signal.baseline_envelope(tx, sy, event_onsets, fs=1000, lp=2, prominence_thr=80, interp_method='cubic')[source]#

Extract baseline based on the lower envelope of the (filtered) signal.

Steps:

  • filter away noise

  • detect high-prominence troughs in the signal

  • calculate lower envelope based on these peaks

Parameters:

  • tx (np.ndarray) – time-vector (any unit; the baseline is evaluated at tx)

  • sy (np.ndarray) – raw pupil signal (must not contain NaN)

  • event_onsets (np.ndarray) – onsets of events (stimuli/responses); currently not used

  • fs (float) – sampling rate in Hz

  • lp (float) – low-pass filter cutoff in Hz for removing random noise

  • prominence_thr (float) – percentile (in [0, 100]) of the prominence distribution (of the peaks) to use for determining prominent peaks (see scipy.signal.peak_prominences())

  • interp_method ({"cubic", "linear", "spline"}) –

    • “linear”: linear interpolation between the high-prominence peaks

    • ”cubic”: cubic interpolation through all high-prominence peaks

    • ”spline”: a smoothing spline that is guaranteed to go through all high-prominence peaks and smoothes through all the other (lower-prominence) peaks

Returns:

base (np.ndarray) – baseline estimate (same length as tx)

Raises:

ValueError – if interp_method is not one of the supported methods