mne.preprocessing.peak_finder#

mne.preprocessing.peak_finder(x0, thresh=None, extrema=1, verbose=None)[source]#

Noise-tolerant fast peak-finding algorithm.

Parameters

x01d array: A real vector from the maxima will be found (required).
threshfloat | None: The amount above surrounding data for a peak to be identified. Larger values mean the algorithm is more selective in finding peaks. If None, use the default of (max(x0) - min(x0)) / 4.
extrema{-1, 1}: 1 if maxima are desired, -1 if minima are desired (default = maxima, 1).
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns

peak_locarray: The indices of the identified peaks in x0.
peak_magarray: The magnitude of the identified peaks.

Notes

If repeated values are found the first is identified as the peak. Conversion from initial Matlab code from: Nathanael C. Yoder (ncyoder@purdue.edu)

Examples

>>> import numpy as np
>>> from mne.preprocessing import peak_finder
>>> t = np.arange(0, 3, 0.01)
>>> x = np.sin(np.pi*t) - np.sin(0.5*np.pi*t)
>>> peak_locs, peak_mags = peak_finder(x) 
>>> peak_locs 
array([36, 260]) 
>>> peak_mags 
array([0.36900026, 1.76007351]) 

mne.preprocessing.oversampled_temporal_projection

mne.preprocessing.read_ica