scipy.signal.find_peaks_cwt(vector, widths, wavelet=None, max_distances=None, gap_thresh=None, min_length=None, min_snr=1, noise_perc=10, window_size=None)[source]#

Find peaks in a 1-D array with wavelet transformation.

The general approach is to smooth vector by convolving it with wavelet(width) for each width in widths. Relative maxima which appear at enough length scales, and with sufficiently high SNR, are accepted.


1-D array in which to find the peaks.

widthsfloat or sequence

Single width or 1-D array-like of widths to use for calculating the CWT matrix. In general, this range should cover the expected width of peaks of interest.

waveletcallable, optional

Should take two parameters and return a 1-D array to convolve with vector. The first parameter determines the number of points of the returned wavelet array, the second parameter is the scale (width) of the wavelet. Should be normalized and symmetric. Default is the ricker wavelet.

max_distancesndarray, optional

At each row, a ridge line is only connected if the relative max at row[n] is within max_distances[n] from the relative max at row[n+1]. Default value is widths/4.

gap_threshfloat, optional

If a relative maximum is not found within max_distances, there will be a gap. A ridge line is discontinued if there are more than gap_thresh points without connecting a new relative maximum. Default is the first value of the widths array i.e. widths[0].

min_lengthint, optional

Minimum length a ridge line needs to be acceptable. Default is cwt.shape[0] / 4, ie 1/4-th the number of widths.

min_snrfloat, optional

Minimum SNR ratio. Default 1. The signal is the maximum CWT coefficient on the largest ridge line. The noise is noise_perc th percentile of datapoints contained within the same ridge line.

noise_percfloat, optional

When calculating the noise floor, percentile of data points examined below which to consider noise. Calculated using stats.scoreatpercentile. Default is 10.

window_sizeint, optional

Size of window to use to calculate noise floor. Default is cwt.shape[1] / 20.


Indices of the locations in the vector where peaks were found. The list is sorted.

See also


Find peaks inside a signal based on peak properties.


This approach was designed for finding sharp peaks among noisy data, however with proper parameter selection it should function well for different peak shapes.

The algorithm is as follows:
  1. Perform a continuous wavelet transform on vector, for the supplied widths. This is a convolution of vector with wavelet(width) for each width in widths. See cwt.

  2. Identify “ridge lines” in the cwt matrix. These are relative maxima at each row, connected across adjacent rows. See identify_ridge_lines

  3. Filter the ridge_lines using filter_ridge_lines.

Added in version 0.11.0.



Bioinformatics (2006) 22 (17): 2059-2065. DOI:10.1093/bioinformatics/btl355


>>> import numpy as np
>>> from scipy import signal
>>> xs = np.arange(0, np.pi, 0.05)
>>> data = np.sin(xs)
>>> peakind = signal.find_peaks_cwt(data, np.arange(1,10))
>>> peakind, xs[peakind], data[peakind]
([32], array([ 1.6]), array([ 0.9995736]))