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bundles / scipy latest / scipy / signal / _peak_finding / find_peaks_cwt

function

scipy.signal._peak_finding:find_peaks_cwt

source: /scipy/signal/_peak_finding.py :1201

Signature

def   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 )

Summary

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

Extended Summary

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.

Parameters

vector : ndarray

1-D array in which to find the peaks.

widths : float 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.

wavelet : callable, 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_distances : ndarray, 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_thresh : float, 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_length : int, 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_snr : float, 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_perc : float, optional

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

window_size : int, optional

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

Returns

peaks_indices : ndarray

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

Notes

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:

  • 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.

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

  • Filter the ridge_lines using filter_ridge_lines.

Array API Standard Support

find_peaks_cwt has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ⛔                   
PyTorch               ⛔                     ⛔                   
JAX                   ⛔                     ⛔                   
Dask                  ⛔                     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

Examples

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]
✗

See also

find_peaks

Find peaks inside a signal based on peak properties.

Aliases

  • scipy.signal.find_peaks_cwt

Referenced by

This package