Skip to main content
`Ctrl`+`K`
scipy.signal.

# find_peaks_cwt

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)

  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.

  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.

  See also

  `find_peaks`

    Find peaks inside a signal based on peak properties.

  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:

    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.

  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 Support for the array API standard for more information.

  References

  [1]

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

  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]
      ([32], array([ 1.6]), array([ 0.9995736]))