code: def number_cwt_peaks(x, n):
      
      return len(find_peaks_cwt(vector=x, widths=np.array(list(range(1, n + 1))), wavelet=ricker))
docs: This feature calculator searches for different peaks in x. To do so, x is smoothed by a ricker wavelet and for
      widths ranging from 1 to n. This feature calculator returns the number of peaks that occur at enough width scales
      and with sufficiently high Signal-to-Noise-Ratio (SNR)
  
      :param x: the time series to calculate the feature of
      :type x: numpy.ndarray
      :param n: maximum width to consider
      :type n: int
      :return: the value of this feature
      :return type: int