code: def _mwi(self):
          
          wavelet_filter = signal.ricker(self.qrs_width, 4)
  
          self.sig_i = signal.filtfilt(wavelet_filter, [1], self.sig_f,
                                       axis=0) ** 2
  
          # Save the mwi gain (x2 due to double filtering) and the total
          # gain from raw to mwi
          self.mwi_gain = get_filter_gain(wavelet_filter, [1],
                           np.mean([self.fc_low, self.fc_high]), self.fs) * 2
          self.transform_gain = self.filter_gain * self.mwi_gain
          self.peak_inds_i = find_local_peaks(self.sig_i, radius=self.qrs_radius)
          self.n_peaks_i = len(self.peak_inds_i)
docs: Apply moving wave integration (mwi) with a ricker (Mexican hat)
          wavelet onto the filtered signal, and save the square of the
          integrated signal.
  
          The width of the hat is equal to the qrs width
  
          After integration, find all local peaks in the mwi signal.