docs: def frequency_grid_evaluation(self, fmin=0.0, fmax=1.0, fresolution=1e-4, n_local_max=10):
          """ 
          Computes the selected criterion over a grid of frequencies 
          with limits and resolution specified by the inputs. After that
          the best local maxima are evaluated over a finer frequency grid
          
          Parameters
          ---------
          fmin: float
              starting frequency
          fmax: float
              stopping frequency
          fresolution: float
              step size in the frequency grid
          
          """
          self.fres_grid = fresolution
          freq = np.arange(np.amax([fmin, fresolution]), fmax, step=fresolution).astype('float32')
          Nf = len(freq)
          per = np.zeros(shape=(Nf,)).astype('float32')     
                
          for k in range(0, Nf):
              per[k] = self.compute_metric(freq[k])
          self.freq = freq
          self.per = per