docs: def calc_gamma_from_energy_autocorrelation_fit(self, GammaGuess=None, silent=False, MakeFig=True, show_fig=True):
          """
          Calculates the total damping, i.e. Gamma, by calculating the energy each 
          point in time. This energy array is then used for the autocorrleation. 
          The autocorrelation is fitted with an exponential relaxation function and
          the function returns the parameters with errors.
  
          Parameters
          ----------
          GammaGuess : float, optional
              Inital guess for BigGamma (in radians)
          silent : bool, optional
              Whether it prints the values fitted or is silent.
          MakeFig : bool, optional
              Whether to construct and return the figure object showing
              the fitting. defaults to True
          show_fig : bool, optional
              Whether to show the figure object when it has been created.
              defaults to True
  
          Returns
          -------
          Gamma : ufloat
              Big Gamma, the total damping in radians
          fig : matplotlib.figure.Figure object
              The figure object created showing the autocorrelation
              of the data with the fit
          ax : matplotlib.axes.Axes object
              The axes object created showing the autocorrelation
              of the data with the fit
  
          """
          autocorrelation = calc_autocorrelation(self.voltage[:-1]**2*self.OmegaTrap.n**2+(_np.diff(self.voltage)*self.SampleFreq)**2)
          time = self.time.get_array()[:len(autocorrelation)]
  
          if GammaGuess==None:
              Gamma_Initial = (time[4]-time[0])/(autocorrelation[0]-autocorrelation[4])
          else:
              Gamma_Initial = GammaGuess
          
          if MakeFig == True:
              Params, ParamsErr, fig, ax = fit_autocorrelation(
                  autocorrelation, time, Gamma_Initial, MakeFig=MakeFig, show_fig=show_fig)
          else:
              Params, ParamsErr, _ , _ = fit_autocorrelation(
                  autocorrelation, time, Gamma_Initial, MakeFig=MakeFig, show_fig=show_fig)
  
          if silent == False:
              print("\n")
              print(
                  "Big Gamma: {} +- {}% ".format(Params[0], ParamsErr[0] / Params[0] * 100))
  
          Gamma = _uncertainties.ufloat(Params[0], ParamsErr[0])
          
          if MakeFig == True:
              return Gamma, fig, ax
          else:
              return Gamma, None, None