docs: def calc_gamma_from_position_autocorrelation_fit(self, GammaGuess=None, FreqTrapGuess=None, silent=False, MakeFig=True, show_fig=True):
          """
          Calculates the total damping, i.e. Gamma, by calculating the autocorrleation 
          of the position-time trace. The autocorrelation is fitted with an exponential 
          relaxation function derived in Tongcang Li's 2013 thesis (DOI: 10.1007/978-1-4614-6031-2)
          and the function (equation 4.20 in the thesis) returns the parameters with errors.
  
          Parameters
          ----------
          GammaGuess : float, optional
              Inital guess for BigGamma (in radians)
          FreqTrapGuess : float, optional
              Inital guess for the trapping Frequency in Hz
          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
          OmegaTrap : ufloat
              Trapping frequency 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)
          time = self.time.get_array()[:len(autocorrelation)]
  
          if GammaGuess==None:
              Gamma_Initial = (autocorrelation[0]-autocorrelation[int(self.SampleFreq/self.FTrap.n)])/(time[int(self.SampleFreq/self.FTrap.n)]-time[0])*2*_np.pi
          else:
              Gamma_Initial = GammaGuess
  
          if FreqTrapGuess==None:
              FreqTrap_Initial = self.FTrap.n
          else:
              FreqTrap_Initial = FreqTrapGuess
              
          if MakeFig == True:
              Params, ParamsErr, fig, ax = fit_autocorrelation(
                  autocorrelation, time, Gamma_Initial, FreqTrap_Initial, method='position', MakeFig=MakeFig, show_fig=show_fig)
          else:
              Params, ParamsErr, _ , _ = fit_autocorrelation(
                  autocorrelation, time, Gamma_Initial, FreqTrap_Initial, method='position', MakeFig=MakeFig, show_fig=show_fig)
  
          if silent == False:
              print("\n")
              print(
                  "Big Gamma: {} +- {}% ".format(Params[0], ParamsErr[0] / Params[0] * 100))
              print(
                  "Trap Frequency: {} +- {}% ".format(Params[1], ParamsErr[1] / Params[1] * 100))
              
          Gamma = _uncertainties.ufloat(Params[0], ParamsErr[0])
          OmegaTrap = _uncertainties.ufloat(Params[1], ParamsErr[1])
          
          if MakeFig == True:
              return Gamma, OmegaTrap, fig, ax
          else:
              return Gamma, OmegaTrap, None, None