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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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