def calc_gamma_from_variance_autocorrelation_fit(self, NumberOfOscillations, GammaGuess=None, silent=False, MakeFig=True, show_fig=True):
        """
        Calculates the total damping, i.e. Gamma, by splitting the time trace
        into chunks of NumberOfOscillations oscillations and calculated the
        variance of each of these chunks. This array of varainces is then used
        for the autocorrleation. The autocorrelation is fitted with an exponential 
        relaxation function and the function returns the parameters with errors.

        Parameters
        ----------
        NumberOfOscillations : int
            The number of oscillations each chunk of the timetrace 
            used to calculate the variance should contain.
        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

        """
        try:
            SplittedArraySize = int(self.SampleFreq/self.FTrap.n) * NumberOfOscillations
        except KeyError:
            ValueError('You forgot to do the spectrum fit to specify self.FTrap exactly.')
        VoltageArraySize = len(self.voltage)
        SnippetsVariances = _np.var(self.voltage[:VoltageArraySize-_np.mod(VoltageArraySize,SplittedArraySize)].reshape(-1,SplittedArraySize),axis=1)
        autocorrelation = calc_autocorrelation(SnippetsVariances)
        time = _np.array(range(len(autocorrelation))) * SplittedArraySize / self.SampleFreq

        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
Calculates the total damping, i.e. Gamma, by splitting the time trace
        into chunks of NumberOfOscillations oscillations and calculated the
        variance of each of these chunks. This array of varainces is then used
        for the autocorrleation. The autocorrelation is fitted with an exponential 
        relaxation function and the function returns the parameters with errors.

        Parameters
        ----------
        NumberOfOscillations : int
            The number of oscillations each chunk of the timetrace 
            used to calculate the variance should contain.
        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