def fit_peaks(self, type, initial_guesses):
        """
        Fit the data to a specified function.

        Parameters:
        initial_guesses (tuple): Initial guesses for the fit parameters.

        Returns:
        tuple: Fitted parameters and covariance matrix.
        """
        parameters = getattr(self, f"{type}_parameters")
        x = getattr(self, f"{type}_channel")
        y = getattr(self, f"{type}_counts")

        # Fit the data to the specified function
        popt, pcov = curve_fit(gauss_lin,
                               x,
                               y,
                               p0=initial_guesses)
        # print(f"Linear background is: y= {popt[0]:.2f}x + {popt[1]:.2f}")

        parameters = popt

        peak_amplitude = getattr(self, f"{type}_peak_amplitude")
        peak_mean = getattr(self, f"{type}_peak_mean")
        peak_sigma = getattr(self, f"{type}_peak_sigma")

        for i in range(2, len(popt), 3):
            peak_amplitude.append(popt[i])
            peak_mean.append(popt[i + 1])
            peak_sigma.append(popt[i + 2])