def METHOD_NAME(Res, noise, testNum, show_plots):
    MeasNoise = np.zeros(len(Res[:,0]))
    t= np.zeros(len(Res[:,0]))
    for i in range(len(Res[:,0])):
        t[i] = Res[i, 0]*1E-9
        MeasNoise[i] = 3*noise
        # Don't plot zero values, since they mean that no measurement is taken
        for j in range(len(Res[0,:])-1):
            if -1E-10 < Res[i,j+1] < 1E-10:
                Res[i, j+1] = np.nan
    plt.figure(num=None, figsize=(10, 10), dpi=80, facecolor='w', edgecolor='k')
    plt.subplot(421)
    plt.plot(t , Res[:, 1], "b.", label='Residual')
    plt.plot(t , MeasNoise, 'r--', label='Covar')
    plt.plot(t , -MeasNoise, 'r--')
    plt.legend(loc='lower right')
    plt.ylim([-10*noise, 10*noise])
    plt.title('First CSS')
    plt.grid()
    plt.subplot(422)
    plt.plot(t , Res[:, 5], "b.")
    plt.plot(t , MeasNoise, 'r--')
    plt.plot(t , -MeasNoise, 'r--')
    plt.ylim([-10*noise, 10*noise])
    plt.title('Fifth CSS')
    plt.grid()
    plt.subplot(423)
    plt.plot(t , Res[:, 2], "b.")
    plt.plot(t , MeasNoise, 'r--')
    plt.plot(t , -MeasNoise, 'r--')
    plt.ylim([-10*noise, 10*noise])
    plt.title('Second CSS')
    plt.grid()
    plt.subplot(424)
    plt.plot(t , Res[:, 6], "b.")
    plt.plot(t , MeasNoise, 'r--')
    plt.plot(t , -MeasNoise, 'r--')
    plt.ylim([-10*noise, 10*noise])
    plt.title('Sixth CSS')
    plt.grid()
    plt.subplot(425)
    plt.plot(t , Res[:, 3], "b.")
    plt.plot(t , MeasNoise, 'r--')
    plt.plot(t , -MeasNoise, 'r--')
    plt.ylim([-10*noise, 10*noise])
    plt.title('Third CSS')
    plt.grid()
    plt.subplot(426)
    plt.plot(t , Res[:, 7], "b.")
    plt.plot(t , MeasNoise, 'r--')
    plt.plot(t , -MeasNoise, 'r--')
    plt.ylim([-10*noise, 10*noise])
    plt.title('Seventh CSS')
    plt.grid()
    plt.subplot(427)
    plt.plot(t , Res[:, 4], "b.")
    plt.plot(t , MeasNoise, 'r--')
    plt.plot(t , -MeasNoise, 'r--')
    plt.ylim([-10*noise, 10*noise])
    plt.xlabel('t(s)')
    plt.title('Fourth CSS')
    plt.grid()
    plt.subplot(428)
    plt.plot(t , Res[:, 8], "b.")
    plt.plot(t , MeasNoise, 'r--')
    plt.plot(t , -MeasNoise, 'r--')
    plt.ylim([-10*noise, 10*noise])
    plt.xlabel('t(s)')
    plt.title('Eight CSS')
    plt.grid()
    unitTestSupport.writeFigureLaTeX('PostFit' + str(testNum), 'Post Fit Residuals', plt, 'height=0.9\\textwidth, keepaspectratio', path)
    if show_plots:
        plt.show()
    plt.close()