def METHOD_NAME(a,b):
    df = pd.read_hdf('{}/t2_run{}.h5'.format(tier_dir,sys.argv[1]))
    df['e_cal'] = pd.read_hdf('{}/Spectrum_{}.hdf5'.format(meta_dir,sys.argv[1]))['e_cal']
    df = df.loc[(df.e_cal>=float(a))&(df.e_cal<=float(b))]
    df_2 = pd.read_hdf('{}/t2_run{}.h5'.format(tier_dir,sys.argv[2]))
    df_2['e_cal'] = pd.read_hdf('{}/Spectrum_{}.hdf5'.format(meta_dir,sys.argv[2]))['e_cal']
    df_2 = df_2.loc[(df_2.e_cal>=float(a))&(df_2.e_cal<=float(b))]
    plt.hist(df['current_max']/df['e_cal'], np.arange(0,.2,.0010), histtype='step', density=True, label='run {}, {} < E < {} keV'.format(sys.argv[1],a,b))
    plt.hist(df_2['current_max']/df_2['e_cal'], np.arange(0,.2,.0010), histtype='step', density=True, label='run {}, {} < E < {} keV'.format(sys.argv[2],a,b))
    plt.xlabel('A/E', ha='right', x=1.0)
    plt.ylabel('Counts (normalized)', ha='right', y=1.0)
    plt.legend(frameon=True, loc='best', fontsize='small')
    plt.show()