def METHOD_NAME(data):
    """compares the test data with the correct data"""
    rlen = data["rlen"]
    corr_dat = numpy.fromfile(get_correct_data_for_test()).reshape(-1, rlen)
    tst_dat = numpy.array(data["data"]).reshape(-1, rlen)
    t1 = corr_dat[:, 0]
    t2 = tst_dat[:, 0]
    # remove any initial t that are greter than the next t
    # (removes times before 0) in correct data
    c = 0
    while c < len(t1) - 1 and t1[c] > t1[c + 1]:
        c = c + 1
    t1 = numpy.delete(t1, range(c))
    corr_dat = numpy.delete(corr_dat, range(c), 0)
    # remove any initial t that are greter than the next t
    # (removes times before 0) in test data
    c = 0
    while c < len(t2) - 1 and t2[c] > t2[c + 1]:
        c = c + 1
    t2 = numpy.delete(t2, range(c))
    tst_dat = numpy.delete(tst_dat, range(c), 0)
    # get rid of repeating t in correct data (otherwise interpolation fails)
    c = 0
    while c < len(t1) - 1:
        c1 = c + 1
        while c1 < len(t1) and abs(t1[c] - t1[c1]) < dt_eps:
            c1 = c1 + 1
        t1 = numpy.delete(t1, range(c, c1 - 1))
        corr_dat = numpy.delete(corr_dat, range(c, c1 - 1), 0)
        c = c + 1
    # get rid of the test data outside of the correct data time interval
    t2_n = len(t2)
    t2_0 = 0
    while t2[t2_n - 1] > t1[-1]:
        t2_n = t2_n - 1
    while t2[t2_0] < t1[0]:
        t2_0 = t2_0 + 1
    # interpolate and compare
    corr_vals = numpy.array(
        [numpy.interp(t2[t2_0:t2_n], t1, corr_dat[:, i].T) for i in range(1, rlen)]
    )
    max_err = numpy.amax(abs(corr_vals.T - tst_dat[t2_0:t2_n, 1:]))
    return max_err