<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def sigmasq_series(htilde, psd=None, low_frequency_cutoff=None, high_frequency_cutoff=None):
 """Return a cumulative sigmasq frequency series. Return a frequency series containing the accumulated power in the input up to that frequency. Parameters htilde : TimeSeries or FrequencySeries The input vector psd : {None, FrequencySeries}, optional The psd used to weight the accumulated power. low_frequency_cutoff : {None, float}, optional The frequency to begin accumulating power. If None, start at the beginning of the vector. high_frequency_cutoff : {None, float}, optional The frequency to stop considering accumulated power. If None, continue until the end of the input vector. Returns ------- Frequency Series: FrequencySeries A frequency series containing the cumulative sigmasq. """

    htilde = make_frequency_series(htilde)
    N = (len(htilde)-1) * 2
    norm = 4.0 * htilde.delta_f
    kmin, kmax = get_cutoff_indices(low_frequency_cutoff,
                                   high_frequency_cutoff, htilde.delta_f, N)

    sigma_vec = FrequencySeries(zeros(len(htilde), dtype=real_same_precision_as(htilde)),
                                delta_f = htilde.delta_f, copy=False)

    mag = htilde.squared_norm()

    if psd is not None:
        mag /= psd

    sigma_vec[kmin:kmax] = mag[kmin:kmax].cumsum()

    return sigma_vec*norm