def METHOD_NAME(freq_data, noise_data, length, delta_f, low_freq_cutoff):
    """Interpolate n PSD (as two 1-dimensional arrays of frequency and data)
    to the desired length, delta_f and low frequency cutoff.
    Parameters
    ----------
    freq_data : array
        Array of frequencies.
    noise_data : array
        PSD values corresponding to frequencies in freq_arr.
    length : int
        Length of the frequency series in samples.
    delta_f : float
        Frequency resolution of the frequency series in Herz.
    low_freq_cutoff : float
        Frequencies below this value are set to zero.
    Returns
    -------
    psd : FrequencySeries
        The generated frequency series.
    """
    # Only include points above the low frequency cutoff
    if freq_data[0] > low_freq_cutoff:
        raise ValueError('Lowest frequency in input data '
          ' is higher than requested low-frequency cutoff ' + str(low_freq_cutoff))
    kmin = int(low_freq_cutoff / delta_f)
    flow = kmin * delta_f
    data_start = (0 if freq_data[0]==low_freq_cutoff else numpy.searchsorted(freq_data, flow) - 1)
    # If the cutoff is exactly in the file, start there
    if freq_data[data_start+1] == low_freq_cutoff:
        data_start += 1
    freq_data = freq_data[data_start:]
    noise_data = noise_data[data_start:]
    if (length - 1) * delta_f > freq_data[-1]:
        logging.warning('Requested number of samples exceeds the highest '
                        'available frequency in the input data, '
                        'will use max available frequency instead. '
                        '(requested %f Hz, available %f Hz)',
                        (length - 1) * delta_f, freq_data[-1])
        length = int(freq_data[-1]/delta_f + 1)
    flog = numpy.log(freq_data)
    slog = numpy.log(noise_data)
    psd_interp = scipy.interpolate.interp1d(
        flog, slog, fill_value=(slog[0], slog[-1]), bounds_error=False)
    psd = numpy.zeros(length, dtype=numpy.float64)
    vals = numpy.log(numpy.arange(kmin, length) * delta_f)
    psd[kmin:] =  numpy.exp(psd_interp(vals))
    return FrequencySeries(psd, delta_f=delta_f)