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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def mt_deconvolve(data_a, data_b, delta, nfft=None, time_bandwidth=None, number_of_tapers=None, weights="adaptive", demean=True, fmax=0.0):
 """ Deconvolve two time series using multitapers. This uses the eigencoefficients and the weights from the multitaper spectral estimations and more or less follows this paper: .. |br| raw:: html <br /> **Receiver Functions from Multiple-Taper Spectral Correlation Estimates** *Jeffrey Park, Vadim Levin* |br| Bulletin of the Seismological Society of America Dec 2000, 90 (6) 1507-1520 http://dx.doi.org/10.1785/0119990122 :type data_a: :class:`numpy.ndarray` :param data_a: Data for first time series. :type data_b: :class:`numpy.ndarray` :param data_b: Data for second time series. :type delta: float :param delta: Sample spacing of the data. :type nfft: int :param nfft: Number of points for the FFT. If ``nfft == None``, no zero padding will be applied before the FFT. :type time_bandwidth: float :param time_bandwidth: Time-bandwidth product. Common values are 2, 3, 4, and numbers in between. :type number_of_tapers: int :param number_of_tapers: Number of tapers to use. Defaults to ``int(2*time_bandwidth) - 1``. This is maximum senseful amount. More tapers will have no great influence on the final spectrum but increase the calculation time. Use fewer tapers for a faster calculation. :type weights: str :param weights: ``"adaptive"`` or ``"constant"`` weights. :type deman: bool :param demean: Force the complex TF to be demeaned. :type fmax: float :param fmax: Maximum frequency for lowpass cosine filter. Set this to zero to not have a filter. :return: Returns a dictionary with 5 :class:`numpy.ndarray`'s. See the note below. .. note:: Returns a dictionary with five arrays: * ``"deconvolved"``: Deconvolved time series. * ``"spectrum_a"``: Spectrum of the first time series. * ``"spectrum_b"``: Spectrum of the second time series. * ``"spectral_ratio"``: The ratio of both spectra. * ``"frequencies"``: The used frequency bins for the spectra. """

    npts = len(data_a)
    if len(data_b) != npts:
        raise ValueError("Input arrays must have the same length!")

    if nfft is None:
        nfft = npts
    elif nfft < npts:
        raise ValueError("nfft must be larger then the number of samples in "
                         "the array.")

    # Deconvolution utilizes the 32bit version.
    mt = _MtspecType("float32")

    # Use the optimal number of tapers in case no number is specified.
    if number_of_tapers is None:
        number_of_tapers = int(2 * time_bandwidth) - 1

    # Transform the data to work with the library.
    data_a = np.require(data_a, mt.float, requirements=[mt.order])
    data_b = np.require(data_b, mt.float, requirements=[mt.order])

    nf = nfft // 2 + 1

    # Internally uses integers
    if demean:
        demean = 1
    else:
        demean = 0

    # iad = 0 are adaptive, iad = 1 are constant weight - this is
    # counter intuitive.
    if weights == "constant":
        adaptive = 1
    elif weights == "adaptive":
        adaptive = 0
    else:
        raise ValueError('Weights must be either "adaptive" or "constant".')

    tfun = mt.empty(nfft)
    freq = mt.empty(nf)
    spec_ratio = mt.empty(nf)
    speci = mt.empty(nf)
    specj = mt.empty(nf)

    mtspeclib.mt_deconv_(
        C.byref(C.c_int(int(npts))),
        C.byref(C.c_int(int(nfft))),
        C.byref(C.c_float(float(delta))),
        mt.p(data_a),
        mt.p(data_b),
        C.byref(C.c_float(float(time_bandwidth))),
        C.byref(C.c_int(int(number_of_tapers))),
        C.byref(C.c_int(int(nf))),
        C.byref(C.c_int(adaptive)),
        mt.p(freq),
        mt.p(tfun),
        mt.p(spec_ratio),
        mt.p(speci),
        mt.p(specj),
        C.byref(C.c_int(demean)),
        C.byref(C.c_float(fmax)))

    return {
        "frequencies": freq,
        "deconvolved": tfun,
        "spectral_ratio": spec_ratio,
        "spectrum_a": speci,
        "spectrum_b": specj
    }