human
Give me a Python code to compute multiterm periodograms relatively quickly given the following:
Complete the below
def lombscargle_fastchi2(t, y, dy, f0, df, Nf, normalization='standard', fit_mean=True, center_data=True, nterms=1, use_fft=True, trig_sum_kwds=None):

    ''''Lomb-Scargle Periodogram
    This implements a fast chi-squared periodogram using the algorithm
    outlined in [4]_. The result is identical to the standard Lomb-Scargle
    periodogram. The advantage of this algorithm is the
    ability to compute multiterm periodograms relatively quickly.
    Parameters
    t, y, dy : array_like  (NOT astropy.Quantities)
    times, values, and errors of the data points. These should be
    broadcastable to the same shape.
    f0, df, Nf : (float, float, int)
    parameters describing the frequency grid, f = f0 + df * arange(Nf).
    normalization : string (optional, default=\'standard\')
    Normalization to use for the periodogram.
    Options are \'standard\', \'model\', \'log\', or \'psd\'.
    fit_mean : bool (optional, default=True)
    if True, include a constant offset as part of the model at each
    frequency. This can lead to more accurate results, especially in the
    case of incomplete phase coverage.
    center_data : bool (optional, default=True)
    if True, pre-center the data by subtracting the weighted mean
    of the input data. This is especially important if ``fit_mean = False``
    nterms : int (optional, default=1)
    Number of Fourier terms in the fit
    Returns
    power : array_like
    Lomb-Scargle power associated with each frequency.
    Units of the result depend on the normalization.
    References
    .. [1] M. Zechmeister and M. Kurster, A&A 496, 577-584 (2009)
    .. [2] W. Press et al, Numerical Recipes in C (2002)
    .. [3] Scargle, J.D. ApJ 263:835-853 (1982)
    .. [4] Palmer, J. ApJ 695:496-502 (2009)'
    '''
assistant
#Here is Python code to compute multiterm periodograms relatively quickly.
    if ((nterms == 0) and (not fit_mean)):
        raise ValueError('Cannot    have    nterms    =    0    without    fitting    bias')
    if (dy is None):
        dy = 1
    (t, y, dy) = np.broadcast_arrays(t, y, dy)
    if (t.ndim != 1):
        raise ValueError('t,    y,    dy    should    be    one    dimensional')
    if (f0 < 0):
        raise ValueError('Frequencies    must    be    positive')
    if (df <= 0):
        raise ValueError('Frequency    steps    must    be    positive')
    if (Nf <= 0):
        raise ValueError('Number    of    frequencies    must    be    positive')
    w = (dy ** (-2.0))
    ws = np.sum(w)
    if (center_data or fit_mean):
        y = (y - (np.dot(w, y) / ws))
    yw = (y / dy)
    chi2_ref = np.dot(yw, yw)
    kwargs = dict.copy((trig_sum_kwds or {}))
    kwargs.update(f0=f0, df=df, use_fft=use_fft, N=Nf)
    yws = np.sum((y * w))
    SCw = [(np.zeros(Nf), (ws * np.ones(Nf)))]
    SCw.extend([trig_sum(t, w, freq_factor=i, **kwargs) for i in range(1, ((2 * nterms) + 1))])
    (Sw, Cw) = zip(*SCw)
    SCyw = [(np.zeros(Nf), (yws * np.ones(Nf)))]
    SCyw.extend([trig_sum(t, (w * y), freq_factor=i, **kwargs) for i in range(1, (nterms + 1))])
    (Syw, Cyw) = zip(*SCyw)
    order = ([('C', 0)] if fit_mean else [])
    order.extend(sum([[('S', i), ('C', i)] for i in range(1, (nterms + 1))], []))
    funcs = dict(S=(lambda m, i: Syw[m][i]), C=(lambda m, i: Cyw[m][i]), SS=(lambda m, n, i: (0.5 * (Cw[abs((m - n))][i] - Cw[(m + n)][i]))), CC=(lambda m, n, i: (0.5 * (Cw[abs((m - n))][i] + Cw[(m + n)][i]))), SC=(lambda m, n, i: (0.5 * ((np.sign((m - n)) * Sw[abs((m - n))][i]) + Sw[(m + n)][i]))), CS=(lambda m, n, i: (0.5 * ((np.sign((n - m)) * Sw[abs((n - m))][i]) + Sw[(n + m)][i]))))
    def compute_power(i):
        XTX = np.array([[funcs[(A[0] + B[0])](A[1], B[1], i) for A in order] for B in order])
        XTy = np.array([funcs[A[0]](A[1], i) for A in order])
        return np.dot(XTy.T, np.linalg.solve(XTX, XTy))
    p = np.array([compute_power(i) for i in range(Nf)])
   ...