human
Solve the following python programming problem given the following code:
@verbose
def dics_source_power(info, forward, noise_csds, data_csds, reg=0.05, label=None, pick_ori=None, real_filter=False, verbose=None):
assistant
#Here is the Python program you requested.

    """Dynamic Imaging of Coherent Sources (DICS).
    Calculate source power in time and frequency windows specified in the
    calculation of the data cross-spectral density matrix or matrices. Source
    power is normalized by noise power.
    NOTE : This implementation has not been heavily tested so please
    report any issues or suggestions.
    Parameters
    info : dict
    Measurement info, e.g. epochs.info.
    forward : dict
    Forward operator.
    noise_csds : instance or list of instances of CrossSpectralDensity
    The noise cross-spectral density matrix for a single frequency or a
    list of matrices for multiple frequencies.
    data_csds : instance or list of instances of CrossSpectralDensity
    The data cross-spectral density matrix for a single frequency or a list
    of matrices for multiple frequencies.
    reg : float
    The regularization for the cross-spectral density.
    label : Label | None
    Restricts the solution to a given label.
    pick_ori : None | \normal\
    If \normal\, rather than pooling the orientations by taking the norm,
    only the radial component is kept.
    real_filter : bool
    If True, take only the real part of the cross-spectral-density matrices
    to compute real filters.
    verbose : bool, str, int, or None
    If not None, override default verbose level (see :func:`mne.verbose`
    and :ref:`Logging documentation <tut_logging>` for more).
    Returns
    stc : SourceEstimate | VolSourceEstimate
    Source power with frequency instead of time.
    Notes
    The original reference is:
    Gross et al. Dynamic imaging of coherent sources: Studying neural
    interactions in the human brain. PNAS (2001) vol. 98 (2) pp. 694-699
    """
    if isinstance(data_csds, CrossSpectralDensity):
        data_csds = [data_csds]
    if isinstance(noise_csds, CrossSpectralDensity):
        noise_csds = [noise_csds]
    def csd_shapes(x):
        return tuple((c.data.shape for c in x))
    if ((csd_shapes(data_csds) != csd_shapes(noise_csds)) or any(((len(set(csd_shapes(c))) > 1) for c in [data_csds, noise_csds]))):
        raise ValueError('One    noise    CSD    matrix    should    be    provided    for    each    data    CSD    matrix    and    vice    versa.    All    CSD    matrices    should    have    identical    shape.')
    frequencies = []
    for (data_csd, noise_csd) in zip(data_csds, noise_csds):
        if (not np.allclose(data_csd.frequencies, noise_csd.frequencies)):
            raise ValueError('Data    and    noise    CSDs    should    be    calculated    at    identical    frequencies')
        if (len(data_csd.frequencies) > 1):
            frequencies.append(np.mean(data_csd.frequencies))
        else:
            frequencies.append(data_csd.frequencies[0])
    fmin = frequencies[0]
    if (len(frequencies) > 2):
        fstep = []
        for i in range((len(frequencies) - 1)):
            fstep.append((frequencies[(i + 1)] - frequencies[i]))
        if (not np.allclose(fstep, np.mean(fstep), 1e-05)):
            warn('Uneven    frequency    spacing    in    CSD    object,    frequencies    in    the    resulting    stc    file    will    be    inaccurate.')
        fstep = fstep[0]
    elif (len(frequencies) > 1):
        fstep = (frequencies[1] - frequencies[0])
    else:
        fstep = 1
    picks = _setup_picks(picks=None, info=info, forward=forward)
    (is_free_ori, _, proj, vertno, G) = _prepare_beamformer_input(info, forward, label, picks=picks, pick_ori=pick_ori)
    n_orient = (3 if is_free_ori else 1)
    n_sources = (G.shape[1] // n_orient)
    source_power = np.zeros((n_sources, len(data_csds)))
    n_csds = len(data_csds)
    logger.info('Computing    DICS    source    power...')
    for (i, (data_csd, noise_csd))...