interpolation.py 文件源码

def _make_interpolation_matrix(pos_from, pos_to, alpha=1e-5):
    """Compute interpolation matrix based on spherical splines

    Implementation based on [1]

    Parameters
    ----------
    pos_from : np.ndarray of float, shape(n_good_sensors, 3)
        The positions to interpoloate from.
    pos_to : np.ndarray of float, shape(n_bad_sensors, 3)
        The positions to interpoloate.
    alpha : float
        Regularization parameter. Defaults to 1e-5.

    Returns
    -------
    interpolation : np.ndarray of float, shape(len(pos_from), len(pos_to))
        The interpolation matrix that maps good signals to the location
        of bad signals.

    References
    ----------
    [1] Perrin, F., Pernier, J., Bertrand, O. and Echallier, JF. (1989).
        Spherical splines for scalp potential and current density mapping.
        Electroencephalography Clinical Neurophysiology, Feb; 72(2):184-7.
    """

    pos_from = pos_from.copy()
    pos_to = pos_to.copy()

    # normalize sensor positions to sphere
    _normalize_vectors(pos_from)
    _normalize_vectors(pos_to)

    # cosine angles between source positions
    cosang_from = pos_from.dot(pos_from.T)
    cosang_to_from = pos_to.dot(pos_from.T)
    G_from = _calc_g(cosang_from)
    G_to_from = _calc_g(cosang_to_from)

    if alpha is not None:
        G_from.flat[::len(G_from) + 1] += alpha

    n_channels = G_from.shape[0]  # G_from should be square matrix
    C = np.r_[np.c_[G_from, np.ones((n_channels, 1))],
              np.c_[np.ones((1, n_channels)), 0]]
    C_inv = linalg.pinv(C)

    interpolation = np.c_[G_to_from,
                          np.ones((G_to_from.shape[0], 1))].dot(C_inv[:, :-1])
    return interpolation