Python linalg.eigvals_banded方法代碼示例

# 需要導入模塊: from scipy import linalg [as 別名]
# 或者: from scipy.linalg import eigvals_banded [as 別名]
def _gen_roots_and_weights(n, mu0, an_func, bn_func, f, df, symmetrize, mu):
    """[x,w] = gen_roots_and_weights(n,an_func,sqrt_bn_func,mu)

    Returns the roots (x) of an nth order orthogonal polynomial,
    and weights (w) to use in appropriate Gaussian quadrature with that
    orthogonal polynomial.

    The polynomials have the recurrence relation
          P_n+1(x) = (x - A_n) P_n(x) - B_n P_n-1(x)

    an_func(n)          should return A_n
    sqrt_bn_func(n)     should return sqrt(B_n)
    mu ( = h_0 )        is the integral of the weight over the orthogonal
                        interval
    """
    k = np.arange(n, dtype='d')
    c = np.zeros((2, n))
    c[0,1:] = bn_func(k[1:])
    c[1,:] = an_func(k)
    x = linalg.eigvals_banded(c, overwrite_a_band=True)

    # improve roots by one application of Newton's method
    y = f(n, x)
    dy = df(n, x)
    x -= y/dy

    fm = f(n-1, x)
    fm /= np.abs(fm).max()
    dy /= np.abs(dy).max()
    w = 1.0 / (fm * dy)

    if symmetrize:
        w = (w + w[::-1]) / 2
        x = (x - x[::-1]) / 2

    w *= mu0 / w.sum()

    if mu:
        return x, w, mu0
    else:
        return x, w

# Jacobi Polynomials 1               P^(alpha,beta)_n(x) 

# 需要導入模塊: from scipy import linalg [as 別名]
# 或者: from scipy.linalg import eigvals_banded [as 別名]
def _find_tapers_from_optimization(n_time_samples_per_window, time_index,
                                   half_bandwidth, n_tapers):
    '''here we want to set up an optimization problem to find a sequence
    whose energy is maximally concentrated within band
    [-half_bandwidth, half_bandwidth]. Thus,
    the measure lambda(T, half_bandwidth) is the ratio between the
    energy within that band, and the total energy. This leads to the
    eigen-system (A - (l1)I)v = 0, where the eigenvector corresponding
    to the largest eigenvalue is the sequence with maximally
    concentrated energy. The collection of eigenvectors of this system
    are called Slepian sequences, or discrete prolate spheroidal
    sequences (DPSS). Only the first K, K = 2NW/dt orders of DPSS will
    exhibit good spectral concentration
    [see http://en.wikipedia.org/wiki/Spectral_concentration_problem]

    Here I set up an alternative symmetric tri-diagonal eigenvalue
    problem such that
    (B - (l2)I)v = 0, and v are our DPSS (but eigenvalues l2 != l1)
    the main diagonal = ([n_time_samples_per_window-1-2*t]/2)**2 cos(2PIW),
    t=[0,1,2,...,n_time_samples_per_window-1] and the first off-diagonal =
    t(n_time_samples_per_window-t)/2, t=[1,2,...,
    n_time_samples_per_window-1] [see Percival and Walden, 1993]'''
    diagonal = (
        ((n_time_samples_per_window - 1 - 2 * time_index) / 2.) ** 2
        * np.cos(2 * np.pi * half_bandwidth))
    off_diag = np.zeros_like(time_index)
    off_diag[:-1] = (
        time_index[1:] * (n_time_samples_per_window - time_index[1:]) / 2.)
    # put the diagonals in LAPACK 'packed' storage
    ab = np.zeros((2, n_time_samples_per_window), dtype='d')
    ab[1] = diagonal
    ab[0, 1:] = off_diag[:-1]
    # only calculate the highest n_tapers eigenvalues
    w = eigvals_banded(
        ab, select='i',
        select_range=(n_time_samples_per_window - n_tapers,
                      n_time_samples_per_window - 1))
    w = w[::-1]

    # find the corresponding eigenvectors via inverse iteration
    t = np.linspace(0, np.pi, n_time_samples_per_window)
    tapers = np.zeros((n_tapers, n_time_samples_per_window), dtype='d')
    for taper_ind in range(n_tapers):
        tapers[taper_ind, :] = tridi_inverse_iteration(
            diagonal, off_diag, w[taper_ind],
            x0=np.sin((taper_ind + 1) * t))
    return tapers