pyerrors.mpm

import numpy as np
import scipy.linalg
from .obs import Obs
from .linalg import svd, eig


def matrix_pencil_method(corrs, k=1, p=None, **kwargs):
    """Matrix pencil method to extract k energy levels from data

    Implementation of the matrix pencil method based on
    eq. (2.17) of Y. Hua, T. K. Sarkar, IEEE Trans. Acoust. 38, 814-824 (1990)

    Parameters
    ----------
    data : list
        can be a list of Obs for the analysis of a single correlator, or a list of lists
        of Obs if several correlators are to analyzed at once.
    k : int
        Number of states to extract (default 1).
    p : int
        matrix pencil parameter which filters noise. The optimal value is expected between
        len(data)/3 and 2*len(data)/3. The computation is more expensive the closer p is
        to len(data)/2 but could possibly suppress more noise (default len(data)//2).
    """
    if isinstance(corrs[0], Obs):
        data = [corrs]
    else:
        data = corrs

    lengths = [len(d) for d in data]
    if lengths.count(lengths[0]) != len(lengths):
        raise Exception('All datasets have to have the same length.')

    data_sets = len(data)
    n_data = len(data[0])

    if p is None:
        p = max(n_data // 2, k)
    if n_data <= p:
        raise Exception('The pencil p has to be smaller than the number of data samples.')
    if p < k or n_data - p < k:
        raise Exception('Cannot extract', k, 'energy levels with p=', p, 'and N-p=', n_data - p)

    # Construct the hankel matrices
    matrix = []
    for n in range(data_sets):
        matrix.append(scipy.linalg.hankel(data[n][:n_data - p], data[n][n_data - p - 1:]))
    matrix = np.array(matrix)
    # Construct y1 and y2
    y1 = np.concatenate(matrix[:, :, :p])
    y2 = np.concatenate(matrix[:, :, 1:])
    # Apply SVD to y2
    u, s, vh = svd(y2, **kwargs)
    # Construct z from y1 and SVD of y2, setting all singular values beyond the kth to zero
    z = np.diag(1. / s[:k]) @ u[:, :k].T @ y1 @ vh.T[:, :k]
    # Return the sorted logarithms of the real eigenvalues as Obs
    energy_levels = np.log(np.abs(eig(z, **kwargs)))
    return sorted(energy_levels, key=lambda x: abs(x.value))

def matrix_pencil_method(corrs, k=1, p=None, **kwargs):
    """Matrix pencil method to extract k energy levels from data

    Implementation of the matrix pencil method based on
    eq. (2.17) of Y. Hua, T. K. Sarkar, IEEE Trans. Acoust. 38, 814-824 (1990)

    Parameters
    ----------
    data : list
        can be a list of Obs for the analysis of a single correlator, or a list of lists
        of Obs if several correlators are to analyzed at once.
    k : int
        Number of states to extract (default 1).
    p : int
        matrix pencil parameter which filters noise. The optimal value is expected between
        len(data)/3 and 2*len(data)/3. The computation is more expensive the closer p is
        to len(data)/2 but could possibly suppress more noise (default len(data)//2).
    """
    if isinstance(corrs[0], Obs):
        data = [corrs]
    else:
        data = corrs

    lengths = [len(d) for d in data]
    if lengths.count(lengths[0]) != len(lengths):
        raise Exception('All datasets have to have the same length.')

    data_sets = len(data)
    n_data = len(data[0])

    if p is None:
        p = max(n_data // 2, k)
    if n_data <= p:
        raise Exception('The pencil p has to be smaller than the number of data samples.')
    if p < k or n_data - p < k:
        raise Exception('Cannot extract', k, 'energy levels with p=', p, 'and N-p=', n_data - p)

    # Construct the hankel matrices
    matrix = []
    for n in range(data_sets):
        matrix.append(scipy.linalg.hankel(data[n][:n_data - p], data[n][n_data - p - 1:]))
    matrix = np.array(matrix)
    # Construct y1 and y2
    y1 = np.concatenate(matrix[:, :, :p])
    y2 = np.concatenate(matrix[:, :, 1:])
    # Apply SVD to y2
    u, s, vh = svd(y2, **kwargs)
    # Construct z from y1 and SVD of y2, setting all singular values beyond the kth to zero
    z = np.diag(1. / s[:k]) @ u[:, :k].T @ y1 @ vh.T[:, :k]
    # Return the sorted logarithms of the real eigenvalues as Obs
    energy_levels = np.log(np.abs(eig(z, **kwargs)))
    return sorted(energy_levels, key=lambda x: abs(x.value))