Vehicle-cpp/hkpc/software/scipy/linalg/_decomp.py

# -*- coding: utf-8 -*-
#
# Author: Pearu Peterson, March 2002
#
# additions by Travis Oliphant, March 2002
# additions by Eric Jones,      June 2002
# additions by Johannes Loehnert, June 2006
# additions by Bart Vandereycken, June 2006
# additions by Andrew D Straw, May 2007
# additions by Tiziano Zito, November 2008
#
# April 2010: Functions for LU, QR, SVD, Schur, and Cholesky decompositions
# were moved to their own files. Still in this file are functions for
# eigenstuff and for the Hessenberg form.

__all__ = ['eig', 'eigvals', 'eigh', 'eigvalsh',
           'eig_banded', 'eigvals_banded',
           'eigh_tridiagonal', 'eigvalsh_tridiagonal', 'hessenberg', 'cdf2rdf']

import warnings

import numpy
from numpy import (array, isfinite, inexact, nonzero, iscomplexobj, cast,
                   flatnonzero, conj, asarray, argsort, empty,
                   iscomplex, zeros, einsum, eye, inf)
# Local imports
from scipy._lib._util import _asarray_validated
from ._misc import LinAlgError, _datacopied, norm
from .lapack import get_lapack_funcs, _compute_lwork


_I = cast['F'](1j)


def _make_complex_eigvecs(w, vin, dtype):
    """
    Produce complex-valued eigenvectors from LAPACK DGGEV real-valued output
    """
    # - see LAPACK man page DGGEV at ALPHAI
    v = numpy.array(vin, dtype=dtype)
    m = (w.imag > 0)
    m[:-1] |= (w.imag[1:] < 0)  # workaround for LAPACK bug, cf. ticket #709
    for i in flatnonzero(m):
        v.imag[:, i] = vin[:, i+1]
        conj(v[:, i], v[:, i+1])
    return v


def _make_eigvals(alpha, beta, homogeneous_eigvals):
    if homogeneous_eigvals:
        if beta is None:
            return numpy.vstack((alpha, numpy.ones_like(alpha)))
        else:
            return numpy.vstack((alpha, beta))
    else:
        if beta is None:
            return alpha
        else:
            w = numpy.empty_like(alpha)
            alpha_zero = (alpha == 0)
            beta_zero = (beta == 0)
            beta_nonzero = ~beta_zero
            w[beta_nonzero] = alpha[beta_nonzero]/beta[beta_nonzero]
            # Use numpy.inf for complex values too since
            # 1/numpy.inf = 0, i.e., it correctly behaves as projective
            # infinity.
            w[~alpha_zero & beta_zero] = numpy.inf
            if numpy.all(alpha.imag == 0):
                w[alpha_zero & beta_zero] = numpy.nan
            else:
                w[alpha_zero & beta_zero] = complex(numpy.nan, numpy.nan)
            return w


def _geneig(a1, b1, left, right, overwrite_a, overwrite_b,
            homogeneous_eigvals):
    ggev, = get_lapack_funcs(('ggev',), (a1, b1))
    cvl, cvr = left, right
    res = ggev(a1, b1, lwork=-1)
    lwork = res[-2][0].real.astype(numpy.int_)
    if ggev.typecode in 'cz':
        alpha, beta, vl, vr, work, info = ggev(a1, b1, cvl, cvr, lwork,
                                               overwrite_a, overwrite_b)
        w = _make_eigvals(alpha, beta, homogeneous_eigvals)
    else:
        alphar, alphai, beta, vl, vr, work, info = ggev(a1, b1, cvl, cvr,
                                                        lwork, overwrite_a,
                                                        overwrite_b)
        alpha = alphar + _I * alphai
        w = _make_eigvals(alpha, beta, homogeneous_eigvals)
    _check_info(info, 'generalized eig algorithm (ggev)')

    only_real = numpy.all(w.imag == 0.0)
    if not (ggev.typecode in 'cz' or only_real):
        t = w.dtype.char
        if left:
            vl = _make_complex_eigvecs(w, vl, t)
        if right:
            vr = _make_complex_eigvecs(w, vr, t)

    # the eigenvectors returned by the lapack function are NOT normalized
    for i in range(vr.shape[0]):
        if right:
            vr[:, i] /= norm(vr[:, i])
        if left:
            vl[:, i] /= norm(vl[:, i])

    if not (left or right):
        return w
    if left:
        if right:
            return w, vl, vr
        return w, vl
    return w, vr


def eig(a, b=None, left=False, right=True, overwrite_a=False,
        overwrite_b=False, check_finite=True, homogeneous_eigvals=False):
    """
    Solve an ordinary or generalized eigenvalue problem of a square matrix.

    Find eigenvalues w and right or left eigenvectors of a general matrix::

        a   vr[:,i] = w[i]        b   vr[:,i]
        a.H vl[:,i] = w[i].conj() b.H vl[:,i]

    where ``.H`` is the Hermitian conjugation.

    Parameters
    ----------
    a : (M, M) array_like
        A complex or real matrix whose eigenvalues and eigenvectors
        will be computed.
    b : (M, M) array_like, optional
        Right-hand side matrix in a generalized eigenvalue problem.
        Default is None, identity matrix is assumed.
    left : bool, optional
        Whether to calculate and return left eigenvectors.  Default is False.
    right : bool, optional
        Whether to calculate and return right eigenvectors.  Default is True.
    overwrite_a : bool, optional
        Whether to overwrite `a`; may improve performance.  Default is False.
    overwrite_b : bool, optional
        Whether to overwrite `b`; may improve performance.  Default is False.
    check_finite : bool, optional
        Whether to check that the input matrices contain only finite numbers.
        Disabling may give a performance gain, but may result in problems
        (crashes, non-termination) if the inputs do contain infinities or NaNs.
    homogeneous_eigvals : bool, optional
        If True, return the eigenvalues in homogeneous coordinates.
        In this case ``w`` is a (2, M) array so that::

            w[1,i] a vr[:,i] = w[0,i] b vr[:,i]

        Default is False.

    Returns
    -------
    w : (M,) or (2, M) double or complex ndarray
        The eigenvalues, each repeated according to its
        multiplicity. The shape is (M,) unless
        ``homogeneous_eigvals=True``.
    vl : (M, M) double or complex ndarray
        The normalized left eigenvector corresponding to the eigenvalue
        ``w[i]`` is the column vl[:,i]. Only returned if ``left=True``.
    vr : (M, M) double or complex ndarray
        The normalized right eigenvector corresponding to the eigenvalue
        ``w[i]`` is the column ``vr[:,i]``.  Only returned if ``right=True``.

    Raises
    ------
    LinAlgError
        If eigenvalue computation does not converge.

    See Also
    --------
    eigvals : eigenvalues of general arrays
    eigh : Eigenvalues and right eigenvectors for symmetric/Hermitian arrays.
    eig_banded : eigenvalues and right eigenvectors for symmetric/Hermitian
        band matrices
    eigh_tridiagonal : eigenvalues and right eiegenvectors for
        symmetric/Hermitian tridiagonal matrices

    Examples
    --------
    >>> import numpy as np
    >>> from scipy import linalg
    >>> a = np.array([[0., -1.], [1., 0.]])
    >>> linalg.eigvals(a)
    array([0.+1.j, 0.-1.j])

    >>> b = np.array([[0., 1.], [1., 1.]])
    >>> linalg.eigvals(a, b)
    array([ 1.+0.j, -1.+0.j])

    >>> a = np.array([[3., 0., 0.], [0., 8., 0.], [0., 0., 7.]])
    >>> linalg.eigvals(a, homogeneous_eigvals=True)
    array([[3.+0.j, 8.+0.j, 7.+0.j],
           [1.+0.j, 1.+0.j, 1.+0.j]])

    >>> a = np.array([[0., -1.], [1., 0.]])
    >>> linalg.eigvals(a) == linalg.eig(a)[0]
    array([ True,  True])
    >>> linalg.eig(a, left=True, right=False)[1] # normalized left eigenvector
    array([[-0.70710678+0.j        , -0.70710678-0.j        ],
           [-0.        +0.70710678j, -0.        -0.70710678j]])
    >>> linalg.eig(a, left=False, right=True)[1] # normalized right eigenvector
    array([[0.70710678+0.j        , 0.70710678-0.j        ],
           [0.        -0.70710678j, 0.        +0.70710678j]])



    """
    a1 = _asarray_validated(a, check_finite=check_finite)
    if len(a1.shape) != 2 or a1.shape[0] != a1.shape[1]:
        raise ValueError('expected square matrix')
    overwrite_a = overwrite_a or (_datacopied(a1, a))
    if b is not None:
        b1 = _asarray_validated(b, check_finite=check_finite)
        overwrite_b = overwrite_b or _datacopied(b1, b)
        if len(b1.shape) != 2 or b1.shape[0] != b1.shape[1]:
            raise ValueError('expected square matrix')
        if b1.shape != a1.shape:
            raise ValueError('a and b must have the same shape')
        return _geneig(a1, b1, left, right, overwrite_a, overwrite_b,
                       homogeneous_eigvals)

    geev, geev_lwork = get_lapack_funcs(('geev', 'geev_lwork'), (a1,))
    compute_vl, compute_vr = left, right

    lwork = _compute_lwork(geev_lwork, a1.shape[0],
                           compute_vl=compute_vl,
                           compute_vr=compute_vr)

    if geev.typecode in 'cz':
        w, vl, vr, info = geev(a1, lwork=lwork,
                               compute_vl=compute_vl,
                               compute_vr=compute_vr,
                               overwrite_a=overwrite_a)
        w = _make_eigvals(w, None, homogeneous_eigvals)
    else:
        wr, wi, vl, vr, info = geev(a1, lwork=lwork,
                                    compute_vl=compute_vl,
                                    compute_vr=compute_vr,
                                    overwrite_a=overwrite_a)
        t = {'f': 'F', 'd': 'D'}[wr.dtype.char]
        w = wr + _I * wi
        w = _make_eigvals(w, None, homogeneous_eigvals)

    _check_info(info, 'eig algorithm (geev)',
                positive='did not converge (only eigenvalues '
                         'with order >= %d have converged)')

    only_real = numpy.all(w.imag == 0.0)
    if not (geev.typecode in 'cz' or only_real):
        t = w.dtype.char
        if left:
            vl = _make_complex_eigvecs(w, vl, t)
        if right:
            vr = _make_complex_eigvecs(w, vr, t)
    if not (left or right):
        return w
    if left:
        if right:
            return w, vl, vr
        return w, vl
    return w, vr


def eigh(a, b=None, lower=True, eigvals_only=False, overwrite_a=False,
         overwrite_b=False, turbo=False, eigvals=None, type=1,
         check_finite=True, subset_by_index=None, subset_by_value=None,
         driver=None):
    """
    Solve a standard or generalized eigenvalue problem for a complex
    Hermitian or real symmetric matrix.

    Find eigenvalues array ``w`` and optionally eigenvectors array ``v`` of
    array ``a``, where ``b`` is positive definite such that for every
    eigenvalue λ (i-th entry of w) and its eigenvector ``vi`` (i-th column of
    ``v``) satisfies::

                      a @ vi = λ * b @ vi
        vi.conj().T @ a @ vi = λ
        vi.conj().T @ b @ vi = 1

    In the standard problem, ``b`` is assumed to be the identity matrix.

    Parameters
    ----------
    a : (M, M) array_like
        A complex Hermitian or real symmetric matrix whose eigenvalues and
        eigenvectors will be computed.
    b : (M, M) array_like, optional
        A complex Hermitian or real symmetric definite positive matrix in.
        If omitted, identity matrix is assumed.
    lower : bool, optional
        Whether the pertinent array data is taken from the lower or upper
        triangle of ``a`` and, if applicable, ``b``. (Default: lower)
    eigvals_only : bool, optional
        Whether to calculate only eigenvalues and no eigenvectors.
        (Default: both are calculated)
    subset_by_index : iterable, optional
        If provided, this two-element iterable defines the start and the end
        indices of the desired eigenvalues (ascending order and 0-indexed).
        To return only the second smallest to fifth smallest eigenvalues,
        ``[1, 4]`` is used. ``[n-3, n-1]`` returns the largest three. Only
        available with "evr", "evx", and "gvx" drivers. The entries are
        directly converted to integers via ``int()``.
    subset_by_value : iterable, optional
        If provided, this two-element iterable defines the half-open interval
        ``(a, b]`` that, if any, only the eigenvalues between these values
        are returned. Only available with "evr", "evx", and "gvx" drivers. Use
        ``np.inf`` for the unconstrained ends.
    driver : str, optional
        Defines which LAPACK driver should be used. Valid options are "ev",
        "evd", "evr", "evx" for standard problems and "gv", "gvd", "gvx" for
        generalized (where b is not None) problems. See the Notes section.
        The default for standard problems is "evr". For generalized problems,
        "gvd" is used for full set, and "gvx" for subset requested cases.
    type : int, optional
        For the generalized problems, this keyword specifies the problem type
        to be solved for ``w`` and ``v`` (only takes 1, 2, 3 as possible
        inputs)::

            1 =>     a @ v = w @ b @ v
            2 => a @ b @ v = w @ v
            3 => b @ a @ v = w @ v

        This keyword is ignored for standard problems.
    overwrite_a : bool, optional
        Whether to overwrite data in ``a`` (may improve performance). Default
        is False.
    overwrite_b : bool, optional
        Whether to overwrite data in ``b`` (may improve performance). Default
        is False.
    check_finite : bool, optional
        Whether to check that the input matrices contain only finite numbers.
        Disabling may give a performance gain, but may result in problems
        (crashes, non-termination) if the inputs do contain infinities or NaNs.
    turbo : bool, optional, deprecated
            .. deprecated:: 1.5.0
                `eigh` keyword argument `turbo` is deprecated in favour of
                ``driver=gvd`` keyword instead and will be removed in SciPy
                1.12.0.
    eigvals : tuple (lo, hi), optional, deprecated
            .. deprecated:: 1.5.0
                `eigh` keyword argument `eigvals` is deprecated in favour of
                `subset_by_index` keyword instead and will be removed in SciPy
                1.12.0.

    Returns
    -------
    w : (N,) ndarray
        The N (1<=N<=M) selected eigenvalues, in ascending order, each
        repeated according to its multiplicity.
    v : (M, N) ndarray
        (if ``eigvals_only == False``)

    Raises
    ------
    LinAlgError
        If eigenvalue computation does not converge, an error occurred, or
        b matrix is not definite positive. Note that if input matrices are
        not symmetric or Hermitian, no error will be reported but results will
        be wrong.

    See Also
    --------
    eigvalsh : eigenvalues of symmetric or Hermitian arrays
    eig : eigenvalues and right eigenvectors for non-symmetric arrays
    eigh_tridiagonal : eigenvalues and right eiegenvectors for
        symmetric/Hermitian tridiagonal matrices

    Notes
    -----
    This function does not check the input array for being Hermitian/symmetric
    in order to allow for representing arrays with only their upper/lower
    triangular parts. Also, note that even though not taken into account,
    finiteness check applies to the whole array and unaffected by "lower"
    keyword.

    This function uses LAPACK drivers for computations in all possible keyword
    combinations, prefixed with ``sy`` if arrays are real and ``he`` if
    complex, e.g., a float array with "evr" driver is solved via
    "syevr", complex arrays with "gvx" driver problem is solved via "hegvx"
    etc.

    As a brief summary, the slowest and the most robust driver is the
    classical ``<sy/he>ev`` which uses symmetric QR. ``<sy/he>evr`` is seen as
    the optimal choice for the most general cases. However, there are certain
    occasions that ``<sy/he>evd`` computes faster at the expense of more
    memory usage. ``<sy/he>evx``, while still being faster than ``<sy/he>ev``,
    often performs worse than the rest except when very few eigenvalues are
    requested for large arrays though there is still no performance guarantee.


    For the generalized problem, normalization with respect to the given
    type argument::

            type 1 and 3 :      v.conj().T @ a @ v = w
            type 2       : inv(v).conj().T @ a @ inv(v) = w

            type 1 or 2  :      v.conj().T @ b @ v  = I
            type 3       : v.conj().T @ inv(b) @ v  = I


    Examples
    --------
    >>> import numpy as np
    >>> from scipy.linalg import eigh
    >>> A = np.array([[6, 3, 1, 5], [3, 0, 5, 1], [1, 5, 6, 2], [5, 1, 2, 2]])
    >>> w, v = eigh(A)
    >>> np.allclose(A @ v - v @ np.diag(w), np.zeros((4, 4)))
    True

    Request only the eigenvalues

    >>> w = eigh(A, eigvals_only=True)

    Request eigenvalues that are less than 10.

    >>> A = np.array([[34, -4, -10, -7, 2],
    ...               [-4, 7, 2, 12, 0],
    ...               [-10, 2, 44, 2, -19],
    ...               [-7, 12, 2, 79, -34],
    ...               [2, 0, -19, -34, 29]])
    >>> eigh(A, eigvals_only=True, subset_by_value=[-np.inf, 10])
    array([6.69199443e-07, 9.11938152e+00])

    Request the second smallest eigenvalue and its eigenvector

    >>> w, v = eigh(A, subset_by_index=[1, 1])
    >>> w
    array([9.11938152])
    >>> v.shape  # only a single column is returned
    (5, 1)

    """
    if turbo:
        warnings.warn("Keyword argument 'turbo' is deprecated in favour of '"
                      "driver=gvd' keyword instead and will be removed in "
                      "SciPy 1.12.0.",
                      DeprecationWarning, stacklevel=2)
    if eigvals:
        warnings.warn("Keyword argument 'eigvals' is deprecated in favour of "
                      "'subset_by_index' keyword instead and will be removed "
                      "in SciPy 1.12.0.",
                      DeprecationWarning, stacklevel=2)

    # set lower
    uplo = 'L' if lower else 'U'
    # Set job for Fortran routines
    _job = 'N' if eigvals_only else 'V'

    drv_str = [None, "ev", "evd", "evr", "evx", "gv", "gvd", "gvx"]
    if driver not in drv_str:
        raise ValueError('"{}" is unknown. Possible values are "None", "{}".'
                         ''.format(driver, '", "'.join(drv_str[1:])))

    a1 = _asarray_validated(a, check_finite=check_finite)
    if len(a1.shape) != 2 or a1.shape[0] != a1.shape[1]:
        raise ValueError('expected square "a" matrix')
    overwrite_a = overwrite_a or (_datacopied(a1, a))
    cplx = True if iscomplexobj(a1) else False
    n = a1.shape[0]
    drv_args = {'overwrite_a': overwrite_a}

    if b is not None:
        b1 = _asarray_validated(b, check_finite=check_finite)
        overwrite_b = overwrite_b or _datacopied(b1, b)
        if len(b1.shape) != 2 or b1.shape[0] != b1.shape[1]:
            raise ValueError('expected square "b" matrix')

        if b1.shape != a1.shape:
            raise ValueError("wrong b dimensions {}, should "
                             "be {}".format(b1.shape, a1.shape))

        if type not in [1, 2, 3]:
            raise ValueError('"type" keyword only accepts 1, 2, and 3.')

        cplx = True if iscomplexobj(b1) else (cplx or False)
        drv_args.update({'overwrite_b': overwrite_b, 'itype': type})

    # backwards-compatibility handling
    subset_by_index = subset_by_index if (eigvals is None) else eigvals

    subset = (subset_by_index is not None) or (subset_by_value is not None)

    # Both subsets can't be given
    if subset_by_index and subset_by_value:
        raise ValueError('Either index or value subset can be requested.')

    # Take turbo into account if all conditions are met otherwise ignore
    if turbo and b is not None:
        driver = 'gvx' if subset else 'gvd'

    # Check indices if given
    if subset_by_index:
        lo, hi = [int(x) for x in subset_by_index]
        if not (0 <= lo <= hi < n):
            raise ValueError('Requested eigenvalue indices are not valid. '
                             'Valid range is [0, {}] and start <= end, but '
                             'start={}, end={} is given'.format(n-1, lo, hi))
        # fortran is 1-indexed
        drv_args.update({'range': 'I', 'il': lo + 1, 'iu': hi + 1})

    if subset_by_value:
        lo, hi = subset_by_value
        if not (-inf <= lo < hi <= inf):
            raise ValueError('Requested eigenvalue bounds are not valid. '
                             'Valid range is (-inf, inf) and low < high, but '
                             'low={}, high={} is given'.format(lo, hi))

        drv_args.update({'range': 'V', 'vl': lo, 'vu': hi})

    # fix prefix for lapack routines
    pfx = 'he' if cplx else 'sy'

    # decide on the driver if not given
    # first early exit on incompatible choice
    if driver:
        if b is None and (driver in ["gv", "gvd", "gvx"]):
            raise ValueError('{} requires input b array to be supplied '
                             'for generalized eigenvalue problems.'
                             ''.format(driver))
        if (b is not None) and (driver in ['ev', 'evd', 'evr', 'evx']):
            raise ValueError('"{}" does not accept input b array '
                             'for standard eigenvalue problems.'
                             ''.format(driver))
        if subset and (driver in ["ev", "evd", "gv", "gvd"]):
            raise ValueError('"{}" cannot compute subsets of eigenvalues'
                             ''.format(driver))

    # Default driver is evr and gvd
    else:
        driver = "evr" if b is None else ("gvx" if subset else "gvd")

    lwork_spec = {
                  'syevd': ['lwork', 'liwork'],
                  'syevr': ['lwork', 'liwork'],
                  'heevd': ['lwork', 'liwork', 'lrwork'],
                  'heevr': ['lwork', 'lrwork', 'liwork'],
                  }

    if b is None:  # Standard problem
        drv, drvlw = get_lapack_funcs((pfx + driver, pfx+driver+'_lwork'),
                                      [a1])
        clw_args = {'n': n, 'lower': lower}
        if driver == 'evd':
            clw_args.update({'compute_v': 0 if _job == "N" else 1})

        lw = _compute_lwork(drvlw, **clw_args)
        # Multiple lwork vars
        if isinstance(lw, tuple):
            lwork_args = dict(zip(lwork_spec[pfx+driver], lw))
        else:
            lwork_args = {'lwork': lw}

        drv_args.update({'lower': lower, 'compute_v': 0 if _job == "N" else 1})
        w, v, *other_args, info = drv(a=a1, **drv_args, **lwork_args)

    else:  # Generalized problem
        # 'gvd' doesn't have lwork query
        if driver == "gvd":
            drv = get_lapack_funcs(pfx + "gvd", [a1, b1])
            lwork_args = {}
        else:
            drv, drvlw = get_lapack_funcs((pfx + driver, pfx+driver+'_lwork'),
                                          [a1, b1])
            # generalized drivers use uplo instead of lower
            lw = _compute_lwork(drvlw, n, uplo=uplo)
            lwork_args = {'lwork': lw}

        drv_args.update({'uplo': uplo, 'jobz': _job})

        w, v, *other_args, info = drv(a=a1, b=b1, **drv_args, **lwork_args)

    # m is always the first extra argument
    w = w[:other_args[0]] if subset else w
    v = v[:, :other_args[0]] if (subset and not eigvals_only) else v

    # Check if we had a  successful exit
    if info == 0:
        if eigvals_only:
            return w
        else:
            return w, v
    else:
        if info < -1:
            raise LinAlgError('Illegal value in argument {} of internal {}'
                              ''.format(-info, drv.typecode + pfx + driver))
        elif info > n:
            raise LinAlgError('The leading minor of order {} of B is not '
                              'positive definite. The factorization of B '
                              'could not be completed and no eigenvalues '
                              'or eigenvectors were computed.'.format(info-n))
        else:
            drv_err = {'ev': 'The algorithm failed to converge; {} '
                             'off-diagonal elements of an intermediate '
                             'tridiagonal form did not converge to zero.',
                       'evx': '{} eigenvectors failed to converge.',
                       'evd': 'The algorithm failed to compute an eigenvalue '
                              'while working on the submatrix lying in rows '
                              'and columns {0}/{1} through mod({0},{1}).',
                       'evr': 'Internal Error.'
                       }
            if driver in ['ev', 'gv']:
                msg = drv_err['ev'].format(info)
            elif driver in ['evx', 'gvx']:
                msg = drv_err['evx'].format(info)
            elif driver in ['evd', 'gvd']:
                if eigvals_only:
                    msg = drv_err['ev'].format(info)
                else:
                    msg = drv_err['evd'].format(info, n+1)
            else:
                msg = drv_err['evr']

            raise LinAlgError(msg)


_conv_dict = {0: 0, 1: 1, 2: 2,
              'all': 0, 'value': 1, 'index': 2,
              'a': 0, 'v': 1, 'i': 2}


def _check_select(select, select_range, max_ev, max_len):
    """Check that select is valid, convert to Fortran style."""
    if isinstance(select, str):
        select = select.lower()
    try:
        select = _conv_dict[select]
    except KeyError as e:
        raise ValueError('invalid argument for select') from e
    vl, vu = 0., 1.
    il = iu = 1
    if select != 0:  # (non-all)
        sr = asarray(select_range)
        if sr.ndim != 1 or sr.size != 2 or sr[1] < sr[0]:
            raise ValueError('select_range must be a 2-element array-like '
                             'in nondecreasing order')
        if select == 1:  # (value)
            vl, vu = sr
            if max_ev == 0:
                max_ev = max_len
        else:  # 2 (index)
            if sr.dtype.char.lower() not in 'hilqp':
                raise ValueError('when using select="i", select_range must '
                                 'contain integers, got dtype %s (%s)'
                                 % (sr.dtype, sr.dtype.char))
            # translate Python (0 ... N-1) into Fortran (1 ... N) with + 1
            il, iu = sr + 1
            if min(il, iu) < 1 or max(il, iu) > max_len:
                raise ValueError('select_range out of bounds')
            max_ev = iu - il + 1
    return select, vl, vu, il, iu, max_ev


def eig_banded(a_band, lower=False, eigvals_only=False, overwrite_a_band=False,
               select='a', select_range=None, max_ev=0, check_finite=True):
    """
    Solve real symmetric or complex Hermitian band matrix eigenvalue problem.

    Find eigenvalues w and optionally right eigenvectors v of a::

        a v[:,i] = w[i] v[:,i]
        v.H v    = identity

    The matrix a is stored in a_band either in lower diagonal or upper
    diagonal ordered form:

        a_band[u + i - j, j] == a[i,j]        (if upper form; i <= j)
        a_band[    i - j, j] == a[i,j]        (if lower form; i >= j)

    where u is the number of bands above the diagonal.

    Example of a_band (shape of a is (6,6), u=2)::

        upper form:
        *   *   a02 a13 a24 a35
        *   a01 a12 a23 a34 a45
        a00 a11 a22 a33 a44 a55

        lower form:
        a00 a11 a22 a33 a44 a55
        a10 a21 a32 a43 a54 *
        a20 a31 a42 a53 *   *

    Cells marked with * are not used.

    Parameters
    ----------
    a_band : (u+1, M) array_like
        The bands of the M by M matrix a.
    lower : bool, optional
        Is the matrix in the lower form. (Default is upper form)
    eigvals_only : bool, optional
        Compute only the eigenvalues and no eigenvectors.
        (Default: calculate also eigenvectors)
    overwrite_a_band : bool, optional
        Discard data in a_band (may enhance performance)
    select : {'a', 'v', 'i'}, optional
        Which eigenvalues to calculate

        ======  ========================================
        select  calculated
        ======  ========================================
        'a'     All eigenvalues
        'v'     Eigenvalues in the interval (min, max]
        'i'     Eigenvalues with indices min <= i <= max
        ======  ========================================
    select_range : (min, max), optional
        Range of selected eigenvalues
    max_ev : int, optional
        For select=='v', maximum number of eigenvalues expected.
        For other values of select, has no meaning.

        In doubt, leave this parameter untouched.

    check_finite : bool, optional
        Whether to check that the input matrix contains only finite numbers.
        Disabling may give a performance gain, but may result in problems
        (crashes, non-termination) if the inputs do contain infinities or NaNs.

    Returns
    -------
    w : (M,) ndarray
        The eigenvalues, in ascending order, each repeated according to its
        multiplicity.
    v : (M, M) float or complex ndarray
        The normalized eigenvector corresponding to the eigenvalue w[i] is
        the column v[:,i].

    Raises
    ------
    LinAlgError
        If eigenvalue computation does not converge.

    See Also
    --------
    eigvals_banded : eigenvalues for symmetric/Hermitian band matrices
    eig : eigenvalues and right eigenvectors of general arrays.
    eigh : eigenvalues and right eigenvectors for symmetric/Hermitian arrays
    eigh_tridiagonal : eigenvalues and right eigenvectors for
        symmetric/Hermitian tridiagonal matrices

    Examples
    --------
    >>> import numpy as np
    >>> from scipy.linalg import eig_banded
    >>> A = np.array([[1, 5, 2, 0], [5, 2, 5, 2], [2, 5, 3, 5], [0, 2, 5, 4]])
    >>> Ab = np.array([[1, 2, 3, 4], [5, 5, 5, 0], [2, 2, 0, 0]])
    >>> w, v = eig_banded(Ab, lower=True)
    >>> np.allclose(A @ v - v @ np.diag(w), np.zeros((4, 4)))
    True
    >>> w = eig_banded(Ab, lower=True, eigvals_only=True)
    >>> w
    array([-4.26200532, -2.22987175,  3.95222349, 12.53965359])

    Request only the eigenvalues between ``[-3, 4]``

    >>> w, v = eig_banded(Ab, lower=True, select='v', select_range=[-3, 4])
    >>> w
    array([-2.22987175,  3.95222349])

    """
    if eigvals_only or overwrite_a_band:
        a1 = _asarray_validated(a_band, check_finite=check_finite)
        overwrite_a_band = overwrite_a_band or (_datacopied(a1, a_band))
    else:
        a1 = array(a_band)
        if issubclass(a1.dtype.type, inexact) and not isfinite(a1).all():
            raise ValueError("array must not contain infs or NaNs")
        overwrite_a_band = 1

    if len(a1.shape) != 2:
        raise ValueError('expected a 2-D array')
    select, vl, vu, il, iu, max_ev = _check_select(
        select, select_range, max_ev, a1.shape[1])
    del select_range
    if select == 0:
        if a1.dtype.char in 'GFD':
            # FIXME: implement this somewhen, for now go with builtin values
            # FIXME: calc optimal lwork by calling ?hbevd(lwork=-1)
            #        or by using calc_lwork.f ???
            # lwork = calc_lwork.hbevd(bevd.typecode, a1.shape[0], lower)
            internal_name = 'hbevd'
        else:  # a1.dtype.char in 'fd':
            # FIXME: implement this somewhen, for now go with builtin values
            #         see above
            # lwork = calc_lwork.sbevd(bevd.typecode, a1.shape[0], lower)
            internal_name = 'sbevd'
        bevd, = get_lapack_funcs((internal_name,), (a1,))
        w, v, info = bevd(a1, compute_v=not eigvals_only,
                          lower=lower, overwrite_ab=overwrite_a_band)
    else:  # select in [1, 2]
        if eigvals_only:
            max_ev = 1
        # calculate optimal abstol for dsbevx (see manpage)
        if a1.dtype.char in 'fF':  # single precision
            lamch, = get_lapack_funcs(('lamch',), (array(0, dtype='f'),))
        else:
            lamch, = get_lapack_funcs(('lamch',), (array(0, dtype='d'),))
        abstol = 2 * lamch('s')
        if a1.dtype.char in 'GFD':
            internal_name = 'hbevx'
        else:  # a1.dtype.char in 'gfd'
            internal_name = 'sbevx'
        bevx, = get_lapack_funcs((internal_name,), (a1,))
        w, v, m, ifail, info = bevx(
            a1, vl, vu, il, iu, compute_v=not eigvals_only, mmax=max_ev,
            range=select, lower=lower, overwrite_ab=overwrite_a_band,
            abstol=abstol)
        # crop off w and v
        w = w[:m]
        if not eigvals_only:
            v = v[:, :m]
    _check_info(info, internal_name)

    if eigvals_only:
        return w
    return w, v


def eigvals(a, b=None, overwrite_a=False, check_finite=True,
            homogeneous_eigvals=False):
    """
    Compute eigenvalues from an ordinary or generalized eigenvalue problem.

    Find eigenvalues of a general matrix::

        a   vr[:,i] = w[i]        b   vr[:,i]

    Parameters
    ----------
    a : (M, M) array_like
        A complex or real matrix whose eigenvalues and eigenvectors
        will be computed.
    b : (M, M) array_like, optional
        Right-hand side matrix in a generalized eigenvalue problem.
        If omitted, identity matrix is assumed.
    overwrite_a : bool, optional
        Whether to overwrite data in a (may improve performance)
    check_finite : bool, optional
        Whether to check that the input matrices contain only finite numbers.
        Disabling may give a performance gain, but may result in problems
        (crashes, non-termination) if the inputs do contain infinities
        or NaNs.
    homogeneous_eigvals : bool, optional
        If True, return the eigenvalues in homogeneous coordinates.
        In this case ``w`` is a (2, M) array so that::

            w[1,i] a vr[:,i] = w[0,i] b vr[:,i]

        Default is False.

    Returns
    -------
    w : (M,) or (2, M) double or complex ndarray
        The eigenvalues, each repeated according to its multiplicity
        but not in any specific order. The shape is (M,) unless
        ``homogeneous_eigvals=True``.

    Raises
    ------
    LinAlgError
        If eigenvalue computation does not converge

    See Also
    --------
    eig : eigenvalues and right eigenvectors of general arrays.
    eigvalsh : eigenvalues of symmetric or Hermitian arrays
    eigvals_banded : eigenvalues for symmetric/Hermitian band matrices
    eigvalsh_tridiagonal : eigenvalues of symmetric/Hermitian tridiagonal
        matrices

    Examples
    --------
    >>> import numpy as np
    >>> from scipy import linalg
    >>> a = np.array([[0., -1.], [1., 0.]])
    >>> linalg.eigvals(a)
    array([0.+1.j, 0.-1.j])

    >>> b = np.array([[0., 1.], [1., 1.]])
    >>> linalg.eigvals(a, b)
    array([ 1.+0.j, -1.+0.j])

    >>> a = np.array([[3., 0., 0.], [0., 8., 0.], [0., 0., 7.]])
    >>> linalg.eigvals(a, homogeneous_eigvals=True)
    array([[3.+0.j, 8.+0.j, 7.+0.j],
           [1.+0.j, 1.+0.j, 1.+0.j]])

    """
    return eig(a, b=b, left=0, right=0, overwrite_a=overwrite_a,
               check_finite=check_finite,
               homogeneous_eigvals=homogeneous_eigvals)


def eigvalsh(a, b=None, lower=True, overwrite_a=False,
             overwrite_b=False, turbo=False, eigvals=None, type=1,
             check_finite=True, subset_by_index=None, subset_by_value=None,
             driver=None):
    """
    Solves a standard or generalized eigenvalue problem for a complex
    Hermitian or real symmetric matrix.

    Find eigenvalues array ``w`` of array ``a``, where ``b`` is positive
    definite such that for every eigenvalue λ (i-th entry of w) and its
    eigenvector vi (i-th column of v) satisfies::

                      a @ vi = λ * b @ vi
        vi.conj().T @ a @ vi = λ
        vi.conj().T @ b @ vi = 1

    In the standard problem, b is assumed to be the identity matrix.

    Parameters
    ----------
    a : (M, M) array_like
        A complex Hermitian or real symmetric matrix whose eigenvalues will
        be computed.
    b : (M, M) array_like, optional
        A complex Hermitian or real symmetric definite positive matrix in.
        If omitted, identity matrix is assumed.
    lower : bool, optional
        Whether the pertinent array data is taken from the lower or upper
        triangle of ``a`` and, if applicable, ``b``. (Default: lower)
    overwrite_a : bool, optional
        Whether to overwrite data in ``a`` (may improve performance). Default
        is False.
    overwrite_b : bool, optional
        Whether to overwrite data in ``b`` (may improve performance). Default
        is False.
    type : int, optional
        For the generalized problems, this keyword specifies the problem type
        to be solved for ``w`` and ``v`` (only takes 1, 2, 3 as possible
        inputs)::

            1 =>     a @ v = w @ b @ v
            2 => a @ b @ v = w @ v
            3 => b @ a @ v = w @ v

        This keyword is ignored for standard problems.
    check_finite : bool, optional
        Whether to check that the input matrices contain only finite numbers.
        Disabling may give a performance gain, but may result in problems
        (crashes, non-termination) if the inputs do contain infinities or NaNs.
    subset_by_index : iterable, optional
        If provided, this two-element iterable defines the start and the end
        indices of the desired eigenvalues (ascending order and 0-indexed).
        To return only the second smallest to fifth smallest eigenvalues,
        ``[1, 4]`` is used. ``[n-3, n-1]`` returns the largest three. Only
        available with "evr", "evx", and "gvx" drivers. The entries are
        directly converted to integers via ``int()``.
    subset_by_value : iterable, optional
        If provided, this two-element iterable defines the half-open interval
        ``(a, b]`` that, if any, only the eigenvalues between these values
        are returned. Only available with "evr", "evx", and "gvx" drivers. Use
        ``np.inf`` for the unconstrained ends.
    driver : str, optional
        Defines which LAPACK driver should be used. Valid options are "ev",
        "evd", "evr", "evx" for standard problems and "gv", "gvd", "gvx" for
        generalized (where b is not None) problems. See the Notes section of
        `scipy.linalg.eigh`.
    turbo : bool, optional, deprecated
        .. deprecated:: 1.5.0
            'eigvalsh' keyword argument `turbo` is deprecated in favor of
            ``driver=gvd`` option and will be removed in SciPy 1.12.0.

    eigvals : tuple (lo, hi), optional
        .. deprecated:: 1.5.0
            'eigvalsh' keyword argument `eigvals` is deprecated in favor of
            `subset_by_index` option and will be removed in SciPy 1.12.0.

    Returns
    -------
    w : (N,) ndarray
        The ``N`` (``1<=N<=M``) selected eigenvalues, in ascending order, each
        repeated according to its multiplicity.

    Raises
    ------
    LinAlgError
        If eigenvalue computation does not converge, an error occurred, or
        b matrix is not definite positive. Note that if input matrices are
        not symmetric or Hermitian, no error will be reported but results will
        be wrong.

    See Also
    --------
    eigh : eigenvalues and right eigenvectors for symmetric/Hermitian arrays
    eigvals : eigenvalues of general arrays
    eigvals_banded : eigenvalues for symmetric/Hermitian band matrices
    eigvalsh_tridiagonal : eigenvalues of symmetric/Hermitian tridiagonal
        matrices

    Notes
    -----
    This function does not check the input array for being Hermitian/symmetric
    in order to allow for representing arrays with only their upper/lower
    triangular parts.

    This function serves as a one-liner shorthand for `scipy.linalg.eigh` with
    the option ``eigvals_only=True`` to get the eigenvalues and not the
    eigenvectors. Here it is kept as a legacy convenience. It might be
    beneficial to use the main function to have full control and to be a bit
    more pythonic.

    Examples
    --------
    For more examples see `scipy.linalg.eigh`.

    >>> import numpy as np
    >>> from scipy.linalg import eigvalsh
    >>> A = np.array([[6, 3, 1, 5], [3, 0, 5, 1], [1, 5, 6, 2], [5, 1, 2, 2]])
    >>> w = eigvalsh(A)
    >>> w
    array([-3.74637491, -0.76263923,  6.08502336, 12.42399079])

    """
    return eigh(a, b=b, lower=lower, eigvals_only=True,
                overwrite_a=overwrite_a, overwrite_b=overwrite_b,
                turbo=turbo, eigvals=eigvals, type=type,
                check_finite=check_finite, subset_by_index=subset_by_index,
                subset_by_value=subset_by_value, driver=driver)


def eigvals_banded(a_band, lower=False, overwrite_a_band=False,
                   select='a', select_range=None, check_finite=True):
    """
    Solve real symmetric or complex Hermitian band matrix eigenvalue problem.

    Find eigenvalues w of a::

        a v[:,i] = w[i] v[:,i]
        v.H v    = identity

    The matrix a is stored in a_band either in lower diagonal or upper
    diagonal ordered form:

        a_band[u + i - j, j] == a[i,j]        (if upper form; i <= j)
        a_band[    i - j, j] == a[i,j]        (if lower form; i >= j)

    where u is the number of bands above the diagonal.

    Example of a_band (shape of a is (6,6), u=2)::

        upper form:
        *   *   a02 a13 a24 a35
        *   a01 a12 a23 a34 a45
        a00 a11 a22 a33 a44 a55

        lower form:
        a00 a11 a22 a33 a44 a55
        a10 a21 a32 a43 a54 *
        a20 a31 a42 a53 *   *

    Cells marked with * are not used.

    Parameters
    ----------
    a_band : (u+1, M) array_like
        The bands of the M by M matrix a.
    lower : bool, optional
        Is the matrix in the lower form. (Default is upper form)
    overwrite_a_band : bool, optional
        Discard data in a_band (may enhance performance)
    select : {'a', 'v', 'i'}, optional
        Which eigenvalues to calculate

        ======  ========================================
        select  calculated
        ======  ========================================
        'a'     All eigenvalues
        'v'     Eigenvalues in the interval (min, max]
        'i'     Eigenvalues with indices min <= i <= max
        ======  ========================================
    select_range : (min, max), optional
        Range of selected eigenvalues
    check_finite : bool, optional
        Whether to check that the input matrix contains only finite numbers.
        Disabling may give a performance gain, but may result in problems
        (crashes, non-termination) if the inputs do contain infinities or NaNs.

    Returns
    -------
    w : (M,) ndarray
        The eigenvalues, in ascending order, each repeated according to its
        multiplicity.

    Raises
    ------
    LinAlgError
        If eigenvalue computation does not converge.

    See Also
    --------
    eig_banded : eigenvalues and right eigenvectors for symmetric/Hermitian
        band matrices
    eigvalsh_tridiagonal : eigenvalues of symmetric/Hermitian tridiagonal
        matrices
    eigvals : eigenvalues of general arrays
    eigh : eigenvalues and right eigenvectors for symmetric/Hermitian arrays
    eig : eigenvalues and right eigenvectors for non-symmetric arrays

    Examples
    --------
    >>> import numpy as np
    >>> from scipy.linalg import eigvals_banded
    >>> A = np.array([[1, 5, 2, 0], [5, 2, 5, 2], [2, 5, 3, 5], [0, 2, 5, 4]])
    >>> Ab = np.array([[1, 2, 3, 4], [5, 5, 5, 0], [2, 2, 0, 0]])
    >>> w = eigvals_banded(Ab, lower=True)
    >>> w
    array([-4.26200532, -2.22987175,  3.95222349, 12.53965359])
    """
    return eig_banded(a_band, lower=lower, eigvals_only=1,
                      overwrite_a_band=overwrite_a_band, select=select,
                      select_range=select_range, check_finite=check_finite)


def eigvalsh_tridiagonal(d, e, select='a', select_range=None,
                         check_finite=True, tol=0., lapack_driver='auto'):
    """
    Solve eigenvalue problem for a real symmetric tridiagonal matrix.

    Find eigenvalues `w` of ``a``::

        a v[:,i] = w[i] v[:,i]
        v.H v    = identity

    For a real symmetric matrix ``a`` with diagonal elements `d` and
    off-diagonal elements `e`.

    Parameters
    ----------
    d : ndarray, shape (ndim,)
        The diagonal elements of the array.
    e : ndarray, shape (ndim-1,)
        The off-diagonal elements of the array.
    select : {'a', 'v', 'i'}, optional
        Which eigenvalues to calculate

        ======  ========================================
        select  calculated
        ======  ========================================
        'a'     All eigenvalues
        'v'     Eigenvalues in the interval (min, max]
        'i'     Eigenvalues with indices min <= i <= max
        ======  ========================================
    select_range : (min, max), optional
        Range of selected eigenvalues
    check_finite : bool, optional
        Whether to check that the input matrix contains only finite numbers.
        Disabling may give a performance gain, but may result in problems
        (crashes, non-termination) if the inputs do contain infinities or NaNs.
    tol : float
        The absolute tolerance to which each eigenvalue is required
        (only used when ``lapack_driver='stebz'``).
        An eigenvalue (or cluster) is considered to have converged if it
        lies in an interval of this width. If <= 0. (default),
        the value ``eps*|a|`` is used where eps is the machine precision,
        and ``|a|`` is the 1-norm of the matrix ``a``.
    lapack_driver : str
        LAPACK function to use, can be 'auto', 'stemr', 'stebz',  'sterf',
        or 'stev'. When 'auto' (default), it will use 'stemr' if ``select='a'``
        and 'stebz' otherwise. 'sterf' and 'stev' can only be used when
        ``select='a'``.

    Returns
    -------
    w : (M,) ndarray
        The eigenvalues, in ascending order, each repeated according to its
        multiplicity.

    Raises
    ------
    LinAlgError
        If eigenvalue computation does not converge.

    See Also
    --------
    eigh_tridiagonal : eigenvalues and right eiegenvectors for
        symmetric/Hermitian tridiagonal matrices

    Examples
    --------
    >>> import numpy as np
    >>> from scipy.linalg import eigvalsh_tridiagonal, eigvalsh
    >>> d = 3*np.ones(4)
    >>> e = -1*np.ones(3)
    >>> w = eigvalsh_tridiagonal(d, e)
    >>> A = np.diag(d) + np.diag(e, k=1) + np.diag(e, k=-1)
    >>> w2 = eigvalsh(A)  # Verify with other eigenvalue routines
    >>> np.allclose(w - w2, np.zeros(4))
    True
    """
    return eigh_tridiagonal(
        d, e, eigvals_only=True, select=select, select_range=select_range,
        check_finite=check_finite, tol=tol, lapack_driver=lapack_driver)


def eigh_tridiagonal(d, e, eigvals_only=False, select='a', select_range=None,
                     check_finite=True, tol=0., lapack_driver='auto'):
    """
    Solve eigenvalue problem for a real symmetric tridiagonal matrix.

    Find eigenvalues `w` and optionally right eigenvectors `v` of ``a``::

        a v[:,i] = w[i] v[:,i]
        v.H v    = identity

    For a real symmetric matrix ``a`` with diagonal elements `d` and
    off-diagonal elements `e`.

    Parameters
    ----------
    d : ndarray, shape (ndim,)
        The diagonal elements of the array.
    e : ndarray, shape (ndim-1,)
        The off-diagonal elements of the array.
    select : {'a', 'v', 'i'}, optional
        Which eigenvalues to calculate

        ======  ========================================
        select  calculated
        ======  ========================================
        'a'     All eigenvalues
        'v'     Eigenvalues in the interval (min, max]
        'i'     Eigenvalues with indices min <= i <= max
        ======  ========================================
    select_range : (min, max), optional
        Range of selected eigenvalues
    check_finite : bool, optional
        Whether to check that the input matrix contains only finite numbers.
        Disabling may give a performance gain, but may result in problems
        (crashes, non-termination) if the inputs do contain infinities or NaNs.
    tol : float
        The absolute tolerance to which each eigenvalue is required
        (only used when 'stebz' is the `lapack_driver`).
        An eigenvalue (or cluster) is considered to have converged if it
        lies in an interval of this width. If <= 0. (default),
        the value ``eps*|a|`` is used where eps is the machine precision,
        and ``|a|`` is the 1-norm of the matrix ``a``.
    lapack_driver : str
        LAPACK function to use, can be 'auto', 'stemr', 'stebz', 'sterf',
        or 'stev'. When 'auto' (default), it will use 'stemr' if ``select='a'``
        and 'stebz' otherwise. When 'stebz' is used to find the eigenvalues and
        ``eigvals_only=False``, then a second LAPACK call (to ``?STEIN``) is
        used to find the corresponding eigenvectors. 'sterf' can only be
        used when ``eigvals_only=True`` and ``select='a'``. 'stev' can only
        be used when ``select='a'``.

    Returns
    -------
    w : (M,) ndarray
        The eigenvalues, in ascending order, each repeated according to its
        multiplicity.
    v : (M, M) ndarray
        The normalized eigenvector corresponding to the eigenvalue ``w[i]`` is
        the column ``v[:,i]``.

    Raises
    ------
    LinAlgError
        If eigenvalue computation does not converge.

    See Also
    --------
    eigvalsh_tridiagonal : eigenvalues of symmetric/Hermitian tridiagonal
        matrices
    eig : eigenvalues and right eigenvectors for non-symmetric arrays
    eigh : eigenvalues and right eigenvectors for symmetric/Hermitian arrays
    eig_banded : eigenvalues and right eigenvectors for symmetric/Hermitian
        band matrices

    Notes
    -----
    This function makes use of LAPACK ``S/DSTEMR`` routines.

    Examples
    --------
    >>> import numpy as np
    >>> from scipy.linalg import eigh_tridiagonal
    >>> d = 3*np.ones(4)
    >>> e = -1*np.ones(3)
    >>> w, v = eigh_tridiagonal(d, e)
    >>> A = np.diag(d) + np.diag(e, k=1) + np.diag(e, k=-1)
    >>> np.allclose(A @ v - v @ np.diag(w), np.zeros((4, 4)))
    True
    """
    d = _asarray_validated(d, check_finite=check_finite)
    e = _asarray_validated(e, check_finite=check_finite)
    for check in (d, e):
        if check.ndim != 1:
            raise ValueError('expected a 1-D array')
        if check.dtype.char in 'GFD':  # complex
            raise TypeError('Only real arrays currently supported')
    if d.size != e.size + 1:
        raise ValueError('d (%s) must have one more element than e (%s)'
                         % (d.size, e.size))
    select, vl, vu, il, iu, _ = _check_select(
        select, select_range, 0, d.size)
    if not isinstance(lapack_driver, str):
        raise TypeError('lapack_driver must be str')
    drivers = ('auto', 'stemr', 'sterf', 'stebz', 'stev')
    if lapack_driver not in drivers:
        raise ValueError('lapack_driver must be one of %s, got %s'
                         % (drivers, lapack_driver))
    if lapack_driver == 'auto':
        lapack_driver = 'stemr' if select == 0 else 'stebz'
    func, = get_lapack_funcs((lapack_driver,), (d, e))
    compute_v = not eigvals_only
    if lapack_driver == 'sterf':
        if select != 0:
            raise ValueError('sterf can only be used when select == "a"')
        if not eigvals_only:
            raise ValueError('sterf can only be used when eigvals_only is '
                             'True')
        w, info = func(d, e)
        m = len(w)
    elif lapack_driver == 'stev':
        if select != 0:
            raise ValueError('stev can only be used when select == "a"')
        w, v, info = func(d, e, compute_v=compute_v)
        m = len(w)
    elif lapack_driver == 'stebz':
        tol = float(tol)
        internal_name = 'stebz'
        stebz, = get_lapack_funcs((internal_name,), (d, e))
        # If getting eigenvectors, needs to be block-ordered (B) instead of
        # matrix-ordered (E), and we will reorder later
        order = 'E' if eigvals_only else 'B'
        m, w, iblock, isplit, info = stebz(d, e, select, vl, vu, il, iu, tol,
                                           order)
    else:   # 'stemr'
        # ?STEMR annoyingly requires size N instead of N-1
        e_ = empty(e.size+1, e.dtype)
        e_[:-1] = e
        stemr_lwork, = get_lapack_funcs(('stemr_lwork',), (d, e))
        lwork, liwork, info = stemr_lwork(d, e_, select, vl, vu, il, iu,
                                          compute_v=compute_v)
        _check_info(info, 'stemr_lwork')
        m, w, v, info = func(d, e_, select, vl, vu, il, iu,
                             compute_v=compute_v, lwork=lwork, liwork=liwork)
    _check_info(info, lapack_driver + ' (eigh_tridiagonal)')
    w = w[:m]
    if eigvals_only:
        return w
    else:
        # Do we still need to compute the eigenvalues?
        if lapack_driver == 'stebz':
            func, = get_lapack_funcs(('stein',), (d, e))
            v, info = func(d, e, w, iblock, isplit)
            _check_info(info, 'stein (eigh_tridiagonal)',
                        positive='%d eigenvectors failed to converge')
            # Convert block-order to matrix-order
            order = argsort(w)
            w, v = w[order], v[:, order]
        else:
            v = v[:, :m]
        return w, v


def _check_info(info, driver, positive='did not converge (LAPACK info=%d)'):
    """Check info return value."""
    if info < 0:
        raise ValueError('illegal value in argument %d of internal %s'
                         % (-info, driver))
    if info > 0 and positive:
        raise LinAlgError(("%s " + positive) % (driver, info,))


def hessenberg(a, calc_q=False, overwrite_a=False, check_finite=True):
    """
    Compute Hessenberg form of a matrix.

    The Hessenberg decomposition is::

        A = Q H Q^H

    where `Q` is unitary/orthogonal and `H` has only zero elements below
    the first sub-diagonal.

    Parameters
    ----------
    a : (M, M) array_like
        Matrix to bring into Hessenberg form.
    calc_q : bool, optional
        Whether to compute the transformation matrix.  Default is False.
    overwrite_a : bool, optional
        Whether to overwrite `a`; may improve performance.
        Default is False.
    check_finite : bool, optional
        Whether to check that the input matrix contains only finite numbers.
        Disabling may give a performance gain, but may result in problems
        (crashes, non-termination) if the inputs do contain infinities or NaNs.

    Returns
    -------
    H : (M, M) ndarray
        Hessenberg form of `a`.
    Q : (M, M) ndarray
        Unitary/orthogonal similarity transformation matrix ``A = Q H Q^H``.
        Only returned if ``calc_q=True``.

    Examples
    --------
    >>> import numpy as np
    >>> from scipy.linalg import hessenberg
    >>> A = np.array([[2, 5, 8, 7], [5, 2, 2, 8], [7, 5, 6, 6], [5, 4, 4, 8]])
    >>> H, Q = hessenberg(A, calc_q=True)
    >>> H
    array([[  2.        , -11.65843866,   1.42005301,   0.25349066],
           [ -9.94987437,  14.53535354,  -5.31022304,   2.43081618],
           [  0.        ,  -1.83299243,   0.38969961,  -0.51527034],
           [  0.        ,   0.        ,  -3.83189513,   1.07494686]])
    >>> np.allclose(Q @ H @ Q.conj().T - A, np.zeros((4, 4)))
    True
    """
    a1 = _asarray_validated(a, check_finite=check_finite)
    if len(a1.shape) != 2 or (a1.shape[0] != a1.shape[1]):
        raise ValueError('expected square matrix')
    overwrite_a = overwrite_a or (_datacopied(a1, a))

    # if 2x2 or smaller: already in Hessenberg
    if a1.shape[0] <= 2:
        if calc_q:
            return a1, eye(a1.shape[0])
        return a1

    gehrd, gebal, gehrd_lwork = get_lapack_funcs(('gehrd', 'gebal',
                                                  'gehrd_lwork'), (a1,))
    ba, lo, hi, pivscale, info = gebal(a1, permute=0, overwrite_a=overwrite_a)
    _check_info(info, 'gebal (hessenberg)', positive=False)
    n = len(a1)

    lwork = _compute_lwork(gehrd_lwork, ba.shape[0], lo=lo, hi=hi)

    hq, tau, info = gehrd(ba, lo=lo, hi=hi, lwork=lwork, overwrite_a=1)
    _check_info(info, 'gehrd (hessenberg)', positive=False)
    h = numpy.triu(hq, -1)
    if not calc_q:
        return h

    # use orghr/unghr to compute q
    orghr, orghr_lwork = get_lapack_funcs(('orghr', 'orghr_lwork'), (a1,))
    lwork = _compute_lwork(orghr_lwork, n, lo=lo, hi=hi)

    q, info = orghr(a=hq, tau=tau, lo=lo, hi=hi, lwork=lwork, overwrite_a=1)
    _check_info(info, 'orghr (hessenberg)', positive=False)
    return h, q


def cdf2rdf(w, v):
    """
    Converts complex eigenvalues ``w`` and eigenvectors ``v`` to real
    eigenvalues in a block diagonal form ``wr`` and the associated real
    eigenvectors ``vr``, such that::

        vr @ wr = X @ vr

    continues to hold, where ``X`` is the original array for which ``w`` and
    ``v`` are the eigenvalues and eigenvectors.

    .. versionadded:: 1.1.0

    Parameters
    ----------
    w : (..., M) array_like
        Complex or real eigenvalues, an array or stack of arrays

        Conjugate pairs must not be interleaved, else the wrong result
        will be produced. So ``[1+1j, 1, 1-1j]`` will give a correct result,
        but ``[1+1j, 2+1j, 1-1j, 2-1j]`` will not.

    v : (..., M, M) array_like
        Complex or real eigenvectors, a square array or stack of square arrays.

    Returns
    -------
    wr : (..., M, M) ndarray
        Real diagonal block form of eigenvalues
    vr : (..., M, M) ndarray
        Real eigenvectors associated with ``wr``

    See Also
    --------
    eig : Eigenvalues and right eigenvectors for non-symmetric arrays
    rsf2csf : Convert real Schur form to complex Schur form

    Notes
    -----
    ``w``, ``v`` must be the eigenstructure for some *real* matrix ``X``.
    For example, obtained by ``w, v = scipy.linalg.eig(X)`` or
    ``w, v = numpy.linalg.eig(X)`` in which case ``X`` can also represent
    stacked arrays.

    .. versionadded:: 1.1.0

    Examples
    --------
    >>> import numpy as np
    >>> X = np.array([[1, 2, 3], [0, 4, 5], [0, -5, 4]])
    >>> X
    array([[ 1,  2,  3],
           [ 0,  4,  5],
           [ 0, -5,  4]])

    >>> from scipy import linalg
    >>> w, v = linalg.eig(X)
    >>> w
    array([ 1.+0.j,  4.+5.j,  4.-5.j])
    >>> v
    array([[ 1.00000+0.j     , -0.01906-0.40016j, -0.01906+0.40016j],
           [ 0.00000+0.j     ,  0.00000-0.64788j,  0.00000+0.64788j],
           [ 0.00000+0.j     ,  0.64788+0.j     ,  0.64788-0.j     ]])

    >>> wr, vr = linalg.cdf2rdf(w, v)
    >>> wr
    array([[ 1.,  0.,  0.],
           [ 0.,  4.,  5.],
           [ 0., -5.,  4.]])
    >>> vr
    array([[ 1.     ,  0.40016, -0.01906],
           [ 0.     ,  0.64788,  0.     ],
           [ 0.     ,  0.     ,  0.64788]])

    >>> vr @ wr
    array([[ 1.     ,  1.69593,  1.9246 ],
           [ 0.     ,  2.59153,  3.23942],
           [ 0.     , -3.23942,  2.59153]])
    >>> X @ vr
    array([[ 1.     ,  1.69593,  1.9246 ],
           [ 0.     ,  2.59153,  3.23942],
           [ 0.     , -3.23942,  2.59153]])
    """
    w, v = _asarray_validated(w), _asarray_validated(v)

    # check dimensions
    if w.ndim < 1:
        raise ValueError('expected w to be at least 1D')
    if v.ndim < 2:
        raise ValueError('expected v to be at least 2D')
    if v.ndim != w.ndim + 1:
        raise ValueError('expected eigenvectors array to have exactly one '
                         'dimension more than eigenvalues array')

    # check shapes
    n = w.shape[-1]
    M = w.shape[:-1]
    if v.shape[-2] != v.shape[-1]:
        raise ValueError('expected v to be a square matrix or stacked square '
                         'matrices: v.shape[-2] = v.shape[-1]')
    if v.shape[-1] != n:
        raise ValueError('expected the same number of eigenvalues as '
                         'eigenvectors')

    # get indices for each first pair of complex eigenvalues
    complex_mask = iscomplex(w)
    n_complex = complex_mask.sum(axis=-1)

    # check if all complex eigenvalues have conjugate pairs
    if not (n_complex % 2 == 0).all():
        raise ValueError('expected complex-conjugate pairs of eigenvalues')

    # find complex indices
    idx = nonzero(complex_mask)
    idx_stack = idx[:-1]
    idx_elem = idx[-1]

    # filter them to conjugate indices, assuming pairs are not interleaved
    j = idx_elem[0::2]
    k = idx_elem[1::2]
    stack_ind = ()
    for i in idx_stack:
        # should never happen, assuming nonzero orders by the last axis
        assert (i[0::2] == i[1::2]).all(),\
                "Conjugate pair spanned different arrays!"
        stack_ind += (i[0::2],)

    # all eigenvalues to diagonal form
    wr = zeros(M + (n, n), dtype=w.real.dtype)
    di = range(n)
    wr[..., di, di] = w.real

    # complex eigenvalues to real block diagonal form
    wr[stack_ind + (j, k)] = w[stack_ind + (j,)].imag
    wr[stack_ind + (k, j)] = w[stack_ind + (k,)].imag

    # compute real eigenvectors associated with real block diagonal eigenvalues
    u = zeros(M + (n, n), dtype=numpy.cdouble)
    u[..., di, di] = 1.0
    u[stack_ind + (j, j)] = 0.5j
    u[stack_ind + (j, k)] = 0.5
    u[stack_ind + (k, j)] = -0.5j
    u[stack_ind + (k, k)] = 0.5

    # multipy matrices v and u (equivalent to v @ u)
    vr = einsum('...ij,...jk->...ik', v, u).real

    return wr, vr