laywerrobot/lib/python3.6/site-packages/scipy/linalg/_interpolative_backend.py

#******************************************************************************
#   Copyright (C) 2013 Kenneth L. Ho
#
#   Redistribution and use in source and binary forms, with or without
#   modification, are permitted provided that the following conditions are met:
#
#   Redistributions of source code must retain the above copyright notice, this
#   list of conditions and the following disclaimer. Redistributions in binary
#   form must reproduce the above copyright notice, this list of conditions and
#   the following disclaimer in the documentation and/or other materials
#   provided with the distribution.
#
#   None of the names of the copyright holders may be used to endorse or
#   promote products derived from this software without specific prior written
#   permission.
#
#   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
#   AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
#   IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
#   ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
#   LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
#   CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
#   SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
#   INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
#   CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
#   ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
#   POSSIBILITY OF SUCH DAMAGE.
#******************************************************************************

"""
Direct wrappers for Fortran `id_dist` backend.
"""

import scipy.linalg._interpolative as _id
import numpy as np

_RETCODE_ERROR = RuntimeError("nonzero return code")


#------------------------------------------------------------------------------
# id_rand.f
#------------------------------------------------------------------------------

def id_srand(n):
    """
    Generate standard uniform pseudorandom numbers via a very efficient lagged
    Fibonacci method.

    :param n:
        Number of pseudorandom numbers to generate.
    :type n: int

    :return:
        Pseudorandom numbers.
    :rtype: :class:`numpy.ndarray`
    """
    return _id.id_srand(n)


def id_srandi(t):
    """
    Initialize seed values for :func:`id_srand` (any appropriately random
    numbers will do).

    :param t:
        Array of 55 seed values.
    :type t: :class:`numpy.ndarray`
    """
    t = np.asfortranarray(t)
    _id.id_srandi(t)


def id_srando():
    """
    Reset seed values to their original values.
    """
    _id.id_srando()


#------------------------------------------------------------------------------
# idd_frm.f
#------------------------------------------------------------------------------

def idd_frm(n, w, x):
    """
    Transform real vector via a composition of Rokhlin's random transform,
    random subselection, and an FFT.

    In contrast to :func:`idd_sfrm`, this routine works best when the length of
    the transformed vector is the power-of-two integer output by
    :func:`idd_frmi`, or when the length is not specified but instead
    determined a posteriori from the output. The returned transformed vector is
    randomly permuted.

    :param n:
        Greatest power-of-two integer satisfying `n <= x.size` as obtained from
        :func:`idd_frmi`; `n` is also the length of the output vector.
    :type n: int
    :param w:
        Initialization array constructed by :func:`idd_frmi`.
    :type w: :class:`numpy.ndarray`
    :param x:
        Vector to be transformed.
    :type x: :class:`numpy.ndarray`

    :return:
        Transformed vector.
    :rtype: :class:`numpy.ndarray`
    """
    return _id.idd_frm(n, w, x)


def idd_sfrm(l, n, w, x):
    """
    Transform real vector via a composition of Rokhlin's random transform,
    random subselection, and an FFT.

    In contrast to :func:`idd_frm`, this routine works best when the length of
    the transformed vector is known a priori.

    :param l:
        Length of transformed vector, satisfying `l <= n`.
    :type l: int
    :param n:
        Greatest power-of-two integer satisfying `n <= x.size` as obtained from
        :func:`idd_sfrmi`.
    :type n: int
    :param w:
        Initialization array constructed by :func:`idd_sfrmi`.
    :type w: :class:`numpy.ndarray`
    :param x:
        Vector to be transformed.
    :type x: :class:`numpy.ndarray`

    :return:
        Transformed vector.
    :rtype: :class:`numpy.ndarray`
    """
    return _id.idd_sfrm(l, n, w, x)


def idd_frmi(m):
    """
    Initialize data for :func:`idd_frm`.

    :param m:
        Length of vector to be transformed.
    :type m: int

    :return:
        Greatest power-of-two integer `n` satisfying `n <= m`.
    :rtype: int
    :return:
        Initialization array to be used by :func:`idd_frm`.
    :rtype: :class:`numpy.ndarray`
    """
    return _id.idd_frmi(m)


def idd_sfrmi(l, m):
    """
    Initialize data for :func:`idd_sfrm`.

    :param l:
        Length of output transformed vector.
    :type l: int
    :param m:
        Length of the vector to be transformed.
    :type m: int

    :return:
        Greatest power-of-two integer `n` satisfying `n <= m`.
    :rtype: int
    :return:
        Initialization array to be used by :func:`idd_sfrm`.
    :rtype: :class:`numpy.ndarray`
    """
    return _id.idd_sfrmi(l, m)


#------------------------------------------------------------------------------
# idd_id.f
#------------------------------------------------------------------------------

def iddp_id(eps, A):
    """
    Compute ID of a real matrix to a specified relative precision.

    :param eps:
        Relative precision.
    :type eps: float
    :param A:
        Matrix.
    :type A: :class:`numpy.ndarray`

    :return:
        Rank of ID.
    :rtype: int
    :return:
        Column index array.
    :rtype: :class:`numpy.ndarray`
    :return:
        Interpolation coefficients.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    k, idx, rnorms = _id.iddp_id(eps, A)
    n = A.shape[1]
    proj = A.T.ravel()[:k*(n-k)].reshape((k, n-k), order='F')
    return k, idx, proj


def iddr_id(A, k):
    """
    Compute ID of a real matrix to a specified rank.

    :param A:
        Matrix.
    :type A: :class:`numpy.ndarray`
    :param k:
        Rank of ID.
    :type k: int

    :return:
        Column index array.
    :rtype: :class:`numpy.ndarray`
    :return:
        Interpolation coefficients.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    idx, rnorms = _id.iddr_id(A, k)
    n = A.shape[1]
    proj = A.T.ravel()[:k*(n-k)].reshape((k, n-k), order='F')
    return idx, proj


def idd_reconid(B, idx, proj):
    """
    Reconstruct matrix from real ID.

    :param B:
        Skeleton matrix.
    :type B: :class:`numpy.ndarray`
    :param idx:
        Column index array.
    :type idx: :class:`numpy.ndarray`
    :param proj:
        Interpolation coefficients.
    :type proj: :class:`numpy.ndarray`

    :return:
        Reconstructed matrix.
    :rtype: :class:`numpy.ndarray`
    """
    B = np.asfortranarray(B)
    if proj.size > 0:
        return _id.idd_reconid(B, idx, proj)
    else:
        return B[:, np.argsort(idx)]


def idd_reconint(idx, proj):
    """
    Reconstruct interpolation matrix from real ID.

    :param idx:
        Column index array.
    :type idx: :class:`numpy.ndarray`
    :param proj:
        Interpolation coefficients.
    :type proj: :class:`numpy.ndarray`

    :return:
        Interpolation matrix.
    :rtype: :class:`numpy.ndarray`
    """
    return _id.idd_reconint(idx, proj)


def idd_copycols(A, k, idx):
    """
    Reconstruct skeleton matrix from real ID.

    :param A:
        Original matrix.
    :type A: :class:`numpy.ndarray`
    :param k:
        Rank of ID.
    :type k: int
    :param idx:
        Column index array.
    :type idx: :class:`numpy.ndarray`

    :return:
        Skeleton matrix.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    return _id.idd_copycols(A, k, idx)


#------------------------------------------------------------------------------
# idd_id2svd.f
#------------------------------------------------------------------------------

def idd_id2svd(B, idx, proj):
    """
    Convert real ID to SVD.

    :param B:
        Skeleton matrix.
    :type B: :class:`numpy.ndarray`
    :param idx:
        Column index array.
    :type idx: :class:`numpy.ndarray`
    :param proj:
        Interpolation coefficients.
    :type proj: :class:`numpy.ndarray`

    :return:
        Left singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Right singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Singular values.
    :rtype: :class:`numpy.ndarray`
    """
    B = np.asfortranarray(B)
    U, V, S, ier = _id.idd_id2svd(B, idx, proj)
    if ier:
        raise _RETCODE_ERROR
    return U, V, S


#------------------------------------------------------------------------------
# idd_snorm.f
#------------------------------------------------------------------------------

def idd_snorm(m, n, matvect, matvec, its=20):
    """
    Estimate spectral norm of a real matrix by the randomized power method.

    :param m:
        Matrix row dimension.
    :type m: int
    :param n:
        Matrix column dimension.
    :type n: int
    :param matvect:
        Function to apply the matrix transpose to a vector, with call signature
        `y = matvect(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matvect: function
    :param matvec:
        Function to apply the matrix to a vector, with call signature
        `y = matvec(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matvec: function
    :param its:
        Number of power method iterations.
    :type its: int

    :return:
        Spectral norm estimate.
    :rtype: float
    """
    snorm, v = _id.idd_snorm(m, n, matvect, matvec, its)
    return snorm


def idd_diffsnorm(m, n, matvect, matvect2, matvec, matvec2, its=20):
    """
    Estimate spectral norm of the difference of two real matrices by the
    randomized power method.

    :param m:
        Matrix row dimension.
    :type m: int
    :param n:
        Matrix column dimension.
    :type n: int
    :param matvect:
        Function to apply the transpose of the first matrix to a vector, with
        call signature `y = matvect(x)`, where `x` and `y` are the input and
        output vectors, respectively.
    :type matvect: function
    :param matvect2:
        Function to apply the transpose of the second matrix to a vector, with
        call signature `y = matvect2(x)`, where `x` and `y` are the input and
        output vectors, respectively.
    :type matvect2: function
    :param matvec:
        Function to apply the first matrix to a vector, with call signature
        `y = matvec(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matvec: function
    :param matvec2:
        Function to apply the second matrix to a vector, with call signature
        `y = matvec2(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matvec2: function
    :param its:
        Number of power method iterations.
    :type its: int

    :return:
        Spectral norm estimate of matrix difference.
    :rtype: float
    """
    return _id.idd_diffsnorm(m, n, matvect, matvect2, matvec, matvec2, its)


#------------------------------------------------------------------------------
# idd_svd.f
#------------------------------------------------------------------------------

def iddr_svd(A, k):
    """
    Compute SVD of a real matrix to a specified rank.

    :param A:
        Matrix.
    :type A: :class:`numpy.ndarray`
    :param k:
        Rank of SVD.
    :type k: int

    :return:
        Left singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Right singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Singular values.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    U, V, S, ier = _id.iddr_svd(A, k)
    if ier:
        raise _RETCODE_ERROR
    return U, V, S


def iddp_svd(eps, A):
    """
    Compute SVD of a real matrix to a specified relative precision.

    :param eps:
        Relative precision.
    :type eps: float
    :param A:
        Matrix.
    :type A: :class:`numpy.ndarray`

    :return:
        Left singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Right singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Singular values.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    m, n = A.shape
    k, iU, iV, iS, w, ier = _id.iddp_svd(eps, A)
    if ier:
        raise _RETCODE_ERROR
    U = w[iU-1:iU+m*k-1].reshape((m, k), order='F')
    V = w[iV-1:iV+n*k-1].reshape((n, k), order='F')
    S = w[iS-1:iS+k-1]
    return U, V, S


#------------------------------------------------------------------------------
# iddp_aid.f
#------------------------------------------------------------------------------

def iddp_aid(eps, A):
    """
    Compute ID of a real matrix to a specified relative precision using random
    sampling.

    :param eps:
        Relative precision.
    :type eps: float
    :param A:
        Matrix.
    :type A: :class:`numpy.ndarray`

    :return:
        Rank of ID.
    :rtype: int
    :return:
        Column index array.
    :rtype: :class:`numpy.ndarray`
    :return:
        Interpolation coefficients.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    m, n = A.shape
    n2, w = idd_frmi(m)
    proj = np.empty(n*(2*n2 + 1) + n2 + 1, order='F')
    k, idx, proj = _id.iddp_aid(eps, A, w, proj)
    proj = proj[:k*(n-k)].reshape((k, n-k), order='F')
    return k, idx, proj


def idd_estrank(eps, A):
    """
    Estimate rank of a real matrix to a specified relative precision using
    random sampling.

    The output rank is typically about 8 higher than the actual rank.

    :param eps:
        Relative precision.
    :type eps: float
    :param A:
        Matrix.
    :type A: :class:`numpy.ndarray`

    :return:
        Rank estimate.
    :rtype: int
    """
    A = np.asfortranarray(A)
    m, n = A.shape
    n2, w = idd_frmi(m)
    ra = np.empty(n*n2 + (n + 1)*(n2 + 1), order='F')
    k, ra = _id.idd_estrank(eps, A, w, ra)
    return k


#------------------------------------------------------------------------------
# iddp_asvd.f
#------------------------------------------------------------------------------

def iddp_asvd(eps, A):
    """
    Compute SVD of a real matrix to a specified relative precision using random
    sampling.

    :param eps:
        Relative precision.
    :type eps: float
    :param A:
        Matrix.
    :type A: :class:`numpy.ndarray`

    :return:
        Left singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Right singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Singular values.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    m, n = A.shape
    n2, winit = _id.idd_frmi(m)
    w = np.empty(
        max((min(m, n) + 1)*(3*m + 5*n + 1) + 25*min(m, n)**2,
            (2*n + 1)*(n2 + 1)),
        order='F')
    k, iU, iV, iS, w, ier = _id.iddp_asvd(eps, A, winit, w)
    if ier:
        raise _RETCODE_ERROR
    U = w[iU-1:iU+m*k-1].reshape((m, k), order='F')
    V = w[iV-1:iV+n*k-1].reshape((n, k), order='F')
    S = w[iS-1:iS+k-1]
    return U, V, S


#------------------------------------------------------------------------------
# iddp_rid.f
#------------------------------------------------------------------------------

def iddp_rid(eps, m, n, matvect):
    """
    Compute ID of a real matrix to a specified relative precision using random
    matrix-vector multiplication.

    :param eps:
        Relative precision.
    :type eps: float
    :param m:
        Matrix row dimension.
    :type m: int
    :param n:
        Matrix column dimension.
    :type n: int
    :param matvect:
        Function to apply the matrix transpose to a vector, with call signature
        `y = matvect(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matvect: function

    :return:
        Rank of ID.
    :rtype: int
    :return:
        Column index array.
    :rtype: :class:`numpy.ndarray`
    :return:
        Interpolation coefficients.
    :rtype: :class:`numpy.ndarray`
    """
    proj = np.empty(m + 1 + 2*n*(min(m, n) + 1), order='F')
    k, idx, proj, ier = _id.iddp_rid(eps, m, n, matvect, proj)
    if ier != 0:
        raise _RETCODE_ERROR
    proj = proj[:k*(n-k)].reshape((k, n-k), order='F')
    return k, idx, proj


def idd_findrank(eps, m, n, matvect):
    """
    Estimate rank of a real matrix to a specified relative precision using
    random matrix-vector multiplication.

    :param eps:
        Relative precision.
    :type eps: float
    :param m:
        Matrix row dimension.
    :type m: int
    :param n:
        Matrix column dimension.
    :type n: int
    :param matvect:
        Function to apply the matrix transpose to a vector, with call signature
        `y = matvect(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matvect: function

    :return:
        Rank estimate.
    :rtype: int
    """
    k, ra, ier = _id.idd_findrank(eps, m, n, matvect)
    if ier:
        raise _RETCODE_ERROR
    return k


#------------------------------------------------------------------------------
# iddp_rsvd.f
#------------------------------------------------------------------------------

def iddp_rsvd(eps, m, n, matvect, matvec):
    """
    Compute SVD of a real matrix to a specified relative precision using random
    matrix-vector multiplication.

    :param eps:
        Relative precision.
    :type eps: float
    :param m:
        Matrix row dimension.
    :type m: int
    :param n:
        Matrix column dimension.
    :type n: int
    :param matvect:
        Function to apply the matrix transpose to a vector, with call signature
        `y = matvect(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matvect: function
    :param matvec:
        Function to apply the matrix to a vector, with call signature
        `y = matvec(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matvec: function

    :return:
        Left singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Right singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Singular values.
    :rtype: :class:`numpy.ndarray`
    """
    k, iU, iV, iS, w, ier = _id.iddp_rsvd(eps, m, n, matvect, matvec)
    if ier:
        raise _RETCODE_ERROR
    U = w[iU-1:iU+m*k-1].reshape((m, k), order='F')
    V = w[iV-1:iV+n*k-1].reshape((n, k), order='F')
    S = w[iS-1:iS+k-1]
    return U, V, S


#------------------------------------------------------------------------------
# iddr_aid.f
#------------------------------------------------------------------------------

def iddr_aid(A, k):
    """
    Compute ID of a real matrix to a specified rank using random sampling.

    :param A:
        Matrix.
    :type A: :class:`numpy.ndarray`
    :param k:
        Rank of ID.
    :type k: int

    :return:
        Column index array.
    :rtype: :class:`numpy.ndarray`
    :return:
        Interpolation coefficients.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    m, n = A.shape
    w = iddr_aidi(m, n, k)
    idx, proj = _id.iddr_aid(A, k, w)
    if k == n:
        proj = np.array([], dtype='float64', order='F')
    else:
        proj = proj.reshape((k, n-k), order='F')
    return idx, proj


def iddr_aidi(m, n, k):
    """
    Initialize array for :func:`iddr_aid`.

    :param m:
        Matrix row dimension.
    :type m: int
    :param n:
        Matrix column dimension.
    :type n: int
    :param k:
        Rank of ID.
    :type k: int

    :return:
        Initialization array to be used by :func:`iddr_aid`.
    :rtype: :class:`numpy.ndarray`
    """
    return _id.iddr_aidi(m, n, k)


#------------------------------------------------------------------------------
# iddr_asvd.f
#------------------------------------------------------------------------------

def iddr_asvd(A, k):
    """
    Compute SVD of a real matrix to a specified rank using random sampling.

    :param A:
        Matrix.
    :type A: :class:`numpy.ndarray`
    :param k:
        Rank of SVD.
    :type k: int

    :return:
        Left singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Right singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Singular values.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    m, n = A.shape
    w = np.empty((2*k + 28)*m + (6*k + 21)*n + 25*k**2 + 100, order='F')
    w_ = iddr_aidi(m, n, k)
    w[:w_.size] = w_
    U, V, S, ier = _id.iddr_asvd(A, k, w)
    if ier != 0:
        raise _RETCODE_ERROR
    return U, V, S


#------------------------------------------------------------------------------
# iddr_rid.f
#------------------------------------------------------------------------------

def iddr_rid(m, n, matvect, k):
    """
    Compute ID of a real matrix to a specified rank using random matrix-vector
    multiplication.

    :param m:
        Matrix row dimension.
    :type m: int
    :param n:
        Matrix column dimension.
    :type n: int
    :param matvect:
        Function to apply the matrix transpose to a vector, with call signature
        `y = matvect(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matvect: function
    :param k:
        Rank of ID.
    :type k: int

    :return:
        Column index array.
    :rtype: :class:`numpy.ndarray`
    :return:
        Interpolation coefficients.
    :rtype: :class:`numpy.ndarray`
    """
    idx, proj = _id.iddr_rid(m, n, matvect, k)
    proj = proj[:k*(n-k)].reshape((k, n-k), order='F')
    return idx, proj


#------------------------------------------------------------------------------
# iddr_rsvd.f
#------------------------------------------------------------------------------

def iddr_rsvd(m, n, matvect, matvec, k):
    """
    Compute SVD of a real matrix to a specified rank using random matrix-vector
    multiplication.

    :param m:
        Matrix row dimension.
    :type m: int
    :param n:
        Matrix column dimension.
    :type n: int
    :param matvect:
        Function to apply the matrix transpose to a vector, with call signature
        `y = matvect(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matvect: function
    :param matvec:
        Function to apply the matrix to a vector, with call signature
        `y = matvec(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matvec: function
    :param k:
        Rank of SVD.
    :type k: int

    :return:
        Left singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Right singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Singular values.
    :rtype: :class:`numpy.ndarray`
    """
    U, V, S, ier = _id.iddr_rsvd(m, n, matvect, matvec, k)
    if ier != 0:
        raise _RETCODE_ERROR
    return U, V, S


#------------------------------------------------------------------------------
# idz_frm.f
#------------------------------------------------------------------------------

def idz_frm(n, w, x):
    """
    Transform complex vector via a composition of Rokhlin's random transform,
    random subselection, and an FFT.

    In contrast to :func:`idz_sfrm`, this routine works best when the length of
    the transformed vector is the power-of-two integer output by
    :func:`idz_frmi`, or when the length is not specified but instead
    determined a posteriori from the output. The returned transformed vector is
    randomly permuted.

    :param n:
        Greatest power-of-two integer satisfying `n <= x.size` as obtained from
        :func:`idz_frmi`; `n` is also the length of the output vector.
    :type n: int
    :param w:
        Initialization array constructed by :func:`idz_frmi`.
    :type w: :class:`numpy.ndarray`
    :param x:
        Vector to be transformed.
    :type x: :class:`numpy.ndarray`

    :return:
        Transformed vector.
    :rtype: :class:`numpy.ndarray`
    """
    return _id.idz_frm(n, w, x)


def idz_sfrm(l, n, w, x):
    """
    Transform complex vector via a composition of Rokhlin's random transform,
    random subselection, and an FFT.

    In contrast to :func:`idz_frm`, this routine works best when the length of
    the transformed vector is known a priori.

    :param l:
        Length of transformed vector, satisfying `l <= n`.
    :type l: int
    :param n:
        Greatest power-of-two integer satisfying `n <= x.size` as obtained from
        :func:`idz_sfrmi`.
    :type n: int
    :param w:
        Initialization array constructed by :func:`idd_sfrmi`.
    :type w: :class:`numpy.ndarray`
    :param x:
        Vector to be transformed.
    :type x: :class:`numpy.ndarray`

    :return:
        Transformed vector.
    :rtype: :class:`numpy.ndarray`
    """
    return _id.idz_sfrm(l, n, w, x)


def idz_frmi(m):
    """
    Initialize data for :func:`idz_frm`.

    :param m:
        Length of vector to be transformed.
    :type m: int

    :return:
        Greatest power-of-two integer `n` satisfying `n <= m`.
    :rtype: int
    :return:
        Initialization array to be used by :func:`idz_frm`.
    :rtype: :class:`numpy.ndarray`
    """
    return _id.idz_frmi(m)


def idz_sfrmi(l, m):
    """
    Initialize data for :func:`idz_sfrm`.

    :param l:
        Length of output transformed vector.
    :type l: int
    :param m:
        Length of the vector to be transformed.
    :type m: int

    :return:
        Greatest power-of-two integer `n` satisfying `n <= m`.
    :rtype: int
    :return:
        Initialization array to be used by :func:`idz_sfrm`.
    :rtype: :class:`numpy.ndarray`
    """
    return _id.idz_sfrmi(l, m)


#------------------------------------------------------------------------------
# idz_id.f
#------------------------------------------------------------------------------

def idzp_id(eps, A):
    """
    Compute ID of a complex matrix to a specified relative precision.

    :param eps:
        Relative precision.
    :type eps: float
    :param A:
        Matrix.
    :type A: :class:`numpy.ndarray`

    :return:
        Rank of ID.
    :rtype: int
    :return:
        Column index array.
    :rtype: :class:`numpy.ndarray`
    :return:
        Interpolation coefficients.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    k, idx, rnorms = _id.idzp_id(eps, A)
    n = A.shape[1]
    proj = A.T.ravel()[:k*(n-k)].reshape((k, n-k), order='F')
    return k, idx, proj


def idzr_id(A, k):
    """
    Compute ID of a complex matrix to a specified rank.

    :param A:
        Matrix.
    :type A: :class:`numpy.ndarray`
    :param k:
        Rank of ID.
    :type k: int

    :return:
        Column index array.
    :rtype: :class:`numpy.ndarray`
    :return:
        Interpolation coefficients.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    idx, rnorms = _id.idzr_id(A, k)
    n = A.shape[1]
    proj = A.T.ravel()[:k*(n-k)].reshape((k, n-k), order='F')
    return idx, proj


def idz_reconid(B, idx, proj):
    """
    Reconstruct matrix from complex ID.

    :param B:
        Skeleton matrix.
    :type B: :class:`numpy.ndarray`
    :param idx:
        Column index array.
    :type idx: :class:`numpy.ndarray`
    :param proj:
        Interpolation coefficients.
    :type proj: :class:`numpy.ndarray`

    :return:
        Reconstructed matrix.
    :rtype: :class:`numpy.ndarray`
    """
    B = np.asfortranarray(B)
    if proj.size > 0:
        return _id.idz_reconid(B, idx, proj)
    else:
        return B[:, np.argsort(idx)]


def idz_reconint(idx, proj):
    """
    Reconstruct interpolation matrix from complex ID.

    :param idx:
        Column index array.
    :type idx: :class:`numpy.ndarray`
    :param proj:
        Interpolation coefficients.
    :type proj: :class:`numpy.ndarray`

    :return:
        Interpolation matrix.
    :rtype: :class:`numpy.ndarray`
    """
    return _id.idz_reconint(idx, proj)


def idz_copycols(A, k, idx):
    """
    Reconstruct skeleton matrix from complex ID.

    :param A:
        Original matrix.
    :type A: :class:`numpy.ndarray`
    :param k:
        Rank of ID.
    :type k: int
    :param idx:
        Column index array.
    :type idx: :class:`numpy.ndarray`

    :return:
        Skeleton matrix.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    return _id.idz_copycols(A, k, idx)


#------------------------------------------------------------------------------
# idz_id2svd.f
#------------------------------------------------------------------------------

def idz_id2svd(B, idx, proj):
    """
    Convert complex ID to SVD.

    :param B:
        Skeleton matrix.
    :type B: :class:`numpy.ndarray`
    :param idx:
        Column index array.
    :type idx: :class:`numpy.ndarray`
    :param proj:
        Interpolation coefficients.
    :type proj: :class:`numpy.ndarray`

    :return:
        Left singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Right singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Singular values.
    :rtype: :class:`numpy.ndarray`
    """
    B = np.asfortranarray(B)
    U, V, S, ier = _id.idz_id2svd(B, idx, proj)
    if ier:
        raise _RETCODE_ERROR
    return U, V, S


#------------------------------------------------------------------------------
# idz_snorm.f
#------------------------------------------------------------------------------

def idz_snorm(m, n, matveca, matvec, its=20):
    """
    Estimate spectral norm of a complex matrix by the randomized power method.

    :param m:
        Matrix row dimension.
    :type m: int
    :param n:
        Matrix column dimension.
    :type n: int
    :param matveca:
        Function to apply the matrix adjoint to a vector, with call signature
        `y = matveca(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matveca: function
    :param matvec:
        Function to apply the matrix to a vector, with call signature
        `y = matvec(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matvec: function
    :param its:
        Number of power method iterations.
    :type its: int

    :return:
        Spectral norm estimate.
    :rtype: float
    """
    snorm, v = _id.idz_snorm(m, n, matveca, matvec, its)
    return snorm


def idz_diffsnorm(m, n, matveca, matveca2, matvec, matvec2, its=20):
    """
    Estimate spectral norm of the difference of two complex matrices by the
    randomized power method.

    :param m:
        Matrix row dimension.
    :type m: int
    :param n:
        Matrix column dimension.
    :type n: int
    :param matveca:
        Function to apply the adjoint of the first matrix to a vector, with
        call signature `y = matveca(x)`, where `x` and `y` are the input and
        output vectors, respectively.
    :type matveca: function
    :param matveca2:
        Function to apply the adjoint of the second matrix to a vector, with
        call signature `y = matveca2(x)`, where `x` and `y` are the input and
        output vectors, respectively.
    :type matveca2: function
    :param matvec:
        Function to apply the first matrix to a vector, with call signature
        `y = matvec(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matvec: function
    :param matvec2:
        Function to apply the second matrix to a vector, with call signature
        `y = matvec2(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matvec2: function
    :param its:
        Number of power method iterations.
    :type its: int

    :return:
        Spectral norm estimate of matrix difference.
    :rtype: float
    """
    return _id.idz_diffsnorm(m, n, matveca, matveca2, matvec, matvec2, its)


#------------------------------------------------------------------------------
# idz_svd.f
#------------------------------------------------------------------------------

def idzr_svd(A, k):
    """
    Compute SVD of a complex matrix to a specified rank.

    :param A:
        Matrix.
    :type A: :class:`numpy.ndarray`
    :param k:
        Rank of SVD.
    :type k: int

    :return:
        Left singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Right singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Singular values.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    U, V, S, ier = _id.idzr_svd(A, k)
    if ier:
        raise _RETCODE_ERROR
    return U, V, S


def idzp_svd(eps, A):
    """
    Compute SVD of a complex matrix to a specified relative precision.

    :param eps:
        Relative precision.
    :type eps: float
    :param A:
        Matrix.
    :type A: :class:`numpy.ndarray`

    :return:
        Left singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Right singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Singular values.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    m, n = A.shape
    k, iU, iV, iS, w, ier = _id.idzp_svd(eps, A)
    if ier:
        raise _RETCODE_ERROR
    U = w[iU-1:iU+m*k-1].reshape((m, k), order='F')
    V = w[iV-1:iV+n*k-1].reshape((n, k), order='F')
    S = w[iS-1:iS+k-1]
    return U, V, S


#------------------------------------------------------------------------------
# idzp_aid.f
#------------------------------------------------------------------------------

def idzp_aid(eps, A):
    """
    Compute ID of a complex matrix to a specified relative precision using
    random sampling.

    :param eps:
        Relative precision.
    :type eps: float
    :param A:
        Matrix.
    :type A: :class:`numpy.ndarray`

    :return:
        Rank of ID.
    :rtype: int
    :return:
        Column index array.
    :rtype: :class:`numpy.ndarray`
    :return:
        Interpolation coefficients.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    m, n = A.shape
    n2, w = idz_frmi(m)
    proj = np.empty(n*(2*n2 + 1) + n2 + 1, dtype='complex128', order='F')
    k, idx, proj = _id.idzp_aid(eps, A, w, proj)
    proj = proj[:k*(n-k)].reshape((k, n-k), order='F')
    return k, idx, proj


def idz_estrank(eps, A):
    """
    Estimate rank of a complex matrix to a specified relative precision using
    random sampling.

    The output rank is typically about 8 higher than the actual rank.

    :param eps:
        Relative precision.
    :type eps: float
    :param A:
        Matrix.
    :type A: :class:`numpy.ndarray`

    :return:
        Rank estimate.
    :rtype: int
    """
    A = np.asfortranarray(A)
    m, n = A.shape
    n2, w = idz_frmi(m)
    ra = np.empty(n*n2 + (n + 1)*(n2 + 1), dtype='complex128', order='F')
    k, ra = _id.idz_estrank(eps, A, w, ra)
    return k


#------------------------------------------------------------------------------
# idzp_asvd.f
#------------------------------------------------------------------------------

def idzp_asvd(eps, A):
    """
    Compute SVD of a complex matrix to a specified relative precision using
    random sampling.

    :param eps:
        Relative precision.
    :type eps: float
    :param A:
        Matrix.
    :type A: :class:`numpy.ndarray`

    :return:
        Left singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Right singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Singular values.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    m, n = A.shape
    n2, winit = _id.idz_frmi(m)
    w = np.empty(
        max((min(m, n) + 1)*(3*m + 5*n + 11) + 8*min(m, n)**2,
            (2*n + 1)*(n2 + 1)),
        dtype=np.complex128, order='F')
    k, iU, iV, iS, w, ier = _id.idzp_asvd(eps, A, winit, w)
    if ier:
        raise _RETCODE_ERROR
    U = w[iU-1:iU+m*k-1].reshape((m, k), order='F')
    V = w[iV-1:iV+n*k-1].reshape((n, k), order='F')
    S = w[iS-1:iS+k-1]
    return U, V, S


#------------------------------------------------------------------------------
# idzp_rid.f
#------------------------------------------------------------------------------

def idzp_rid(eps, m, n, matveca):
    """
    Compute ID of a complex matrix to a specified relative precision using
    random matrix-vector multiplication.

    :param eps:
        Relative precision.
    :type eps: float
    :param m:
        Matrix row dimension.
    :type m: int
    :param n:
        Matrix column dimension.
    :type n: int
    :param matveca:
        Function to apply the matrix adjoint to a vector, with call signature
        `y = matveca(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matveca: function

    :return:
        Rank of ID.
    :rtype: int
    :return:
        Column index array.
    :rtype: :class:`numpy.ndarray`
    :return:
        Interpolation coefficients.
    :rtype: :class:`numpy.ndarray`
    """
    proj = np.empty(
        m + 1 + 2*n*(min(m, n) + 1),
        dtype=np.complex128, order='F')
    k, idx, proj, ier = _id.idzp_rid(eps, m, n, matveca, proj)
    if ier:
        raise _RETCODE_ERROR
    proj = proj[:k*(n-k)].reshape((k, n-k), order='F')
    return k, idx, proj


def idz_findrank(eps, m, n, matveca):
    """
    Estimate rank of a complex matrix to a specified relative precision using
    random matrix-vector multiplication.

    :param eps:
        Relative precision.
    :type eps: float
    :param m:
        Matrix row dimension.
    :type m: int
    :param n:
        Matrix column dimension.
    :type n: int
    :param matveca:
        Function to apply the matrix adjoint to a vector, with call signature
        `y = matveca(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matveca: function

    :return:
        Rank estimate.
    :rtype: int
    """
    k, ra, ier = _id.idz_findrank(eps, m, n, matveca)
    if ier:
        raise _RETCODE_ERROR
    return k


#------------------------------------------------------------------------------
# idzp_rsvd.f
#------------------------------------------------------------------------------

def idzp_rsvd(eps, m, n, matveca, matvec):
    """
    Compute SVD of a complex matrix to a specified relative precision using
    random matrix-vector multiplication.

    :param eps:
        Relative precision.
    :type eps: float
    :param m:
        Matrix row dimension.
    :type m: int
    :param n:
        Matrix column dimension.
    :type n: int
    :param matveca:
        Function to apply the matrix adjoint to a vector, with call signature
        `y = matveca(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matveca: function
    :param matvec:
        Function to apply the matrix to a vector, with call signature
        `y = matvec(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matvec: function

    :return:
        Left singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Right singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Singular values.
    :rtype: :class:`numpy.ndarray`
    """
    k, iU, iV, iS, w, ier = _id.idzp_rsvd(eps, m, n, matveca, matvec)
    if ier:
        raise _RETCODE_ERROR
    U = w[iU-1:iU+m*k-1].reshape((m, k), order='F')
    V = w[iV-1:iV+n*k-1].reshape((n, k), order='F')
    S = w[iS-1:iS+k-1]
    return U, V, S


#------------------------------------------------------------------------------
# idzr_aid.f
#------------------------------------------------------------------------------

def idzr_aid(A, k):
    """
    Compute ID of a complex matrix to a specified rank using random sampling.

    :param A:
        Matrix.
    :type A: :class:`numpy.ndarray`
    :param k:
        Rank of ID.
    :type k: int

    :return:
        Column index array.
    :rtype: :class:`numpy.ndarray`
    :return:
        Interpolation coefficients.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    m, n = A.shape
    w = idzr_aidi(m, n, k)
    idx, proj = _id.idzr_aid(A, k, w)
    if k == n:
        proj = np.array([], dtype='complex128', order='F')
    else:
        proj = proj.reshape((k, n-k), order='F')
    return idx, proj


def idzr_aidi(m, n, k):
    """
    Initialize array for :func:`idzr_aid`.

    :param m:
        Matrix row dimension.
    :type m: int
    :param n:
        Matrix column dimension.
    :type n: int
    :param k:
        Rank of ID.
    :type k: int

    :return:
        Initialization array to be used by :func:`idzr_aid`.
    :rtype: :class:`numpy.ndarray`
    """
    return _id.idzr_aidi(m, n, k)


#------------------------------------------------------------------------------
# idzr_asvd.f
#------------------------------------------------------------------------------

def idzr_asvd(A, k):
    """
    Compute SVD of a complex matrix to a specified rank using random sampling.

    :param A:
        Matrix.
    :type A: :class:`numpy.ndarray`
    :param k:
        Rank of SVD.
    :type k: int

    :return:
        Left singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Right singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Singular values.
    :rtype: :class:`numpy.ndarray`
    """
    A = np.asfortranarray(A)
    m, n = A.shape
    w = np.empty(
        (2*k + 22)*m + (6*k + 21)*n + 8*k**2 + 10*k + 90,
        dtype='complex128', order='F')
    w_ = idzr_aidi(m, n, k)
    w[:w_.size] = w_
    U, V, S, ier = _id.idzr_asvd(A, k, w)
    if ier:
        raise _RETCODE_ERROR
    return U, V, S


#------------------------------------------------------------------------------
# idzr_rid.f
#------------------------------------------------------------------------------

def idzr_rid(m, n, matveca, k):
    """
    Compute ID of a complex matrix to a specified rank using random
    matrix-vector multiplication.

    :param m:
        Matrix row dimension.
    :type m: int
    :param n:
        Matrix column dimension.
    :type n: int
    :param matveca:
        Function to apply the matrix adjoint to a vector, with call signature
        `y = matveca(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matveca: function
    :param k:
        Rank of ID.
    :type k: int

    :return:
        Column index array.
    :rtype: :class:`numpy.ndarray`
    :return:
        Interpolation coefficients.
    :rtype: :class:`numpy.ndarray`
    """
    idx, proj = _id.idzr_rid(m, n, matveca, k)
    proj = proj[:k*(n-k)].reshape((k, n-k), order='F')
    return idx, proj


#------------------------------------------------------------------------------
# idzr_rsvd.f
#------------------------------------------------------------------------------

def idzr_rsvd(m, n, matveca, matvec, k):
    """
    Compute SVD of a complex matrix to a specified rank using random
    matrix-vector multiplication.

    :param m:
        Matrix row dimension.
    :type m: int
    :param n:
        Matrix column dimension.
    :type n: int
    :param matveca:
        Function to apply the matrix adjoint to a vector, with call signature
        `y = matveca(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matveca: function
    :param matvec:
        Function to apply the matrix to a vector, with call signature
        `y = matvec(x)`, where `x` and `y` are the input and output vectors,
        respectively.
    :type matvec: function
    :param k:
        Rank of SVD.
    :type k: int

    :return:
        Left singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Right singular vectors.
    :rtype: :class:`numpy.ndarray`
    :return:
        Singular values.
    :rtype: :class:`numpy.ndarray`
    """
    U, V, S, ier = _id.idzr_rsvd(m, n, matveca, matvec, k)
    if ier:
        raise _RETCODE_ERROR
    return U, V, S