"""
Pure SciPy implementation of Locally Optimal Block Preconditioned Conjugate
Gradient Method (LOBPCG), see
https://bitbucket.org/joseroman/blopex

License: BSD

Authors: Robert Cimrman, Andrew Knyazev

Examples in tests directory contributed by Nils Wagner.
"""

from __future__ import division, print_function, absolute_import
import numpy as np
from numpy.testing import assert_allclose
from scipy._lib.six import xrange
from scipy.linalg import inv, eigh, cho_factor, cho_solve, cholesky
from scipy.sparse.linalg import aslinearoperator, LinearOperator

__all__ = ['lobpcg']


def save(ar, fileName):
    # Used only when verbosity level > 10.
    from numpy import savetxt
    savetxt(fileName, ar)


def _report_nonhermitian(M, a, b, name):
    """
    Report if `M` is not a hermitian matrix given the tolerances `a`, `b`.
    """
    from scipy.linalg import norm

    md = M - M.T.conj()

    nmd = norm(md, 1)
    tol = np.spacing(max(10**a, (10**b)*norm(M, 1)))
    if nmd > tol:
        print('matrix %s is not sufficiently Hermitian for a=%d, b=%d:'
              % (name, a, b))
        print('condition: %.e < %e' % (nmd, tol))

##
# 21.05.2007, c


def as2d(ar):
    """
    If the input array is 2D return it, if it is 1D, append a dimension,
    making it a column vector.
    """
    if ar.ndim == 2:
        return ar
    else:  # Assume 1!
        aux = np.array(ar, copy=False)
        aux.shape = (ar.shape[0], 1)
        return aux


def _makeOperator(operatorInput, expectedShape):
    """Takes a dense numpy array or a sparse matrix or
    a function and makes an operator performing matrix * blockvector
    products.

    Examples
    --------
    >>> A = _makeOperator( arrayA, (n, n) )
    >>> vectorB = A( vectorX )

    """
    if operatorInput is None:
        def ident(x):
            return x
        operator = LinearOperator(expectedShape, ident, matmat=ident)
    else:
        operator = aslinearoperator(operatorInput)

    if operator.shape != expectedShape:
        raise ValueError('operator has invalid shape')

    return operator


def _applyConstraints(blockVectorV, factYBY, blockVectorBY, blockVectorY):
    """Changes blockVectorV in place."""
    gramYBV = np.dot(blockVectorBY.T.conj(), blockVectorV)
    tmp = cho_solve(factYBY, gramYBV)
    blockVectorV -= np.dot(blockVectorY, tmp)


def _b_orthonormalize(B, blockVectorV, blockVectorBV=None, retInvR=False):
    if blockVectorBV is None:
        if B is not None:
            blockVectorBV = B(blockVectorV)
        else:
            blockVectorBV = blockVectorV  # Shared data!!!
    gramVBV = np.dot(blockVectorV.T.conj(), blockVectorBV)
    gramVBV = cholesky(gramVBV)
    gramVBV = inv(gramVBV, overwrite_a=True)
    # gramVBV is now R^{-1}.
    blockVectorV = np.dot(blockVectorV, gramVBV)
    if B is not None:
        blockVectorBV = np.dot(blockVectorBV, gramVBV)

    if retInvR:
        return blockVectorV, blockVectorBV, gramVBV
    else:
        return blockVectorV, blockVectorBV

def _get_indx(_lambda, num, largest):
    """Get `num` indices into `_lambda` depending on `largest` option."""
    ii = np.argsort(_lambda)
    if largest:
        ii = ii[:-num-1:-1]
    else:
        ii = ii[:num]

    return ii

def lobpcg(A, X,
            B=None, M=None, Y=None,
            tol=None, maxiter=20,
            largest=True, verbosityLevel=0,
            retLambdaHistory=False, retResidualNormsHistory=False):
    """Locally Optimal Block Preconditioned Conjugate Gradient Method (LOBPCG)

    LOBPCG is a preconditioned eigensolver for large symmetric positive
    definite (SPD) generalized eigenproblems.

    Parameters
    ----------
    A : {sparse matrix, dense matrix, LinearOperator}
        The symmetric linear operator of the problem, usually a
        sparse matrix.  Often called the "stiffness matrix".
    X : array_like
        Initial approximation to the k eigenvectors. If A has
        shape=(n,n) then X should have shape shape=(n,k).
    B : {dense matrix, sparse matrix, LinearOperator}, optional
        the right hand side operator in a generalized eigenproblem.
        by default, B = Identity
        often called the "mass matrix"
    M : {dense matrix, sparse matrix, LinearOperator}, optional
        preconditioner to A; by default M = Identity
        M should approximate the inverse of A
    Y : array_like, optional
        n-by-sizeY matrix of constraints, sizeY < n
        The iterations will be performed in the B-orthogonal complement
        of the column-space of Y. Y must be full rank.

    Returns
    -------
    w : array
        Array of k eigenvalues
    v : array
        An array of k eigenvectors.  V has the same shape as X.

    Other Parameters
    ----------------
    tol : scalar, optional
        Solver tolerance (stopping criterion)
        by default: tol=n*sqrt(eps)
    maxiter : integer, optional
        maximum number of iterations
        by default: maxiter=min(n,20)
    largest : bool, optional
        when True, solve for the largest eigenvalues, otherwise the smallest
    verbosityLevel : integer, optional
        controls solver output.  default: verbosityLevel = 0.
    retLambdaHistory : boolean, optional
        whether to return eigenvalue history
    retResidualNormsHistory : boolean, optional
        whether to return history of residual norms

    Examples
    --------

    Solve A x = lambda B x with constraints and preconditioning.

    >>> from scipy.sparse import spdiags, issparse
    >>> from scipy.sparse.linalg import lobpcg, LinearOperator
    >>> n = 100
    >>> vals = [np.arange(n, dtype=np.float64) + 1]
    >>> A = spdiags(vals, 0, n, n)
    >>> A.toarray()
    array([[   1.,    0.,    0., ...,    0.,    0.,    0.],
           [   0.,    2.,    0., ...,    0.,    0.,    0.],
           [   0.,    0.,    3., ...,    0.,    0.,    0.],
           ...,
           [   0.,    0.,    0., ...,   98.,    0.,    0.],
           [   0.,    0.,    0., ...,    0.,   99.,    0.],
           [   0.,    0.,    0., ...,    0.,    0.,  100.]])

    Constraints.

    >>> Y = np.eye(n, 3)

    Initial guess for eigenvectors, should have linearly independent
    columns. Column dimension = number of requested eigenvalues.

    >>> X = np.random.rand(n, 3)

    Preconditioner -- inverse of A (as an abstract linear operator).

    >>> invA = spdiags([1./vals[0]], 0, n, n)
    >>> def precond( x ):
    ...     return invA  * x
    >>> M = LinearOperator(matvec=precond, shape=(n, n), dtype=float)

    Here, ``invA`` could of course have been used directly as a preconditioner.
    Let us then solve the problem:

    >>> eigs, vecs = lobpcg(A, X, Y=Y, M=M, tol=1e-4, maxiter=40, largest=False)
    >>> eigs
    array([ 4.,  5.,  6.])

    Note that the vectors passed in Y are the eigenvectors of the 3 smallest
    eigenvalues. The results returned are orthogonal to those.

    Notes
    -----
    If both retLambdaHistory and retResidualNormsHistory are True,
    the return tuple has the following format
    (lambda, V, lambda history, residual norms history).

    In the following ``n`` denotes the matrix size and ``m`` the number
    of required eigenvalues (smallest or largest).

    The LOBPCG code internally solves eigenproblems of the size 3``m`` on every
    iteration by calling the "standard" dense eigensolver, so if ``m`` is not
    small enough compared to ``n``, it does not make sense to call the LOBPCG
    code, but rather one should use the "standard" eigensolver,
    e.g. numpy or scipy function in this case.
    If one calls the LOBPCG algorithm for 5``m``>``n``,
    it will most likely break internally, so the code tries to call the standard
    function instead.

    It is not that n should be large for the LOBPCG to work, but rather the
    ratio ``n``/``m`` should be large. It you call the LOBPCG code with ``m``=1
    and ``n``=10, it should work, though ``n`` is small. The method is intended
    for extremely large ``n``/``m``, see e.g., reference [28] in
    https://arxiv.org/abs/0705.2626

    The convergence speed depends basically on two factors:

    1.  How well relatively separated the seeking eigenvalues are
        from the rest of the eigenvalues.
        One can try to vary ``m`` to make this better.

    2.  How well conditioned the problem is. This can be changed by using proper
        preconditioning. For example, a rod vibration test problem (under tests
        directory) is ill-conditioned for large ``n``, so convergence will be
        slow, unless efficient preconditioning is used.
        For this specific problem, a good simple preconditioner function would
        be a linear solve for A, which is easy to code since A is tridiagonal.

    *Acknowledgements*

    lobpcg.py code was written by Robert Cimrman.
    Many thanks belong to Andrew Knyazev, the author of the algorithm,
    for lots of advice and support.

    References
    ----------
    .. [1] A. V. Knyazev (2001),
           Toward the Optimal Preconditioned Eigensolver: Locally Optimal
           Block Preconditioned Conjugate Gradient Method.
           SIAM Journal on Scientific Computing 23, no. 2,
           pp. 517-541. :doi:`10.1137/S1064827500366124`

    .. [2] A. V. Knyazev, I. Lashuk, M. E. Argentati, and E. Ovchinnikov (2007),
           Block Locally Optimal Preconditioned Eigenvalue Xolvers (BLOPEX)
           in hypre and PETSc.  https://arxiv.org/abs/0705.2626

    .. [3] A. V. Knyazev's C and MATLAB implementations:
           https://bitbucket.org/joseroman/blopex

    """
    blockVectorX = X
    blockVectorY = Y
    residualTolerance = tol
    maxIterations = maxiter

    if blockVectorY is not None:
        sizeY = blockVectorY.shape[1]
    else:
        sizeY = 0

    # Block size.
    if len(blockVectorX.shape) != 2:
        raise ValueError('expected rank-2 array for argument X')

    n, sizeX = blockVectorX.shape
    if sizeX > n:
        raise ValueError('X column dimension exceeds the row dimension')

    A = _makeOperator(A, (n,n))
    B = _makeOperator(B, (n,n))
    M = _makeOperator(M, (n,n))

    if (n - sizeY) < (5 * sizeX):
        # warn('The problem size is small compared to the block size.' \
        #        ' Using dense eigensolver instead of LOBPCG.')

        if blockVectorY is not None:
            raise NotImplementedError('The dense eigensolver '
                    'does not support constraints.')

        # Define the closed range of indices of eigenvalues to return.
        if largest:
            eigvals = (n - sizeX, n-1)
        else:
            eigvals = (0, sizeX-1)

        A_dense = A(np.eye(n))
        B_dense = None if B is None else B(np.eye(n))

        vals, vecs = eigh(A_dense, B_dense, eigvals=eigvals, check_finite=False)
        if largest:
            # Reverse order to be compatible with eigs() in 'LM' mode.
            vals = vals[::-1]
            vecs = vecs[:, ::-1]

        return vals, vecs

    if residualTolerance is None:
        residualTolerance = np.sqrt(1e-15) * n

    maxIterations = min(n, maxIterations)

    if verbosityLevel:
        aux = "Solving "
        if B is None:
            aux += "standard"
        else:
            aux += "generalized"
        aux += " eigenvalue problem with"
        if M is None:
            aux += "out"
        aux += " preconditioning\n\n"
        aux += "matrix size %d\n" % n
        aux += "block size %d\n\n" % sizeX
        if blockVectorY is None:
            aux += "No constraints\n\n"
        else:
            if sizeY > 1:
                aux += "%d constraints\n\n" % sizeY
            else:
                aux += "%d constraint\n\n" % sizeY
        print(aux)

    ##
    # Apply constraints to X.
    if blockVectorY is not None:

        if B is not None:
            blockVectorBY = B(blockVectorY)
        else:
            blockVectorBY = blockVectorY

        # gramYBY is a dense array.
        gramYBY = np.dot(blockVectorY.T.conj(), blockVectorBY)
        try:
            # gramYBY is a Cholesky factor from now on...
            gramYBY = cho_factor(gramYBY)
        except Exception:
            raise ValueError('cannot handle linearly dependent constraints')

        _applyConstraints(blockVectorX, gramYBY, blockVectorBY, blockVectorY)

    ##
    # B-orthonormalize X.
    blockVectorX, blockVectorBX = _b_orthonormalize(B, blockVectorX)

    ##
    # Compute the initial Ritz vectors: solve the eigenproblem.
    blockVectorAX = A(blockVectorX)
    gramXAX = np.dot(blockVectorX.T.conj(), blockVectorAX)

    _lambda, eigBlockVector = eigh(gramXAX, check_finite=False)
    ii = _get_indx(_lambda, sizeX, largest)
    _lambda = _lambda[ii]

    eigBlockVector = np.asarray(eigBlockVector[:,ii])
    blockVectorX = np.dot(blockVectorX, eigBlockVector)
    blockVectorAX = np.dot(blockVectorAX, eigBlockVector)
    if B is not None:
        blockVectorBX = np.dot(blockVectorBX, eigBlockVector)

    ##
    # Active index set.
    activeMask = np.ones((sizeX,), dtype=bool)

    lambdaHistory = [_lambda]
    residualNormsHistory = []

    previousBlockSize = sizeX
    ident = np.eye(sizeX, dtype=A.dtype)
    ident0 = np.eye(sizeX, dtype=A.dtype)

    ##
    # Main iteration loop.

    blockVectorP = None  # set during iteration
    blockVectorAP = None
    blockVectorBP = None

    for iterationNumber in xrange(maxIterations):
        if verbosityLevel > 0:
            print('iteration %d' % iterationNumber)

        aux = blockVectorBX * _lambda[np.newaxis,:]
        blockVectorR = blockVectorAX - aux

        aux = np.sum(blockVectorR.conjugate() * blockVectorR, 0)
        residualNorms = np.sqrt(aux)

        residualNormsHistory.append(residualNorms)

        ii = np.where(residualNorms > residualTolerance, True, False)
        activeMask = activeMask & ii
        if verbosityLevel > 2:
            print(activeMask)

        currentBlockSize = activeMask.sum()
        if currentBlockSize != previousBlockSize:
            previousBlockSize = currentBlockSize
            ident = np.eye(currentBlockSize, dtype=A.dtype)

        if currentBlockSize == 0:
            break

        if verbosityLevel > 0:
            print('current block size:', currentBlockSize)
            print('eigenvalue:', _lambda)
            print('residual norms:', residualNorms)
        if verbosityLevel > 10:
            print(eigBlockVector)

        activeBlockVectorR = as2d(blockVectorR[:,activeMask])

        if iterationNumber > 0:
            activeBlockVectorP = as2d(blockVectorP[:,activeMask])
            activeBlockVectorAP = as2d(blockVectorAP[:,activeMask])
            activeBlockVectorBP = as2d(blockVectorBP[:,activeMask])

        if M is not None:
            # Apply preconditioner T to the active residuals.
            activeBlockVectorR = M(activeBlockVectorR)

        ##
        # Apply constraints to the preconditioned residuals.
        if blockVectorY is not None:
            _applyConstraints(activeBlockVectorR,
                              gramYBY, blockVectorBY, blockVectorY)

        ##
        # B-orthonormalize the preconditioned residuals.

        aux = _b_orthonormalize(B, activeBlockVectorR)
        activeBlockVectorR, activeBlockVectorBR = aux

        activeBlockVectorAR = A(activeBlockVectorR)

        if iterationNumber > 0:
            aux = _b_orthonormalize(B, activeBlockVectorP,
                                    activeBlockVectorBP, retInvR=True)
            activeBlockVectorP, activeBlockVectorBP, invR = aux
            activeBlockVectorAP = np.dot(activeBlockVectorAP, invR)

        ##
        # Perform the Rayleigh Ritz Procedure:
        # Compute symmetric Gram matrices:

        xaw = np.dot(blockVectorX.T.conj(), activeBlockVectorAR)
        waw = np.dot(activeBlockVectorR.T.conj(), activeBlockVectorAR)
        xbw = np.dot(blockVectorX.T.conj(), activeBlockVectorBR)

        if iterationNumber > 0:
            xap = np.dot(blockVectorX.T.conj(), activeBlockVectorAP)
            wap = np.dot(activeBlockVectorR.T.conj(), activeBlockVectorAP)
            pap = np.dot(activeBlockVectorP.T.conj(), activeBlockVectorAP)
            xbp = np.dot(blockVectorX.T.conj(), activeBlockVectorBP)
            wbp = np.dot(activeBlockVectorR.T.conj(), activeBlockVectorBP)

            gramA = np.bmat([[np.diag(_lambda), xaw, xap],
                              [xaw.T.conj(), waw, wap],
                              [xap.T.conj(), wap.T.conj(), pap]])

            gramB = np.bmat([[ident0, xbw, xbp],
                              [xbw.T.conj(), ident, wbp],
                              [xbp.T.conj(), wbp.T.conj(), ident]])
        else:
            gramA = np.bmat([[np.diag(_lambda), xaw],
                              [xaw.T.conj(), waw]])
            gramB = np.bmat([[ident0, xbw],
                              [xbw.T.conj(), ident]])

        if verbosityLevel > 0:
            _report_nonhermitian(gramA, 3, -1, 'gramA')
            _report_nonhermitian(gramB, 3, -1, 'gramB')

        if verbosityLevel > 10:
            save(gramA, 'gramA')
            save(gramB, 'gramB')

        # Solve the generalized eigenvalue problem.
        _lambda, eigBlockVector = eigh(gramA, gramB, check_finite=False)
        ii = _get_indx(_lambda, sizeX, largest)
        if verbosityLevel > 10:
            print(ii)

        _lambda = _lambda[ii]
        eigBlockVector = eigBlockVector[:,ii]

        lambdaHistory.append(_lambda)

        if verbosityLevel > 10:
            print('lambda:', _lambda)
##         # Normalize eigenvectors!
##         aux = np.sum( eigBlockVector.conjugate() * eigBlockVector, 0 )
##         eigVecNorms = np.sqrt( aux )
##         eigBlockVector = eigBlockVector / eigVecNorms[np.newaxis,:]
#        eigBlockVector, aux = _b_orthonormalize( B, eigBlockVector )

        if verbosityLevel > 10:
            print(eigBlockVector)

        ##
        # Compute Ritz vectors.
        if iterationNumber > 0:
            eigBlockVectorX = eigBlockVector[:sizeX]
            eigBlockVectorR = eigBlockVector[sizeX:sizeX+currentBlockSize]
            eigBlockVectorP = eigBlockVector[sizeX+currentBlockSize:]

            pp = np.dot(activeBlockVectorR, eigBlockVectorR)
            pp += np.dot(activeBlockVectorP, eigBlockVectorP)

            app = np.dot(activeBlockVectorAR, eigBlockVectorR)
            app += np.dot(activeBlockVectorAP, eigBlockVectorP)

            bpp = np.dot(activeBlockVectorBR, eigBlockVectorR)
            bpp += np.dot(activeBlockVectorBP, eigBlockVectorP)
        else:
            eigBlockVectorX = eigBlockVector[:sizeX]
            eigBlockVectorR = eigBlockVector[sizeX:]

            pp = np.dot(activeBlockVectorR, eigBlockVectorR)
            app = np.dot(activeBlockVectorAR, eigBlockVectorR)
            bpp = np.dot(activeBlockVectorBR, eigBlockVectorR)

        if verbosityLevel > 10:
            print(pp)
            print(app)
            print(bpp)

        blockVectorX = np.dot(blockVectorX, eigBlockVectorX) + pp
        blockVectorAX = np.dot(blockVectorAX, eigBlockVectorX) + app
        blockVectorBX = np.dot(blockVectorBX, eigBlockVectorX) + bpp

        blockVectorP, blockVectorAP, blockVectorBP = pp, app, bpp

    aux = blockVectorBX * _lambda[np.newaxis,:]
    blockVectorR = blockVectorAX - aux

    aux = np.sum(blockVectorR.conjugate() * blockVectorR, 0)
    residualNorms = np.sqrt(aux)

    if verbosityLevel > 0:
        print('final eigenvalue:', _lambda)
        print('final residual norms:', residualNorms)

    if retLambdaHistory:
        if retResidualNormsHistory:
            return _lambda, blockVectorX, lambdaHistory, residualNormsHistory
        else:
            return _lambda, blockVectorX, lambdaHistory
    else:
        if retResidualNormsHistory:
            return _lambda, blockVectorX, residualNormsHistory
        else:
            return _lambda, blockVectorX
