import cvxopt # to ensure cvxopt is installed
import cvxopt.base
from openopt.kernel.ooMisc import Len

matrix = cvxopt.base.matrix
sparse = cvxopt.base.sparse
Sparse = cvxopt.spmatrix

from numpy import asfarray, copy, array, prod
from openopt.kernel.nonOptMisc import isspmatrix

def Matrix(x):
    if x is None or (hasattr(x, 'shape') and prod(x.shape) == 0): return None
    if isspmatrix(x):
        if min(x.shape) > 1:
            from scipy.sparse import find
            I, J, values = find(x)
            return Sparse(array(values, float).tolist(), I.tolist(), J.tolist(), x.shape)
        else:
            x = x.toarray()
    
    x = asfarray(x)
    if x.ndim > 1 and x.nonzero()[0].size < 0.3*x.size: #todo: replace 0.3 by prob param
        return sparse(x.tolist()).T # without tolist currently it doesn't work
    else:  return matrix(x, tc='d')

def xBounds2cvxoptMatrix(p):
    """
    transforms lb - ub bounds into (A, x) <= b, (Aeq, x) = beq conditions
    this func is developed for those solvers that can handle lb, ub only via c(x)<=0, h(x)=0
    """

    #TODO: is reshape/flatten required in newest numpy versions?
    indLB, indUB, indEQ = \
    where(isfinite(p.lb) & ~(p.lb == p.ub))[0], \
    where(isfinite(p.ub) & ~(p.lb == p.ub))[0], \
    where(p.lb == p.ub)[0]    
    
    initLenB = Len(p.b)
    initLenBeq = Len(p.beq)
    nLB, nUB, nEQ = Len(indLB), Len(indUB), Len(indEQ)

    if  nLB>0 or nUB>0:
        A, b = copy(p.A), copy(p.b)
        p.A = zeros([Len(p.b) + nLB+nUB, p.n])
        p.b = zeros(Len(p.b) + nLB+nUB)
        p.b[:Len(b)] = b.flatten() # sometimes flatten is needed when called before runProbSolver(), from tests
        p.A[:Len(b)] = A
        for i in  range(len(indLB)):
            p.A[initLenB+i, indLB[i]] = -1
            p.b[initLenB+i] = -p.lb[indLB[i]]
        for i in  range(len(indUB)):
            p.A[initLenB+len(indLB)+i, indUB[i]] = 1
            p.b[initLenB+len(indLB)+i] = p.ub[indUB[i]]

    if nEQ>0:
        Aeq, beq = copy(p.Aeq), copy(p.beq)
        p.Aeq = zeros([Len(p.beq) + nEQ, p.n])
        p.beq = zeros(Len(p.beq) + nEQ)
        p.beq[:Len(beq)] = beq
        p.Aeq[:Len(beq)] = Aeq
        for i in range(len(indEQ)):
            p.Aeq[initLenBeq+i, indEQ[i]] = 1
            p.beq[initLenBeq+i] = p.lb[indEQ[i]] # = p.ub[indEQ[i]], because they are the same

    p.lb = -inf*ones(p.n)
    p.ub = inf*ones(p.n)

66f4860fea8346768e42ed7fadc85da0b6cceb3a
<mask token>


def Matrix(x):
    if x is None or hasattr(x, 'shape') and prod(x.shape) == 0:
        return None
    if isspmatrix(x):
        if min(x.shape) > 1:
            from scipy.sparse import find
            I, J, values = find(x)
            return Sparse(array(values, float).tolist(), I.tolist(), J.
                tolist(), x.shape)
        else:
            x = x.toarray()
    x = asfarray(x)
    if x.ndim > 1 and x.nonzero()[0].size < 0.3 * x.size:
        return sparse(x.tolist()).T
    else:
        return matrix(x, tc='d')


<mask token>

<mask token>


def Matrix(x):
    if x is None or hasattr(x, 'shape') and prod(x.shape) == 0:
        return None
    if isspmatrix(x):
        if min(x.shape) > 1:
            from scipy.sparse import find
            I, J, values = find(x)
            return Sparse(array(values, float).tolist(), I.tolist(), J.
                tolist(), x.shape)
        else:
            x = x.toarray()
    x = asfarray(x)
    if x.ndim > 1 and x.nonzero()[0].size < 0.3 * x.size:
        return sparse(x.tolist()).T
    else:
        return matrix(x, tc='d')


def xBounds2cvxoptMatrix(p):
    """
    transforms lb - ub bounds into (A, x) <= b, (Aeq, x) = beq conditions
    this func is developed for those solvers that can handle lb, ub only via c(x)<=0, h(x)=0
    """
    indLB, indUB, indEQ = where(isfinite(p.lb) & ~(p.lb == p.ub))[0], where(
        isfinite(p.ub) & ~(p.lb == p.ub))[0], where(p.lb == p.ub)[0]
    initLenB = Len(p.b)
    initLenBeq = Len(p.beq)
    nLB, nUB, nEQ = Len(indLB), Len(indUB), Len(indEQ)
    if nLB > 0 or nUB > 0:
        A, b = copy(p.A), copy(p.b)
        p.A = zeros([Len(p.b) + nLB + nUB, p.n])
        p.b = zeros(Len(p.b) + nLB + nUB)
        p.b[:Len(b)] = b.flatten()
        p.A[:Len(b)] = A
        for i in range(len(indLB)):
            p.A[initLenB + i, indLB[i]] = -1
            p.b[initLenB + i] = -p.lb[indLB[i]]
        for i in range(len(indUB)):
            p.A[initLenB + len(indLB) + i, indUB[i]] = 1
            p.b[initLenB + len(indLB) + i] = p.ub[indUB[i]]
    if nEQ > 0:
        Aeq, beq = copy(p.Aeq), copy(p.beq)
        p.Aeq = zeros([Len(p.beq) + nEQ, p.n])
        p.beq = zeros(Len(p.beq) + nEQ)
        p.beq[:Len(beq)] = beq
        p.Aeq[:Len(beq)] = Aeq
        for i in range(len(indEQ)):
            p.Aeq[initLenBeq + i, indEQ[i]] = 1
            p.beq[initLenBeq + i] = p.lb[indEQ[i]]
    p.lb = -inf * ones(p.n)
    p.ub = inf * ones(p.n)

<mask token>
matrix = cvxopt.base.matrix
sparse = cvxopt.base.sparse
Sparse = cvxopt.spmatrix
<mask token>


def Matrix(x):
    if x is None or hasattr(x, 'shape') and prod(x.shape) == 0:
        return None
    if isspmatrix(x):
        if min(x.shape) > 1:
            from scipy.sparse import find
            I, J, values = find(x)
            return Sparse(array(values, float).tolist(), I.tolist(), J.
                tolist(), x.shape)
        else:
            x = x.toarray()
    x = asfarray(x)
    if x.ndim > 1 and x.nonzero()[0].size < 0.3 * x.size:
        return sparse(x.tolist()).T
    else:
        return matrix(x, tc='d')


def xBounds2cvxoptMatrix(p):
    """
    transforms lb - ub bounds into (A, x) <= b, (Aeq, x) = beq conditions
    this func is developed for those solvers that can handle lb, ub only via c(x)<=0, h(x)=0
    """
    indLB, indUB, indEQ = where(isfinite(p.lb) & ~(p.lb == p.ub))[0], where(
        isfinite(p.ub) & ~(p.lb == p.ub))[0], where(p.lb == p.ub)[0]
    initLenB = Len(p.b)
    initLenBeq = Len(p.beq)
    nLB, nUB, nEQ = Len(indLB), Len(indUB), Len(indEQ)
    if nLB > 0 or nUB > 0:
        A, b = copy(p.A), copy(p.b)
        p.A = zeros([Len(p.b) + nLB + nUB, p.n])
        p.b = zeros(Len(p.b) + nLB + nUB)
        p.b[:Len(b)] = b.flatten()
        p.A[:Len(b)] = A
        for i in range(len(indLB)):
            p.A[initLenB + i, indLB[i]] = -1
            p.b[initLenB + i] = -p.lb[indLB[i]]
        for i in range(len(indUB)):
            p.A[initLenB + len(indLB) + i, indUB[i]] = 1
            p.b[initLenB + len(indLB) + i] = p.ub[indUB[i]]
    if nEQ > 0:
        Aeq, beq = copy(p.Aeq), copy(p.beq)
        p.Aeq = zeros([Len(p.beq) + nEQ, p.n])
        p.beq = zeros(Len(p.beq) + nEQ)
        p.beq[:Len(beq)] = beq
        p.Aeq[:Len(beq)] = Aeq
        for i in range(len(indEQ)):
            p.Aeq[initLenBeq + i, indEQ[i]] = 1
            p.beq[initLenBeq + i] = p.lb[indEQ[i]]
    p.lb = -inf * ones(p.n)
    p.ub = inf * ones(p.n)

import cvxopt
import cvxopt.base
from openopt.kernel.ooMisc import Len
matrix = cvxopt.base.matrix
sparse = cvxopt.base.sparse
Sparse = cvxopt.spmatrix
from numpy import asfarray, copy, array, prod
from openopt.kernel.nonOptMisc import isspmatrix


def Matrix(x):
    if x is None or hasattr(x, 'shape') and prod(x.shape) == 0:
        return None
    if isspmatrix(x):
        if min(x.shape) > 1:
            from scipy.sparse import find
            I, J, values = find(x)
            return Sparse(array(values, float).tolist(), I.tolist(), J.
                tolist(), x.shape)
        else:
            x = x.toarray()
    x = asfarray(x)
    if x.ndim > 1 and x.nonzero()[0].size < 0.3 * x.size:
        return sparse(x.tolist()).T
    else:
        return matrix(x, tc='d')


def xBounds2cvxoptMatrix(p):
    """
    transforms lb - ub bounds into (A, x) <= b, (Aeq, x) = beq conditions
    this func is developed for those solvers that can handle lb, ub only via c(x)<=0, h(x)=0
    """
    indLB, indUB, indEQ = where(isfinite(p.lb) & ~(p.lb == p.ub))[0], where(
        isfinite(p.ub) & ~(p.lb == p.ub))[0], where(p.lb == p.ub)[0]
    initLenB = Len(p.b)
    initLenBeq = Len(p.beq)
    nLB, nUB, nEQ = Len(indLB), Len(indUB), Len(indEQ)
    if nLB > 0 or nUB > 0:
        A, b = copy(p.A), copy(p.b)
        p.A = zeros([Len(p.b) + nLB + nUB, p.n])
        p.b = zeros(Len(p.b) + nLB + nUB)
        p.b[:Len(b)] = b.flatten()
        p.A[:Len(b)] = A
        for i in range(len(indLB)):
            p.A[initLenB + i, indLB[i]] = -1
            p.b[initLenB + i] = -p.lb[indLB[i]]
        for i in range(len(indUB)):
            p.A[initLenB + len(indLB) + i, indUB[i]] = 1
            p.b[initLenB + len(indLB) + i] = p.ub[indUB[i]]
    if nEQ > 0:
        Aeq, beq = copy(p.Aeq), copy(p.beq)
        p.Aeq = zeros([Len(p.beq) + nEQ, p.n])
        p.beq = zeros(Len(p.beq) + nEQ)
        p.beq[:Len(beq)] = beq
        p.Aeq[:Len(beq)] = Aeq
        for i in range(len(indEQ)):
            p.Aeq[initLenBeq + i, indEQ[i]] = 1
            p.beq[initLenBeq + i] = p.lb[indEQ[i]]
    p.lb = -inf * ones(p.n)
    p.ub = inf * ones(p.n)

import cvxopt # to ensure cvxopt is installed
import cvxopt.base
from openopt.kernel.ooMisc import Len

matrix = cvxopt.base.matrix
sparse = cvxopt.base.sparse
Sparse = cvxopt.spmatrix

from numpy import asfarray, copy, array, prod
from openopt.kernel.nonOptMisc import isspmatrix

def Matrix(x):
    if x is None or (hasattr(x, 'shape') and prod(x.shape) == 0): return None
    if isspmatrix(x):
        if min(x.shape) > 1:
            from scipy.sparse import find
            I, J, values = find(x)
            return Sparse(array(values, float).tolist(), I.tolist(), J.tolist(), x.shape)
        else:
            x = x.toarray()
    
    x = asfarray(x)
    if x.ndim > 1 and x.nonzero()[0].size < 0.3*x.size: #todo: replace 0.3 by prob param
        return sparse(x.tolist()).T # without tolist currently it doesn't work
    else:  return matrix(x, tc='d')

def xBounds2cvxoptMatrix(p):
    """
    transforms lb - ub bounds into (A, x) <= b, (Aeq, x) = beq conditions
    this func is developed for those solvers that can handle lb, ub only via c(x)<=0, h(x)=0
    """

    #TODO: is reshape/flatten required in newest numpy versions?
    indLB, indUB, indEQ = \
    where(isfinite(p.lb) & ~(p.lb == p.ub))[0], \
    where(isfinite(p.ub) & ~(p.lb == p.ub))[0], \
    where(p.lb == p.ub)[0]    
    
    initLenB = Len(p.b)
    initLenBeq = Len(p.beq)
    nLB, nUB, nEQ = Len(indLB), Len(indUB), Len(indEQ)

    if  nLB>0 or nUB>0:
        A, b = copy(p.A), copy(p.b)
        p.A = zeros([Len(p.b) + nLB+nUB, p.n])
        p.b = zeros(Len(p.b) + nLB+nUB)
        p.b[:Len(b)] = b.flatten() # sometimes flatten is needed when called before runProbSolver(), from tests
        p.A[:Len(b)] = A
        for i in  range(len(indLB)):
            p.A[initLenB+i, indLB[i]] = -1
            p.b[initLenB+i] = -p.lb[indLB[i]]
        for i in  range(len(indUB)):
            p.A[initLenB+len(indLB)+i, indUB[i]] = 1
            p.b[initLenB+len(indLB)+i] = p.ub[indUB[i]]

    if nEQ>0:
        Aeq, beq = copy(p.Aeq), copy(p.beq)
        p.Aeq = zeros([Len(p.beq) + nEQ, p.n])
        p.beq = zeros(Len(p.beq) + nEQ)
        p.beq[:Len(beq)] = beq
        p.Aeq[:Len(beq)] = Aeq
        for i in range(len(indEQ)):
            p.Aeq[initLenBeq+i, indEQ[i]] = 1
            p.beq[initLenBeq+i] = p.lb[indEQ[i]] # = p.ub[indEQ[i]], because they are the same

    p.lb = -inf*ones(p.n)
    p.ub = inf*ones(p.n)
