#ifndef EIGEN_PIESSENS_H
#define EIGEN_PIESSENS_H

namespace Eigen
{

    /**
    * \ingroup NumericalIntegration_Module
    *
    * \class Piessens
    *
    * \brief This class computes Kronrod abscissae & weights for arbitrary precision
    *
    * \tparam Scalar floating point type
    *
    * This class is based on the work by R. Piessens, et.al,published in the
    * journal Mathematics of Computation, Volume 28, Number 125, January, 1974.
    */
    template <typename Scalar>
    class Piessens
    {
    public:

        /**
        * \brief kronrod adds n+1 points to an n-point Gaussian rule.
        *
        *    This function is a C++ implementation of original work by R. Piessens, et.al,
        *    published in the journal Mathematics of Computation, Volume 28, Number 125,
        *    January, 1974.  Where possible strucutre and variable naming convention
        *    has been aligned to favor work of the QUADPACK Gaus-Kronrod integration
        *    routines created by individuals of the same group.
        *
        *    This function calculates the abscissas and weights of the (2n+1)-point
        *    Gauss-Kronrod quadrature formula which is obtained from the n-point
        *    Gauss quadrature formula with the optimal addition of (n+1)-points.
        *
        *    The optimally added points are the Kronrod abscissae.  The
        *    abscissas and weights for both the Gauss and Gauss Kronrod rules
        *    are calculated for integration over the interval (-1, +1).
        *
        *    Because the quadrature formula is symmetric with respect to the origin,
        *    only the positive abscissas are calculated.  Weights corresponding to the
        *    symetric abscissae are equal.  Weights of weightGauss are calculated as well.
        *
        *    Work by Dr. John Burkhardt made note that the code published in Mathematics of
        *    Computation omitted the definition of the second Chebyshev coefficient (chebCoeff2),
        *    and Dr. Burkhardt's contributions are reflected here with permission.
        *
        *    The arrays abcsGaussKronrod, weightGaussKronrod and weightGauss contain the
        *    positive abscissae in decreasing order, and the weights of each abscissa in
        *    the Gauss-Kronrod and Gauss rules, respectively.
        *
        *    Ported to C++/Eigen and templated for multiprecision by Mark Sauder,
        *    Sreekumar Thaithara Balan, Matt Beall, and R. Jeff Jenkins - September 2014.
        *
        * Input Parameters:
        * \param[in] n, the order of the Gauss rule.
        *
        * Return Parameters:
        * \param[in,out] abscGaussKronrod[n+1] The Gauss-Kronrod abscissae.
        * \param[in,out] weightGaussKronrod[n+1] The weights for the Gauss-Kronrod rule.
        * \param[in,out] weightGauss[n+1] The weights for the Gauss rule.
        */
        static void kronrod(unsigned int nNodes,
                            Eigen::Array<Scalar, Eigen::Dynamic, 1>& abscGaussKronrod,
                            Eigen::Array<Scalar, Eigen::Dynamic, 1>& weightGaussKronrod,
                            Eigen::Array<Scalar, Eigen::Dynamic, 1>& weightGauss)
        {
            unsigned int arraySize = nNodes + 1;
            abscGaussKronrod = Eigen::Array<Scalar, Eigen::Dynamic, 1>::Zero(arraySize);
            weightGaussKronrod = Eigen::Array<Scalar, Eigen::Dynamic, 1>::Zero(arraySize);
            weightGauss = Eigen::Array<Scalar, Eigen::Dynamic, 1>::Zero(arraySize / 2);

            Scalar aN(0.0);
            Scalar d(2.0);

            for (size_t i = 0; i < nNodes; ++i)
            {
                aN += Scalar(1.0);
                d *= aN / (aN + Scalar(0.5));
            }

            unsigned int m = (nNodes + 1) / 2;
            bool even = (nNodes == 2 * m);

            // aK is an index variable to account for calculating only the positive abscissae
            Scalar aK = aN;

            // Calculation of the Chebyshev coefficients of the orthogonal polynomial.
            Eigen::Array<Scalar, Eigen::Dynamic, 1> tau(m);
            tau(0) = (aN + Scalar(2.0)) / (Scalar(2) * aN + Scalar(3.0));

            Eigen::Array<Scalar, Eigen::Dynamic, 1> betaCoeffs(m + 1);
            betaCoeffs(m - 1) = tau(0) - Scalar(1.0);

            for (size_t k = 1; k < m; ++k)
            {
                // This step accounts for both positive and negative abscissae
                aK += Scalar(2.0);

                tau(k) = ((aK - Scalar(1.0)) * aK - aN * (aN + Scalar(1.0) )) * (aK + Scalar(2.0) ) * tau(k-1) /
                         (aK * ((aK + Scalar(3.0) ) * (aK + Scalar(2.0) ) - aN * (aN + Scalar(1.0) )));

                betaCoeffs(m-k-1) = tau(k);

                for (size_t i = 1; i <= k; ++i)
                {
                    betaCoeffs(m - k - 1) = betaCoeffs(m - k + i - 1) * tau(i - 1) + betaCoeffs(m - k - 1);
                }
            }

            betaCoeffs(m) = Scalar(1.);

            // Calculation of approximate values for the abscissae as inital values
            // for the Newton-Raphson iterative solution.  These values are derived
            // from Pythagorean identities to the original code to more closely follow
            // the mathematics of the 1974 CoM paper.

            // @TODO The usage of constant Pi with fixed precision needs to be changed to the following for multiprecision
            //RealScalar pi = NumTraits<RealScalar>::Pi();

            Scalar s1 = sin((M_PI / Scalar(2) ) / (Scalar(2.) * aN + Scalar(1.0) ));
            Scalar c1 = cos((M_PI / Scalar(2) ) / (Scalar(2.) * aN + Scalar(1.0) ));

            Scalar s2 = sin((M_PI) / (Scalar(2.) * aN + Scalar(1.0) ));
            Scalar c2 = cos((M_PI) / (Scalar(2.) * aN + Scalar(1.0) ));

            // Coefficient for Gauss and Kronrod abscissae and weights
            Scalar chebCoeff1 = Scalar(1.0) - Scalar(1.0) / (Scalar(8.0) * aN * aN) + Scalar(1.0) / (Scalar(8.0) * aN * aN * aN);
            Scalar chebCoeff2 = Scalar(2.0) / (Scalar(2. * nNodes + 1));

            for (size_t i = 1; i <= nNodes; ++i)
            {
                chebCoeff2 =  Scalar(4.0) * chebCoeff2 * i / (nNodes + i);
            }

            Scalar abscK = chebCoeff1 * c1;
            Scalar temp(0.);

            // Calculation of the K-th (Kronrod) abscissa and the corresponding weight.
            for (size_t k = 0; k < nNodes; ++k)
            {
                abscWeightKronrod(nNodes, m, even, chebCoeff2, betaCoeffs, abscK, weightGaussKronrod(k));
                abscGaussKronrod(k) = abscK;
                ++k;

                temp = c1;
                c1 = temp * c2 - s1 * s2;
                s1 = temp * s2 + s1 * c2;
                abscK = chebCoeff1 * c1;

                // Calculation of the k+1 (Gauss) abscissa and the corresponding weights.
                abscWeightGauss(nNodes, m, even, chebCoeff2, betaCoeffs, abscK, weightGaussKronrod(k),
                                weightGauss(k/2));
                abscGaussKronrod(k) = abscK;

                temp = c1;
                c1 = temp * c2 - s1 * s2;
                s1 = temp * s2 + s1 * c2;
                abscK = chebCoeff1 * c1;
            }

            // Add a Kronrod abscissa at the origin if n is even.
            if (even)
            {
                abscWeightKronrod(nNodes, m, even, chebCoeff2, betaCoeffs, abscK, weightGaussKronrod(nNodes));
            }

            // Set the abscissa value at the origin to zero and exit the function.
            abscGaussKronrod(nNodes) = Scalar(0.0);
            return;
        }


        /**
        * \brief abscWeightKronrod calculates a Kronrod abscissa and weight.
        *
        *  Input Parameters:
        * \param[in] betaCoeffs[m+1] The Chebyshev coefficients.
        * \param[in] chebCoeff A value needed to compute weights.
        * \param[in] even A boolean variable that is TRUE if n is even.
        * \param[in] n The order of the Gauss rule.
        * \param[in] m The value of ( n + 1 ) / 2.
        *
        *  Input/output:
        * \param[in,out] abscGaussKronrod An estimate for the abscissa on input and the computed abscissa on output.
        * \param[in,out] weightGaussKronrod The Gauss-Kronrod weight.
        */
        static void abscWeightKronrod(unsigned int nNodes,
                                      unsigned int m,
                                      bool even,
                                      Scalar chebCoeff,
                                      Eigen::Array<Scalar,
                                      Eigen::Dynamic, 1> betaCoeffs,
                                      Scalar& abscGaussKronrod,
                                      Scalar& weightGaussKronrod)
        {
            Scalar ai;

            Scalar b0(0.);
            Scalar b1(0.);
            Scalar b2(0.);

            Scalar d0(0.);
            Scalar d1(0.);
            Scalar d2(0.);

            Scalar delta(1.);
            Scalar dif(0.);

            Scalar f(0.);
            Scalar fd(0.);

            Scalar yy(0.);

            int i = 0;

            size_t iter = 0;
            size_t iterationLimit = 50;

            // Iterative process for the computation of a Kronrod abscissa.
            while (abs(delta) > machineEpsilon())
            {
                ++iter;

                b1 = Scalar(0.0);
                b2 = betaCoeffs(m);

                yy = Scalar(4.0) * abscGaussKronrod * abscGaussKronrod - Scalar(2.0);
                d1 = Scalar(0.0);

                if (even)
                {
                    ai = Scalar(m + m + 1);
                    d2 = ai * betaCoeffs(m);
                    dif = Scalar(2.0);
                }
                else
                {
                    ai = Scalar(m + 1);
                    d2 = Scalar(0.0);
                    dif = Scalar(1.0);
                }

                for (size_t k = 0; k < m; ++k)
                {
                    ai -= dif;
                    i = m - (int)k - 1;
                    b0 = b1;
                    b1 = b2;
                    d0 = d1;
                    d1 = d2;
                    b2 = yy * b1 - b0 + betaCoeffs(i);

                    if (!even)
                    {
                        i += 1;
                    }

                    d2 = yy * d1 - d0 + ai * betaCoeffs(i);
                }

                if (even)
                {
                    f = abscGaussKronrod * (b2 - b1);
                    fd = d2 + d1;
                }
                else
                {
                    f = Scalar(0.5) * (b2 - b0);
                    fd = Scalar(4.0) * abscGaussKronrod * d2;
                }

                // Newton correction.
                delta = f / fd;
                abscGaussKronrod -= delta;

                if (abscGaussKronrod == Scalar(0.))
                {
                    break;
                }

                // Identify non-convergence of the iterative solver after 50 iterations
                if (iter > iterationLimit)
                {
                    std::cout << "@abscWeightKronrod Newton-Raphson iterative abscissae solver failed."<<std::endl;
                    return;
                }
            }

            // Computation of the weight.
            d0 = Scalar(1.);
            d1 = abscGaussKronrod;
            ai = Scalar(0.);

            for (size_t k = 0; k < nNodes - 1; ++k)
            {
                ai = ai + Scalar(1.);
                d2 = ((ai + ai + Scalar(1.)) * (abscGaussKronrod * d1) - (ai * d0)) / (ai + Scalar(1.));
                d0 = d1;
                d1 = d2;
            }

            weightGaussKronrod = chebCoeff / (fd * d2);
            return;
        }

        /**
        * \brief abscWeightGauss calculates a Gaussian abscissa and two weights.
        *
        *   Input Parameters
        * \param[in] betaCoeffs[m+1] The Chebyshev coefficients.
        * \param[in] chebCoeff A value needed to compute weights.
        * \param[in] even A boolean variable that is TRUE if n is even.
        * \param[in] nNodes The order of the Gauss rule.
        * \param[in] m The value of ( nNodes + 1 ) / 2.
        *
        *   Input/Output
        * \param[in,out] abscGaussKronrod An estimate for the abscissa on input and the computed abscissa on output.
        * \param[in,out] weightGaussKronrod The Gauss-Kronrod weight.
        * \param[in,out] weightGauss The Gauss weight.
        */
        static void abscWeightGauss(unsigned int nNodes,
                                    unsigned int m,
                                    bool even,
                                    const Scalar& chebCoeff,
                                    Eigen::Array<Scalar, Eigen::Dynamic, 1> betaCoeffs,
                                    Scalar& abscGaussKronrod,
                                    Scalar& weightGaussKronrod,
                                    Scalar& weightGauss)
        {
            Scalar ai(0.);
            Scalar delta(1.);

            Scalar p0(0.);
            Scalar p1(0.);
            Scalar p2(0.);

            Scalar pd0(0.);
            Scalar pd1(0.);
            Scalar pd2(0.);

            Scalar yy(0.);

            size_t iter = 0;
            size_t iterationLimit = 50;

            //  Iterative process for the computation of a Gaussian abscissa.
            while (abs(delta) > machineEpsilon())
            {
                ++iter;
                p0 = Scalar(1.);
                p1 = abscGaussKronrod;
                pd0 = Scalar(0.);
                pd1 = Scalar(1.);

                // If nNodes <= 1, initialize p2 and pd2 to avoid problems calculating delta.
                if (nNodes <= 1)
                {
                    if (machineEpsilon() < abs(abscGaussKronrod))
                    {
                        p2 = (Scalar(3.0) * (abscGaussKronrod) * (abscGaussKronrod) - Scalar(1.0)) / Scalar(2.0);
                        pd2 = Scalar(3.0) * (abscGaussKronrod);
                    }
                    else
                    {
                        p2 = Scalar(3.0) * (abscGaussKronrod);
                        pd2 = Scalar(3.0);
                    }
                }

                ai = Scalar(0.0);

                for (size_t k = 0; k < nNodes - 1; ++k)
                {
                    ai = ai + Scalar(1.0);
                    p2 = ((ai + ai + Scalar(1.0) ) * abscGaussKronrod * p1 - ai * p0) / (ai + Scalar(1.0) );
                    pd2 = ((ai + ai + Scalar(1.0) ) * (p1 + abscGaussKronrod * pd1) - ai * pd0) / (ai + Scalar(1.0) );
                    p0 = p1;
                    p1 = p2;
                    pd0 = pd1;
                    pd1 = pd2;
                }

                // Newton correction.
                delta = p2 / pd2;

                if (abscGaussKronrod == Scalar(0.0))
                {
                    abscGaussKronrod -= delta;
                    break;
                }
                else
                {
                    abscGaussKronrod -= delta;
                }

                // Identify non-convergence of the iterative solver after iteration limit.
                if (iter > iterationLimit)
                {
                    std::cout << "@abscWeightGauss Newton-Raphson iterative abscissae solver failed."<<std::endl;;
                    return;
                }
            }

            // Computation of the Gauss weight.
            Scalar aN = nNodes;
            weightGauss = Scalar(2.0) / (aN * pd2 * p0);

            // Initialize
            p1 = Scalar(0.0);
            p2 = betaCoeffs(m);
            yy = Scalar(4.0) * (abscGaussKronrod) * (abscGaussKronrod) - Scalar(2.0);

            for (size_t k = 1; k <= m; ++k)
            {
                p0 = p1;
                p1 = p2;
                p2 = yy * p1 - p0 + betaCoeffs(m - k);
            }

            if (even)
            {
                weightGaussKronrod = weightGauss + chebCoeff / (abscGaussKronrod * pd2 * (p2 - p1));
            }
            else
            {
                weightGaussKronrod = weightGauss + (Scalar(2.0) * chebCoeff) / (pd2 * (p2 - p0));
            }

            return;
        }

        /**
        * \brief machineEpsilon returns the machine precision roundoff error.
        * \returns Returns the machine precision round-off error for double precision
        */
        static Scalar machineEpsilon()
        {
            // ISO standard machine precision values
            // half precision:      2^-10   (9.76563e-04)
            // single precision:    2^-23   (1.1920928955078125e-07)
            // double precision:    2^-52   (2.22044604925031308e-16)
            // extended precision:  2^-63   (1.08420217248550443e-19)
            // quad precision:      2^-112  (1.9259299e-34)

            return NumTraits<Scalar>::epsilon();
        }

        /**
         * \brief A method ofr computing Gauss/Kronrod abscissae and weights
         * \param nNodes Gauss-Legendre degree
         * \param abscGaussKronrod Gauss/Kronrod abscissae
         * \param weightGaussKronrod Gauss/Kronrod weights
         * \param abscGauss Gauss abscissae
         * \param weightGauss Gauss weights
         */
        static void computeAbscissaeAndWeights(unsigned int nNodes,
            Eigen::Array<Scalar, Eigen::Dynamic, 1> &abscGaussKronrod,
            Eigen::Array<Scalar, Eigen::Dynamic, 1> &weightGaussKronrod,
            Eigen::Array<Scalar, Eigen::Dynamic, 1> abscGauss,
            Eigen::Array<Scalar, Eigen::Dynamic, 1> &weightGauss)
        {
            Piessens::kronrod(nNodes,abscGaussKronrod,weightGaussKronrod,weightGauss);
            abscGauss = Eigen::Array<Scalar, Eigen::Dynamic, 1>::Zero(abscGauss.rows());
        }
    };

}//namespace Eigen
#endif //EIGEN_PIESSENS_H