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```// Copyright Nick Thompson, 2019
// Use, modification and distribution are subject to the
// Boost Software License, Version 1.0.
#include <utility> // for std::pair.
#include <vector>
#include <iostream>
#include <boost/math/special_functions/expm1.hpp>
#include <boost/math/special_functions/sin_pi.hpp>
#include <boost/math/special_functions/cos_pi.hpp>
#include <boost/math/constants/constants.hpp>
#include <boost/math/tools/config.hpp>

#include <mutex>
#include <atomic>
#endif

namespace boost { namespace math { namespace quadrature { namespace detail {

// Ooura and Mori, A robust double exponential formula for Fourier-type integrals,
// eta is the argument to the exponential in equation 3.3:
template<class Real>
std::pair<Real, Real> ooura_eta(Real x, Real alpha) {
using std::expm1;
using std::exp;
using std::abs;
Real expx = exp(x);
Real eta_prime = 2 + alpha/expx + expx/4;
Real eta;
// This is the fast branch:
if (abs(x) > 0.125) {
eta = 2*x - alpha*(1/expx - 1) + (expx - 1)/4;
}
else {// this is the slow branch using expm1 for small x:
eta = 2*x - alpha*expm1(-x) + expm1(x)/4;
}
return {eta, eta_prime};
}

// Ooura and Mori, A robust double exponential formula for Fourier-type integrals,
// equation 3.6:
template<class Real>
Real calculate_ooura_alpha(Real h)
{
using boost::math::constants::pi;
using std::log1p;
using std::sqrt;
Real x = sqrt(16 + 4*log1p(pi<Real>()/h)/h);
return 1/x;
}

template<class Real>
std::pair<Real, Real> ooura_sin_node_and_weight(long n, Real h, Real alpha)
{
using std::expm1;
using std::exp;
using std::abs;
using boost::math::constants::pi;
using std::isnan;

if (n == 0) {
// Equation 44 of https://arxiv.org/pdf/0911.4796.pdf
// Fourier Transform of the Stretched Exponential Function: Analytic Error Bounds,
// Double Exponential Transform, and Open-Source Implementation,
// Joachim Wuttke,
// The C library libkww provides functions to compute the Kohlrausch-Williams-Watts function,
// the Laplace-Fourier transform of the stretched (or compressed) exponential function exp(-t^beta)
// for exponent beta between 0.1 and 1.9 with sixteen decimal digits accuracy.

Real eta_prime_0 = Real(2) + alpha + Real(1)/Real(4);
Real node = pi<Real>()/(eta_prime_0*h);
Real weight = pi<Real>()*boost::math::sin_pi(1/(eta_prime_0*h));
Real eta_dbl_prime = -alpha + Real(1)/Real(4);
Real phi_prime_0 = (1 - eta_dbl_prime/(eta_prime_0*eta_prime_0))/2;
weight *= phi_prime_0;
return {node, weight};
}
Real x = n*h;
auto p = ooura_eta(x, alpha);
auto eta = p.first;
auto eta_prime = p.second;

Real expm1_meta = expm1(-eta);
Real exp_meta = exp(-eta);
Real node = -n*pi<Real>()/expm1_meta;

// I have verified that this is not a significant source of inaccuracy in the weight computation:
Real phi_prime = -(expm1_meta + x*exp_meta*eta_prime)/(expm1_meta*expm1_meta);

// The main source of inaccuracy is in computation of sin_pi.
// But I've agonized over this, and I think it's as good as it can get:
Real s = pi<Real>();
Real arg;
if(eta > 1) {
arg = n/( 1/exp_meta - 1 );
s *= boost::math::sin_pi(arg);
if (n&1) {
s *= -1;
}
}
else if (eta < -1) {
arg = n/(1-exp_meta);
s *= boost::math::sin_pi(arg);
}
else {
arg = -n*exp_meta/expm1_meta;
s *= boost::math::sin_pi(arg);
if (n&1) {
s *= -1;
}
}

Real weight = s*phi_prime;
return {node, weight};
}

#ifdef BOOST_MATH_INSTRUMENT_OOURA
template<class Real>
void print_ooura_estimate(size_t i, Real I0, Real I1, Real omega) {
using std::abs;
std::cout << std::defaultfloat
<< std::setprecision(std::numeric_limits<Real>::digits10)
<< std::fixed;
std::cout << "h = " << Real(1)/Real(1<<i) << ", I_h = " << I0/omega
<< " = " << std::hexfloat << I0/omega << ", absolute error estimate = "
<< std::defaultfloat << std::scientific << abs(I0-I1)  << std::endl;
}
#endif

template<class Real>
std::pair<Real, Real> ooura_cos_node_and_weight(long n, Real h, Real alpha)
{
using std::expm1;
using std::exp;
using std::abs;
using boost::math::constants::pi;

Real x = h*(n-Real(1)/Real(2));
auto p = ooura_eta(x, alpha);
auto eta = p.first;
auto eta_prime = p.second;
Real expm1_meta = expm1(-eta);
Real exp_meta = exp(-eta);
Real node = pi<Real>()*(Real(1)/Real(2)-n)/expm1_meta;

Real phi_prime = -(expm1_meta + x*exp_meta*eta_prime)/(expm1_meta*expm1_meta);

// Takuya Ooura and Masatake Mori,
// Journal of Computational and Applied Mathematics, 112 (1999) 229-241.
// A robust double exponential formula for Fourier-type integrals.
// Equation 4.6
Real s = pi<Real>();
Real arg;
if (eta < -1) {
arg = -(n-Real(1)/Real(2))/expm1_meta;
s *= boost::math::cos_pi(arg);
}
else {
arg = -(n-Real(1)/Real(2))*exp_meta/expm1_meta;
s *= boost::math::sin_pi(arg);
if (n&1) {
s *= -1;
}
}

Real weight = s*phi_prime;
return {node, weight};
}

template<class Real>
class ooura_fourier_sin_detail {
public:
ooura_fourier_sin_detail(const Real relative_error_goal, size_t levels) {
#ifdef BOOST_MATH_INSTRUMENT_OOURA
std::cout << "ooura_fourier_sin with relative error goal " << relative_error_goal
<< " & " << levels << " levels." << std::endl;
#endif // BOOST_MATH_INSTRUMENT_OOURA
if (relative_error_goal < std::numeric_limits<Real>::epsilon() * 2) {
throw std::domain_error("The relative error goal cannot be smaller than the unit roundoff.");
}
using std::abs;
requested_levels_ = levels;
starting_level_ = 0;
rel_err_goal_ = relative_error_goal;
big_nodes_.reserve(levels);
bweights_.reserve(levels);
little_nodes_.reserve(levels);
lweights_.reserve(levels);

for (size_t i = 0; i < levels; ++i) {
if (std::is_same<Real, float>::value) {
}
else if (std::is_same<Real, double>::value) {
}
else {
}
}
}

std::vector<std::vector<Real>> const & big_nodes() const {
return big_nodes_;
}

std::vector<std::vector<Real>> const & weights_for_big_nodes() const {
return bweights_;
}

std::vector<std::vector<Real>> const & little_nodes() const {
return little_nodes_;
}

std::vector<std::vector<Real>> const & weights_for_little_nodes() const {
return lweights_;
}

template<class F>
std::pair<Real,Real> integrate(F const & f, Real omega) {
using std::abs;
using std::max;
using boost::math::constants::pi;

if (omega == 0) {
return {Real(0), Real(0)};
}
if (omega < 0) {
auto p = this->integrate(f, -omega);
return {-p.first, p.second};
}

Real I1 = std::numeric_limits<Real>::quiet_NaN();
Real relative_error_estimate = std::numeric_limits<Real>::quiet_NaN();
// As we compute integrals, we learn about their structure.
// Assuming we compute f(t)sin(wt) for many different omega, this gives some
// a posteriori ability to choose a refinement level that is roughly appropriate.
size_t i = starting_level_;
do {
Real I0 = estimate_integral(f, omega, i);
#ifdef BOOST_MATH_INSTRUMENT_OOURA
print_ooura_estimate(i, I0, I1, omega);
#endif
Real absolute_error_estimate = abs(I0-I1);
Real scale = (max)(abs(I0), abs(I1));
if (!isnan(I1) && absolute_error_estimate <= rel_err_goal_*scale) {
starting_level_ = (max)(long(i) - 1, long(0));
return {I0/omega, absolute_error_estimate/scale};
}
I1 = I0;
} while(++i < big_nodes_.size());

// We've used up all our precomputed levels.
// Now we need to add more.
// It might seems reasonable to just keep adding levels indefinitely, if that's what the user wants.
// But in fact the nodes and weights just merge into each other and the error gets worse after a certain number.
// This value for max_additional_levels was chosen by observation of a slowly converging oscillatory integral:
// f(x) := cos(7cos(x))sin(x)/x
while (big_nodes_.size() < requested_levels_ + max_additional_levels) {
size_t ii = big_nodes_.size();
if (std::is_same<Real, float>::value) {
}
else if (std::is_same<Real, double>::value) {
}
else {
}
Real I0 = estimate_integral(f, omega, ii);
Real absolute_error_estimate = abs(I0-I1);
Real scale = (max)(abs(I0), abs(I1));
#ifdef BOOST_MATH_INSTRUMENT_OOURA
print_ooura_estimate(ii, I0, I1, omega);
#endif
if (absolute_error_estimate <= rel_err_goal_*scale) {
starting_level_ = (max)(long(ii) - 1, long(0));
return {I0/omega, absolute_error_estimate/scale};
}
I1 = I0;
++ii;
}

starting_level_ = static_cast<long>(big_nodes_.size() - 2);
return {I1/omega, relative_error_estimate};
}

private:

template<class PreciseReal>
using std::abs;
size_t current_num_levels = big_nodes_.size();
Real unit_roundoff = std::numeric_limits<Real>::epsilon()/2;
// h0 = 1. Then all further levels have h_i = 1/2^i.
// Since the nodes don't nest, we could conceivably divide h by (say) 1.5, or 3.
// It's not clear how much benefit (or loss) would be obtained from this.
PreciseReal h = PreciseReal(1)/PreciseReal(1<<i);

std::vector<Real> bnode_row;
std::vector<Real> bweight_row;

// This is a pretty good estimate for how many elements will be placed in the vector:
bnode_row.reserve((static_cast<size_t>(1)<<i)*sizeof(Real));
bweight_row.reserve((static_cast<size_t>(1)<<i)*sizeof(Real));

std::vector<Real> lnode_row;
std::vector<Real> lweight_row;

lnode_row.reserve((static_cast<size_t>(1)<<i)*sizeof(Real));
lweight_row.reserve((static_cast<size_t>(1)<<i)*sizeof(Real));

Real max_weight = 1;
auto alpha = calculate_ooura_alpha(h);
long n = 0;
Real w;
do {
auto precise_nw = ooura_sin_node_and_weight(n, h, alpha);
Real node = static_cast<Real>(precise_nw.first);
Real weight = static_cast<Real>(precise_nw.second);
w = weight;
if (bnode_row.size() == bnode_row.capacity()) {
bnode_row.reserve(2*bnode_row.size());
bweight_row.reserve(2*bnode_row.size());
}

bnode_row.push_back(node);
bweight_row.push_back(weight);
if (abs(weight) > max_weight) {
max_weight = abs(weight);
}
++n;
// f(t)->0 as t->infty, which is why the weights are computed up to the unit roundoff.
} while(abs(w) > unit_roundoff*max_weight);

// This class tends to consume a lot of memory; shrink the vectors back down to size:
bnode_row.shrink_to_fit();
bweight_row.shrink_to_fit();
// Why we are splitting the nodes into regimes where t_n >> 1 and t_n << 1?
// It will create the opportunity to sensibly truncate the quadrature sum to significant terms.
n = -1;
do {
auto precise_nw = ooura_sin_node_and_weight(n, h, alpha);
Real node = static_cast<Real>(precise_nw.first);
if (node <= 0) {
break;
}
Real weight = static_cast<Real>(precise_nw.second);
w = weight;
using std::isnan;
if (isnan(node)) {
// This occurs at n = -11 in quad precision:
break;
}
if (lnode_row.size() > 0) {
if (lnode_row[lnode_row.size()-1] == node) {
// The nodes have fused into each other:
break;
}
}
if (lnode_row.size() == lnode_row.capacity()) {
lnode_row.reserve(2*lnode_row.size());
lweight_row.reserve(2*lnode_row.size());
}
lnode_row.push_back(node);
lweight_row.push_back(weight);
if (abs(weight) > max_weight) {
max_weight = abs(weight);
}
--n;
// f(t)->infty is possible as t->0, hence compute up to the min.
} while(abs(w) > (std::numeric_limits<Real>::min)()*max_weight);

lnode_row.shrink_to_fit();
lweight_row.shrink_to_fit();

// std::scoped_lock once C++17 is more common?
std::lock_guard<std::mutex> lock(node_weight_mutex_);
#endif
// Another thread might have already finished this calculation and appended it to the nodes/weights:
if (current_num_levels == big_nodes_.size()) {
big_nodes_.push_back(bnode_row);
bweights_.push_back(bweight_row);

little_nodes_.push_back(lnode_row);
lweights_.push_back(lweight_row);
}
}

template<class F>
Real estimate_integral(F const & f, Real omega, size_t i) {
// Because so few function evaluations are required to get high accuracy on the integrals in the tests,
// Kahan summation doesn't really help.
//auto cond = boost::math::tools::summation_condition_number<Real, true>(0);
Real I0 = 0;
auto const & b_nodes = big_nodes_[i];
auto const & b_weights = bweights_[i];
// Will benchmark if this is helpful:
Real inv_omega = 1/omega;
for(size_t j = 0 ; j < b_nodes.size(); ++j) {
I0 += f(b_nodes[j]*inv_omega)*b_weights[j];
}

auto const & l_nodes = little_nodes_[i];
auto const & l_weights = lweights_[i];
// If f decays rapidly as |t|->infty, not all of these calls are necessary.
for (size_t j = 0; j < l_nodes.size(); ++j) {
I0 += f(l_nodes[j]*inv_omega)*l_weights[j];
}
return I0;
}

std::mutex node_weight_mutex_;
#endif
// Nodes for n >= 0, giving t_n = pi*phi(nh)/h. Generally t_n >> 1.
std::vector<std::vector<Real>> big_nodes_;
// The term bweights_ will indicate that these are weights corresponding
// to the big nodes:
std::vector<std::vector<Real>> bweights_;

// Nodes for n < 0: Generally t_n << 1, and an invariant is that t_n > 0.
std::vector<std::vector<Real>> little_nodes_;
std::vector<std::vector<Real>> lweights_;
Real rel_err_goal_;

std::atomic<long> starting_level_{};
#else
long starting_level_;
#endif
size_t requested_levels_;
};

template<class Real>
class ooura_fourier_cos_detail {
public:
ooura_fourier_cos_detail(const Real relative_error_goal, size_t levels) {
#ifdef BOOST_MATH_INSTRUMENT_OOURA
std::cout << "ooura_fourier_cos with relative error goal " << relative_error_goal
<< " & " << levels << " levels." << std::endl;
std::cout << "epsilon for type = " << std::numeric_limits<Real>::epsilon() << std::endl;
#endif // BOOST_MATH_INSTRUMENT_OOURA
if (relative_error_goal < std::numeric_limits<Real>::epsilon() * 2) {
throw std::domain_error("The relative error goal cannot be smaller than the unit roundoff!");
}

using std::abs;
requested_levels_ = levels;
starting_level_ = 0;
rel_err_goal_ = relative_error_goal;
big_nodes_.reserve(levels);
bweights_.reserve(levels);
little_nodes_.reserve(levels);
lweights_.reserve(levels);

for (size_t i = 0; i < levels; ++i) {
if (std::is_same<Real, float>::value) {
}
else if (std::is_same<Real, double>::value) {
}
else {
}
}

}

template<class F>
std::pair<Real,Real> integrate(F const & f, Real omega) {
using std::abs;
using std::max;
using boost::math::constants::pi;

if (omega == 0) {
throw std::domain_error("At omega = 0, the integral is not oscillatory. The user must choose an appropriate method for this case.\n");
}

if (omega < 0) {
return this->integrate(f, -omega);
}

Real I1 = std::numeric_limits<Real>::quiet_NaN();
Real absolute_error_estimate = std::numeric_limits<Real>::quiet_NaN();
Real scale = std::numeric_limits<Real>::quiet_NaN();
size_t i = starting_level_;
do {
Real I0 = estimate_integral(f, omega, i);
#ifdef BOOST_MATH_INSTRUMENT_OOURA
print_ooura_estimate(i, I0, I1, omega);
#endif
absolute_error_estimate = abs(I0-I1);
scale = (max)(abs(I0), abs(I1));
if (!isnan(I1) && absolute_error_estimate <= rel_err_goal_*scale) {
starting_level_ = (max)(long(i) - 1, long(0));
return {I0/omega, absolute_error_estimate/scale};
}
I1 = I0;
} while(++i < big_nodes_.size());

while (big_nodes_.size() < requested_levels_ + max_additional_levels) {
size_t ii = big_nodes_.size();
if (std::is_same<Real, float>::value) {
}
else if (std::is_same<Real, double>::value) {
}
else {
}
Real I0 = estimate_integral(f, omega, ii);
#ifdef BOOST_MATH_INSTRUMENT_OOURA
print_ooura_estimate(ii, I0, I1, omega);
#endif
absolute_error_estimate = abs(I0-I1);
scale = (max)(abs(I0), abs(I1));
if (absolute_error_estimate <= rel_err_goal_*scale) {
starting_level_ = (max)(long(ii) - 1, long(0));
return {I0/omega, absolute_error_estimate/scale};
}
I1 = I0;
++ii;
}

starting_level_ = static_cast<long>(big_nodes_.size() - 2);
return {I1/omega, absolute_error_estimate/scale};
}

private:

template<class PreciseReal>
using std::abs;
size_t current_num_levels = big_nodes_.size();
Real unit_roundoff = std::numeric_limits<Real>::epsilon()/2;
PreciseReal h = PreciseReal(1)/PreciseReal(1<<i);

std::vector<Real> bnode_row;
std::vector<Real> bweight_row;
bnode_row.reserve((static_cast<size_t>(1)<<i)*sizeof(Real));
bweight_row.reserve((static_cast<size_t>(1)<<i)*sizeof(Real));

std::vector<Real> lnode_row;
std::vector<Real> lweight_row;

lnode_row.reserve((static_cast<size_t>(1)<<i)*sizeof(Real));
lweight_row.reserve((static_cast<size_t>(1)<<i)*sizeof(Real));

Real max_weight = 1;
auto alpha = calculate_ooura_alpha(h);
long n = 0;
Real w;
do {
auto precise_nw = ooura_cos_node_and_weight(n, h, alpha);
Real node = static_cast<Real>(precise_nw.first);
Real weight = static_cast<Real>(precise_nw.second);
w = weight;
if (bnode_row.size() == bnode_row.capacity()) {
bnode_row.reserve(2*bnode_row.size());
bweight_row.reserve(2*bnode_row.size());
}

bnode_row.push_back(node);
bweight_row.push_back(weight);
if (abs(weight) > max_weight) {
max_weight = abs(weight);
}
++n;
// f(t)->0 as t->infty, which is why the weights are computed up to the unit roundoff.
} while(abs(w) > unit_roundoff*max_weight);

bnode_row.shrink_to_fit();
bweight_row.shrink_to_fit();
n = -1;
do {
auto precise_nw = ooura_cos_node_and_weight(n, h, alpha);
Real node = static_cast<Real>(precise_nw.first);
// The function cannot be singular at zero,
// so zero is not a unreasonable node,
// unlike in the case of the Fourier Sine.
// Hence only break if the node is negative.
if (node < 0) {
break;
}
Real weight = static_cast<Real>(precise_nw.second);
w = weight;
if (lnode_row.size() > 0) {
if (lnode_row.back() == node) {
// The nodes have fused into each other:
break;
}
}
if (lnode_row.size() == lnode_row.capacity()) {
lnode_row.reserve(2*lnode_row.size());
lweight_row.reserve(2*lnode_row.size());
}

lnode_row.push_back(node);
lweight_row.push_back(weight);
if (abs(weight) > max_weight) {
max_weight = abs(weight);
}
--n;
} while(abs(w) > (std::numeric_limits<Real>::min)()*max_weight);

lnode_row.shrink_to_fit();
lweight_row.shrink_to_fit();

std::lock_guard<std::mutex> lock(node_weight_mutex_);
#endif

// Another thread might have already finished this calculation and appended it to the nodes/weights:
if (current_num_levels == big_nodes_.size()) {
big_nodes_.push_back(bnode_row);
bweights_.push_back(bweight_row);

little_nodes_.push_back(lnode_row);
lweights_.push_back(lweight_row);
}
}

template<class F>
Real estimate_integral(F const & f, Real omega, size_t i) {
Real I0 = 0;
auto const & b_nodes = big_nodes_[i];
auto const & b_weights = bweights_[i];
Real inv_omega = 1/omega;
for(size_t j = 0 ; j < b_nodes.size(); ++j) {
I0 += f(b_nodes[j]*inv_omega)*b_weights[j];
}

auto const & l_nodes = little_nodes_[i];
auto const & l_weights = lweights_[i];
for (size_t j = 0; j < l_nodes.size(); ++j) {
I0 += f(l_nodes[j]*inv_omega)*l_weights[j];
}
return I0;
}

std::mutex node_weight_mutex_;
#endif

std::vector<std::vector<Real>> big_nodes_;
std::vector<std::vector<Real>> bweights_;

std::vector<std::vector<Real>> little_nodes_;
std::vector<std::vector<Real>> lweights_;
Real rel_err_goal_;