479 lines
15 KiB
C++
479 lines
15 KiB
C++
/*
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* Copyright Nick Thompson, John Maddock 2020
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* Use, modification and distribution are subject to the
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* Boost Software License, Version 1.0. (See accompanying file
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* LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
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*/
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#ifndef BOOST_MATH_SPECIAL_DAUBECHIES_SCALING_HPP
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#define BOOST_MATH_SPECIAL_DAUBECHIES_SCALING_HPP
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#include <vector>
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#include <array>
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#include <cmath>
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#include <thread>
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#include <future>
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#include <iostream>
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#include <boost/math/special_functions/detail/daubechies_scaling_integer_grid.hpp>
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#include <boost/math/filters/daubechies.hpp>
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#include <boost/math/interpolators/detail/cubic_hermite_detail.hpp>
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#include <boost/math/interpolators/detail/quintic_hermite_detail.hpp>
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#include <boost/math/interpolators/detail/septic_hermite_detail.hpp>
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namespace boost::math {
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template<class Real, int p, int order>
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std::vector<Real> daubechies_scaling_dyadic_grid(int64_t j_max)
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{
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using std::isnan;
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using std::sqrt;
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auto c = boost::math::filters::daubechies_scaling_filter<Real, p>();
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Real scale = sqrt(static_cast<Real>(2))*(1 << order);
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for (auto & x : c)
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{
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x *= scale;
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}
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auto phik = detail::daubechies_scaling_integer_grid<Real, p, order>();
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// Maximum sensible j for 32 bit floats is j_max = 22:
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if (std::is_same_v<Real, float>)
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{
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if (j_max > 23)
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{
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throw std::logic_error("Requested dyadic grid more dense than number of representables on the interval.");
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}
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}
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std::vector<Real> v(2*p + (2*p-1)*((1<<j_max) -1), std::numeric_limits<Real>::quiet_NaN());
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v[0] = 0;
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v[v.size()-1] = 0;
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for (int64_t i = 0; i < static_cast<int64_t>(phik.size()); ++i) {
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v[i*(1uLL<<j_max)] = phik[i];
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}
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for (int64_t j = 1; j <= j_max; ++j)
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{
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int64_t k_max = v.size()/(int64_t(1) << (j_max-j));
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for (int64_t k = 1; k < k_max; k += 2)
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{
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// Where this value will go:
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int64_t delivery_idx = k*(1uLL << (j_max-j));
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// This is a nice check, but we've tested this exhaustively, and it's an expensive check:
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//if (delivery_idx >= static_cast<int64_t>(v.size())) {
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// std::cerr << "Delivery index out of range!\n";
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// continue;
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//}
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Real term = 0;
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for (int64_t l = 0; l < static_cast<int64_t>(c.size()); ++l)
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{
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int64_t idx = k*(int64_t(1) << (j_max - j + 1)) - l*(int64_t(1) << j_max);
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if (idx < 0)
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{
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break;
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}
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if (idx < static_cast<int64_t>(v.size()))
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{
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term += c[l]*v[idx];
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}
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}
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// Again, another nice check:
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//if (!isnan(v[delivery_idx])) {
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// std::cerr << "Delivery index already populated!, = " << v[delivery_idx] << "\n";
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// std::cerr << "would overwrite with " << term << "\n";
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//}
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v[delivery_idx] = term;
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}
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}
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return v;
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}
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namespace detail {
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template<class RandomAccessContainer>
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class matched_holder {
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public:
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using Real = typename RandomAccessContainer::value_type;
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matched_holder(RandomAccessContainer && y, RandomAccessContainer && dydx, int grid_refinements, Real x0) : x0_{x0}, y_{std::move(y)}, dy_{std::move(dydx)}
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{
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inv_h_ = (1 << grid_refinements);
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Real h = 1/inv_h_;
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for (auto & dy : dy_)
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{
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dy *= h;
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}
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}
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inline Real operator()(Real x) const
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{
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using std::floor;
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using std::sqrt;
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// This is the exact Holder exponent, but it's pessimistic almost everywhere!
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// It's only exactly right at dyadic rationals.
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//Real const alpha = 2 - log(1+sqrt(Real(3)))/log(Real(2));
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// We're gonna use alpha = 1/2, rather than 0.5500...
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Real s = (x-x0_)*inv_h_;
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Real ii = floor(s);
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auto i = static_cast<decltype(y_.size())>(ii);
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Real t = s - ii;
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Real dphi = dy_[i+1];
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Real diff = y_[i+1] - y_[i];
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return y_[i] + (2*dphi - diff)*t + 2*sqrt(t)*(diff-dphi);
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}
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int64_t bytes() const
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{
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return 2*y_.size()*sizeof(Real) + sizeof(this);
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}
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private:
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Real x0_;
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Real inv_h_;
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RandomAccessContainer y_;
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RandomAccessContainer dy_;
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};
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template<class RandomAccessContainer>
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class matched_holder_aos {
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public:
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using Point = typename RandomAccessContainer::value_type;
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using Real = typename Point::value_type;
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matched_holder_aos(RandomAccessContainer && data, int grid_refinements, Real x0) : x0_{x0}, data_{std::move(data)}
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{
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inv_h_ = Real(1uLL << grid_refinements);
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Real h = 1/inv_h_;
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for (auto & datum : data_)
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{
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datum[1] *= h;
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}
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}
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inline Real operator()(Real x) const
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{
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using std::floor;
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using std::sqrt;
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Real s = (x-x0_)*inv_h_;
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Real ii = floor(s);
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auto i = static_cast<decltype(data_.size())>(ii);
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Real t = s - ii;
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Real y0 = data_[i][0];
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Real y1 = data_[i+1][0];
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Real dphi = data_[i+1][1];
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Real diff = y1 - y0;
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return y0 + (2*dphi - diff)*t + 2*sqrt(t)*(diff-dphi);
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}
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int64_t bytes() const
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{
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return data_.size()*data_[0].size()*sizeof(Real) + sizeof(this);
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}
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private:
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Real x0_;
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Real inv_h_;
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RandomAccessContainer data_;
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};
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template<class RandomAccessContainer>
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class linear_interpolation {
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public:
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using Real = typename RandomAccessContainer::value_type;
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linear_interpolation(RandomAccessContainer && y, RandomAccessContainer && dydx, int grid_refinements) : y_{std::move(y)}, dydx_{std::move(dydx)}
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{
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s_ = (1 << grid_refinements);
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}
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inline Real operator()(Real x) const
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{
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using std::floor;
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Real y = x*s_;
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Real k = floor(y);
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int64_t kk = static_cast<int64_t>(k);
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Real t = y - k;
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return (1-t)*y_[kk] + t*y_[kk+1];
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}
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inline Real prime(Real x) const
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{
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using std::floor;
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Real y = x*s_;
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Real k = floor(y);
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int64_t kk = static_cast<int64_t>(k);
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Real t = y - k;
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return (1-t)*dydx_[kk] + t*dydx_[kk+1];
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}
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int64_t bytes() const
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{
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return (1 + y_.size() + dydx_.size())*sizeof(Real) + sizeof(y_) + sizeof(dydx_);
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}
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private:
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Real s_;
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RandomAccessContainer y_;
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RandomAccessContainer dydx_;
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};
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template<class RandomAccessContainer>
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class linear_interpolation_aos {
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public:
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using Point = typename RandomAccessContainer::value_type;
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using Real = typename Point::value_type;
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linear_interpolation_aos(RandomAccessContainer && data, int grid_refinements, Real x0) : x0_{x0}, data_{std::move(data)}
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{
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s_ = Real(1uLL << grid_refinements);
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}
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inline Real operator()(Real x) const
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{
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using std::floor;
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Real y = (x-x0_)*s_;
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Real k = floor(y);
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int64_t kk = static_cast<int64_t>(k);
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Real t = y - k;
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return (t != 0) ? (1-t)*data_[kk][0] + t*data_[kk+1][0] : data_[kk][0];
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}
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inline Real prime(Real x) const
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{
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using std::floor;
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Real y = (x-x0_)*s_;
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Real k = floor(y);
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int64_t kk = static_cast<int64_t>(k);
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Real t = y - k;
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return t != 0 ? (1-t)*data_[kk][1] + t*data_[kk+1][1] : data_[kk][1];
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}
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int64_t bytes() const
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{
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return sizeof(this) + data_.size()*data_[0].size()*sizeof(Real);
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}
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private:
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Real x0_;
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Real s_;
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RandomAccessContainer data_;
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};
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template <class T>
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struct daubechies_eval_type
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{
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typedef T type;
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static const std::vector<T>& vector_cast(const std::vector<T>& v) { return v; }
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};
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template <>
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struct daubechies_eval_type<float>
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{
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typedef double type;
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inline static std::vector<float> vector_cast(const std::vector<double>& v)
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{
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std::vector<float> result(v.size());
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for (unsigned i = 0; i < v.size(); ++i)
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result[i] = static_cast<float>(v[i]);
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return result;
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}
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};
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template <>
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struct daubechies_eval_type<double>
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{
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typedef long double type;
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inline static std::vector<double> vector_cast(const std::vector<long double>& v)
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{
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std::vector<double> result(v.size());
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for (unsigned i = 0; i < v.size(); ++i)
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result[i] = static_cast<double>(v[i]);
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return result;
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}
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};
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struct null_interpolator
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{
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template <class T>
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T operator()(const T&)
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{
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return 1;
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}
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};
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} // namespace detail
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template<class Real, int p>
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class daubechies_scaling {
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//
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// Some type manipulation so we know the type of the interpolator, and the vector type it requires:
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//
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typedef std::vector<std::array<Real, p < 6 ? 2 : p < 10 ? 3 : 4>> vector_type;
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//
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// List our interpolators:
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//
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typedef std::tuple<
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detail::null_interpolator, detail::matched_holder_aos<vector_type>, detail::linear_interpolation_aos<vector_type>,
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interpolators::detail::cardinal_cubic_hermite_detail_aos<vector_type>, interpolators::detail::cardinal_quintic_hermite_detail_aos<vector_type>,
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interpolators::detail::cardinal_septic_hermite_detail_aos<vector_type> > interpolator_list;
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//
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// Select the one we need:
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//
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typedef std::tuple_element_t<
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p == 1 ? 0 :
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p == 2 ? 1 :
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p == 3 ? 2 :
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p <= 5 ? 3 :
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p <= 9 ? 4 : 5, interpolator_list> interpolator_type;
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public:
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daubechies_scaling(int grid_refinements = -1)
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{
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static_assert(p < 20, "Daubechies scaling functions are only implemented for p < 20.");
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static_assert(p > 0, "Daubechies scaling functions must have at least 1 vanishing moment.");
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if constexpr (p == 1)
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{
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return;
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}
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else {
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if (grid_refinements < 0)
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{
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if (std::is_same_v<Real, float>)
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{
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if (grid_refinements == -2)
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{
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// Control absolute error:
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// p= 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19
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std::array<int, 20> r{ -1, -1, 18, 19, 16, 11, 8, 7, 7, 7, 5, 5, 4, 4, 4, 4, 3, 3, 3, 3 };
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grid_refinements = r[p];
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}
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else
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{
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// Control relative error:
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// p= 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19
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std::array<int, 20> r{ -1, -1, 21, 21, 21, 17, 16, 15, 14, 13, 12, 11, 11, 11, 11, 11, 11, 11, 11, 11 };
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grid_refinements = r[p];
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}
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}
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else if (std::is_same_v<Real, double>)
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{
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// p= 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19
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std::array<int, 20> r{ -1, -1, 21, 21, 21, 21, 21, 21, 21, 21, 20, 20, 19, 19, 18, 18, 18, 18, 18, 18 };
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grid_refinements = r[p];
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}
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else
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{
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grid_refinements = 21;
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}
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}
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// Compute the refined grid:
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// In fact for float precision I know the grid must be computed in double precision and then cast back down, or else parts of the support are systematically inaccurate.
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std::future<std::vector<Real>> t0 = std::async(std::launch::async, [&grid_refinements]() {
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// Computing in higher precision and downcasting is essential for 1ULP evaluation in float precision:
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auto v = daubechies_scaling_dyadic_grid<typename detail::daubechies_eval_type<Real>::type, p, 0>(grid_refinements);
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return detail::daubechies_eval_type<Real>::vector_cast(v);
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});
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// Compute the derivative of the refined grid:
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std::future<std::vector<Real>> t1 = std::async(std::launch::async, [&grid_refinements]() {
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auto v = daubechies_scaling_dyadic_grid<typename detail::daubechies_eval_type<Real>::type, p, 1>(grid_refinements);
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return detail::daubechies_eval_type<Real>::vector_cast(v);
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});
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// if necessary, compute the second and third derivative:
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std::vector<Real> d2ydx2;
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std::vector<Real> d3ydx3;
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if constexpr (p >= 6) {
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std::future<std::vector<Real>> t3 = std::async(std::launch::async, [&grid_refinements]() {
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auto v = daubechies_scaling_dyadic_grid<typename detail::daubechies_eval_type<Real>::type, p, 2>(grid_refinements);
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return detail::daubechies_eval_type<Real>::vector_cast(v);
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});
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if constexpr (p >= 10) {
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std::future<std::vector<Real>> t4 = std::async(std::launch::async, [&grid_refinements]() {
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auto v = daubechies_scaling_dyadic_grid<typename detail::daubechies_eval_type<Real>::type, p, 3>(grid_refinements);
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return detail::daubechies_eval_type<Real>::vector_cast(v);
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});
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d3ydx3 = t4.get();
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}
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d2ydx2 = t3.get();
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}
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auto y = t0.get();
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auto dydx = t1.get();
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if constexpr (p >= 2)
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{
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vector_type data(y.size());
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for (size_t i = 0; i < y.size(); ++i)
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{
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data[i][0] = y[i];
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data[i][1] = dydx[i];
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if constexpr (p >= 6)
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data[i][2] = d2ydx2[i];
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if constexpr (p >= 10)
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data[i][3] = d3ydx3[i];
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}
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if constexpr (p <= 3)
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m_interpolator = std::make_shared<interpolator_type>(std::move(data), grid_refinements, Real(0));
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else
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m_interpolator = std::make_shared<interpolator_type>(std::move(data), Real(0), Real(1) / (1 << grid_refinements));
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}
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else
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m_interpolator = std::make_shared<detail::null_interpolator>();
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}
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}
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inline Real operator()(Real x) const
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{
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if (x <= 0 || x >= 2*p-1)
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{
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return 0;
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}
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return (*m_interpolator)(x);
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}
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inline Real prime(Real x) const
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{
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static_assert(p > 2, "The 3-vanishing moment Daubechies scaling function is the first which is continuously differentiable.");
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if (x <= 0 || x >= 2*p-1)
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{
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return 0;
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}
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return m_interpolator->prime(x);
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}
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inline Real double_prime(Real x) const
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{
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static_assert(p >= 6, "Second derivatives require at least 6 vanishing moments.");
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if (x <= 0 || x >= 2*p - 1)
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{
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return Real(0);
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}
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return m_interpolator->double_prime(x);
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}
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std::pair<Real, Real> support() const
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{
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return {Real(0), Real(2*p-1)};
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}
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int64_t bytes() const
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{
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return m_interpolator->bytes() + sizeof(m_interpolator);
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}
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private:
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std::shared_ptr<interpolator_type> m_interpolator;
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};
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}
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#endif
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