bbm::precomputed::holzschuchpacanowski Namespace Reference

Variables
static const tab< float, std::array{25}, decltype([](const auto &p) { return(5.0 - p) *5.0;}) >	betamax
static const tab< named< std::tuple< float, float >, "scale", "u">, std::array{43, 11, 25, 10}, decltype([](const auto &u, const auto &, const auto &, const auto &) { std::decay_t< decltype(u)> index=0;auto mask=(u<=1.0);if(bbm::any(mask)) index=bbm::select(mask, u/0.05, index);mask=(u > 1) &&(u<=10);if(bbm::any(mask)) index=bbm::select(mask, u+19.0, index);mask=(u > 10) &&(u<=50);if(bbm::any(mask)) index=bbm::select(mask,(u - 10.0)/5.0+29.0, index);mask=(u > 50);if(bbm::any(mask)) index=bbm::select(mask,(u-50.0)/10.0+37.0, index);return index;}), decltype([](const auto &, const auto &c, const auto &, const auto &) { return 5.0 *(3.02 - c);}), decltype([](const auto &, const auto &, const auto &p, const auto &) { return 25.0 - 5.0 *p;}), decltype([](const auto &, const auto &, const auto &p, const auto &beta) { return 10.0 *beta/bbm::get<"value">(betamax.interpolate< std::decay_t< decltype(beta)> >(p)) - 0.5;}) >	convolution
static const tab< named< std::tuple< float, float >, "scale", "u">, std::array{43, 11, 25, 10}, decltype([](const auto &u, const auto &, const auto &, const auto &) { std::decay_t< decltype(u)> index=0;auto mask=(u<=1.0);if(bbm::any(mask)) index=bbm::select(mask, u/0.05, index);mask=(u > 1) &&(u<=10);if(bbm::any(mask)) index=bbm::select(mask, u+19.0, index);mask=(u > 10) &&(u<=50);if(bbm::any(mask)) index=bbm::select(mask,(u - 10.0)/5.0+29.0, index);mask=(u > 50);if(bbm::any(mask)) index=bbm::select(mask,(u-50.0)/10.0+37.0, index);return index;}), decltype([](const auto &, const auto &c, const auto &, const auto &) { return 5.0 *(3.02 - c);}), decltype([](const auto &, const auto &, const auto &p, const auto &) { return 25.0 - 5.0 *p;}), decltype([](const auto &, const auto &, const auto &p, const auto &beta) { return 10.0 *beta/bbm::get<"value">(betamax.interpolate< std::decay_t< decltype(beta)> >(p)) - 0.5;}) >	convolution_cos
static const tab< float, std::array{256}, decltype([](const auto &p) { return p *256.0/5.0;}) >	distributionNormalization
static const tab< float, std::array{100, 1000}, decltype([](const auto &p) { return 5.0/p - 1.0;}), decltype([](const auto &t) { return bbm::exp(-bbm::exp(bbm::log(bbm::rcp(t)) *0.05)) *1000.0 - 1.0;}) >	G1
static const tab< float, std::array{100, 100, 100}, decltype([](const auto &b) { return 101.0 *(10.0/(b+10.0)) -1.0;}), decltype([](const auto &c) { return 101.0 *(c-1)/c-1.0;}), decltype([](const auto &sinTheta) { return sinTheta *100.0;}) >	renormalization

Variable Documentation

betamax

const tab<float, std::array{25}, decltype( [](const auto& p) { return (5.0 - p) * 5.0; } ) > betamax

static

Initial value:

= {                                                    
           0.058, 0.060, 0.062, 0.064, 0.066,       
           0.068, 0.071, 0.074, 0.078, 0.083,       
           0.085, 0.087, 0.090, 0.093, 0.098,       
           0.104, 0.113, 0.127, 0.153, 0.211,       
           0.348, 0.461, 0.448, 0.261, 0.015        
      }

convolution

const tab<named<std::tuple<float,float>, "scale", "u">, std::array{43,11,25,10}, decltype( [](const auto& u, const auto&, const auto&, const auto&) { std::decay_t<decltype(u)> index = 0; auto mask = (u <= 1.0); if(bbm::any(mask)) index = bbm::select(mask, u / 0.05, index); mask = (u > 1) && (u <= 10); if(bbm::any(mask)) index = bbm::select(mask, u + 19.0, index); mask = (u > 10) && (u <= 50); if(bbm::any(mask)) index = bbm::select(mask, (u - 10.0) / 5.0 + 29.0, index); mask = (u > 50); if(bbm::any(mask)) index = bbm::select(mask, (u-50.0) / 10.0 + 37.0, index); return index; } ), decltype( [](const auto&, const auto& c, const auto&, const auto&) { return 5.0*(3.02 - c); } ), decltype( [](const auto&, const auto&, const auto& p, const auto&) { return 25.0 - 5.0*p; } ), decltype( [](const auto&, const auto&, const auto& p, const auto& beta) { return 10.0 * beta / bbm::get<"value">(betamax.interpolate<std::decay_t<decltype(beta)>>(p)) - 0.5; } ) > convolution

static

convolution_cos

const tab<named<std::tuple<float,float>, "scale", "u">, std::array{43,11,25,10}, decltype( [](const auto& u, const auto&, const auto&, const auto&) { std::decay_t<decltype(u)> index = 0; auto mask = (u <= 1.0); if(bbm::any(mask)) index = bbm::select(mask, u / 0.05, index); mask = (u > 1) && (u <= 10); if(bbm::any(mask)) index = bbm::select(mask, u + 19.0, index); mask = (u > 10) && (u <= 50); if(bbm::any(mask)) index = bbm::select(mask, (u - 10.0) / 5.0 + 29.0, index); mask = (u > 50); if(bbm::any(mask)) index = bbm::select(mask, (u-50.0) / 10.0 + 37.0, index); return index; } ), decltype( [](const auto&, const auto& c, const auto&, const auto&) { return 5.0*(3.02 - c); } ), decltype( [](const auto&, const auto&, const auto& p, const auto&) { return 25.0 - 5.0*p; } ), decltype( [](const auto&, const auto&, const auto& p, const auto& beta) { return 10.0 * beta / bbm::get<"value">(betamax.interpolate<std::decay_t<decltype(beta)>>(p)) - 0.5; } ) > convolution_cos

static

distributionNormalization

const tab<float, std::array{256}, decltype( [](const auto& p) { return p * 256.0 / 5.0; } ) > distributionNormalization

static

G1

const tab<float, std::array{100,1000}, decltype( [](const auto& p) { return 5.0 / p - 1.0; } ), decltype( [](const auto& t) { return bbm::exp(-bbm::exp(bbm::log(bbm::rcp(t)) * 0.05)) * 1000.0 - 1.0; } ) > G1

static

renormalization

const tab<float, std::array{100,100,100}, decltype( [](const auto& b) { return 101.0*(10.0/(b+10.0))-1.0; } ), decltype( [](const auto& c) { return 101.0*(c-1)/c-1.0; } ), decltype( [](const auto& sinTheta) { return sinTheta*100.0; } ) > renormalization

static