cosmo-replay/main.cpp

136 lines
No EOL
4.1 KiB
C++

#include <algorithm>
#include <array>
#include <boost/numeric/odeint.hpp>
#include <fstream>
#include <gsl/gsl_spline.h>
#include <iostream>
#include <numeric>
#include <string>
#include <stdexcept>
#include <vector>
#include "loadtxt.h"
// Our units are {kiloparsec, solar mass, gigayear}
constexpr double G = 4.498317481097514e-06;
class Interp {
// This is a wrapper around GSL spline interpolation. I tried to use
// boost::math::interpolators but as of version 1.72 none were suitable:
// barycentric_rational is the one suitalbe for non-uniform sampling but it
// is very slow. I also tried to resample the data uniformly using
// barycentric rational interpolation and then using cardinal_cubic_b_spline
// on the result, but was still slower than GSL.
public:
Interp(std::vector<double>& x, std::vector<double>& y)
{
acc = gsl_interp_accel_alloc();
spline = gsl_spline_alloc(gsl_interp_cspline, x.size());
gsl_spline_init(spline, x.data(), y.data(), x.size());
}
Interp() {}
inline double operator()(double x) const
{
return gsl_spline_eval(spline, x, acc);
}
private:
gsl_interp_accel *acc;
gsl_spline *spline;
};
class Plummer {
public:
Plummer(double M, double b)
: M(M), b(b) {}
void calc_acceleration(const double *pos, double *acc)
{
double r2 = (pos[0]*pos[0] + pos[1]*pos[1] + pos[2]*pos[2] + b*b);
double r = sqrt(r2);
double r3_inv = 1/(r*r2);
acc[0] = -G*M*pos[0]*r3_inv;
acc[1] = -G*M*pos[1]*r3_inv;
acc[2] = -G*M*pos[2]*r3_inv;
}
private:
double M, b;
};
class Galaxy {
public:
Galaxy(std::string file_name)
{
auto data = Loadtxt("file.dat", {1, 2, 3}).get_cols();
auto& t_data = data[0];
auto& halo_m_data = data[1];
auto& halo_b_data = data[2];
std::transform(begin(t_data), end(t_data), begin(t_data), [t0=t_data[0]](const double& x){ return x-t0; });
std::transform(halo_b_data.begin(), halo_b_data.end(), halo_b_data.begin(), [](const double& x){ return x*0.7664209365408798; });
interp_halo_m = Interp(t_data, halo_m_data);
interp_halo_b = Interp(t_data, halo_b_data);
}
void func(const std::array<double, 6> &y, std::array<double, 6> &f, const double t)
{
double halo_m = interp_halo_m(t);
double halo_b = interp_halo_b(t);
Plummer plummer(halo_m, halo_b);
f[0] = y[3]; // vx -> x'
f[1] = y[4]; // vy -> y'
f[2] = y[5]; // vz -> z'
plummer.calc_acceleration(y.data(), f.data()+3); // a -> v'
}
private:
Interp interp_halo_m;
Interp interp_halo_b;
} galaxy("file.dat");
extern "C"
int integrate(const double y0[], const double t_max, const double stride_size, double y[])
{
using namespace boost::numeric::odeint;
using Coordinates = std::array<double, 6>;
auto stepper = bulirsch_stoer<Coordinates>(1E-7, 0);
auto function_wrapper = [](const Coordinates &x, Coordinates &dxdt, const double t) { return galaxy.func(x, dxdt, t); };
const int stride_count = t_max / stride_size;
Coordinates y_current;
std::copy(y0, y0+6, begin(y_current));
std::copy(y0, y0+6, y);
double t = 0;
const double h = 1./4096.;
for (int i=0; i<stride_count; i++) {
integrate_adaptive(stepper, function_wrapper, y_current, t, t+stride_size, h);
// NOTE h here is just a recommended initial step size for the stepper,
// the actual step is adapted as needed. Since the integration is
// interrupted here in order for the data to be saved, the result
// somewhat depends on stride_size.
std::copy(begin(y_current), end(y_current), y+(i+1)*6);
t += stride_size;
}
}
int main()
{
std::cout << "Hi" << std::endl;
double y[12];
double y0[] = {80,0,0,0,80,0};
for (int i=0; i<8000; i++)
integrate(y0, 10, 10, y);
/*double y0[] = {80,0,0,0,80,0};
double y[12];
integrate(y0, 10, 10, y);
for (int i=0; i<12; i++) std::cout << y[i] << std::endl;*/
//std::vector<double> w = {8, 0.12, 0.04, -0.08, 200, -0.001};
std::cout << "Bye" << std::endl;
return 0;
}