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/*
Perform a double-beta density model fitting to the surface brightness data
Author: Junhua Gu
Last modified: 2011.01.01
This code is distributed with no warrant
*/
//#define HAVE_X_ERROR
#include <iomanip>
#include <iostream>
#include <sstream>
#include <fstream>
#include <models/pl1d.hpp>
#include <models/lin1d.hpp>
#include "statistics/chisq.hpp"
#include "statistics/leastsq.hpp"
#include "statistics/robust_chisq.hpp"
#include <data_sets/default_data_set.hpp>
#include <methods/powell/powell_method.hpp>
#include <core/freeze_param.hpp>
using namespace std;
using namespace opt_utilities;
//double s=5.63136645E20;
const double kpc=3.086E21;//kpc in cm
const double Mpc=kpc*1000;
const double pi=4*atan(1);
double std_norm_rand()
{
double u=0;
double v=0;
while(u<=0||v<=0)
{
u=rand()/(double)RAND_MAX;
rand();
v=rand()/(double)RAND_MAX;
}
double x=std::sqrt(-log(u))*cos(2*pi*v);
return x;
}
double shuffle_data(double xc,double xl,double xu)
{
if(std_norm_rand()>0)
{
double result=xc-std::abs(std_norm_rand()*xl);
return result;
}
else
{
double result= xc+std::abs(std_norm_rand()*xu);
return result;
}
}
int main(int argc,char* argv[])
{
if(argc!=3)
{
cerr<<"Usage:"<<argv[0]<<" <a 6 column file with T -Terr +Terr M -Merr +Merr> <lower T limit>"<<endl;
return -1;
}
double T_lower_limit(atof(argv[2]));
ifstream ifs_data(argv[1]);
default_data_set<double,double> ds;
ofstream ofs_result("m-t_result.qdp");
ofs_result<<"read terr 1 2"<<endl;
ofs_result<<"skip single"<<endl;
ofs_result<<"log"<<endl;
//ofs_result<<"li on 2"<<endl;
ofs_result<<"time off"<<endl;
ofs_result<<"la f"<<endl;
ofs_result<<"la x temperature (keV)"<<endl;
ofs_result<<"la y mass (M\\dsun\\u)"<<endl;
double sxx=0;
double s1=0;
double sx=0;
double sy=0;
double sxy=0;
bool is_first_nonono=true;
for(;;)
{
double T,Tl,Tu;
double M,Ml,Mu;
std::string line;
getline(ifs_data,line);
//ifs_data>>T>>Tl>>Tu>>M>>Ml>>Mu;
if(!ifs_data.good())
{
break;
}
line+=" ";
istringstream iss(line);
if(line[0]=='#')
{
if(!is_first_nonono)
{
ofs_result<<"no no no"<<endl;
}
else
{
is_first_nonono=false;
}
continue;
}
iss>>T>>Tl>>Tu>>M>>Ml>>Mu;
//std::cerr<<L<<"\t"<<Lerr<<endl;
if(!iss.good())
{
continue;
}
if(T<T_lower_limit||M<0)
{
continue;
}
if(std::abs(Mu)<M*.1||std::abs(Ml)<M*.1)
{
cerr<<"mass error less than 10%, skipped"<<endl;
cerr<<line<<endl;
continue;
}
#if 1
if(std::abs(Tu)<.1||std::abs(Tl)<.1)
{
cerr<<"T error less than 10%, skipped"<<endl;
cerr<<line<<endl;
continue;
}
#endif
if(std::abs(Mu)+std::abs(Ml)<M*.1)
{
double k=M*.1/(std::abs(Mu)+std::abs(Ml));
Mu*=k;
Ml*=k;
}
Tl=std::abs(Tl);
Tu=std::abs(Tu);
Ml=std::abs(Ml);
Mu=std::abs(Mu);
ofs_result<<T<<"\t"<<-std::abs(Tl)<<"\t"<<+std::abs(Tu)<<"\t"<<M<<"\t"<<-std::abs(Ml)<<"\t"<<+std::abs(Mu)<<endl;
double x=log(T);
double y=log(M);
double xu=log(T+Tu)-log(T);
double xl=log(T-Tl)-log(T);
double yu=log(M+Mu)-log(M);
double yl=log(M-Ml)-log(M);
if(isnan(x)||isnan(y)||isnan(yl)||isnan(yu)||
isnan(xl)||isnan(xu))
{
std::cerr<<"one data with error > data, skipped"<<endl;
std::cerr<<line<<endl;
continue;
}
sxx+=x*x;
sx+=x;
sy+=y;
sxy+=y*x;
s1+=1;
data<double,double> d(x,y,std::abs(yl),std::abs(yu),
std::abs(xl),std::abs(xu));
ds.add_data(d);
}
double M=sxx*s1-sx*sx;
double Ma=sxy*s1-sy*sx;
double Mb=sxx*sy-sx*sxy;
double k0=Ma/M;
double b0=Mb/M;
ofs_result<<"no no no"<<endl;
fitter<double,double,vector<double>,double,std::string> fit;
fit.set_opt_method(powell_method<double,vector<double> >());
fit.set_statistic(chisq<double,double,vector<double>,double,std::string>());
//fit.set_statistic(robust_chisq<double,double,vector<double>,double,std::string>());
//fit.set_statistic(leastsq<double,double,vector<double>,double,std::string>());
fit.set_model(lin1d<double>());
fit.load_data(ds);
cerr<<"k0="<<k0<<endl;
cerr<<"b0="<<b0<<endl;
cerr<<"Ampl0="<<exp(b0)<<endl;
cerr<<"gamma0="<<k0<<endl;
fit.set_param_value("k",k0);
fit.set_param_value("b",b0);
std::vector<double> p=fit.get_all_params();
std::cout<<"chi="<<fit.get_statistic().eval(p)<<std::endl;
fit.fit();
fit.fit();
p=fit.fit();
std::cout<<"chi="<<fit.get_statistic().eval(p)<<std::endl;
for(double i=.5;i<12;i*=1.01)
{
ofs_result<<i<<"\t0\t0\t"<<exp(fit.eval_model_raw(log(i),p))<<"\t0\t0\n";
}
ofstream ofs_resid("resid.qdp");
ofs_resid<<"read terr 1 2 3"<<endl;
ofs_resid<<"skip single"<<endl;
ofs_resid<<"ma 3 on 1"<<endl;
ofs_resid<<"log x"<<endl;
for(size_t i=0;i<ds.size();++i)
{
double x=ds.get_data(i).get_x();
double y=ds.get_data(i).get_y();
//double xe1=-ds.get_data(i).get_x_lower_err()*0;
//double xe2=ds.get_data(i).get_x_upper_err()*0;
double ye1=-ds.get_data(i).get_y_lower_err();
double ye2=ds.get_data(i).get_y_upper_err();
ofs_resid<<exp(x)<<"\t"<<0<<"\t"<<0<<"\t"<<y-fit.eval_model_raw(x,p)<<"\t"<<ye1<<"\t"<<ye2<<"\t"<<"0\t0\t0"<<endl;
}
double mean_A=0;
double mean_A2=0;
double mean_g=0;
double mean_g2=0;
int cnt=0;
for(int n=0;n<100;++n)
{
++cnt;
cerr<<".";
opt_utilities::default_data_set<double,double> ds1;
for(size_t i=0;i<ds.size();++i)
{
double new_x=shuffle_data(ds.get_data(i).get_x(),
ds.get_data(i).get_x_lower_err(),
ds.get_data(i).get_x_upper_err());
double new_y=shuffle_data(ds.get_data(i).get_y(),
ds.get_data(i).get_y_lower_err(),
ds.get_data(i).get_y_upper_err());
ds1.add_data(data<double,double>(new_x,new_y,
ds.get_data(i).get_y_lower_err(),
ds.get_data(i).get_y_upper_err(),
ds.get_data(i).get_y_lower_err(),
ds.get_data(i).get_y_upper_err()));
}
fit.load_data(ds1);
fit.fit();
double k=fit.get_param_value("k");
double b=fit.get_param_value("b");
double A=exp(b);
double g=k;
mean_A+=A;
mean_A2+=A*A;
mean_g+=g;
mean_g2+=g*g;
}
std::cerr<<endl;
mean_A/=cnt;
mean_A2/=cnt;
mean_g/=cnt;
mean_g2/=cnt;
double std_A=std::sqrt(mean_A2-mean_A*mean_A);
double std_g=std::sqrt(mean_g2-mean_g*mean_g);
std::cerr<<"M=M0*T^gamma"<<endl;
std::cout<<"M0= "<<exp(p[1])<<"+/-"<<std_A<<endl;
std::cout<<"gamma= "<<p[0]<<"+/-"<<std_g<<endl;
std::cout<<"Num of sources:"<<ds.size()<<endl;
}
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