diff options
Diffstat (limited to 'mass_profile/fit_lt_bpl.cpp')
| -rw-r--r-- | mass_profile/fit_lt_bpl.cpp | 42 |
1 files changed, 21 insertions, 21 deletions
diff --git a/mass_profile/fit_lt_bpl.cpp b/mass_profile/fit_lt_bpl.cpp index 5637dca..0c06b22 100644 --- a/mass_profile/fit_lt_bpl.cpp +++ b/mass_profile/fit_lt_bpl.cpp @@ -45,7 +45,7 @@ double shuffle_data(double xc,double xl,double xu) double lxc=log(xc); double lxl=log(xc-xl)-log(xc); double lxu=log(xc+xu)-log(xc); - + if(std_norm_rand()>0) { double result=std::exp(lxc-std::abs(std_norm_rand()*lxl)); @@ -108,7 +108,7 @@ int main(int argc,char* argv[]) } line+=" "; istringstream iss(line); - + if(line[0]=='#') { if(!is_first_nonono) @@ -190,16 +190,16 @@ int main(int argc,char* argv[]) double gamma0l=k0l; double gamma0u=k0u; - + double ampl0l=exp(b0l)*pow(Tb,gamma0l); double ampl0u=exp(b0u)*pow(Tb,gamma0u);; - + 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(logchisq<double,double,vector<double>,double,std::string>()); //fit.set_statistic(leastsq<double,double,vector<double>,double,std::string>()); fit.set_model(bpl1d<double>()); @@ -208,7 +208,7 @@ int main(int argc,char* argv[]) cerr<<"k0l="<<k0l<<endl; cerr<<"k0u="<<k0u<<endl; cerr<<"Ampl0="<<(ampl0l+ampl0u)/2<<endl; - + fit.set_param_value("bpx",Tb); fit.set_param_value("bpy",(ampl0l+ampl0u)/2); fit.set_param_value("gamma1",gamma0l); @@ -225,7 +225,7 @@ int main(int argc,char* argv[]) ofs_result<<i<<"\t0\t0\t"<<fit.eval_model_raw(i,p)/yunit<<"\t0\t0\n"; } - + std::vector<double> mean_p(p.size()); std::vector<double> mean_p2(p.size()); int cnt=0; @@ -238,7 +238,7 @@ int main(int argc,char* argv[]) double sxl=0; double syl=0; double sxyl=0; - + double sxxu=0; double s1u=0; double sxu=0; @@ -246,7 +246,7 @@ int main(int argc,char* argv[]) double sxyu=0; opt_utilities::default_data_set<double,double> ds1; - for(int i=0;i<ds.size();++i) + 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(); @@ -254,14 +254,14 @@ int main(int argc,char* argv[]) double xu=ds.get_data(i).get_x_upper_err(); double yl=ds.get_data(i).get_y_lower_err(); double yu=ds.get_data(i).get_y_upper_err(); - + double new_x=shuffle_data(x, xl, xu); double new_y=shuffle_data(y, yl, yu); - + ds1.add_data(data<double,double>(new_x,new_y, yl/y*new_y, yu/y*new_y, @@ -292,40 +292,40 @@ int main(int argc,char* argv[]) double Mbl=sxxl*syl-sxl*sxyl; double k0l=Mal/Ml; double b0l=Mbl/Ml; - + double Mu=sxxu*s1u-sxu*sxu; double Mau=sxyu*s1u-syu*sxu; double Mbu=sxxu*syu-sxu*sxyu; double k0u=Mau/Mu; double b0u=Mbu/Mu; - + double gamma0l=k0l; double gamma0u=k0u; - + double ampl0l=exp(b0l)*pow(Tb,gamma0l); double ampl0u=exp(b0u)*pow(Tb,gamma0u);; - + fit.set_param_value("bpx",Tb); fit.set_param_value("bpy",(ampl0l+ampl0u)/2); fit.set_param_value("gamma1",gamma0l); fit.set_param_value("gamma2",gamma0u); - - + + fit.load_data(ds1); - + fit.fit(); vector<double> p=fit.fit(); - for(int i=0;i<p.size();++i) + for(size_t i=0;i<p.size();++i) { mean_p[i]+=p[i]; mean_p2[i]+=p[i]*p[i]; } //cerr<<fit.get_param_value("gamma1")<<"\t"<<fit.get_param_value("gamma2")<<endl; - + } vector<double> std_p(p.size()); cerr<<endl; - for(int i=0;i<mean_p.size();++i) + for(size_t i=0;i<mean_p.size();++i) { mean_p[i]/=cnt; mean_p2[i]/=cnt; |