double MLFitterGSL::iteration(){
//do one itteration
//lock the model so user can not add or remove components during itteration
modelptr->setlocked(true);
//if the model has changed (a new or removed component eg.) create a new solver
if (modelptr->has_changed()){
createmodelinfo(); //this automaticaly calls initfitter() via created_modelinfo()
}
//do an itteration step
try{
const int status=gsl_multifit_fdfsolver_iterate(solver);
#ifdef FITTER_DEBUG
std::cout <<"solver status: "<<gsl_strerror(status)<<"\n";
std::cout <<"chi square/dof: "<<gsl_blas_dnrm2(solver->f)<<"\n";
#endif
this->setstatus(gsl_strerror(status)); //update status info of fitter
convertxtoparam(solver->x);
}
catch(...){
Saysomething mysay(0,"Error","Problem with call to gsl_multifit_fdfsolver_iterate, exit MLFitterGSL",true);
delete(this);
}
//unlock the model so user can add or remove components
modelptr->setlocked(false);
//put the goodness in the goodnessqueue to check for convergence
const double newgoodness= goodness_of_fit();
addgoodness(newgoodness);
return newgoodness;
}
std::string WLSQFitter::goodness_of_fit_string()const{
//returns a string which says how good the fit is
char s[256];
double chisq=goodness_of_fit();
sprintf(s,"Normalized Weighted Chi square: %e",chisq/(modelptr->getnpoints()));
std::string f=s;
return f;
}
void fit_halo_profile(struct halo *HALO)
{
double c=0, r=0, rho0, rho0_halo, rs, chi, gof, per;
double *x, *y, *e, *R, *y_th, *x_bin, *y_bin, *e_bin, rMin, rMax;
int bins, skip, N, j=0;
r = HALO->Rvir;
c = HALO->c;
bins = HALO->n_bins;
skip = HALO->neg_r_bins;
N = bins - skip;
rho0 = HALO->rho0;
rs = HALO->r2;
x = (double*) calloc(N, sizeof(double));
y = (double*) calloc(N, sizeof(double));
e = (double*) calloc(N, sizeof(double));
R = (double*) calloc(N, sizeof(double));
for(j=0; j<N; j++)
{
x[j] = HALO->radius[j+skip];
y[j] = HALO->rho[j+skip];
e[j] = HALO->err[j+skip];
R[j] = HALO->radius[j+skip]/r;
}
best_fit_nfw(rho0, rs, N, x, y, e);
HALO->fit_nfw.rho0 = rho0;
HALO->fit_nfw.rs = rs;
HALO->fit_nfw.c = HALO->Rvir/rs;
y_th = (double*) calloc(bins-skip,sizeof(double));
for(j=skip; j<bins; j++)
{
y_th[j-skip] = nfw(HALO->radius[j], HALO->fit_nfw.rs, HALO->fit_nfw.rho0);
//fprintf(stderr, "%d) R=%e, %e %e %e\n", j, R[j-skip], rho0, y[j-skip], y_th[j-skip]);
}
// Various estimators for the goodness of fit
chi = chi_square(y_th, y, e, N);
gof = goodness_of_fit(y_th, y, N);
per = percentage_error(y_th, y, N);
chi /= (double) (bins-skip);
HALO->fit_nfw.chi = chi;
HALO->fit_nfw.gof = gof;
HALO->fit_nfw.per = per;
x_bin = (double*) calloc(BIN_PROFILE+1, sizeof(double));
y_bin = (double*) calloc(BIN_PROFILE, sizeof(double));
e_bin = (double*) calloc(BIN_PROFILE, sizeof(double));
rMin = 2 * Rvir_frac_min;
rMax = F_MAX * 1.01; //HALO->radius[bins-1]/r;
x_bin = log_stepper(rMin, rMax, BIN_PROFILE+1);
average_bin(R, y, x_bin, y_bin, e_bin, BIN_PROFILE+1, N);
for(j=0; j<BIN_PROFILE; j++)
{
HALO->nfw.x[j] = 0.5 * (x_bin[j] + x_bin[j+1]);
HALO->nfw.y[j] = y_bin[j];
//fprintf(stderr, "%d %e %e\n", j, x_bin[j+1], y_bin[j]);
}
free(x);
free(y);
free(R);
free(y_th);
free(e);
// fprintf(stderr, "ThisTask=%d, skip=%d, bins=%d, rho=%f, rs=%f, ChiSquare=%lf, Red=%lf\n",
// ThisTask, skip, bins, rho0, rs, chi_sq, chi_sq/(bins-skip));
}
