real fit_acf(int ncorr, int fitfn, const gmx_output_env_t *oenv, gmx_bool bVerbose,
real tbeginfit, real tendfit, real dt, real c1[], real *fit)
{
double fitparm[3];
double tStart, tail_corr, sum, sumtot = 0, c_start, ct_estimate;
real *sig;
int i, j, jmax, nf_int;
gmx_bool bPrint;
bPrint = bVerbose || bDebugMode();
if (bPrint)
{
printf("COR:\n");
}
if (tendfit <= 0)
{
tendfit = ncorr*dt;
}
nf_int = std::min(ncorr, (int)(tendfit/dt));
sum = print_and_integrate(debug, nf_int, dt, c1, NULL, 1);
if (bPrint)
{
printf("COR: Correlation time (plain integral from %6.3f to %6.3f ps) = %8.5f ps\n",
0.0, dt*nf_int, sum);
printf("COR: Relaxation times are computed as fit to an exponential:\n");
printf("COR: %s\n", effnDescription(fitfn));
printf("COR: Fit to correlation function from %6.3f ps to %6.3f ps, results in a\n", tbeginfit, std::min(ncorr*dt, tendfit));
}
tStart = 0;
if (bPrint)
{
printf("COR:%11s%11s%11s%11s%11s%11s%11s\n",
"Fit from", "Integral", "Tail Value", "Sum (ps)", " a1 (ps)",
(effnNparams(fitfn) >= 2) ? " a2 ()" : "",
(effnNparams(fitfn) >= 3) ? " a3 (ps)" : "");
}
snew(sig, ncorr);
if (tbeginfit > 0)
{
jmax = 3;
}
else
{
jmax = 1;
}
for (j = 0; ((j < jmax) && (tStart < tendfit) && (tStart < ncorr*dt)); j++)
{
/* Estimate the correlation time for better fitting */
c_start = -1;
ct_estimate = 0;
for (i = 0; (i < ncorr) && (dt*i < tStart || c1[i] > 0); i++)
{
if (c_start < 0)
{
if (dt*i >= tStart)
{
c_start = c1[i];
ct_estimate = 0.5*c1[i];
}
}
else
{
ct_estimate += c1[i];
}
}
if (c_start > 0)
{
ct_estimate *= dt/c_start;
}
else
{
/* The data is strange, so we need to choose somehting */
ct_estimate = tendfit;
}
if (debug)
{
fprintf(debug, "tStart %g ct_estimate: %g\n", tStart, ct_estimate);
}
if (fitfn == effnEXPEXP)
{
fitparm[0] = 0.002*ncorr*dt;
fitparm[1] = 0.95;
fitparm[2] = 0.2*ncorr*dt;
}
else
{
/* Good initial guess, this increases the probability of convergence */
fitparm[0] = ct_estimate;
fitparm[1] = 1.0;
fitparm[2] = 1.0;
}
/* Generate more or less appropriate sigma's */
for (i = 0; i < ncorr; i++)
{
sig[i] = sqrt(ct_estimate+dt*i);
}
nf_int = std::min(ncorr, (int)((tStart+1e-4)/dt));
sum = print_and_integrate(debug, nf_int, dt, c1, NULL, 1);
tail_corr = do_lmfit(ncorr, c1, sig, dt, NULL, tStart, tendfit, oenv,
bDebugMode(), fitfn, fitparm, 0, NULL);
sumtot = sum+tail_corr;
if (fit && ((jmax == 1) || (j == 1)))
{
double mfp[3];
for (i = 0; (i < 3); i++)
{
mfp[i] = fitparm[i];
}
for (i = 0; (i < ncorr); i++)
{
fit[i] = lmcurves[fitfn](i*dt, mfp);
}
}
if (bPrint)
{
printf("COR:%11.4e%11.4e%11.4e%11.4e", tStart, sum, tail_corr, sumtot);
for (i = 0; (i < effnNparams(fitfn)); i++)
{
printf(" %11.4e", fitparm[i]);
}
printf("\n");
}
tStart += tbeginfit;
}
sfree(sig);
return sumtot;
}
static void do_fit(FILE *out, int n, gmx_bool bYdy,
int ny, real *x0, real **val,
int npargs, t_pargs *ppa, const gmx_output_env_t *oenv,
const char *fn_fitted)
{
real *c1 = NULL, *sig = NULL;
double *fitparm;
real tendfit, tbeginfit;
int i, efitfn, nparm;
efitfn = get_acffitfn();
nparm = effnNparams(efitfn);
fprintf(out, "Will fit to the following function:\n");
fprintf(out, "%s\n", effnDescription(efitfn));
c1 = val[n];
if (bYdy)
{
c1 = val[n];
sig = val[n+1];
fprintf(out, "Using two columns as y and sigma values\n");
}
else
{
snew(sig, ny);
}
if (opt2parg_bSet("-beginfit", npargs, ppa))
{
tbeginfit = opt2parg_real("-beginfit", npargs, ppa);
}
else
{
tbeginfit = x0[0];
}
if (opt2parg_bSet("-endfit", npargs, ppa))
{
tendfit = opt2parg_real("-endfit", npargs, ppa);
}
else
{
tendfit = x0[ny-1];
}
snew(fitparm, nparm);
switch (efitfn)
{
case effnEXP1:
fitparm[0] = 0.5;
break;
case effnEXP2:
fitparm[0] = 0.5;
fitparm[1] = c1[0];
break;
case effnEXPEXP:
fitparm[0] = 1.0;
fitparm[1] = 0.5*c1[0];
fitparm[2] = 10.0;
break;
case effnEXP5:
fitparm[0] = fitparm[2] = 0.5*c1[0];
fitparm[1] = 10;
fitparm[3] = 40;
fitparm[4] = 0;
break;
case effnEXP7:
fitparm[0] = fitparm[2] = fitparm[4] = 0.33*c1[0];
fitparm[1] = 1;
fitparm[3] = 10;
fitparm[5] = 100;
fitparm[6] = 0;
break;
case effnEXP9:
fitparm[0] = fitparm[2] = fitparm[4] = fitparm[6] = 0.25*c1[0];
fitparm[1] = 0.1;
fitparm[3] = 1;
fitparm[5] = 10;
fitparm[7] = 100;
fitparm[8] = 0;
break;
default:
fprintf(out, "Warning: don't know how to initialize the parameters\n");
for (i = 0; (i < nparm); i++)
{
fitparm[i] = 1;
}
}
fprintf(out, "Starting parameters:\n");
for (i = 0; (i < nparm); i++)
{
fprintf(out, "a%-2d = %12.5e\n", i+1, fitparm[i]);
}
if (do_lmfit(ny, c1, sig, 0, x0, tbeginfit, tendfit,
oenv, bDebugMode(), efitfn, fitparm, 0,
fn_fitted) > 0)
{
for (i = 0; (i < nparm); i++)
{
fprintf(out, "a%-2d = %12.5e\n", i+1, fitparm[i]);
}
}
else
{
fprintf(out, "No solution was found\n");
}
}
