*/
#include "gmxpre.h"
-#include <math.h>
-#include <stdlib.h>
-#include <string.h>
+#include <cmath>
+#include <cstdlib>
+#include <cstring>
#include "gromacs/commandline/pargs.h"
#include "gromacs/correlationfunctions/autocorr.h"
{
if (t[0] > 0)
{
- x[i] = log(t[i]);
+ x[i] = std::log(t[i]);
}
}
}
fprintf(stdout, "First time is not larger than 0, using index number as time for power fit\n");
for (i = 0; i < n; i++)
{
- x[i] = gmx_log1p(i);
+ x[i] = gmx_log1p(static_cast<real>(i));
}
}
for (s = 0; s < nset; s++)
{
- i = 0;
for (i = 0; i < n && val[s][i] >= 0; i++)
{
- y[i] = log(val[s][i]);
+ y[i] = std::log(val[s][i]);
}
if (i < n)
{
}
lsq_y_ax_b(i, x, y, &a, &b, &r, &quality);
fprintf(stdout, "Power fit set %3d: error %.3f a %g b %g\n",
- s+1, quality, a, exp(b));
+ s+1, quality, a, std::exp(b));
}
sfree(y);
yyint = 0;
for (i = 0; i < n; i++)
{
- cosyint += cos(fac*i)*y[i];
+ cosyint += std::cos(fac*i)*y[i];
yyint += y[i]*y[i];
}
{
FILE *fp;
int i, s;
- double min, max;
+ double minval, maxval;
int nbin;
gmx_int64_t *histo;
- min = val[0][0];
- max = val[0][0];
+ minval = val[0][0];
+ maxval = val[0][0];
for (s = 0; s < nset; s++)
{
for (i = 0; i < n; i++)
{
- if (val[s][i] < min)
+ if (val[s][i] < minval)
{
- min = val[s][i];
+ minval = val[s][i];
}
- else if (val[s][i] > max)
+ else if (val[s][i] > maxval)
{
- max = val[s][i];
+ maxval = val[s][i];
}
}
}
- min = binwidth*floor(min/binwidth);
- max = binwidth*ceil(max/binwidth);
- if (min != 0)
+ minval = binwidth*std::floor(minval/binwidth);
+ maxval = binwidth*std::ceil(maxval/binwidth);
+ if (minval != 0)
{
- min -= binwidth;
+ minval -= binwidth;
}
- max += binwidth;
+ maxval += binwidth;
- nbin = (int)((max - min)/binwidth + 0.5) + 1;
+ nbin = static_cast<int>(((maxval - minval)/binwidth + 0.5) + 1);
fprintf(stderr, "Making distributions with %d bins\n", nbin);
snew(histo, nbin);
fp = xvgropen(distfile, "Distribution", "", "", oenv);
}
for (i = 0; i < n; i++)
{
- histo[(int)((val[s][i] - min)/binwidth + 0.5)]++;
+ histo[static_cast<int>((val[s][i] - minval)/binwidth + 0.5)]++;
}
for (i = 0; i < nbin; i++)
{
- fprintf(fp, " %g %g\n", min+i*binwidth, (double)histo[i]/(n*binwidth));
+ fprintf(fp, " %g %g\n", minval+i*binwidth, static_cast<double>(histo[i])/(n*binwidth));
}
if (s < nset-1)
{
{
snew(tmp, nset);
fprintf(fp, "@TYPE xydydy\n");
- edge = (int)(nset*0.05+0.5);
+ edge = static_cast<int>(nset*0.05+0.5);
fprintf(stdout, "Errorbars: discarding %d points on both sides: %d%%"
- " interval\n", edge, (int)(100*(nset-2*edge)/nset+0.5));
+ " interval\n", edge, static_cast<int>(100*(nset-2*edge)/nset+0.5));
}
else
{
}
if (avbar_opt == avbarSTDDEV)
{
- err = sqrt(var/nset);
+ err = std::sqrt(var/nset);
}
else
{
- err = sqrt(var/(nset*(nset-1)));
+ err = std::sqrt(var/(nset*(nset-1)));
}
fprintf(fp, " %g", err);
}
return 0;
}
- return sigma*sqrt(2*ss/tTotal);
+ return sigma*std::sqrt(2*ss/tTotal);
}
static void estimate_error(const char *eefile, int nb_min, int resol, int n,
xvgr_legend(fp, 2*nset, (const char**)leg, oenv);
sfree(leg);
- spacing = pow(2, 1.0/resol);
+ spacing = std::pow(2.0, 1.0/resol);
snew(tbs, n);
snew(ybs, n);
snew(fitsig, n);
nbr = nb_min;
while (nbr <= n)
{
- bs = n/(int)nbr;
+ bs = n/static_cast<int>(nbr);
if (bs != prev_bs)
{
nb = n/bs;
nbs++;
}
nbr *= spacing;
- nb = (int)(nbr+0.5);
prev_bs = bs;
}
if (sig[s] == 0)
* For a single exponential autocorrelation: ybs(tau1) = 2/e tau1
* From this we take our initial guess for tau1.
*/
- twooe = 2/exp(1);
+ twooe = 2.0/std::exp(1.0);
i = -1;
do
{
{
dens = 0.5*(tbs[i+1]/tbs[i-1] - 1);
}
- fitsig[i] = sqrt((tau_sig + tbs[i])/dens);
+ fitsig[i] = std::sqrt((tau_sig + tbs[i])/dens);
}
if (!bSingleExpFit)
/* We set the initial guess for tau2
* to halfway between tau1_est and the total time (on log scale).
*/
- fitparm[2] = sqrt(tau1_est*(n-1)*dt);
+ fitparm[2] = std::sqrt(tau1_est*(n-1)*dt);
do_lmfit(nbs, ybs, fitsig, 0, tbs, 0, dt*n, oenv,
bDebugMode(), effnERREST, fitparm, 0,
NULL);
}
for (i = 0; i < nbs; i++)
{
- fprintf(fp, "%g %g %g\n", tbs[i], sig[s]*sqrt(ybs[i]/(n*dt)),
- sig[s]*sqrt(fit_function(effnERREST, fitparm, tbs[i])/(n*dt)));
+ fprintf(fp, "%g %g %g\n", tbs[i], sig[s]*std::sqrt(ybs[i]/(n*dt)),
+ sig[s]*std::sqrt(fit_function(effnERREST, fitparm, tbs[i])/(n*dt)));
}
if (bFitAc)
ac[i] = val[s][i] - av[s];
if (i > 0)
{
- fitsig[i] = sqrt(i);
+ fitsig[i] = std::sqrt(static_cast<real>(i));
}
else
{
/* Generate more or less appropriate sigma's */
for (i = 0; i <= fitlen; i++)
{
- fitsig[i] = sqrt(acint + dt*i);
+ fitsig[i] = std::sqrt(acint + dt*i);
}
ac_fit[0] = 0.5*acint;
for (i = 0; i < nbs; i++)
{
fprintf(fp, "%g %g\n", tbs[i],
- sig[s]*sqrt(fit_function(effnERREST, ac_fit, tbs[i]))/(n*dt));
+ sig[s]*std::sqrt(fit_function(effnERREST, ac_fit, tbs[i]))/(n*dt));
}
sfree(ac);
static void luzar_correl(int nn, real *time, int nset, real **val, real temp,
gmx_bool bError, real fit_start)
{
- const real tol = 1e-8;
real *kt;
- real weight, d2;
+ real d2;
int j;
please_cite(stdout, "Spoel2006b");
{
d2 += sqr(kt[j] - val[3][j]);
}
- fprintf(debug, "RMS difference in derivatives is %g\n", sqrt(d2/nn));
+ fprintf(debug, "RMS difference in derivatives is %g\n", std::sqrt(d2/nn));
}
analyse_corr(nn, time, val[0], val[2], kt, NULL, NULL, NULL, fit_start,
temp);
int f, s, i, j;
double *filt, sum, vf, fluc, fluctot;
- f = (int)(flen/(2*dt));
+ f = static_cast<int>(flen/(2*dt));
snew(filt, f+1);
filt[0] = 1;
sum = 1;
for (i = 1; i <= f; i++)
{
- filt[i] = cos(M_PI*dt*i/flen);
+ filt[i] = std::cos(M_PI*dt*i/flen);
sum += 2*filt[i];
}
for (i = 0; i <= f; i++)
}
fluc /= n - 2*f;
fluctot += fluc;
- fprintf(stdout, "Set %3d filtered fluctuation: %12.6e\n", s+1, sqrt(fluc));
+ fprintf(stdout, "Set %3d filtered fluctuation: %12.6e\n", s+1, std::sqrt(fluc));
}
- fprintf(stdout, "Overall filtered fluctuation: %12.6e\n", sqrt(fluctot/nset));
+ fprintf(stdout, "Overall filtered fluctuation: %12.6e\n", std::sqrt(fluctot/nset));
fprintf(stdout, "\n");
sfree(filt);
int s, buflen = 0;
if (NULL != fn_fitted)
{
- buflen = strlen(fn_fitted)+32;
+ buflen = std::strlen(fn_fitted)+32;
snew(buf2, buflen);
- strncpy(buf2, fn_fitted, buflen);
- buf2[strlen(buf2)-4] = '\0';
+ std::strncpy(buf2, fn_fitted, buflen);
+ buf2[std::strlen(buf2)-4] = '\0';
}
for (s = 0; s < nset; s++)
{
static real tb = -1, te = -1, frac = 0.5, filtlen = 0, binwidth = 0.1, aver_start = 0;
static gmx_bool bHaveT = TRUE, bDer = FALSE, bSubAv = TRUE, bAverCorr = FALSE, bXYdy = FALSE;
static gmx_bool bEESEF = FALSE, bEENLC = FALSE, bEeFitAc = FALSE, bPower = FALSE;
- static gmx_bool bIntegrate = FALSE, bRegression = FALSE, bLuzar = FALSE, bLuzarError = FALSE;
- static int nsets_in = 1, d = 1, nb_min = 4, resol = 10, nFitPoints = 100;
- static real temp = 298.15, fit_start = 1, fit_end = 60, diffusion = 5e-5, rcut = 0.35;
+ static gmx_bool bIntegrate = FALSE, bRegression = FALSE, bLuzar = FALSE;
+ static int nsets_in = 1, d = 1, nb_min = 4, resol = 10;
+ static real temp = 298.15, fit_start = 1, fit_end = 60;
/* must correspond to enum avbar* declared at beginning of file */
static const char *avbar_opt[avbarNR+1] = {
};
#define NPA asize(pa)
- FILE *out, *out_fit;
+ FILE *out;
int n, nlast, s, nset, i, j = 0;
real **val, *t, dt, tot, error;
double *av, *sig, cum1, cum2, cum3, cum4, db;
cum3 /= n;
cum4 /= n;
av[s] = cum1;
- sig[s] = sqrt(cum2);
+ sig[s] = std::sqrt(cum2);
if (n > 1)
{
- error = sqrt(cum2/(n-1));
+ error = std::sqrt(cum2/(n-1));
}
else
{
}
printf("SS%d %13.6e %12.6e %12.6e %6.3f %6.3f\n",
s+1, av[s], sig[s], error,
- sig[s] ? cum3/(sig[s]*sig[s]*sig[s]*sqrt(8/M_PI)) : 0,
+ sig[s] ? cum3/(sig[s]*sig[s]*sig[s]*std::sqrt(8/M_PI)) : 0,
sig[s] ? cum4/(sig[s]*sig[s]*sig[s]*sig[s]*3)-1 : 0);
}
printf("\n");
{
out = xvgropen(msdfile, "Mean square displacement",
"time", "MSD (nm\\S2\\N)", oenv);
- nlast = (int)(n*frac);
+ nlast = static_cast<int>(n*frac);
for (s = 0; s < nset; s++)
{
for (j = 0; j <= nlast; j++)
{
tot += sqr(val[s][i]-val[s][i+j]);
}
- tot /= (real)(n-j);
+ tot /= (n-j);
fprintf(out, " %g %8g\n", dt*j, tot);
}
if (s < nset-1)
biod.pnpi.spb.ru / alexxy / gromacs.git / blobdiff