int main(int argc, char *argv[])
{
int warn, i, j, nread, NUMBER_OF_SIGMA_BINS;
double X_s, I_R, s, R[90], R_arcmin[90], sig_p[90], a_dm, a_s, M_s, M_dm, beta;
double R_o[12], sig_p_o[12], numb_error[12], chi2, a_dm_min, a_s_min, M_dm_min, M_s_min, beta_min, sig_p_i;
double chi_min = 100000000000000000.0;
double gamma, gamma_min, aux;
char *infile;
FILE *script,*sigma,*best_model;
double gamma_ini, gamma_end, Delta_gamma;
double adm_ini, adm_end, Delta_adm;
double as_ini, as_end, Delta_as;
double Mdm_ini, Mdm_end, Delta_Mdm;
double Ms_ini, Ms_end, Delta_Ms;
double beta_ini, beta_end, Delta_beta;
infile = argv[1]; // INPUT FILE WITH VELOCITY DISPERSION BINS
NUMBER_OF_SIGMA_BINS = atoi(argv[2]); // EVENTUALLY 12!
if(argc != 3)
{
printf(" Execute as ./exec <infile> <number of lines>\n"); // THIS WILL TELL HOW TO PROPERLY DO THE EXECUTION
exit(0);
}
// READ THE FILE WITH THE OBSERVATIONAL DATA
sigma=fopen(infile,"r");
for(j=0.0; j<NUMBER_OF_SIGMA_BINS; j++)
nread = fscanf(sigma,"%lf %lf %lf", &R_o[j], &sig_p_o[j], &numb_error[j]);
fclose(sigma);
a_s = 2.23; //STELLAR SCALENGTH OF 2.234 PARSECS FROM THE FITTING OF NOYOLA'S DATA
M_s = 20.0; //STELLAR MASS OF 6.61e6 M_sun
// THE VARIATIONS OF THE PARAMETERS
gamma_ini = 0.6; gamma_end = 0.8; Delta_gamma = 0.04;
adm_ini = 3.0; adm_end = 5.0; Delta_adm = 0.04;
Mdm_ini = 1.0; Mdm_end = 2.5; Delta_Mdm = 0.04;
beta_ini = 0.3; beta_end = 0.5; Delta_beta = 0.04;
// THIS LINE CALCULATES THE NUMBER OF ITERATIONS FOR THE USER'S REFERENCE
int Ntotal_iterations, counter, cuenta;
Ntotal_iterations = (int) ( ((beta_end-beta_ini)/Delta_beta)*((gamma_end-gamma_ini)/Delta_gamma)*((adm_end-adm_ini)/Delta_adm)*((Mdm_end-Mdm_ini)/Delta_Mdm) );
printf(" Running %d iterations in parameter space\n", Ntotal_iterations);
counter=0;
//DEFINITION OF THE VECTOR R[i]
for(i=0;i<K;i++)
{
R[i] = (i+1.0);
if(i==0)
R[i] = R[i]*0.1;
}
for(beta = beta_ini; beta <= beta_end; beta = beta + Delta_beta)
{
for(gamma = gamma_ini; gamma <= gamma_end; gamma = gamma + Delta_gamma)
{
for(a_dm = adm_ini; a_dm <= adm_end; a_dm = a_dm + Delta_adm)
{
// THIS IS TO AVOID INDETERMINATIONS IN SOME DENOMINATORS
if (a_s != a_dm)
{
for(M_dm = Mdm_ini; M_dm < Mdm_end; M_dm = M_dm + Delta_Mdm)
{
for(i = 0; i < K; i ++)
{
evaluate_integral(R[i], beta, gamma, a_dm, a_s, M_dm, M_s, &sig_p[i], &R_arcmin[i]);
//printf("%d R = %lf I=%lf\n",i, log10(R[i]), sig_p[i]);
}
// ALLOC MEMORY FOR THE INTERPOLATION IN EACH STEP
gsl_interp_accel *acc = gsl_interp_accel_alloc ();
gsl_spline *spline = gsl_spline_alloc (gsl_interp_cspline, 90);
gsl_spline_init (spline, R_arcmin, sig_p, 90);
chi2 = 0.0;
for(j=0; j<NUMBER_OF_SIGMA_BINS; j++)
{
sig_p_i = gsl_spline_eval (spline, R_o[j], acc);
chi2 += ( (sig_p_i-sig_p_o[j])*(sig_p_i-sig_p_o[j]) ) / numb_error[j];
//printf("%16.8lf\t %16.8lf\n", sig_p_i, chi2);
}
// FREE MEMORY
gsl_spline_free (spline);
gsl_interp_accel_free (acc);
// CALCULATION OF XI SQUARE TO FIND THE OPTIMIZED PARAMETERS
if(chi2 < chi_min)
{
chi_min = chi2;
a_dm_min = a_dm;
a_s_min = a_s;
M_dm_min = M_dm;
M_s_min = M_s;
gamma_min = gamma;
beta_min = beta;
}
if((counter%1000) == 0)
printf("Ready %d iterations of %d\n",counter, Ntotal_iterations);
counter++;
}// for Mdm
}// if for the singularity
}// for adm
}//for gamma
}//for beta
//printf ("\n");
best_model=fopen("best_model.dat","w");
for(i = 0; i < K; i ++)
{
evaluate_integral(R[i], beta_min, gamma_min, a_dm_min, a_s_min, M_dm_min, M_s_min, &sig_p[i], &R_arcmin[i]);
fprintf(best_model,"%16.8e %16.8e\n", R_arcmin[i], sig_p[i]);
}
fclose(best_model);
// THIS ARE THE OPTIMIZED PARAMETERS
printf("xi=%16.8lf beta=%16.8lf a_dm/pc=%16.8lf a_s/pc=%16.8lf M_dm/Msun=%16.8elf M_s/Msun=%16.8e gamma=%lf\n",
chi_min, beta_min, a_dm_min, a_s_min, (1.0e5)*M_dm_min, (1.0e5)*M_s_min, gamma_min);
script = fopen( "script.gpl", "w" );
fprintf( script, "plot 'sigma.dat'pt 7 ps 1.2\n" );
//fprintf( script, "replot 'best_model.dat' u 1:2 w l\n");
//fprintf( script, "reset\n");
fprintf(script, "set grid\nset terminal png\nset output 'sigma.png'\nset nokey\n");
fprintf( script, "set title 'Sigma Proyectada vs R'\n" );
fprintf( script, "set xrange [0:45]\n" );
fprintf( script, "set yrange [4:14]\n" );
fprintf( script, "set xlabel 'Radius in Arcmin'\n" );
fprintf( script, "set ylabel 'Projected velocity dispersion in km/s'\n" );
//fprintf( script, "plot 'sigma.dat'pt 7 ps 1.2\n" );
fprintf( script, "replot 'best_model.dat' u 1:2 w lp\n");
//fprintf( script, "pause -1\n");
fclose(script);
warn = system("gnuplot script.gpl");
return(warn);
}
int gmx_analyze(int argc,char *argv[])
{
static const char *desc[] = {
"g_analyze reads an ascii file and analyzes data sets.",
"A line in the input file may start with a time",
"(see option [TT]-time[tt]) and any number of y values may follow.",
"Multiple sets can also be",
"read when they are seperated by & (option [TT]-n[tt]),",
"in this case only one y value is read from each line.",
"All lines starting with # and @ are skipped.",
"All analyses can also be done for the derivative of a set",
"(option [TT]-d[tt]).[PAR]",
"All options, except for [TT]-av[tt] and [TT]-power[tt] assume that the",
"points are equidistant in time.[PAR]",
"g_analyze always shows the average and standard deviation of each",
"set. For each set it also shows the relative deviation of the third",
"and forth cumulant from those of a Gaussian distribution with the same",
"standard deviation.[PAR]",
"Option [TT]-ac[tt] produces the autocorrelation function(s).[PAR]",
"Option [TT]-cc[tt] plots the resemblance of set i with a cosine of",
"i/2 periods. The formula is:[BR]"
"2 (int0-T y(t) cos(i pi t) dt)^2 / int0-T y(t) y(t) dt[BR]",
"This is useful for principal components obtained from covariance",
"analysis, since the principal components of random diffusion are",
"pure cosines.[PAR]",
"Option [TT]-msd[tt] produces the mean square displacement(s).[PAR]",
"Option [TT]-dist[tt] produces distribution plot(s).[PAR]",
"Option [TT]-av[tt] produces the average over the sets.",
"Error bars can be added with the option [TT]-errbar[tt].",
"The errorbars can represent the standard deviation, the error",
"(assuming the points are independent) or the interval containing",
"90% of the points, by discarding 5% of the points at the top and",
"the bottom.[PAR]",
"Option [TT]-ee[tt] produces error estimates using block averaging.",
"A set is divided in a number of blocks and averages are calculated for",
"each block. The error for the total average is calculated from",
"the variance between averages of the m blocks B_i as follows:",
"error^2 = Sum (B_i - <B>)^2 / (m*(m-1)).",
"These errors are plotted as a function of the block size.",
"Also an analytical block average curve is plotted, assuming",
"that the autocorrelation is a sum of two exponentials.",
"The analytical curve for the block average is:[BR]",
"f(t) = sigma sqrt(2/T ( a (tau1 ((exp(-t/tau1) - 1) tau1/t + 1)) +[BR]",
" (1-a) (tau2 ((exp(-t/tau2) - 1) tau2/t + 1)))),[BR]"
"where T is the total time.",
"a, tau1 and tau2 are obtained by fitting f^2(t) to error^2.",
"When the actual block average is very close to the analytical curve,",
"the error is sigma*sqrt(2/T (a tau1 + (1-a) tau2)).",
"The complete derivation is given in",
"B. Hess, J. Chem. Phys. 116:209-217, 2002.[PAR]",
"Option [TT]-filter[tt] prints the RMS high-frequency fluctuation",
"of each set and over all sets with respect to a filtered average.",
"The filter is proportional to cos(pi t/len) where t goes from -len/2",
"to len/2. len is supplied with the option [TT]-filter[tt].",
"This filter reduces oscillations with period len/2 and len by a factor",
"of 0.79 and 0.33 respectively.[PAR]",
"Option [TT]-g[tt] fits the data to the function given with option",
"[TT]-fitfn[tt].[PAR]",
"Option [TT]-power[tt] fits the data to b t^a, which is accomplished",
"by fitting to a t + b on log-log scale. All points after the first",
"zero or negative value are ignored.[PAR]"
"Option [TT]-luzar[tt] performs a Luzar & Chandler kinetics analysis",
"on output from [TT]g_hbond[tt]. The input file can be taken directly",
"from [TT]g_hbond -ac[tt], and then the same result should be produced."
};
static real tb=-1,te=-1,frac=0.5,filtlen=0,binwidth=0.1,aver_start=0;
static bool bHaveT=TRUE,bDer=FALSE,bSubAv=TRUE,bAverCorr=FALSE,bXYdy=FALSE;
static bool bEESEF=FALSE,bEENLC=FALSE,bEeFitAc=FALSE,bPower=FALSE;
static bool bIntegrate=FALSE,bRegression=FALSE,bLuzar=FALSE,bLuzarError=FALSE;
static int nsets_in=1,d=1,nb_min=4,resol=10;
static real temp=298.15,fit_start=1,smooth_tail_start=-1;
/* must correspond to enum avbar* declared at beginning of file */
static const char *avbar_opt[avbarNR+1] = {
NULL, "none", "stddev", "error", "90", NULL
};
t_pargs pa[] = {
{ "-time", FALSE, etBOOL, {&bHaveT},
"Expect a time in the input" },
{ "-b", FALSE, etREAL, {&tb},
"First time to read from set" },
{ "-e", FALSE, etREAL, {&te},
"Last time to read from set" },
{ "-n", FALSE, etINT, {&nsets_in},
"Read # sets seperated by &" },
{ "-d", FALSE, etBOOL, {&bDer},
"Use the derivative" },
{ "-dp", FALSE, etINT, {&d},
"HIDDENThe derivative is the difference over # points" },
{ "-bw", FALSE, etREAL, {&binwidth},
"Binwidth for the distribution" },
{ "-errbar", FALSE, etENUM, {avbar_opt},
"Error bars for -av" },
{ "-integrate",FALSE,etBOOL, {&bIntegrate},
"Integrate data function(s) numerically using trapezium rule" },
{ "-aver_start",FALSE, etREAL, {&aver_start},
"Start averaging the integral from here" },
{ "-xydy", FALSE, etBOOL, {&bXYdy},
"Interpret second data set as error in the y values for integrating" },
{ "-regression",FALSE,etBOOL,{&bRegression},
"Perform a linear regression analysis on the data" },
{ "-luzar", FALSE, etBOOL, {&bLuzar},
"Do a Luzar and Chandler analysis on a correlation function and related as produced by g_hbond. When in addition the -xydy flag is given the second and fourth column will be interpreted as errors in c(t) and n(t)." },
{ "-temp", FALSE, etREAL, {&temp},
"Temperature for the Luzar hydrogen bonding kinetics analysis" },
{ "-fitstart", FALSE, etREAL, {&fit_start},
"Time (ps) from which to start fitting the correlation functions in order to obtain the forward and backward rate constants for HB breaking and formation" },
{ "-smooth",FALSE, etREAL, {&smooth_tail_start},
"If >= 0, the tail of the ACF will be smoothed by fitting it to an exponential function: y = A exp(-x/tau)" },
{ "-nbmin", FALSE, etINT, {&nb_min},
"HIDDENMinimum number of blocks for block averaging" },
{ "-resol", FALSE, etINT, {&resol},
"HIDDENResolution for the block averaging, block size increases with"
" a factor 2^(1/#)" },
{ "-eeexpfit", FALSE, etBOOL, {&bEESEF},
"HIDDENAlways use a single exponential fit for the error estimate" },
{ "-eenlc", FALSE, etBOOL, {&bEENLC},
"HIDDENAllow a negative long-time correlation" },
{ "-eefitac", FALSE, etBOOL, {&bEeFitAc},
"HIDDENAlso plot analytical block average using a autocorrelation fit" },
{ "-filter", FALSE, etREAL, {&filtlen},
"Print the high-frequency fluctuation after filtering with a cosine filter of length #" },
{ "-power", FALSE, etBOOL, {&bPower},
"Fit data to: b t^a" },
{ "-subav", FALSE, etBOOL, {&bSubAv},
"Subtract the average before autocorrelating" },
{ "-oneacf", FALSE, etBOOL, {&bAverCorr},
"Calculate one ACF over all sets" }
};
#define NPA asize(pa)
FILE *out,*out_fit;
int n,nlast,s,nset,i,j=0;
real **val,*t,dt,tot,error;
double *av,*sig,cum1,cum2,cum3,cum4,db;
char *acfile,*msdfile,*ccfile,*distfile,*avfile,*eefile,*fitfile;
t_filenm fnm[] = {
{ efXVG, "-f", "graph", ffREAD },
{ efXVG, "-ac", "autocorr", ffOPTWR },
{ efXVG, "-msd", "msd", ffOPTWR },
{ efXVG, "-cc", "coscont", ffOPTWR },
{ efXVG, "-dist", "distr", ffOPTWR },
{ efXVG, "-av", "average", ffOPTWR },
{ efXVG, "-ee", "errest", ffOPTWR },
{ efLOG, "-g", "fitlog", ffOPTWR }
};
#define NFILE asize(fnm)
int npargs;
t_pargs *ppa;
npargs = asize(pa);
ppa = add_acf_pargs(&npargs,pa);
CopyRight(stderr,argv[0]);
parse_common_args(&argc,argv,PCA_CAN_VIEW,
NFILE,fnm,npargs,ppa,asize(desc),desc,0,NULL);
acfile = opt2fn_null("-ac",NFILE,fnm);
msdfile = opt2fn_null("-msd",NFILE,fnm);
ccfile = opt2fn_null("-cc",NFILE,fnm);
distfile = opt2fn_null("-dist",NFILE,fnm);
avfile = opt2fn_null("-av",NFILE,fnm);
eefile = opt2fn_null("-ee",NFILE,fnm);
if (opt2parg_bSet("-fitfn",npargs,ppa))
fitfile = opt2fn("-g",NFILE,fnm);
else
fitfile = opt2fn_null("-g",NFILE,fnm);
val=read_xvg_time(opt2fn("-f",NFILE,fnm),bHaveT,
opt2parg_bSet("-b",npargs,ppa),tb,
opt2parg_bSet("-e",npargs,ppa),te,
nsets_in,&nset,&n,&dt,&t);
printf("Read %d sets of %d points, dt = %g\n\n",nset,n,dt);
if (bDer) {
printf("Calculating the derivative as (f[i+%d]-f[i])/(%d*dt)\n\n",
d,d);
n -= d;
for(s=0; s<nset; s++)
for(i=0; (i<n); i++)
val[s][i] = (val[s][i+d]-val[s][i])/(d*dt);
}
if (bIntegrate) {
real sum,stddev;
printf("Calculating the integral using the trapezium rule\n");
if (bXYdy) {
sum = evaluate_integral(n,t,val[0],val[1],aver_start,&stddev);
printf("Integral %10.3f +/- %10.5f\n",sum,stddev);
}
else {
for(s=0; s<nset; s++) {
sum = evaluate_integral(n,t,val[s],NULL,aver_start,&stddev);
printf("Integral %d %10.5f +/- %10.5f\n",s+1,sum,stddev);
}
}
}
if (fitfile) {
out_fit = ffopen(fitfile,"w");
if (bXYdy && nset>=2) {
do_fit(out_fit,0,TRUE,n,t,val,npargs,ppa);
} else {
for(s=0; s<nset; s++)
do_fit(out_fit,s,FALSE,n,t,val,npargs,ppa);
}
fclose(out_fit);
}
printf(" std. dev. relative deviation of\n");
printf(" standard --------- cumulants from those of\n");
printf("set average deviation sqrt(n-1) a Gaussian distribition\n");
printf(" cum. 3 cum. 4\n");
snew(av,nset);
snew(sig,nset);
for(s=0; (s<nset); s++) {
cum1 = 0;
cum2 = 0;
cum3 = 0;
cum4 = 0;
for(i=0; (i<n); i++)
cum1 += val[s][i];
cum1 /= n;
for(i=0; (i<n); i++) {
db = val[s][i]-cum1;
cum2 += db*db;
cum3 += db*db*db;
cum4 += db*db*db*db;
}
cum2 /= n;
cum3 /= n;
cum4 /= n;
av[s] = cum1;
sig[s] = sqrt(cum2);
if (n > 1)
error = sqrt(cum2/(n-1));
else
error = 0;
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] ? cum4/(sig[s]*sig[s]*sig[s]*sig[s]*3)-1 : 0);
}
printf("\n");
if (filtlen)
filter(filtlen,n,nset,val,dt);
if (msdfile) {
out=xvgropen(msdfile,"Mean square displacement",
"time","MSD (nm\\S2\\N)");
nlast = (int)(n*frac);
for(s=0; s<nset; s++) {
for(j=0; j<=nlast; j++) {
if (j % 100 == 0)
fprintf(stderr,"\r%d",j);
tot=0;
for(i=0; i<n-j; i++)
tot += sqr(val[s][i]-val[s][i+j]);
tot /= (real)(n-j);
fprintf(out," %g %8g\n",dt*j,tot);
}
if (s<nset-1)
fprintf(out,"&\n");
}
fclose(out);
fprintf(stderr,"\r%d, time=%g\n",j-1,(j-1)*dt);
}
if (ccfile)
plot_coscont(ccfile,n,nset,val);
if (distfile)
histogram(distfile,binwidth,n,nset,val);
if (avfile)
average(avfile,nenum(avbar_opt),n,nset,val,t);
if (eefile)
estimate_error(eefile,nb_min,resol,n,nset,av,sig,val,dt,
bEeFitAc,bEESEF,bEENLC);
if (bPower)
power_fit(n,nset,val,t);
if (acfile) {
if (bSubAv)
for(s=0; s<nset; s++)
for(i=0; i<n; i++)
val[s][i] -= av[s];
do_autocorr(acfile,"Autocorrelation",n,nset,val,dt,
eacNormal,bAverCorr);
}
if (bRegression)
regression_analysis(n,bXYdy,t,val);
if (bLuzar)
luzar_correl(n,t,nset,val,temp,bXYdy,fit_start,smooth_tail_start);
view_all(NFILE, fnm);
thanx(stderr);
return 0;
}
int gmx_dos(int argc, char *argv[])
{
const char *desc[] = {
"[TT]g_dos[tt] computes the Density of States from a simulations.",
"In order for this to be meaningful the velocities must be saved",
"in the trajecotry with sufficiently high frequency such as to cover",
"all vibrations. For flexible systems that would be around a few fs",
"between saving. Properties based on the DoS are printed on the",
"standard output."
};
const char *bugs[] = {
"This program needs a lot of memory: total usage equals the number of atoms times 3 times number of frames times 4 (or 8 when run in double precision)."
};
FILE *fp, *fplog;
t_topology top;
int ePBC = -1;
t_trxframe fr;
matrix box;
int gnx;
char title[256];
real t0, t1, m;
t_trxstatus *status;
int nV, nframes, n_alloc, i, j, k, l, fftcode, Nmol, Natom;
double rho, dt, V2sum, Vsum, V, tmass, dostot, dos2, dosabs;
real **c1, **dos, mi, beta, bfac, *nu, *tt, stddev, c1j;
output_env_t oenv;
gmx_fft_t fft;
double cP, S, A, E, DiffCoeff, Delta, f, y, z, sigHS, Shs, Sig, DoS0, recip_fac;
double wCdiff, wSdiff, wAdiff, wEdiff;
static gmx_bool bVerbose = TRUE, bAbsolute = FALSE, bNormalize = FALSE;
static gmx_bool bRecip = FALSE, bDump = FALSE;
static real Temp = 298.15, toler = 1e-6;
t_pargs pa[] = {
{ "-v", FALSE, etBOOL, {&bVerbose},
"Be loud and noisy." },
{ "-recip", FALSE, etBOOL, {&bRecip},
"Use cm^-1 on X-axis instead of 1/ps for DoS plots." },
{ "-abs", FALSE, etBOOL, {&bAbsolute},
"Use the absolute value of the Fourier transform of the VACF as the Density of States. Default is to use the real component only" },
{ "-normdos", FALSE, etBOOL, {&bNormalize},
"Normalize the DoS such that it adds up to 3N. This is a hack that should not be necessary." },
{ "-T", FALSE, etREAL, {&Temp},
"Temperature in the simulation" },
{ "-toler", FALSE, etREAL, {&toler},
"[HIDDEN]Tolerance when computing the fluidicity using bisection algorithm" },
{ "-dump", FALSE, etBOOL, {&bDump},
"[HIDDEN]Dump the y/fy plot corresponding to Fig. 2 inLin2003a and the and the weighting functions corresponding to Fig. 1 in Berens1983a." }
};
t_filenm fnm[] = {
{ efTRN, "-f", NULL, ffREAD },
{ efTPX, "-s", NULL, ffREAD },
{ efNDX, NULL, NULL, ffOPTRD },
{ efXVG, "-vacf", "vacf", ffWRITE },
{ efXVG, "-mvacf", "mvacf", ffWRITE },
{ efXVG, "-dos", "dos", ffWRITE },
{ efLOG, "-g", "dos", ffWRITE },
};
#define NFILE asize(fnm)
int npargs;
t_pargs *ppa;
const char *DoSlegend[] = {
"DoS(v)", "DoS(v)[Solid]", "DoS(v)[Diff]"
};
npargs = asize(pa);
ppa = add_acf_pargs(&npargs, pa);
parse_common_args(&argc, argv, PCA_CAN_VIEW | PCA_CAN_TIME | PCA_BE_NICE,
NFILE, fnm, npargs, ppa, asize(desc), desc,
asize(bugs), bugs, &oenv);
beta = 1/(Temp*BOLTZ);
if (bDump)
{
printf("Dumping reference figures. Thanks for your patience.\n");
dump_fy(oenv, toler);
dump_w(oenv, beta);
exit(0);
}
fplog = gmx_fio_fopen(ftp2fn(efLOG, NFILE, fnm), "w");
fprintf(fplog, "Doing density of states analysis based on trajectory.\n");
please_cite(fplog, "Pascal2011a");
please_cite(fplog, "Caleman2011b");
read_tps_conf(ftp2fn(efTPX, NFILE, fnm), title, &top, &ePBC, NULL, NULL, box,
TRUE);
V = det(box);
tmass = 0;
for (i = 0; (i < top.atoms.nr); i++)
{
tmass += top.atoms.atom[i].m;
}
Natom = top.atoms.nr;
Nmol = top.mols.nr;
gnx = Natom*DIM;
/* Correlation stuff */
snew(c1, gnx);
for (i = 0; (i < gnx); i++)
{
c1[i] = NULL;
}
read_first_frame(oenv, &status, ftp2fn(efTRN, NFILE, fnm), &fr, TRX_NEED_V);
t0 = fr.time;
n_alloc = 0;
nframes = 0;
Vsum = V2sum = 0;
nV = 0;
do
{
if (fr.bBox)
{
V = det(fr.box);
V2sum += V*V;
Vsum += V;
nV++;
}
if (nframes >= n_alloc)
{
n_alloc += 100;
for (i = 0; i < gnx; i++)
{
srenew(c1[i], n_alloc);
}
}
for (i = 0; i < gnx; i += DIM)
{
c1[i+XX][nframes] = fr.v[i/DIM][XX];
c1[i+YY][nframes] = fr.v[i/DIM][YY];
c1[i+ZZ][nframes] = fr.v[i/DIM][ZZ];
}
t1 = fr.time;
nframes++;
}
while (read_next_frame(oenv, status, &fr));
close_trj(status);
dt = (t1-t0)/(nframes-1);
if (nV > 0)
{
V = Vsum/nV;
}
if (bVerbose)
{
printf("Going to do %d fourier transforms of length %d. Hang on.\n",
gnx, nframes);
}
low_do_autocorr(NULL, oenv, NULL, nframes, gnx, nframes, c1, dt, eacNormal, 0, FALSE,
FALSE, FALSE, -1, -1, 0, 0);
snew(dos, DOS_NR);
for (j = 0; (j < DOS_NR); j++)
{
snew(dos[j], nframes+4);
}
if (bVerbose)
{
printf("Going to merge the ACFs into the mass-weighted and plain ACF\n");
}
for (i = 0; (i < gnx); i += DIM)
{
mi = top.atoms.atom[i/DIM].m;
for (j = 0; (j < nframes/2); j++)
{
c1j = (c1[i+XX][j] + c1[i+YY][j] + c1[i+ZZ][j]);
dos[VACF][j] += c1j/Natom;
dos[MVACF][j] += mi*c1j;
}
}
fp = xvgropen(opt2fn("-vacf", NFILE, fnm), "Velocity ACF",
"Time (ps)", "C(t)", oenv);
snew(tt, nframes/2);
for (j = 0; (j < nframes/2); j++)
{
tt[j] = j*dt;
fprintf(fp, "%10g %10g\n", tt[j], dos[VACF][j]);
}
xvgrclose(fp);
fp = xvgropen(opt2fn("-mvacf", NFILE, fnm), "Mass-weighted velocity ACF",
"Time (ps)", "C(t)", oenv);
for (j = 0; (j < nframes/2); j++)
{
fprintf(fp, "%10g %10g\n", tt[j], dos[MVACF][j]);
}
xvgrclose(fp);
if ((fftcode = gmx_fft_init_1d_real(&fft, nframes/2,
GMX_FFT_FLAG_NONE)) != 0)
{
gmx_fatal(FARGS, "gmx_fft_init_1d_real returned %d", fftcode);
}
if ((fftcode = gmx_fft_1d_real(fft, GMX_FFT_REAL_TO_COMPLEX,
(void *)dos[MVACF], (void *)dos[DOS])) != 0)
{
gmx_fatal(FARGS, "gmx_fft_1d_real returned %d", fftcode);
}
/* First compute the DoS */
/* Magic factor of 8 included now. */
bfac = 8*dt*beta/2;
dos2 = 0;
snew(nu, nframes/4);
for (j = 0; (j < nframes/4); j++)
{
nu[j] = 2*j/(t1-t0);
dos2 += sqr(dos[DOS][2*j]) + sqr(dos[DOS][2*j+1]);
if (bAbsolute)
{
dos[DOS][j] = bfac*sqrt(sqr(dos[DOS][2*j]) + sqr(dos[DOS][2*j+1]));
}
else
{
dos[DOS][j] = bfac*dos[DOS][2*j];
}
}
/* Normalize it */
dostot = evaluate_integral(nframes/4, nu, dos[DOS], NULL, nframes/4, &stddev);
if (bNormalize)
{
for (j = 0; (j < nframes/4); j++)
{
dos[DOS][j] *= 3*Natom/dostot;
}
}
/* Now analyze it */
DoS0 = dos[DOS][0];
/* Note this eqn. is incorrect in Pascal2011a! */
Delta = ((2*DoS0/(9*Natom))*sqrt(M_PI*BOLTZ*Temp*Natom/tmass)*
pow((Natom/V), 1.0/3.0)*pow(6/M_PI, 2.0/3.0));
f = calc_fluidicity(Delta, toler);
y = calc_y(f, Delta, toler);
z = calc_compress(y);
Sig = BOLTZ*(5.0/2.0+log(2*M_PI*BOLTZ*Temp/(sqr(PLANCK))*V/(f*Natom)));
Shs = Sig+calc_Shs(f, y);
rho = (tmass*AMU)/(V*NANO*NANO*NANO);
sigHS = pow(6*y*V/(M_PI*Natom), 1.0/3.0);
fprintf(fplog, "System = \"%s\"\n", title);
fprintf(fplog, "Nmol = %d\n", Nmol);
fprintf(fplog, "Natom = %d\n", Natom);
fprintf(fplog, "dt = %g ps\n", dt);
fprintf(fplog, "tmass = %g amu\n", tmass);
fprintf(fplog, "V = %g nm^3\n", V);
fprintf(fplog, "rho = %g g/l\n", rho);
fprintf(fplog, "T = %g K\n", Temp);
fprintf(fplog, "beta = %g mol/kJ\n", beta);
fprintf(fplog, "\nDoS parameters\n");
fprintf(fplog, "Delta = %g\n", Delta);
fprintf(fplog, "fluidicity = %g\n", f);
fprintf(fplog, "hard sphere packing fraction = %g\n", y);
fprintf(fplog, "hard sphere compressibility = %g\n", z);
fprintf(fplog, "ideal gas entropy = %g\n", Sig);
fprintf(fplog, "hard sphere entropy = %g\n", Shs);
fprintf(fplog, "sigma_HS = %g nm\n", sigHS);
fprintf(fplog, "DoS0 = %g\n", DoS0);
fprintf(fplog, "Dos2 = %g\n", dos2);
fprintf(fplog, "DoSTot = %g\n", dostot);
/* Now compute solid (2) and diffusive (3) components */
fp = xvgropen(opt2fn("-dos", NFILE, fnm), "Density of states",
bRecip ? "E (cm\\S-1\\N)" : "\\f{12}n\\f{4} (1/ps)",
"\\f{4}S(\\f{12}n\\f{4})", oenv);
xvgr_legend(fp, asize(DoSlegend), DoSlegend, oenv);
recip_fac = bRecip ? (1e7/SPEED_OF_LIGHT) : 1.0;
for (j = 0; (j < nframes/4); j++)
{
dos[DOS_DIFF][j] = DoS0/(1+sqr(DoS0*M_PI*nu[j]/(6*f*Natom)));
dos[DOS_SOLID][j] = dos[DOS][j]-dos[DOS_DIFF][j];
fprintf(fp, "%10g %10g %10g %10g\n",
recip_fac*nu[j],
dos[DOS][j]/recip_fac,
dos[DOS_SOLID][j]/recip_fac,
dos[DOS_DIFF][j]/recip_fac);
}
xvgrclose(fp);
/* Finally analyze the results! */
wCdiff = 0.5;
wSdiff = Shs/(3*BOLTZ); /* Is this correct? */
wEdiff = 0.5;
wAdiff = wEdiff-wSdiff;
for (j = 0; (j < nframes/4); j++)
{
dos[DOS_CP][j] = (dos[DOS_DIFF][j]*wCdiff +
dos[DOS_SOLID][j]*wCsolid(nu[j], beta));
dos[DOS_S][j] = (dos[DOS_DIFF][j]*wSdiff +
dos[DOS_SOLID][j]*wSsolid(nu[j], beta));
dos[DOS_A][j] = (dos[DOS_DIFF][j]*wAdiff +
dos[DOS_SOLID][j]*wAsolid(nu[j], beta));
dos[DOS_E][j] = (dos[DOS_DIFF][j]*wEdiff +
dos[DOS_SOLID][j]*wEsolid(nu[j], beta));
}
DiffCoeff = evaluate_integral(nframes/2, tt, dos[VACF], NULL, nframes/2, &stddev);
DiffCoeff = 1000*DiffCoeff/3.0;
fprintf(fplog, "Diffusion coefficient from VACF %g 10^-5 cm^2/s\n",
DiffCoeff);
fprintf(fplog, "Diffusion coefficient from DoS %g 10^-5 cm^2/s\n",
1000*DoS0/(12*tmass*beta));
cP = BOLTZ * evaluate_integral(nframes/4, nu, dos[DOS_CP], NULL,
nframes/4, &stddev);
fprintf(fplog, "Heat capacity %g J/mol K\n", 1000*cP/Nmol);
/*
S = BOLTZ * evaluate_integral(nframes/4,nu,dos[DOS_S],NULL,
nframes/4,&stddev);
fprintf(fplog,"Entropy %g J/mol K\n",1000*S/Nmol);
A = BOLTZ * evaluate_integral(nframes/4,nu,dos[DOS_A],NULL,
nframes/4,&stddev);
fprintf(fplog,"Helmholtz energy %g kJ/mol\n",A/Nmol);
E = BOLTZ * evaluate_integral(nframes/4,nu,dos[DOS_E],NULL,
nframes/4,&stddev);
fprintf(fplog,"Internal energy %g kJ/mol\n",E/Nmol);
*/
fprintf(fplog, "\nArrivederci!\n");
gmx_fio_fclose(fplog);
do_view(oenv, ftp2fn(efXVG, NFILE, fnm), "-nxy");
thanx(stderr);
return 0;
}
int main(int argc, char *argv[])
{
int warn, i, j, nread, NUMBER_OF_SIGMA_BINS;
double X_s, I_R, s, R[90], R_arcmin[90], sig_p[90], a_dm, a_s, M_s, M_dm, beta;
double R_o[12], sig_p_o[12], chi2, a_dm_min, a_s_min, M_dm_min, M_s_min, beta_min, sig_p_i;
double chi_min = 1000000.0;
double gamma, gamma_min, aux;
char *infile;
FILE *script,*sigma,*chi_cua;
double gamma_ini, gamma_end, Delta_gamma;
double adm_ini, adm_end, Delta_adm;
double as_ini, as_end, Delta_as;
double Mdm_ini, Mdm_end, Delta_Mdm;
double Ms_ini, Ms_end, Delta_Ms;
double beta_ini, beta_end, Delta_beta;
gsl_interp_accel *acc = gsl_interp_accel_alloc ();
gsl_spline *spline = gsl_spline_alloc (gsl_interp_cspline, 90);
infile = argv[1]; // input file with velocity dispersion bins!
NUMBER_OF_SIGMA_BINS = atoi(argv[2]); // eventually 12!
if(argc != 3)
{
printf(" Execute as ./exec <infile> <number of lines>\n");
exit(0);
}
sigma=fopen(infile,"r");
for(j=0.0; j<NUMBER_OF_SIGMA_BINS; j++)
nread = fscanf(sigma,"%lf %lf", &R_o[j], &sig_p_o[j]);
fclose(sigma);
a_s = 0.00161; //Stellar scalength of 1.61 parsecs from the fitting of Noyola's data
gamma_ini = 0.1; gamma_end = 3.0; Delta_gamma = 0.03;
adm_ini = 0.001; adm_end = 0.06; Delta_adm = 0.005;
Mdm_ini = 0.00001; Mdm_end = 0.0008; Delta_Mdm = 0.00005;
Ms_ini = 0.00001; Ms_end = 0.0008; Delta_Ms = 0.00005;
beta_ini = 0.00001; beta_end = 1.0; Delta_beta = 0.02;
// HAGO LAS VARIACIONES DE LOS PARAMETROS
int Ntotal_iterations, counter, cuenta;
Ntotal_iterations = (int) ( ((beta_end-beta_ini)/Delta_beta)*((gamma_end-gamma_ini)/Delta_gamma)*((adm_end-adm_ini)/Delta_adm)*((Mdm_end-Mdm_ini)/Delta_Mdm)*((Ms_end-Ms_ini)/Delta_Ms) );
printf(" Running %d iterations in parameter space\n", Ntotal_iterations);
counter=0;
/*DEFINICION DEL VECTOR R[i]*/
double ii = 0.0;
for(i=0;i<K;i++)
{
R[i] = (ii+1.0)/1000.0;
if(i==0)
R[i] = R[i]*0.1;
ii = ii +1.0;
}
for(beta = beta_ini; beta <= beta_end; beta = beta + Delta_beta)
{
for(gamma = gamma_ini; gamma <= gamma_end; gamma = gamma + Delta_gamma)
{
for(a_dm = adm_ini; a_dm <= adm_end; a_dm = a_dm + Delta_adm)
{
// PARA EVITAR INDETERMINACIONES EN ALGUNOS DENOMINADORES
if (a_s != a_dm)
{
for(M_dm = Mdm_ini; M_dm < Mdm_end; M_dm = M_dm + Delta_Mdm)
{
for(M_s = Ms_ini; M_s < Ms_end; M_s = M_s + Delta_Ms)
{
// ESTE CICLO ES PARA R (RADIO PROYECTADO) YA QUE SIGMA DEPENDE DE R, ADEMAS VA A SER EL LIMITE INFERIOR DE LA INTEGRAL
for(i = 0; i < K; i ++)
{
evaluate_integral(R[i], beta, gamma, a_dm, a_s, M_dm, M_s, &sig_p[i], &R_arcmin[i]);
}
gsl_spline_init (spline, R_arcmin, sig_p, 90);
chi2 = 0.0;
for(j=0; j<NUMBER_OF_SIGMA_BINS; j++)
{
sig_p_i = gsl_spline_eval (spline, R_o[j], acc);
chi2 += (sig_p_i-sig_p_o[j])*(sig_p_i-sig_p_o[j]);
}
if(chi2 < chi_min)
{
chi_min = chi2;
a_dm_min = a_dm;
a_s_min = a_s;
M_dm_min = M_dm;
M_s_min = M_s;
gamma_min = gamma;
beta_min = beta;
}
if((counter%1000) == 0)
printf("Ready %d iterations of %d\n",counter, Ntotal_iterations);
counter++;
}// for Ms
}// for Mdm
}// if for the singularity
}// for adm
}//for gamma
}//for beta
//printf ("\n");
chi_cua=fopen("chi_cuadrado.dat","w");
for(i = 0; i < K; i ++)
{
evaluate_integral(R[i], beta_min, gamma_min, a_dm_min, a_s_min, M_dm_min, M_s_min, &sig_p[i], &R_arcmin[i]);
fprintf(chi_cua,"%16.8e %16.8e\n", R_arcmin[i], sig_p[i]);
}
fclose(chi_cua);
// MUESTRO CUALES SON LOS PARAMETROS QUE MEJOR SE AJUSTAN A LOS DATOS OBSERVACIONALES
printf("xi=%16.8lf beta=%16.8lf a_dm/pc=%16.8lf a_s/pc=%16.8lf M_dm/Msun=%16.8elf M_s/Msun=%16.8e gamma=%lf\n",
chi_min, beta_min, 1000*a_dm_min, 1000*a_s_min, (1.0e10)*M_dm_min, (1.0e10)*M_s_min, gamma_min);
gsl_spline_free (spline);
gsl_interp_accel_free (acc);
script = fopen( "script.gpl", "w" );
fprintf( script, "plot 'sigma.dat'pt 7 ps 1.2\n" );
fprintf( script, "replot 'chi_cuadrado.dat' u 1:2 w l\n");
fprintf( script, "reset\n");
fprintf(script, "set grid\nset terminal png\nset output 'sigma.png'\nset nokey\n");
fprintf( script, "set title 'Sigma Proyectada vs R'\n" );
fprintf( script, "set xrange [0:40]\n" );
fprintf( script, "set yrange [4:14]\n" );
fprintf( script, "set xlabel 'Radius in Arcmin'\n" );
fprintf( script, "set ylabel 'Projected velocity dispersion in km/s'\n" );
fprintf( script, "plot 'sigma.dat'pt 7 ps 1.2\n" );
fprintf( script, "replot 'chi_cuadrado.dat' u 1:2 w l\n");
fprintf( script, "pause -1\n");
fclose(script);
warn = system("gnuplot script.gpl");
return(warn);
}
int gmx_analyze(int argc, char *argv[])
{
static const char *desc[] = {
"[TT]g_analyze[tt] reads an ASCII file and analyzes data sets.",
"A line in the input file may start with a time",
"(see option [TT]-time[tt]) and any number of [IT]y[it]-values may follow.",
"Multiple sets can also be",
"read when they are separated by & (option [TT]-n[tt]);",
"in this case only one [IT]y[it]-value is read from each line.",
"All lines starting with # and @ are skipped.",
"All analyses can also be done for the derivative of a set",
"(option [TT]-d[tt]).[PAR]",
"All options, except for [TT]-av[tt] and [TT]-power[tt], assume that the",
"points are equidistant in time.[PAR]",
"[TT]g_analyze[tt] always shows the average and standard deviation of each",
"set, as well as the relative deviation of the third",
"and fourth cumulant from those of a Gaussian distribution with the same",
"standard deviation.[PAR]",
"Option [TT]-ac[tt] produces the autocorrelation function(s).",
"Be sure that the time interval between data points is",
"much shorter than the time scale of the autocorrelation.[PAR]",
"Option [TT]-cc[tt] plots the resemblance of set i with a cosine of",
"i/2 periods. The formula is:[BR]"
"[MATH]2 ([INT][FROM]0[from][TO]T[to][int] y(t) [COS]i [GRK]pi[grk] t[cos] dt)^2 / [INT][FROM]0[from][TO]T[to][int] y^2(t) dt[math][BR]",
"This is useful for principal components obtained from covariance",
"analysis, since the principal components of random diffusion are",
"pure cosines.[PAR]",
"Option [TT]-msd[tt] produces the mean square displacement(s).[PAR]",
"Option [TT]-dist[tt] produces distribution plot(s).[PAR]",
"Option [TT]-av[tt] produces the average over the sets.",
"Error bars can be added with the option [TT]-errbar[tt].",
"The errorbars can represent the standard deviation, the error",
"(assuming the points are independent) or the interval containing",
"90% of the points, by discarding 5% of the points at the top and",
"the bottom.[PAR]",
"Option [TT]-ee[tt] produces error estimates using block averaging.",
"A set is divided in a number of blocks and averages are calculated for",
"each block. The error for the total average is calculated from",
"the variance between averages of the m blocks B[SUB]i[sub] as follows:",
"error^2 = [SUM][sum] (B[SUB]i[sub] - [CHEVRON]B[chevron])^2 / (m*(m-1)).",
"These errors are plotted as a function of the block size.",
"Also an analytical block average curve is plotted, assuming",
"that the autocorrelation is a sum of two exponentials.",
"The analytical curve for the block average is:[BR]",
"[MATH]f(t) = [GRK]sigma[grk][TT]*[tt][SQRT]2/T ( [GRK]alpha[grk] ([GRK]tau[grk][SUB]1[sub] (([EXP]-t/[GRK]tau[grk][SUB]1[sub][exp] - 1) [GRK]tau[grk][SUB]1[sub]/t + 1)) +[BR]",
" (1-[GRK]alpha[grk]) ([GRK]tau[grk][SUB]2[sub] (([EXP]-t/[GRK]tau[grk][SUB]2[sub][exp] - 1) [GRK]tau[grk][SUB]2[sub]/t + 1)))[sqrt][math],[BR]"
"where T is the total time.",
"[GRK]alpha[grk], [GRK]tau[grk][SUB]1[sub] and [GRK]tau[grk][SUB]2[sub] are obtained by fitting f^2(t) to error^2.",
"When the actual block average is very close to the analytical curve,",
"the error is [MATH][GRK]sigma[grk][TT]*[tt][SQRT]2/T (a [GRK]tau[grk][SUB]1[sub] + (1-a) [GRK]tau[grk][SUB]2[sub])[sqrt][math].",
"The complete derivation is given in",
"B. Hess, J. Chem. Phys. 116:209-217, 2002.[PAR]",
"Option [TT]-bal[tt] finds and subtracts the ultrafast \"ballistic\"",
"component from a hydrogen bond autocorrelation function by the fitting",
"of a sum of exponentials, as described in e.g.",
"O. Markovitch, J. Chem. Phys. 129:084505, 2008. The fastest term",
"is the one with the most negative coefficient in the exponential,",
"or with [TT]-d[tt], the one with most negative time derivative at time 0.",
"[TT]-nbalexp[tt] sets the number of exponentials to fit.[PAR]",
"Option [TT]-gem[tt] fits bimolecular rate constants ka and kb",
"(and optionally kD) to the hydrogen bond autocorrelation function",
"according to the reversible geminate recombination model. Removal of",
"the ballistic component first is strongly advised. The model is presented in",
"O. Markovitch, J. Chem. Phys. 129:084505, 2008.[PAR]",
"Option [TT]-filter[tt] prints the RMS high-frequency fluctuation",
"of each set and over all sets with respect to a filtered average.",
"The filter is proportional to cos([GRK]pi[grk] t/len) where t goes from -len/2",
"to len/2. len is supplied with the option [TT]-filter[tt].",
"This filter reduces oscillations with period len/2 and len by a factor",
"of 0.79 and 0.33 respectively.[PAR]",
"Option [TT]-g[tt] fits the data to the function given with option",
"[TT]-fitfn[tt].[PAR]",
"Option [TT]-power[tt] fits the data to [MATH]b t^a[math], which is accomplished",
"by fitting to [MATH]a t + b[math] on log-log scale. All points after the first",
"zero or with a negative value are ignored.[PAR]"
"Option [TT]-luzar[tt] performs a Luzar & Chandler kinetics analysis",
"on output from [TT]g_hbond[tt]. The input file can be taken directly",
"from [TT]g_hbond -ac[tt], and then the same result should be produced."
};
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, nBalExp = 4, nFitPoints = 100;
static real temp = 298.15, fit_start = 1, fit_end = 60, smooth_tail_start = -1, balTime = 0.2, diffusion = 5e-5, rcut = 0.35;
/* must correspond to enum avbar* declared at beginning of file */
static const char *avbar_opt[avbarNR+1] = {
NULL, "none", "stddev", "error", "90", NULL
};
t_pargs pa[] = {
{ "-time", FALSE, etBOOL, {&bHaveT},
"Expect a time in the input" },
{ "-b", FALSE, etREAL, {&tb},
"First time to read from set" },
{ "-e", FALSE, etREAL, {&te},
"Last time to read from set" },
{ "-n", FALSE, etINT, {&nsets_in},
"Read this number of sets separated by &" },
{ "-d", FALSE, etBOOL, {&bDer},
"Use the derivative" },
{ "-dp", FALSE, etINT, {&d},
"HIDDENThe derivative is the difference over this number of points" },
{ "-bw", FALSE, etREAL, {&binwidth},
"Binwidth for the distribution" },
{ "-errbar", FALSE, etENUM, {avbar_opt},
"Error bars for [TT]-av[tt]" },
{ "-integrate", FALSE, etBOOL, {&bIntegrate},
"Integrate data function(s) numerically using trapezium rule" },
{ "-aver_start", FALSE, etREAL, {&aver_start},
"Start averaging the integral from here" },
{ "-xydy", FALSE, etBOOL, {&bXYdy},
"Interpret second data set as error in the y values for integrating" },
{ "-regression", FALSE, etBOOL, {&bRegression},
"Perform a linear regression analysis on the data. If [TT]-xydy[tt] is set a second set will be interpreted as the error bar in the Y value. Otherwise, if multiple data sets are present a multilinear regression will be performed yielding the constant A that minimize [MATH][GRK]chi[grk]^2 = (y - A[SUB]0[sub] x[SUB]0[sub] - A[SUB]1[sub] x[SUB]1[sub] - ... - A[SUB]N[sub] x[SUB]N[sub])^2[math] where now Y is the first data set in the input file and x[SUB]i[sub] the others. Do read the information at the option [TT]-time[tt]." },
{ "-luzar", FALSE, etBOOL, {&bLuzar},
"Do a Luzar and Chandler analysis on a correlation function and related as produced by [TT]g_hbond[tt]. When in addition the [TT]-xydy[tt] flag is given the second and fourth column will be interpreted as errors in c(t) and n(t)." },
{ "-temp", FALSE, etREAL, {&temp},
"Temperature for the Luzar hydrogen bonding kinetics analysis (K)" },
{ "-fitstart", FALSE, etREAL, {&fit_start},
"Time (ps) from which to start fitting the correlation functions in order to obtain the forward and backward rate constants for HB breaking and formation" },
{ "-fitend", FALSE, etREAL, {&fit_end},
"Time (ps) where to stop fitting the correlation functions in order to obtain the forward and backward rate constants for HB breaking and formation. Only with [TT]-gem[tt]" },
{ "-smooth", FALSE, etREAL, {&smooth_tail_start},
"If this value is >= 0, the tail of the ACF will be smoothed by fitting it to an exponential function: [MATH]y = A [EXP]-x/[GRK]tau[grk][exp][math]" },
{ "-nbmin", FALSE, etINT, {&nb_min},
"HIDDENMinimum number of blocks for block averaging" },
{ "-resol", FALSE, etINT, {&resol},
"HIDDENResolution for the block averaging, block size increases with"
" a factor 2^(1/resol)" },
{ "-eeexpfit", FALSE, etBOOL, {&bEESEF},
"HIDDENAlways use a single exponential fit for the error estimate" },
{ "-eenlc", FALSE, etBOOL, {&bEENLC},
"HIDDENAllow a negative long-time correlation" },
{ "-eefitac", FALSE, etBOOL, {&bEeFitAc},
"HIDDENAlso plot analytical block average using a autocorrelation fit" },
{ "-filter", FALSE, etREAL, {&filtlen},
"Print the high-frequency fluctuation after filtering with a cosine filter of this length" },
{ "-power", FALSE, etBOOL, {&bPower},
"Fit data to: b t^a" },
{ "-subav", FALSE, etBOOL, {&bSubAv},
"Subtract the average before autocorrelating" },
{ "-oneacf", FALSE, etBOOL, {&bAverCorr},
"Calculate one ACF over all sets" },
{ "-nbalexp", FALSE, etINT, {&nBalExp},
"HIDDENNumber of exponentials to fit to the ultrafast component" },
{ "-baltime", FALSE, etREAL, {&balTime},
"HIDDENTime up to which the ballistic component will be fitted" },
/* { "-gemnp", FALSE, etINT, {&nFitPoints}, */
/* "HIDDENNumber of data points taken from the ACF to use for fitting to rev. gem. recomb. model."}, */
/* { "-rcut", FALSE, etREAL, {&rcut}, */
/* "Cut-off for hydrogen bonds in geminate algorithms" }, */
/* { "-gemtype", FALSE, etENUM, {gemType}, */
/* "What type of gminate recombination to use"}, */
/* { "-D", FALSE, etREAL, {&diffusion}, */
/* "The self diffusion coefficient which is used for the reversible geminate recombination model."} */
};
#define NPA asize(pa)
FILE *out, *out_fit;
int n, nlast, s, nset, i, j = 0;
real **val, *t, dt, tot, error;
double *av, *sig, cum1, cum2, cum3, cum4, db;
const char *acfile, *msdfile, *ccfile, *distfile, *avfile, *eefile, *balfile, *gemfile, *fitfile;
output_env_t oenv;
t_filenm fnm[] = {
{ efXVG, "-f", "graph", ffREAD },
{ efXVG, "-ac", "autocorr", ffOPTWR },
{ efXVG, "-msd", "msd", ffOPTWR },
{ efXVG, "-cc", "coscont", ffOPTWR },
{ efXVG, "-dist", "distr", ffOPTWR },
{ efXVG, "-av", "average", ffOPTWR },
{ efXVG, "-ee", "errest", ffOPTWR },
{ efXVG, "-bal", "ballisitc", ffOPTWR },
/* { efXVG, "-gem", "geminate", ffOPTWR }, */
{ efLOG, "-g", "fitlog", ffOPTWR }
};
#define NFILE asize(fnm)
int npargs;
t_pargs *ppa;
npargs = asize(pa);
ppa = add_acf_pargs(&npargs, pa);
parse_common_args(&argc, argv, PCA_CAN_VIEW,
NFILE, fnm, npargs, ppa, asize(desc), desc, 0, NULL, &oenv);
acfile = opt2fn_null("-ac", NFILE, fnm);
msdfile = opt2fn_null("-msd", NFILE, fnm);
ccfile = opt2fn_null("-cc", NFILE, fnm);
distfile = opt2fn_null("-dist", NFILE, fnm);
avfile = opt2fn_null("-av", NFILE, fnm);
eefile = opt2fn_null("-ee", NFILE, fnm);
balfile = opt2fn_null("-bal", NFILE, fnm);
/* gemfile = opt2fn_null("-gem",NFILE,fnm); */
/* When doing autocorrelation we don't want a fitlog for fitting
* the function itself (not the acf) when the user did not ask for it.
*/
if (opt2parg_bSet("-fitfn", npargs, ppa) && acfile == NULL)
{
fitfile = opt2fn("-g", NFILE, fnm);
}
else
{
fitfile = opt2fn_null("-g", NFILE, fnm);
}
val = read_xvg_time(opt2fn("-f", NFILE, fnm), bHaveT,
opt2parg_bSet("-b", npargs, ppa), tb,
opt2parg_bSet("-e", npargs, ppa), te,
nsets_in, &nset, &n, &dt, &t);
printf("Read %d sets of %d points, dt = %g\n\n", nset, n, dt);
if (bDer)
{
printf("Calculating the derivative as (f[i+%d]-f[i])/(%d*dt)\n\n",
d, d);
n -= d;
for (s = 0; s < nset; s++)
{
for (i = 0; (i < n); i++)
{
val[s][i] = (val[s][i+d]-val[s][i])/(d*dt);
}
}
}
if (bIntegrate)
{
real sum, stddev;
printf("Calculating the integral using the trapezium rule\n");
if (bXYdy)
{
sum = evaluate_integral(n, t, val[0], val[1], aver_start, &stddev);
printf("Integral %10.3f +/- %10.5f\n", sum, stddev);
}
else
{
for (s = 0; s < nset; s++)
{
sum = evaluate_integral(n, t, val[s], NULL, aver_start, &stddev);
printf("Integral %d %10.5f +/- %10.5f\n", s+1, sum, stddev);
}
}
}
if (fitfile != NULL)
{
out_fit = ffopen(fitfile, "w");
if (bXYdy && nset >= 2)
{
do_fit(out_fit, 0, TRUE, n, t, val, npargs, ppa, oenv);
}
else
{
for (s = 0; s < nset; s++)
{
do_fit(out_fit, s, FALSE, n, t, val, npargs, ppa, oenv);
}
}
ffclose(out_fit);
}
printf(" std. dev. relative deviation of\n");
printf(" standard --------- cumulants from those of\n");
printf("set average deviation sqrt(n-1) a Gaussian distribition\n");
printf(" cum. 3 cum. 4\n");
snew(av, nset);
snew(sig, nset);
for (s = 0; (s < nset); s++)
{
cum1 = 0;
cum2 = 0;
cum3 = 0;
cum4 = 0;
for (i = 0; (i < n); i++)
{
cum1 += val[s][i];
}
cum1 /= n;
for (i = 0; (i < n); i++)
{
db = val[s][i]-cum1;
cum2 += db*db;
cum3 += db*db*db;
cum4 += db*db*db*db;
}
cum2 /= n;
cum3 /= n;
cum4 /= n;
av[s] = cum1;
sig[s] = sqrt(cum2);
if (n > 1)
{
error = sqrt(cum2/(n-1));
}
else
{
error = 0;
}
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] ? cum4/(sig[s]*sig[s]*sig[s]*sig[s]*3)-1 : 0);
}
printf("\n");
if (filtlen)
{
filter(filtlen, n, nset, val, dt);
}
if (msdfile)
{
out = xvgropen(msdfile, "Mean square displacement",
"time", "MSD (nm\\S2\\N)", oenv);
nlast = (int)(n*frac);
for (s = 0; s < nset; s++)
{
for (j = 0; j <= nlast; j++)
{
if (j % 100 == 0)
{
fprintf(stderr, "\r%d", j);
}
tot = 0;
for (i = 0; i < n-j; i++)
{
tot += sqr(val[s][i]-val[s][i+j]);
}
tot /= (real)(n-j);
fprintf(out, " %g %8g\n", dt*j, tot);
}
if (s < nset-1)
{
fprintf(out, "&\n");
}
}
ffclose(out);
fprintf(stderr, "\r%d, time=%g\n", j-1, (j-1)*dt);
}
if (ccfile)
{
plot_coscont(ccfile, n, nset, val, oenv);
}
if (distfile)
{
histogram(distfile, binwidth, n, nset, val, oenv);
}
if (avfile)
{
average(avfile, nenum(avbar_opt), n, nset, val, t);
}
if (eefile)
{
estimate_error(eefile, nb_min, resol, n, nset, av, sig, val, dt,
bEeFitAc, bEESEF, bEENLC, oenv);
}
if (balfile)
{
do_ballistic(balfile, n, t, val, nset, balTime, nBalExp, oenv);
}
/* if (gemfile) */
/* do_geminate(gemfile,n,t,val,nset,diffusion,rcut,balTime, */
/* nFitPoints, fit_start, fit_end, oenv); */
if (bPower)
{
power_fit(n, nset, val, t);
}
if (acfile != NULL)
{
if (bSubAv)
{
for (s = 0; s < nset; s++)
{
for (i = 0; i < n; i++)
{
val[s][i] -= av[s];
}
}
}
do_autocorr(acfile, oenv, "Autocorrelation", n, nset, val, dt,
eacNormal, bAverCorr);
}
if (bRegression)
{
regression_analysis(n, bXYdy, t, nset, val);
}
if (bLuzar)
{
luzar_correl(n, t, nset, val, temp, bXYdy, fit_start, smooth_tail_start, oenv);
}
view_all(oenv, NFILE, fnm);
return 0;
}
int gmx_dos(int argc, char *argv[])
{
const char *desc[] = {
"[THISMODULE] computes the Density of States from a simulations.",
"In order for this to be meaningful the velocities must be saved",
"in the trajecotry with sufficiently high frequency such as to cover",
"all vibrations. For flexible systems that would be around a few fs",
"between saving. Properties based on the DoS are printed on the",
"standard output."
"Note that the density of states is calculated from the mass-weighted",
"autocorrelation, and by default only from the square of the real",
"component rather than absolute value. This means the shape can differ",
"substantially from the plain vibrational power spectrum you can",
"calculate with gmx velacc."
};
const char *bugs[] = {
"This program needs a lot of memory: total usage equals the number of atoms times 3 times number of frames times 4 (or 8 when run in double precision)."
};
FILE *fp, *fplog;
t_topology top;
int ePBC = -1;
t_trxframe fr;
matrix box;
int gnx;
real t0, t1;
t_trxstatus *status;
int nV, nframes, n_alloc, i, j, fftcode, Nmol, Natom;
double rho, dt, Vsum, V, tmass, dostot, dos2;
real **c1, **dos, mi, beta, bfac, *nu, *tt, stddev, c1j;
gmx_output_env_t *oenv;
gmx_fft_t fft;
double cP, DiffCoeff, Delta, f, y, z, sigHS, Shs, Sig, DoS0, recip_fac;
double wCdiff, wSdiff, wAdiff, wEdiff;
int grpNatoms;
int *index;
char *grpname;
double invNormalize;
gmx_bool normalizeAutocorrelation;
static gmx_bool bVerbose = TRUE, bAbsolute = FALSE, bNormalizeDos = FALSE;
static gmx_bool bRecip = FALSE;
static real Temp = 298.15, toler = 1e-6;
t_pargs pa[] = {
{ "-v", FALSE, etBOOL, {&bVerbose},
"Be loud and noisy."
},
{ "-recip", FALSE, etBOOL, {&bRecip},
"Use cm^-1 on X-axis instead of 1/ps for DoS plots."
},
{ "-abs", FALSE, etBOOL, {&bAbsolute},
"Use the absolute value of the Fourier transform of the VACF as the Density of States. Default is to use the real component only"
},
{ "-normdos", FALSE, etBOOL, {&bNormalizeDos},
"Normalize the DoS such that it adds up to 3N. This should usually not be necessary."
},
{ "-T", FALSE, etREAL, {&Temp},
"Temperature in the simulation"
},
{ "-toler", FALSE, etREAL, {&toler},
"[HIDDEN]Tolerance when computing the fluidicity using bisection algorithm"
}
};
t_filenm fnm[] = {
{ efTRN, "-f", NULL, ffREAD },
{ efTPR, "-s", NULL, ffREAD },
{ efNDX, NULL, NULL, ffOPTRD },
{ efXVG, "-vacf", "vacf", ffWRITE },
{ efXVG, "-mvacf", "mvacf", ffWRITE },
{ efXVG, "-dos", "dos", ffWRITE },
{ efLOG, "-g", "dos", ffWRITE },
};
#define NFILE asize(fnm)
int npargs;
t_pargs *ppa;
const char *DoSlegend[] = {
"DoS(v)", "DoS(v)[Solid]", "DoS(v)[Diff]"
};
npargs = asize(pa);
ppa = add_acf_pargs(&npargs, pa);
if (!parse_common_args(&argc, argv, PCA_CAN_VIEW | PCA_CAN_TIME,
NFILE, fnm, npargs, ppa, asize(desc), desc,
asize(bugs), bugs, &oenv))
{
return 0;
}
beta = 1/(Temp*BOLTZ);
fplog = gmx_fio_fopen(ftp2fn(efLOG, NFILE, fnm), "w");
fprintf(fplog, "Doing density of states analysis based on trajectory.\n");
please_cite(fplog, "Pascal2011a");
please_cite(fplog, "Caleman2011b");
read_tps_conf(ftp2fn(efTPR, NFILE, fnm), &top, &ePBC, NULL, NULL, box, TRUE);
/* Handle index groups */
get_index(&top.atoms, ftp2fn_null(efNDX, NFILE, fnm), 1, &grpNatoms, &index, &grpname);
V = det(box);
tmass = 0;
for (i = 0; i < grpNatoms; i++)
{
tmass += top.atoms.atom[index[i]].m;
}
Natom = grpNatoms;
Nmol = calcMoleculesInIndexGroup(&top.mols, top.atoms.nr, index, grpNatoms);
gnx = Natom*DIM;
/* Correlation stuff */
snew(c1, gnx);
for (i = 0; (i < gnx); i++)
{
c1[i] = NULL;
}
read_first_frame(oenv, &status, ftp2fn(efTRN, NFILE, fnm), &fr, TRX_NEED_V);
t0 = fr.time;
n_alloc = 0;
nframes = 0;
Vsum = 0;
nV = 0;
do
{
if (fr.bBox)
{
V = det(fr.box);
Vsum += V;
nV++;
}
if (nframes >= n_alloc)
{
n_alloc += 100;
for (i = 0; i < gnx; i++)
{
srenew(c1[i], n_alloc);
}
}
for (i = 0; i < gnx; i += DIM)
{
c1[i+XX][nframes] = fr.v[index[i/DIM]][XX];
c1[i+YY][nframes] = fr.v[index[i/DIM]][YY];
c1[i+ZZ][nframes] = fr.v[index[i/DIM]][ZZ];
}
t1 = fr.time;
nframes++;
}
while (read_next_frame(oenv, status, &fr));
close_trj(status);
dt = (t1-t0)/(nframes-1);
if (nV > 0)
{
V = Vsum/nV;
}
if (bVerbose)
{
printf("Going to do %d fourier transforms of length %d. Hang on.\n",
gnx, nframes);
}
/* Unfortunately the -normalize program option for the autocorrelation
* function calculation is added as a hack with a static variable in the
* autocorrelation.c source. That would work if we called the normal
* do_autocorr(), but this routine overrides that by directly calling
* the low-level functionality. That unfortunately leads to ignoring the
* default value for the option (which is to normalize).
* Since the absolute value seems to be important for the subsequent
* analysis below, we detect the value directly from the option, calculate
* the autocorrelation without normalization, and then apply the
* normalization just to the autocorrelation output
* (or not, if the user asked for a non-normalized autocorrelation).
*/
normalizeAutocorrelation = opt2parg_bool("-normalize", npargs, ppa);
/* Note that we always disable normalization here, regardless of user settings */
low_do_autocorr(NULL, oenv, NULL, nframes, gnx, nframes, c1, dt, eacNormal, 0, FALSE,
FALSE, FALSE, -1, -1, 0);
snew(dos, DOS_NR);
for (j = 0; (j < DOS_NR); j++)
{
snew(dos[j], nframes+4);
}
if (bVerbose)
{
printf("Going to merge the ACFs into the mass-weighted and plain ACF\n");
}
for (i = 0; (i < gnx); i += DIM)
{
mi = top.atoms.atom[index[i/DIM]].m;
for (j = 0; (j < nframes/2); j++)
{
c1j = (c1[i+XX][j] + c1[i+YY][j] + c1[i+ZZ][j]);
dos[VACF][j] += c1j/Natom;
dos[MVACF][j] += mi*c1j;
}
}
fp = xvgropen(opt2fn("-vacf", NFILE, fnm), "Velocity autocorrelation function",
"Time (ps)", "C(t)", oenv);
snew(tt, nframes/2);
invNormalize = normalizeAutocorrelation ? 1.0/dos[VACF][0] : 1.0;
for (j = 0; (j < nframes/2); j++)
{
tt[j] = j*dt;
fprintf(fp, "%10g %10g\n", tt[j], dos[VACF][j] * invNormalize);
}
xvgrclose(fp);
fp = xvgropen(opt2fn("-mvacf", NFILE, fnm), "Mass-weighted velocity autocorrelation function",
"Time (ps)", "C(t)", oenv);
invNormalize = normalizeAutocorrelation ? 1.0/dos[VACF][0] : 1.0;
for (j = 0; (j < nframes/2); j++)
{
fprintf(fp, "%10g %10g\n", tt[j], dos[MVACF][j] * invNormalize);
}
xvgrclose(fp);
if ((fftcode = gmx_fft_init_1d_real(&fft, nframes/2,
GMX_FFT_FLAG_NONE)) != 0)
{
gmx_fatal(FARGS, "gmx_fft_init_1d_real returned %d", fftcode);
}
if ((fftcode = gmx_fft_1d_real(fft, GMX_FFT_REAL_TO_COMPLEX,
(void *)dos[MVACF], (void *)dos[DOS])) != 0)
{
gmx_fatal(FARGS, "gmx_fft_1d_real returned %d", fftcode);
}
/* First compute the DoS */
/* Magic factor of 8 included now. */
bfac = 8*dt*beta/2;
dos2 = 0;
snew(nu, nframes/4);
for (j = 0; (j < nframes/4); j++)
{
nu[j] = 2*j/(t1-t0);
dos2 += gmx::square(dos[DOS][2*j]) + gmx::square(dos[DOS][2*j+1]);
if (bAbsolute)
{
dos[DOS][j] = bfac*std::hypot(dos[DOS][2*j], dos[DOS][2*j+1]);
}
else
{
dos[DOS][j] = bfac*dos[DOS][2*j];
}
}
/* Normalize it */
dostot = evaluate_integral(nframes/4, nu, dos[DOS], NULL, nframes/4, &stddev);
if (bNormalizeDos)
{
for (j = 0; (j < nframes/4); j++)
{
dos[DOS][j] *= 3*Natom/dostot;
}
}
/* Now analyze it */
DoS0 = dos[DOS][0];
/* Note this eqn. is incorrect in Pascal2011a! */
Delta = ((2*DoS0/(9*Natom))*std::sqrt(M_PI*BOLTZ*Temp*Natom/tmass)*
std::pow((Natom/V), 1.0/3.0)*std::pow(6.0/M_PI, 2.0/3.0));
f = calc_fluidicity(Delta, toler);
y = calc_y(f, Delta, toler);
z = calc_compress(y);
Sig = BOLTZ*(5.0/2.0+std::log(2*M_PI*BOLTZ*Temp/(gmx::square(PLANCK))*V/(f*Natom)));
Shs = Sig+calc_Shs(f, y);
rho = (tmass*AMU)/(V*NANO*NANO*NANO);
sigHS = std::cbrt(6*y*V/(M_PI*Natom));
fprintf(fplog, "System = \"%s\"\n", *top.name);
fprintf(fplog, "Nmol = %d\n", Nmol);
fprintf(fplog, "Natom = %d\n", Natom);
fprintf(fplog, "dt = %g ps\n", dt);
fprintf(fplog, "tmass = %g amu\n", tmass);
fprintf(fplog, "V = %g nm^3\n", V);
fprintf(fplog, "rho = %g g/l\n", rho);
fprintf(fplog, "T = %g K\n", Temp);
fprintf(fplog, "beta = %g mol/kJ\n", beta);
fprintf(fplog, "\nDoS parameters\n");
fprintf(fplog, "Delta = %g\n", Delta);
fprintf(fplog, "fluidicity = %g\n", f);
fprintf(fplog, "hard sphere packing fraction = %g\n", y);
fprintf(fplog, "hard sphere compressibility = %g\n", z);
fprintf(fplog, "ideal gas entropy = %g\n", Sig);
fprintf(fplog, "hard sphere entropy = %g\n", Shs);
fprintf(fplog, "sigma_HS = %g nm\n", sigHS);
fprintf(fplog, "DoS0 = %g\n", DoS0);
fprintf(fplog, "Dos2 = %g\n", dos2);
fprintf(fplog, "DoSTot = %g\n", dostot);
/* Now compute solid (2) and diffusive (3) components */
fp = xvgropen(opt2fn("-dos", NFILE, fnm), "Density of states",
bRecip ? "E (cm\\S-1\\N)" : "\\f{12}n\\f{4} (1/ps)",
"\\f{4}S(\\f{12}n\\f{4})", oenv);
xvgr_legend(fp, asize(DoSlegend), DoSlegend, oenv);
recip_fac = bRecip ? (1e7/SPEED_OF_LIGHT) : 1.0;
for (j = 0; (j < nframes/4); j++)
{
dos[DOS_DIFF][j] = DoS0/(1+gmx::square(DoS0*M_PI*nu[j]/(6*f*Natom)));
dos[DOS_SOLID][j] = dos[DOS][j]-dos[DOS_DIFF][j];
fprintf(fp, "%10g %10g %10g %10g\n",
recip_fac*nu[j],
dos[DOS][j]/recip_fac,
dos[DOS_SOLID][j]/recip_fac,
dos[DOS_DIFF][j]/recip_fac);
}
xvgrclose(fp);
/* Finally analyze the results! */
wCdiff = 0.5;
wSdiff = Shs/(3*BOLTZ); /* Is this correct? */
wEdiff = 0.5;
wAdiff = wEdiff-wSdiff;
for (j = 0; (j < nframes/4); j++)
{
dos[DOS_CP][j] = (dos[DOS_DIFF][j]*wCdiff +
dos[DOS_SOLID][j]*wCsolid(nu[j], beta));
dos[DOS_S][j] = (dos[DOS_DIFF][j]*wSdiff +
dos[DOS_SOLID][j]*wSsolid(nu[j], beta));
dos[DOS_A][j] = (dos[DOS_DIFF][j]*wAdiff +
dos[DOS_SOLID][j]*wAsolid(nu[j], beta));
dos[DOS_E][j] = (dos[DOS_DIFF][j]*wEdiff +
dos[DOS_SOLID][j]*wEsolid(nu[j], beta));
}
DiffCoeff = evaluate_integral(nframes/2, tt, dos[VACF], NULL, nframes/2, &stddev);
DiffCoeff = 1000*DiffCoeff/3.0;
fprintf(fplog, "Diffusion coefficient from VACF %g 10^-5 cm^2/s\n",
DiffCoeff);
fprintf(fplog, "Diffusion coefficient from DoS %g 10^-5 cm^2/s\n",
1000*DoS0/(12*tmass*beta));
cP = BOLTZ * evaluate_integral(nframes/4, nu, dos[DOS_CP], NULL,
nframes/4, &stddev);
fprintf(fplog, "Heat capacity %g J/mol K\n", 1000*cP/Nmol);
fprintf(fplog, "\nArrivederci!\n");
gmx_fio_fclose(fplog);
do_view(oenv, ftp2fn(efXVG, NFILE, fnm), "-nxy");
return 0;
}
