#include "vec.h"
#include "macros.h"
#include "index.h"
-#include "random.h"
+#include "gmx_random.h"
#include "pbc.h"
#include "rmpbc.h"
#include "xvgr.h"
}
}
-void mc_optimize(FILE *log, t_mat *m, int maxiter, int *seed, real kT)
+void mc_optimize(FILE *log, t_mat *m, real *time,
+ int maxiter, int nrandom,
+ int seed, real kT,
+ const char *conv, output_env_t oenv)
{
- real e[2], ei, ej, efac;
- int *low_index;
- int cur = 0;
-#define next (1-cur)
- int i, isw, jsw, iisw, jjsw, nn;
+ FILE *fp = NULL;
+ real ecur, enext, emin, prob, enorm;
+ int i, j, iswap, jswap, nn, nuphill = 0;
+ gmx_rng_t rng;
+ t_mat *minimum;
- fprintf(stderr, "\nDoing Monte Carlo clustering\n");
- nn = m->nn;
- snew(low_index, nn);
- cp_index(nn, m->m_ind, low_index);
- if (getenv("TESTMC"))
+ if (m->n1 != m->nn)
{
- e[cur] = mat_energy(m);
- pr_energy(log, e[cur]);
- fprintf(log, "Doing 1000 random swaps\n");
- for (i = 0; (i < 1000); i++)
+ fprintf(stderr,"Can not do Monte Carlo optimization with a non-square matrix.\n");
+ return;
+ }
+ printf("\nDoing Monte Carlo optimization to find the smoothest trajectory\n");
+ printf("by reordering the frames to minimize the path between the two structures\n");
+ printf("that have the largest pairwise RMSD.\n");
+
+ iswap = jswap = -1;
+ enorm = m->mat[0][0];
+ for(i = 0; (i < m->n1); i++)
+ {
+ for(j = 0; (j < m->nn); j++)
{
- do
+ if (m->mat[i][j] > enorm)
{
- isw = nn*rando(seed);
- jsw = nn*rando(seed);
+ enorm = m->mat[i][j];
+ iswap = i;
+ jswap = j;
}
- while ((isw == jsw) || (isw >= nn) || (jsw >= nn));
- iisw = m->m_ind[isw];
- jjsw = m->m_ind[jsw];
- m->m_ind[isw] = jjsw;
- m->m_ind[jsw] = iisw;
}
}
- e[cur] = mat_energy(m);
- pr_energy(log, e[cur]);
+ if ((iswap == -1) || (jswap == -1))
+ {
+ fprintf(stderr, "Matrix contains identical values in all fields\n");
+ return;
+ }
+ swap_rows(m, 0, iswap);
+ swap_rows(m, m->n1-1, jswap);
+ emin = ecur = mat_energy(m);
+ printf("Largest distance %g between %d and %d. Energy: %g.\n",
+ enorm, iswap, jswap, emin);
+
+ rng = gmx_rng_init(seed);
+ nn = m->nn;
+
+ /* Initiate and store global minimum */
+ minimum = init_mat(nn, m->b1D);
+ minimum->nn = nn;
+ copy_t_mat(minimum, m);
+
+ if (NULL != conv)
+ {
+ fp = xvgropen(conv, "Convergence of the MC optimization",
+ "Energy", "Step", oenv);
+ }
for (i = 0; (i < maxiter); i++)
{
+ /* Generate new swapping candidates */
do
{
- isw = nn*rando(seed);
- jsw = nn*rando(seed);
+ iswap = 1+(nn-2)*gmx_rng_uniform_real(rng);
+ jswap = 1+(nn-2)*gmx_rng_uniform_real(rng);
}
- while ((isw == jsw) || (isw >= nn) || (jsw >= nn));
-
- iisw = m->m_ind[isw];
- jjsw = m->m_ind[jsw];
- ei = row_energy(nn, iisw, m->mat[jsw]);
- ej = row_energy(nn, jjsw, m->mat[isw]);
+ while ((iswap == jswap) || (iswap >= nn-1) || (jswap >= nn-1));
- e[next] = e[cur] + (ei+ej-EROW(m, isw)-EROW(m, jsw))/nn;
+ /* Apply swap and compute energy */
+ swap_rows(m, iswap, jswap);
+ enext = mat_energy(m);
- efac = kT ? exp((e[next]-e[cur])/kT) : -1;
- if ((e[next] > e[cur]) || (efac > rando(seed)))
+ /* Compute probability */
+ prob = 0;
+ if ((enext < ecur) || (i < nrandom))
{
-
- if (e[next] > e[cur])
+ prob = 1;
+ if (enext < emin)
{
- cp_index(nn, m->m_ind, low_index);
+ /* Store global minimum */
+ copy_t_mat(minimum, m);
+ emin = enext;
}
- else
+ }
+ else if (kT > 0)
+ {
+ /* Try Monte Carlo step */
+ prob = exp(-(enext-ecur)/(enorm*kT));
+ }
+
+ if ((prob == 1) || (gmx_rng_uniform_real(rng) < prob))
+ {
+ if (enext > ecur)
{
- fprintf(log, "Taking uphill step\n");
+ nuphill++;
}
- /* Now swapping rows */
- m->m_ind[isw] = jjsw;
- m->m_ind[jsw] = iisw;
- EROW(m, isw) = ei;
- EROW(m, jsw) = ej;
- cur = next;
- fprintf(log, "Iter: %d Swapped %4d and %4d (now %g)",
- i, isw, jsw, mat_energy(m));
- pr_energy(log, e[cur]);
+ fprintf(log, "Iter: %d Swapped %4d and %4d (energy: %g prob: %g)\n",
+ i, iswap, jswap, enext, prob);
+ if (NULL != fp)
+ {
+ fprintf(fp, "%6d %10g\n", i, enext);
+ }
+ ecur = enext;
}
+ else
+ {
+ swap_rows(m, jswap, iswap);
+ }
+ }
+ fprintf(log, "%d uphill steps were taken during optimization\n",
+ nuphill);
+
+ /* Now swap the matrix to get it into global minimum mode */
+ copy_t_mat(m, minimum);
+
+ fprintf(log, "Global minimum energy %g\n", mat_energy(minimum));
+ fprintf(log, "Global minimum energy %g\n", mat_energy(m));
+ fprintf(log, "Swapped time and frame indices and RMSD to next neighbor:\n");
+ for(i=0; (i<m->nn); i++)
+ {
+ fprintf(log, "%10g %5d %10g\n",
+ time[m->m_ind[i]],
+ m->m_ind[i],
+ (i<m->nn-1) ? m->mat[m->m_ind[i]][m->m_ind[i+1]] : 0);
+ }
+
+ if (NULL != fp)
+ {
+ fclose(fp);
}
- /* Now restore the highest energy index */
- cp_index(nn, low_index, m->m_ind);
}
static void calc_dist(int nind, rvec x[], real **d)
"of a structure are the M closest structures or all structures within",
"[TT]cutoff[tt].[PAR]",
- "Monte Carlo: reorder the RMSD matrix using Monte Carlo.[PAR]",
+ "Monte Carlo: reorder the RMSD matrix using Monte Carlo such that",
+ "the order of the frames is using the smallest possible increments.",
+ "With this it is possible to make a smooth animation going from one",
+ "structure to another with the largest possible (e.g.) RMSD between",
+ "them, however the intermediate steps should be as small as possible.",
+ "Applications could be to visualize a potential of mean force",
+ "ensemble of simulations or a pulling simulation. Obviously the user",
+ "has to prepare the trajectory well (e.g. by not superimposing frames).",
+ "The final result can be inspect visually by looking at the matrix",
+ "[TT].xpm[tt] file, which should vary smoothly from bottom to top.[PAR]",
"diagonalization: diagonalize the RMSD matrix.[PAR]",
rvec *xtps, *usextps, *x1, **xx = NULL;
const char *fn, *trx_out_fn;
t_clusters clust;
- t_mat *rms;
+ t_mat *rms, *orig=NULL;
real *eigenvalues;
t_topology top;
int ePBC;
static int nlevels = 40, skip = 1;
static real scalemax = -1.0, rmsdcut = 0.1, rmsmin = 0.0;
gmx_bool bRMSdist = FALSE, bBinary = FALSE, bAverage = FALSE, bFit = TRUE;
- static int niter = 10000, seed = 1993, write_ncl = 0, write_nst = 1, minstruct = 1;
+ static int niter = 10000, nrandom = 0, seed = 1993, write_ncl = 0, write_nst = 1, minstruct = 1;
static real kT = 1e-3;
static int M = 10, P = 3;
output_env_t oenv;
{ "-P", FALSE, etINT, {&P},
"Number of identical nearest neighbors required to form a cluster" },
{ "-seed", FALSE, etINT, {&seed},
- "Random number seed for Monte Carlo clustering algorithm" },
+ "Random number seed for Monte Carlo clustering algorithm: <= 0 means generate" },
{ "-niter", FALSE, etINT, {&niter},
"Number of iterations for MC" },
+ { "-nrandom", FALSE, etINT, {&nrandom},
+ "The first iterations for MC may be done complete random, to shuffle the frames" },
{ "-kT", FALSE, etREAL, {&kT},
"Boltzmann weighting factor for Monte Carlo optimization "
"(zero turns off uphill steps)" },
{ efTPS, "-s", NULL, ffOPTRD },
{ efNDX, NULL, NULL, ffOPTRD },
{ efXPM, "-dm", "rmsd", ffOPTRD },
+ { efXPM, "-om", "rmsd-raw", ffWRITE },
{ efXPM, "-o", "rmsd-clust", ffWRITE },
{ efLOG, "-g", "cluster", ffWRITE },
{ efXVG, "-dist", "rmsd-dist", ffOPTWR },
{ efXVG, "-ev", "rmsd-eig", ffOPTWR },
+ { efXVG, "-conv", "mc-conv", ffOPTWR },
{ efXVG, "-sz", "clust-size", ffOPTWR},
{ efXPM, "-tr", "clust-trans", ffOPTWR},
{ efXVG, "-ntr", "clust-trans", ffOPTWR},
rms->minrms, rms->maxrms);
ffprintf_g(stderr, log, buf, "Average RMSD is %g\n", 2*rms->sumrms/(nf*(nf-1)));
ffprintf_d(stderr, log, buf, "Number of structures for matrix %d\n", nf);
- ffprintf_g(stderr, log, buf, "Energy of the matrix is %g nm\n", mat_energy(rms));
+ ffprintf_g(stderr, log, buf, "Energy of the matrix is %g.\n", mat_energy(rms));
if (bUseRmsdCut && (rmsdcut < rms->minrms || rmsdcut > rms->maxrms) )
{
fprintf(stderr, "WARNING: rmsd cutoff %g is outside range of rmsd values "
ffclose(fp);
break;
case m_monte_carlo:
- mc_optimize(log, rms, niter, &seed, kT);
- swap_mat(rms);
- reset_index(rms);
+ orig = init_mat(rms->nn, FALSE);
+ orig->nn = rms->nn;
+ copy_t_mat(orig, rms);
+ mc_optimize(log, rms, time, niter, nrandom, seed, kT,
+ opt2fn_null("-conv",NFILE,fnm), oenv);
break;
case m_jarvis_patrick:
jarvis_patrick(rms->nn, rms->mat, M, P, bJP_RMSD ? rmsdcut : -1, &clust);
if (method == m_monte_carlo || method == m_diagonalize)
{
- fprintf(stderr, "Energy of the matrix after clustering is %g nm\n",
+ fprintf(stderr, "Energy of the matrix after clustering is %g.\n",
mat_energy(rms));
}
}
fprintf(stderr, "\n");
ffclose(fp);
-
+ if (NULL != orig)
+ {
+ fp = opt2FILE("-om", NFILE, fnm, "w");
+ sprintf(buf, "Time (%s)", output_env_get_time_unit(oenv));
+ sprintf(title, "RMS%sDeviation", bRMSdist ? " Distance " : " ");
+ write_xpm(fp, 0, title, "RMSD (nm)", buf, buf,
+ nf, nf, time, time, orig->mat, 0.0, orig->maxrms,
+ rlo_top, rhi_top, &nlevels);
+ ffclose(fp);
+ done_mat(&orig);
+ sfree(orig);
+ }
/* now show what we've done */
do_view(oenv, opt2fn("-o", NFILE, fnm), "-nxy");
do_view(oenv, opt2fn_null("-sz", NFILE, fnm), "-nxy");
do_view(oenv, opt2fn_null("-ntr", NFILE, fnm), "-nxy");
do_view(oenv, opt2fn_null("-clid", NFILE, fnm), "-nxy");
}
+ do_view(oenv, opt2fn_null("-conv", NFILE, fnm), NULL);
return 0;
}
biod.pnpi.spb.ru / alexxy / gromacs.git / commitdiff