int
main(int argc, char* argv[])
{
line* l;
int nl, ind;
parse_cl(argc, argv);
print_title();
base_triangles();
nl = border(sfile->trg, sfile->hdr->nfacets, &l);
printf("Facets %d\n", sfile->hdr->nfacets);
printf("Border segments %d\n", nl);
for(int i=0; i<nl; i++)
print_line(&l[i]);
print_circle();
}
int main(int argc, char *argv[])
{
char *data_file;
char *out_file;
unsigned int step, nsteps, total, out_freq;
int *pos, *dpos, *newpos;
unsigned int *histogram;
const double *grad, *newgrad;
unsigned int offset, newoffset;
int not_accepted;
double dA;
double temp;
double mbeta;
bool meta;
double hill, hill_fact, hill_min;
double rmsd, rmsd_old, rmsd_rel_change, convergence_limit;
bool converged;
unsigned int scale_hill_step;
// Setting default values
nsteps = 0;
temp = 500;
meta = true;
hill = 0.01;
hill_fact = 0.5;
hill_min = 0.0005;
convergence_limit = -0.001;
if (!(data_file = parse_cl(argc, argv, &nsteps, &temp, &meta, &hill, &hill_fact))) {
std::cerr << "\nabf_integrate: MC-based integration of multidimensional free energy gradient\n";
std::cerr << "Version 20110511\n\n";
std::cerr << "Syntax: " << argv[0] <<
" <filename> [-n <nsteps>] [-t <temp>] [-m [0|1] (metadynamics)]"
" [-h <hill_height>] [-f <variable_hill_factor>]\n\n";
exit(1);
}
if (meta) {
std::cout << "\nUsing metadynamics-style sampling with hill height: " << hill << "\n";
if (hill_fact) {
std::cout << "Varying hill height by factor " << hill_fact << "\n";
}
} else {
std::cout << "\nUsing unbiased MC sampling\n";
}
if (nsteps) {
std::cout << "Sampling " << nsteps << " steps at temperature " << temp << "\n\n";
out_freq = nsteps / 10;
scale_hill_step = nsteps / 2;
converged = true;
} else {
std::cout << "Sampling until convergence at temperature " << temp << "\n\n";
out_freq = 1000000;
converged = false;
}
// Inverse temperature in (kcal/mol)-1
mbeta = -1 / (0.001987 * temp);
ABFdata data(data_file);
if (!nsteps) {
scale_hill_step = 2000 * data.scalar_dim;
nsteps = 2 * scale_hill_step;
std::cout << "Setting minimum number of steps to " << nsteps << "\n";
}
srand(time(NULL));
pos = new int[data.Nvars];
dpos = new int[data.Nvars];
newpos = new int[data.Nvars];
do {
for (int i = 0; i < data.Nvars; i++) {
pos[i] = rand() % data.sizes[i];
}
offset = data.offset(pos);
} while ( !data.allowed (offset) );
rmsd = compute_deviation(&data, meta, 0.001987 * temp);
std::cout << "\nInitial gradient RMS is " << rmsd << "\n";
total = 0;
for (step = 1; (step <= nsteps || !converged); step++) {
if ( step % out_freq == 0) {
rmsd_old = rmsd;
rmsd = compute_deviation(&data, meta, 0.001987 * temp);
rmsd_rel_change = (rmsd - rmsd_old) / (rmsd_old * double (out_freq)) * 1000000.0;
std::cout << "Step " << step << " ; gradient RMSD is " << rmsd
<< " ; relative change per 1M steps " << rmsd_rel_change;
if ( rmsd_rel_change > convergence_limit && step >= nsteps ) {
converged = true;
}
if (meta && hill_fact && step > scale_hill_step && hill > hill_min ) {
hill *= hill_fact;
std::cout << " - changing hill height to " << hill << "\n";
} else {
std::cout << "\n";
}
}
offset = data.offset(pos);
data.histogram[offset]++;
if (meta) {
data.bias[offset] += hill;
}
grad = data.gradients + offset * data.Nvars;
not_accepted = 1;
while (not_accepted) {
dA = 0.0;
total++;
for (int i = 0; i < data.Nvars; i++) {
dpos[i] = rand() % 3 - 1;
newpos[i] = pos[i] + dpos[i];
data.wrap(newpos[i], i);
if (newpos[i] == pos[i])
dpos[i] = 0;
if (dpos[i]) {
dA += grad[i] * dpos[i] * data.widths[i];
// usefulness of the interpolation below depends on
// where the grid points are for the histogram wrt to the gradients
// If done, it has to be done in all directions
// the line below is useless
//dA += 0.5 * (newgrad[i] + grad[i]) * dpos[i] * data.widths[i];
}
}
newoffset = data.offset(newpos);
if (meta) {
dA += data.bias[newoffset] - data.bias[offset];
}
if ( data.allowed (newoffset) && (((float) rand()) / RAND_MAX < exp(mbeta * dA)) ) {
// Accept move
for (int i = 0; i < data.Nvars; i++) {
pos[i] = newpos[i];
not_accepted = 0;
}
}
}
}
std::cout << "Run " << total << " total iterations; acceptance ratio is "
<< double (step) / double (total)
<< " ; final gradient RMSD is " << compute_deviation(&data, meta, 0.001987 * temp) << "\n";
out_file = new char[strlen(data_file) + 8];
if (meta) {
sprintf(out_file, "%s.pmf", data_file);
std::cout << "Writing PMF to file " << out_file << "\n";
data.write_bias(out_file);
}
// TODO write a PMF for unbiased MC, too...
sprintf(out_file, "%s.histo", data_file);
std::cout << "Writing sampling histogram to file " << out_file << "\n";
data.write_histogram(out_file);
sprintf(out_file, "%s.est", data_file);
std::cout << "Writing estimated FE gradient to file " << out_file << "\n";
data.write_field(data.estimate, out_file);
sprintf(out_file, "%s.dev", data_file);
std::cout << "Writing FE gradient deviation to file " << out_file << "\n\n";
data.write_field(data.deviation, out_file);
delete [] pos;
delete [] dpos;
delete [] newpos;
delete [] out_file;
exit(0);
}
