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colvargrid.cpp
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Thu, Jul 11, 07:57
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Sat, Jul 13, 07:57 (2 d)
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rLAMMPS lammps
colvargrid.cpp
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/// -*- c++ -*-
#include "colvarmodule.h"
#include "colvarvalue.h"
#include "colvarparse.h"
#include "colvar.h"
#include "colvarcomp.h"
#include "colvargrid.h"
colvar_grid_count::colvar_grid_count()
: colvar_grid<size_t>()
{
mult = 1;
}
colvar_grid_count::colvar_grid_count(std::vector<int> const &nx_i,
size_t const &def_count)
: colvar_grid<size_t>(nx_i, def_count, 1)
{}
colvar_grid_count::colvar_grid_count(std::vector<colvar *> &colvars,
size_t const &def_count)
: colvar_grid<size_t>(colvars, def_count, 1)
{}
std::istream & colvar_grid_count::read_restart(std::istream &is)
{
size_t const start_pos = is.tellg();
std::string key, conf;
if ((is >> key) && (key == std::string("grid_parameters"))) {
is.seekg(start_pos, std::ios::beg);
is >> colvarparse::read_block("grid_parameters", conf);
parse_params(conf);
} else {
cvm::log("Grid parameters are missing in the restart file, using those from the configuration.\n");
is.seekg(start_pos, std::ios::beg);
}
read_raw(is);
return is;
}
std::ostream & colvar_grid_count::write_restart(std::ostream &os)
{
write_params(os);
write_raw(os);
return os;
}
colvar_grid_scalar::colvar_grid_scalar()
: colvar_grid<cvm::real>(), samples(NULL), grad(NULL)
{}
colvar_grid_scalar::colvar_grid_scalar(colvar_grid_scalar const &g)
: colvar_grid<cvm::real>(g), samples(NULL), grad(NULL)
{
grad = new cvm::real[nd];
}
colvar_grid_scalar::colvar_grid_scalar(std::vector<int> const &nx_i)
: colvar_grid<cvm::real>(nx_i, 0.0, 1), samples(NULL)
{
grad = new cvm::real[nd];
}
colvar_grid_scalar::colvar_grid_scalar(std::vector<colvar *> &colvars, bool margin)
: colvar_grid<cvm::real>(colvars, 0.0, 1, margin), samples(NULL)
{
grad = new cvm::real[nd];
}
colvar_grid_scalar::~colvar_grid_scalar()
{
if (grad) {
delete [] grad;
grad = NULL;
}
}
std::istream & colvar_grid_scalar::read_restart(std::istream &is)
{
size_t const start_pos = is.tellg();
std::string key, conf;
if ((is >> key) && (key == std::string("grid_parameters"))) {
is.seekg(start_pos, std::ios::beg);
is >> colvarparse::read_block("grid_parameters", conf);
parse_params(conf);
} else {
cvm::log("Grid parameters are missing in the restart file, using those from the configuration.\n");
is.seekg(start_pos, std::ios::beg);
}
read_raw(is);
return is;
}
std::ostream & colvar_grid_scalar::write_restart(std::ostream &os)
{
write_params(os);
write_raw(os);
return os;
}
cvm::real colvar_grid_scalar::maximum_value() const
{
cvm::real max = data[0];
for (size_t i = 0; i < nt; i++) {
if (data[i] > max) max = data[i];
}
return max;
}
cvm::real colvar_grid_scalar::minimum_value() const
{
cvm::real min = data[0];
for (size_t i = 0; i < nt; i++) {
if (data[i] < min) min = data[i];
}
return min;
}
cvm::real colvar_grid_scalar::integral() const
{
cvm::real sum = 0.0;
for (size_t i = 0; i < nt; i++) {
sum += data[i];
}
cvm::real bin_volume = 1.0;
for (size_t id = 0; id < widths.size(); id++) {
bin_volume *= widths[id];
}
return bin_volume * sum;
}
cvm::real colvar_grid_scalar::entropy() const
{
cvm::real sum = 0.0;
for (size_t i = 0; i < nt; i++) {
sum += -1.0 * data[i] * std::log(data[i]);
}
cvm::real bin_volume = 1.0;
for (size_t id = 0; id < widths.size(); id++) {
bin_volume *= widths[id];
}
return bin_volume * sum;
}
colvar_grid_gradient::colvar_grid_gradient()
: colvar_grid<cvm::real>(), samples(NULL)
{}
colvar_grid_gradient::colvar_grid_gradient(std::vector<int> const &nx_i)
: colvar_grid<cvm::real>(nx_i, 0.0, nx_i.size()), samples(NULL)
{}
colvar_grid_gradient::colvar_grid_gradient(std::vector<colvar *> &colvars)
: colvar_grid<cvm::real>(colvars, 0.0, colvars.size()), samples(NULL)
{}
std::istream & colvar_grid_gradient::read_restart(std::istream &is)
{
size_t const start_pos = is.tellg();
std::string key, conf;
if ((is >> key) && (key == std::string("grid_parameters"))) {
is.seekg(start_pos, std::ios::beg);
is >> colvarparse::read_block("grid_parameters", conf);
parse_params(conf);
} else {
cvm::log("Grid parameters are missing in the restart file, using those from the configuration.\n");
is.seekg(start_pos, std::ios::beg);
}
read_raw(is);
return is;
}
std::ostream & colvar_grid_gradient::write_restart(std::ostream &os)
{
write_params(os);
write_raw(os);
return os;
}
void colvar_grid_gradient::write_1D_integral(std::ostream &os)
{
cvm::real bin, min, integral;
std::vector<cvm::real> int_vals;
os << "# xi A(xi)\n";
if ( cv.size() != 1 ) {
cvm::fatal_error("Cannot write integral for multi-dimensional gradient grids.");
}
integral = 0.0;
int_vals.push_back( 0.0 );
bin = 0.0;
min = 0.0;
// correction for periodic colvars, so that the PMF is periodic
cvm::real corr;
if ( periodic[0] ) {
corr = average();
} else {
corr = 0.0;
}
for (std::vector<int> ix = new_index(); index_ok(ix); incr(ix), bin += 1.0 ) {
if (samples) {
size_t const samples_here = samples->value(ix);
if (samples_here)
integral += (value(ix) / cvm::real(samples_here) - corr) * cv[0]->width;
} else {
integral += (value(ix) - corr) * cv[0]->width;
}
if ( integral < min ) min = integral;
int_vals.push_back( integral );
}
bin = 0.0;
for ( int i = 0; i < nx[0]; i++, bin += 1.0 ) {
os << std::setw(10) << cv[0]->lower_boundary.real_value + cv[0]->width * bin << " "
<< std::setw(cvm::cv_width)
<< std::setprecision(cvm::cv_prec)
<< int_vals[i] - min << "\n";
}
os << std::setw(10) << cv[0]->lower_boundary.real_value + cv[0]->width * bin << " "
<< std::setw(cvm::cv_width)
<< std::setprecision(cvm::cv_prec)
<< int_vals[nx[0]] - min << "\n";
return;
}
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