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lammps/lib/colvars/colvarbias.cpp
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colvarbias.cpp
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// -*- c++ -*-
#include "colvarmodule.h"
#include "colvarvalue.h"
#include "colvarbias.h"
colvarbias::colvarbias(char const *key)
: bias_type(to_lower_cppstr(key))
{
init_cvb_requires();
rank = 1;
if (bias_type == std::string("abf")) {
rank = cvm::n_abf_biases+1;
}
if (bias_type == std::string("harmonic") ||
bias_type == std::string("linear")) {
rank = cvm::n_rest_biases+1;
}
if (bias_type == std::string("histogram")) {
rank = cvm::n_histo_biases+1;
}
if (bias_type == std::string("metadynamics")) {
rank = cvm::n_meta_biases+1;
}
has_data = false;
b_output_energy = false;
reset();
// Start in active state by default
enable(f_cvb_active);
}
int colvarbias::init(std::string const &conf)
{
colvarparse::init(conf);
if (name.size() == 0) {
cvm::log("Initializing a new \""+bias_type+"\" instance.\n");
get_keyval(conf, "name", name, bias_type+cvm::to_str(rank));
{
colvarbias *bias_with_name = cvm::bias_by_name(this->name);
if (bias_with_name != NULL) {
if ((bias_with_name->rank != this->rank) ||
(bias_with_name->bias_type != this->bias_type)) {
cvm::error("Error: this bias cannot have the same name, \""+this->name+
"\", as another bias.\n", INPUT_ERROR);
return INPUT_ERROR;
}
}
}
description = "bias " + name;
{
// lookup the associated colvars
std::vector<std::string> colvar_names;
if (get_keyval(conf, "colvars", colvar_names)) {
if (colvars.size()) {
cvm::error("Error: cannot redefine the colvars that a bias was already defined on.\n",
INPUT_ERROR);
return INPUT_ERROR;
}
for (size_t i = 0; i < colvar_names.size(); i++) {
add_colvar(colvar_names[i]);
}
}
}
if (!colvars.size()) {
cvm::error("Error: no collective variables specified.\n", INPUT_ERROR);
return INPUT_ERROR;
}
} else {
cvm::log("Reinitializing bias \""+name+"\".\n");
}
get_keyval(conf, "outputEnergy", b_output_energy, b_output_energy);
return COLVARS_OK;
}
int colvarbias::reset()
{
bias_energy = 0.0;
for (size_t i = 0; i < colvars.size(); i++) {
colvar_forces[i].reset();
}
return COLVARS_OK;
}
colvarbias::colvarbias()
: colvarparse(), has_data(false)
{}
colvarbias::~colvarbias()
{
colvarbias::clear();
}
int colvarbias::clear()
{
// Remove references to this bias from colvars
for (std::vector<colvar *>::iterator cvi = colvars.begin();
cvi != colvars.end();
++cvi) {
for (std::vector<colvarbias *>::iterator bi = (*cvi)->biases.begin();
bi != (*cvi)->biases.end();
++bi) {
if ( *bi == this) {
(*cvi)->biases.erase(bi);
break;
}
}
}
// ...and from the colvars module
for (std::vector<colvarbias *>::iterator bi = cvm::biases.begin();
bi != cvm::biases.end();
++bi) {
if ( *bi == this) {
cvm::biases.erase(bi);
break;
}
}
return COLVARS_OK;
}
int colvarbias::add_colvar(std::string const &cv_name)
{
if (colvar *cv = cvm::colvar_by_name(cv_name)) {
// Removed this as nor all biases apply forces eg histogram
// cv->enable(colvar::task_gradients);
if (cvm::debug()) {
cvm::log("Applying this bias to collective variable \""+
cv->name+"\".\n");
}
colvars.push_back(cv);
colvar_forces.push_back(colvarvalue());
colvar_forces.back().type(cv->value()); // make sure each force is initialized to zero
colvar_forces.back().reset();
cv->biases.push_back(this); // add back-reference to this bias to colvar
// Add dependency link.
// All biases need at least the value of each colvar
// although possibly not at all timesteps
add_child(cv);
} else {
cvm::error("Error: cannot find a colvar named \""+
cv_name+"\".\n", INPUT_ERROR);
return INPUT_ERROR;
}
return COLVARS_OK;
}
int colvarbias::update()
{
// Note: if anything is added here, it should be added also in the SMP block of calc_biases()
// TODO move here debug msg of bias update
has_data = true;
return COLVARS_OK;
}
void colvarbias::communicate_forces()
{
for (size_t i = 0; i < colvars.size(); i++) {
if (cvm::debug()) {
cvm::log("Communicating a force to colvar \""+
colvars[i]->name+"\".\n");
}
colvars[i]->add_bias_force(colvar_forces[i]);
}
}
void colvarbias::change_configuration(std::string const &conf)
{
cvm::error("Error: change_configuration() not implemented.\n");
}
cvm::real colvarbias::energy_difference(std::string const &conf)
{
cvm::error("Error: energy_difference() not implemented.\n");
return 0.;
}
// So far, these are only implemented in colvarbias_abf
int colvarbias::bin_num()
{
cvm::error("Error: bin_num() not implemented.\n");
return COLVARS_NOT_IMPLEMENTED;
}
int colvarbias::current_bin()
{
cvm::error("Error: current_bin() not implemented.\n");
return COLVARS_NOT_IMPLEMENTED;
}
int colvarbias::bin_count(int bin_index)
{
cvm::error("Error: bin_count() not implemented.\n");
return COLVARS_NOT_IMPLEMENTED;
}
int colvarbias::replica_share()
{
cvm::error("Error: replica_share() not implemented.\n");
return COLVARS_NOT_IMPLEMENTED;
}
std::ostream & colvarbias::write_traj_label(std::ostream &os)
{
os << " ";
if (b_output_energy)
os << " E_"
<< cvm::wrap_string(this->name, cvm::en_width-2);
return os;
}
std::ostream & colvarbias::write_traj(std::ostream &os)
{
os << " ";
if (b_output_energy)
os << " "
<< bias_energy;
return os;
}
// Static members
std::vector<colvardeps::feature *> colvarbias::cvb_features;
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