int colvarbias_abf::update()
{
if (cvm::debug()) cvm::log("Updating ABF bias " + this->name);
size_t i;
for (i = 0; i < num_variables(); i++) {
bin[i] = samples->current_bin_scalar(i);
}
if (cvm::proxy->total_forces_same_step()) {
// e.g. in LAMMPS, total forces are current
force_bin = bin;
}
if (cvm::step_relative() > 0 || cvm::proxy->total_forces_same_step()) {
if (update_bias) {
// if (b_adiabatic_reweighting) {
// // Update gradients non-locally based on conditional distribution of
// // fictitious variable TODO
//
// } else
if (samples->index_ok(force_bin)) {
// Only if requested and within bounds of the grid...
for (i = 0; i < num_variables(); i++) {
// get total forces (lagging by 1 timestep) from colvars
// and subtract previous ABF force if necessary
update_system_force(i);
}
gradients->acc_force(force_bin, system_force);
if ( b_integrate ) {
pmf->update_div_neighbors(force_bin);
}
}
}
if ( z_gradients && update_bias ) {
for (i = 0; i < num_variables(); i++) {
z_bin[i] = z_samples->current_bin_scalar(i);
}
if ( z_samples->index_ok(z_bin) ) {
for (i = 0; i < num_variables(); i++) {
// If we are outside the range of xi, the force has not been obtained above
// the function is just an accessor, so cheap to call again anyway
update_system_force(i);
}
z_gradients->acc_force(z_bin, system_force);
}
}
if ( b_integrate ) {
if ( pabf_freq && cvm::step_relative() % pabf_freq == 0 ) {
cvm::real err;
int iter = pmf->integrate(integrate_steps, integrate_tol, err);
if ( iter == integrate_steps ) {
cvm::log("Warning: PMF integration did not converge to " + cvm::to_str(integrate_tol)
+ " in " + cvm::to_str(integrate_steps)
+ " steps. Residual error: " + cvm::to_str(err));
}
pmf->set_zero_minimum(); // TODO: do this only when necessary
}
}
}
if (!cvm::proxy->total_forces_same_step()) {
// e.g. in NAMD, total forces will be available for next timestep
// hence we store the current colvar bin
force_bin = bin;
}
// Reset biasing forces from previous timestep
for (i = 0; i < num_variables(); i++) {
colvar_forces[i].reset();
}
// Compute and apply the new bias, if applicable
if (is_enabled(f_cvb_apply_force) && samples->index_ok(bin)) {
cvm::real count = samples->value(bin);
cvm::real fact = 1.0;
// Factor that ensures smooth introduction of the force
if ( count < full_samples ) {
fact = (count < min_samples) ? 0.0 :
(cvm::real(count - min_samples)) / (cvm::real(full_samples - min_samples));
}
std::vector<cvm::real> grad(num_variables());
if ( pabf_freq ) {
// In projected ABF, the force is the PMF gradient estimate
pmf->vector_gradient_finite_diff(bin, grad);
} else {
// Normal ABF
gradients->vector_value(bin, grad);
}
// if ( b_adiabatic_reweighting) {
// // Average of force according to conditional distribution of fictitious variable
// // need freshly integrated PMF, gradient TODO
// } else
if ( fact != 0.0 ) {
if ( (num_variables() == 1) && colvars[0]->periodic_boundaries() ) {
// Enforce a zero-mean bias on periodic, 1D coordinates
// in other words: boundary condition is that the biasing potential is periodic
// This is enforced naturally if using integrated PMF
colvar_forces[0].real_value = fact * (grad[0] - gradients->average ());
} else {
for (size_t i = 0; i < num_variables(); i++) {
// subtracting the mean force (opposite of the FE gradient) means adding the gradient
colvar_forces[i].real_value = fact * grad[i];
}
}
if (cap_force) {
for (size_t i = 0; i < num_variables(); i++) {
if ( colvar_forces[i].real_value * colvar_forces[i].real_value > max_force[i] * max_force[i] ) {
colvar_forces[i].real_value = (colvar_forces[i].real_value > 0 ? max_force[i] : -1.0 * max_force[i]);
}
}
}
}
}
// update the output prefix; TODO: move later to setup_output() function
if (cvm::main()->num_biases_feature(colvardeps::f_cvb_calc_pmf) == 1) {
// This is the only bias computing PMFs
output_prefix = cvm::output_prefix();
} else {
output_prefix = cvm::output_prefix() + "." + this->name;
}
if (output_freq && (cvm::step_absolute() % output_freq) == 0) {
if (cvm::debug()) cvm::log("ABF bias trying to write gradients and samples to disk");
write_gradients_samples(output_prefix);
}
if (b_history_files && (cvm::step_absolute() % history_freq) == 0) {
// file already exists iff cvm::step_relative() > 0
// otherwise, backup and replace
write_gradients_samples(output_prefix + ".hist", (cvm::step_relative() > 0));
}
if (shared_on && shared_last_step >= 0 && cvm::step_absolute() % shared_freq == 0) {
// Share gradients and samples for shared ABF.
replica_share();
}
// Prepare for the first sharing.
if (shared_last_step < 0) {
// Copy the current gradient and count values into last.
last_gradients->copy_grid(*gradients);
last_samples->copy_grid(*samples);
shared_last_step = cvm::step_absolute();
cvm::log("Prepared sample and gradient buffers at step "+cvm::to_str(cvm::step_absolute())+".");
}
// update UI estimator every step
if (b_UI_estimator)
{
std::vector<double> x(num_variables(),0);
std::vector<double> y(num_variables(),0);
for (size_t i = 0; i < num_variables(); i++)
{
x[i] = colvars[i]->actual_value();
y[i] = colvars[i]->value();
}
eabf_UI.update_output_filename(output_prefix);
eabf_UI.update(cvm::step_absolute(), x, y);
}
return COLVARS_OK;
}
int colvarbias_abf::write_output_files()
{
write_gradients_samples(output_prefix);
return COLVARS_OK;
}
int colvarbias_abf::update()
{
if (cvm::debug()) cvm::log("Updating ABF bias " + this->name);
if (cvm::step_relative() == 0) {
// At first timestep, do only:
// initialization stuff (file operations relying on n_abf_biases
// compute current value of colvars
for (size_t i = 0; i < colvars.size(); i++) {
bin[i] = samples->current_bin_scalar(i);
}
} else {
for (size_t i = 0; i < colvars.size(); i++) {
bin[i] = samples->current_bin_scalar(i);
}
if ( update_bias && samples->index_ok(force_bin) ) {
// Only if requested and within bounds of the grid...
for (size_t i = 0; i < colvars.size(); i++) {
// get total forces (lagging by 1 timestep) from colvars
// and subtract previous ABF force if necessary
update_system_force(i);
}
gradients->acc_force(force_bin, system_force);
}
if ( z_gradients && update_bias ) {
for (size_t i = 0; i < colvars.size(); i++) {
z_bin[i] = z_samples->current_bin_scalar(i);
}
if ( z_samples->index_ok(z_bin) ) {
for (size_t i = 0; i < colvars.size(); i++) {
// If we are outside the range of xi, the force has not been obtained above
// the function is just an accessor, so cheap to call again anyway
update_system_force(i);
}
z_gradients->acc_force(z_bin, system_force);
}
}
}
// save bin for next timestep
force_bin = bin;
// Reset biasing forces from previous timestep
for (size_t i = 0; i < colvars.size(); i++) {
colvar_forces[i].reset();
}
// Compute and apply the new bias, if applicable
if (is_enabled(f_cvb_apply_force) && samples->index_ok(bin)) {
size_t count = samples->value(bin);
cvm::real fact = 1.0;
// Factor that ensures smooth introduction of the force
if ( count < full_samples ) {
fact = (count < min_samples) ? 0.0 :
(cvm::real(count - min_samples)) / (cvm::real(full_samples - min_samples));
}
const cvm::real * grad = &(gradients->value(bin));
if ( fact != 0.0 ) {
if ( (colvars.size() == 1) && colvars[0]->periodic_boundaries() ) {
// Enforce a zero-mean bias on periodic, 1D coordinates
// in other words: boundary condition is that the biasing potential is periodic
colvar_forces[0].real_value = fact * (grad[0] / cvm::real(count) - gradients->average());
} else {
for (size_t i = 0; i < colvars.size(); i++) {
// subtracting the mean force (opposite of the FE gradient) means adding the gradient
colvar_forces[i].real_value = fact * grad[i] / cvm::real(count);
}
}
if (cap_force) {
for (size_t i = 0; i < colvars.size(); i++) {
if ( colvar_forces[i].real_value * colvar_forces[i].real_value > max_force[i] * max_force[i] ) {
colvar_forces[i].real_value = (colvar_forces[i].real_value > 0 ? max_force[i] : -1.0 * max_force[i]);
}
}
}
}
}
// update the output prefix; TODO: move later to setup_output() function
if (cvm::num_biases_feature(colvardeps::f_cvb_calc_pmf) == 1) {
// This is the only bias computing PMFs
output_prefix = cvm::output_prefix();
} else {
output_prefix = cvm::output_prefix() + "." + this->name;
}
if (output_freq && (cvm::step_absolute() % output_freq) == 0) {
if (cvm::debug()) cvm::log("ABF bias trying to write gradients and samples to disk");
write_gradients_samples(output_prefix);
}
if (b_history_files && (cvm::step_absolute() % history_freq) == 0) {
// file already exists iff cvm::step_relative() > 0
// otherwise, backup and replace
write_gradients_samples(output_prefix + ".hist", (cvm::step_relative() > 0));
}
if (shared_on && shared_last_step >= 0 && cvm::step_absolute() % shared_freq == 0) {
// Share gradients and samples for shared ABF.
replica_share();
}
// Prepare for the first sharing.
if (shared_last_step < 0) {
// Copy the current gradient and count values into last.
last_gradients->copy_grid(*gradients);
last_samples->copy_grid(*samples);
shared_last_step = cvm::step_absolute();
cvm::log("Prepared sample and gradient buffers at step "+cvm::to_str(cvm::step_absolute())+".");
}
return COLVARS_OK;
}