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lammps/lib/colvars/colvarbias_abf.cpp

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// -*- c++ -*-
// This file is part of the Collective Variables module (Colvars).
// The original version of Colvars and its updates are located at:
// https://github.com/Colvars/colvars
// Please update all Colvars source files before making any changes.
// If you wish to distribute your changes, please submit them to the
// Colvars repository at GitHub.
#include "colvarmodule.h"
#include "colvarproxy.h"
#include "colvar.h"
#include "colvarbias_abf.h"
colvarbias_abf::colvarbias_abf(char const *key)
: colvarbias(key),
b_UI_estimator(false),
b_CZAR_estimator(false),
pabf_freq(0),
system_force(NULL),
gradients(NULL),
samples(NULL),
pmf(NULL),
z_gradients(NULL),
z_samples(NULL),
czar_gradients(NULL),
czar_pmf(NULL),
last_gradients(NULL),
last_samples(NULL)
{
}
int colvarbias_abf::init(std::string const &conf)
{
colvarbias::init(conf);
colvarproxy *proxy = cvm::main()->proxy;
enable(f_cvb_scalar_variables);
enable(f_cvb_calc_pmf);
// TODO relax this in case of VMD plugin
if (cvm::temperature() == 0.0)
cvm::log("WARNING: ABF should not be run without a thermostat or at 0 Kelvin!\n");
// ************* parsing general ABF options ***********************
get_keyval_feature((colvarparse *)this, conf, "applyBias", f_cvb_apply_force, true);
if (!is_enabled(f_cvb_apply_force)){
cvm::log("WARNING: ABF biases will *not* be applied!\n");
}
get_keyval(conf, "updateBias", update_bias, true);
if (update_bias) {
enable(f_cvb_history_dependent);
} else {
cvm::log("WARNING: ABF biases will *not* be updated!\n");
}
get_keyval(conf, "hideJacobian", hide_Jacobian, false);
if (hide_Jacobian) {
cvm::log("Jacobian (geometric) forces will be handled internally.\n");
} else {
cvm::log("Jacobian (geometric) forces will be included in reported free energy gradients.\n");
}
get_keyval(conf, "fullSamples", full_samples, 200);
if ( full_samples <= 1 ) full_samples = 1;
min_samples = full_samples / 2;
// full_samples - min_samples >= 1 is guaranteed
get_keyval(conf, "inputPrefix", input_prefix, std::vector<std::string>());
get_keyval(conf, "outputFreq", output_freq, cvm::restart_out_freq);
get_keyval(conf, "historyFreq", history_freq, 0);
b_history_files = (history_freq > 0);
// shared ABF
get_keyval(conf, "shared", shared_on, false);
if (shared_on) {
if ((proxy->replica_enabled() != COLVARS_OK) ||
(proxy->num_replicas() <= 1)) {
return cvm::error("Error: shared ABF requires more than one replica.",
INPUT_ERROR);
}
cvm::log("shared ABF will be applied among "+
cvm::to_str(proxy->num_replicas()) + " replicas.\n");
if (cvm::proxy->smp_enabled() == COLVARS_OK) {
cvm::error("Error: shared ABF is currently not available with SMP parallelism; "
"please set \"SMP off\" at the top of the Colvars configuration file.\n",
COLVARS_NOT_IMPLEMENTED);
return COLVARS_NOT_IMPLEMENTED;
}
// If shared_freq is not set, we default to output_freq
get_keyval(conf, "sharedFreq", shared_freq, output_freq);
}
// ************* checking the associated colvars *******************
if (num_variables() == 0) {
cvm::error("Error: no collective variables specified for the ABF bias.\n");
return COLVARS_ERROR;
}
if (update_bias) {
// Request calculation of total force
if(enable(f_cvb_get_total_force)) return cvm::get_error();
}
bool b_extended = false;
size_t i;
for (i = 0; i < num_variables(); i++) {
if (colvars[i]->value().type() != colvarvalue::type_scalar) {
cvm::error("Error: ABF bias can only use scalar-type variables.\n");
}
colvars[i]->enable(f_cv_grid); // Could be a child dependency of a f_cvb_use_grids feature
if (hide_Jacobian) {
colvars[i]->enable(f_cv_hide_Jacobian);
}
// If any colvar is extended-system, we need to collect the extended
// system gradient
if (colvars[i]->is_enabled(f_cv_extended_Lagrangian))
b_extended = true;
// Cannot mix and match coarse time steps with ABF because it gives
// wrong total force averages - total force needs to be averaged over
// every time step
if (colvars[i]->get_time_step_factor() != time_step_factor) {
cvm::error("Error: " + colvars[i]->description + " has a value of timeStepFactor ("
+ cvm::to_str(colvars[i]->get_time_step_factor()) + ") different from that of "
+ description + " (" + cvm::to_str(time_step_factor) + ").\n");
return COLVARS_ERROR;
}
// Here we could check for orthogonality of the Cartesian coordinates
// and make it just a warning if some parameter is set?
}
if (get_keyval(conf, "maxForce", max_force)) {
if (max_force.size() != num_variables()) {
cvm::error("Error: Number of parameters to maxForce does not match number of colvars.");
}
for (i = 0; i < num_variables(); i++) {
if (max_force[i] < 0.0) {
cvm::error("Error: maxForce should be non-negative.");
}
}
cap_force = true;
} else {
cap_force = false;
}
bin.assign(num_variables(), 0);
force_bin.assign(num_variables(), 0);
system_force = new cvm::real [num_variables()];
// Construct empty grids based on the colvars
if (cvm::debug()) {
cvm::log("Allocating count and free energy gradient grids.\n");
}
samples = new colvar_grid_count(colvars);
gradients = new colvar_grid_gradient(colvars);
gradients->samples = samples;
samples->has_parent_data = true;
// Data for eAB F z-based estimator
if ( b_extended ) {
get_keyval(conf, "CZARestimator", b_CZAR_estimator, true);
// CZAR output files for stratified eABF
get_keyval(conf, "writeCZARwindowFile", b_czar_window_file, false,
colvarparse::parse_silent);
z_bin.assign(num_variables(), 0);
z_samples = new colvar_grid_count(colvars);
z_samples->request_actual_value();
z_gradients = new colvar_grid_gradient(colvars);
z_gradients->request_actual_value();
z_gradients->samples = z_samples;
z_samples->has_parent_data = true;
czar_gradients = new colvar_grid_gradient(colvars);
}
// For now, we integrate on-the-fly iff the grid is < 3D
if ( num_variables() <= 3 ) {
pmf = new integrate_potential(colvars, gradients);
if ( b_CZAR_estimator ) {
czar_pmf = new integrate_potential(colvars, czar_gradients);
}
get_keyval(conf, "integrate", b_integrate, true); // Integrate for output
if ( num_variables() > 1 ) {
// Projected ABF
get_keyval(conf, "pABFintegrateFreq", pabf_freq, 0);
// Parameters for integrating initial (and final) gradient data
get_keyval(conf, "integrateInitMaxIterations", integrate_initial_iterations, 1e4);
get_keyval(conf, "integrateInitTol", integrate_initial_tol, 1e-6);
// for updating the integrated PMF on the fly
get_keyval(conf, "integrateMaxIterations", integrate_iterations, 100);
get_keyval(conf, "integrateTol", integrate_tol, 1e-4);
}
} else {
b_integrate = false;
}
// For shared ABF, we store a second set of grids.
// This used to be only if "shared" was defined,
// but now we allow calling share externally (e.g. from Tcl).
last_samples = new colvar_grid_count(colvars);
last_gradients = new colvar_grid_gradient(colvars);
last_gradients->samples = last_samples;
last_samples->has_parent_data = true;
shared_last_step = -1;
// If custom grids are provided, read them
if ( input_prefix.size() > 0 ) {
read_gradients_samples();
// Update divergence to account for input data
pmf->set_div();
}
// if extendedLangrangian is on, then call UI estimator
if (b_extended) {
get_keyval(conf, "UIestimator", b_UI_estimator, false);
if (b_UI_estimator) {
std::vector<double> UI_lowerboundary;
std::vector<double> UI_upperboundary;
std::vector<double> UI_width;
std::vector<double> UI_krestr;
bool UI_restart = (input_prefix.size() > 0);
for (i = 0; i < num_variables(); i++)
{
UI_lowerboundary.push_back(colvars[i]->lower_boundary);
UI_upperboundary.push_back(colvars[i]->upper_boundary);
UI_width.push_back(colvars[i]->width);
UI_krestr.push_back(colvars[i]->force_constant());
}
eabf_UI = UIestimator::UIestimator(UI_lowerboundary,
UI_upperboundary,
UI_width,
UI_krestr, // force constant in eABF
output_prefix, // the prefix of output files
cvm::restart_out_freq,
UI_restart, // whether restart from a .count and a .grad file
input_prefix, // the prefixes of input files
cvm::temperature());
}
}
cvm::log("Finished ABF setup.\n");
return COLVARS_OK;
}
/// Destructor
colvarbias_abf::~colvarbias_abf()
{
if (samples) {
delete samples;
samples = NULL;
}
if (gradients) {
delete gradients;
gradients = NULL;
}
if (pmf) {
delete pmf;
pmf = NULL;
}
if (z_samples) {
delete z_samples;
z_samples = NULL;
}
if (z_gradients) {
delete z_gradients;
z_gradients = NULL;
}
if (czar_gradients) {
delete czar_gradients;
czar_gradients = NULL;
}
if (czar_pmf) {
delete czar_pmf;
czar_pmf = NULL;
}
// shared ABF
// We used to only do this if "shared" was defined,
// but now we can call shared externally
if (last_samples) {
delete last_samples;
last_samples = NULL;
}
if (last_gradients) {
delete last_gradients;
last_gradients = NULL;
}
if (system_force) {
delete [] system_force;
system_force = NULL;
}
}
/// Update the FE gradient, compute and apply biasing force
/// also output data to disk if needed
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_iterations, integrate_tol, err);
if ( iter == integrate_iterations ) {
cvm::log("Warning: PMF integration did not converge to " + cvm::to_str(integrate_tol)
+ " in " + cvm::to_str(integrate_iterations)
+ " 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);
}
/// Add the bias energy for 1D ABF
bias_energy = calc_energy(NULL);
return COLVARS_OK;
}
int colvarbias_abf::replica_share() {
colvarproxy *proxy = cvm::main()->proxy;
if (proxy->replica_enabled() != COLVARS_OK) {
cvm::error("Error: shared ABF: No replicas.\n");
return COLVARS_ERROR;
}
// We must have stored the last_gradients and last_samples.
if (shared_last_step < 0 ) {
cvm::error("Error: shared ABF: Tried to apply shared ABF before any sampling had occurred.\n");
return COLVARS_ERROR;
}
// Share gradients for shared ABF.
cvm::log("shared ABF: Sharing gradient and samples among replicas at step "+cvm::to_str(cvm::step_absolute()) );
// Count of data items.
size_t data_n = gradients->raw_data_num();
size_t samp_start = data_n*sizeof(cvm::real);
size_t msg_total = data_n*sizeof(size_t) + samp_start;
char* msg_data = new char[msg_total];
if (proxy->replica_index() == 0) {
int p;
// Replica 0 collects the delta gradient and count from the others.
for (p = 1; p < proxy->num_replicas(); p++) {
// Receive the deltas.
proxy->replica_comm_recv(msg_data, msg_total, p);
// Map the deltas from the others into the grids.
last_gradients->raw_data_in((cvm::real*)(&msg_data[0]));
last_samples->raw_data_in((size_t*)(&msg_data[samp_start]));
// Combine the delta gradient and count of the other replicas
// with Replica 0's current state (including its delta).
gradients->add_grid( *last_gradients );
samples->add_grid( *last_samples );
}
// Now we must send the combined gradient to the other replicas.
gradients->raw_data_out((cvm::real*)(&msg_data[0]));
samples->raw_data_out((size_t*)(&msg_data[samp_start]));
for (p = 1; p < proxy->num_replicas(); p++) {
proxy->replica_comm_send(msg_data, msg_total, p);
}
} else {
// All other replicas send their delta gradient and count.
// Calculate the delta gradient and count.
last_gradients->delta_grid(*gradients);
last_samples->delta_grid(*samples);
// Cast the raw char data to the gradient and samples.
last_gradients->raw_data_out((cvm::real*)(&msg_data[0]));
last_samples->raw_data_out((size_t*)(&msg_data[samp_start]));
proxy->replica_comm_send(msg_data, msg_total, 0);
// We now receive the combined gradient from Replica 0.
proxy->replica_comm_recv(msg_data, msg_total, 0);
// We sync to the combined gradient computed by Replica 0.
gradients->raw_data_in((cvm::real*)(&msg_data[0]));
samples->raw_data_in((size_t*)(&msg_data[samp_start]));
}
// Without a barrier it's possible that one replica starts
// share 2 when other replicas haven't finished share 1.
proxy->replica_comm_barrier();
// Done syncing the replicas.
delete[] msg_data;
// 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();
return COLVARS_OK;
}
void colvarbias_abf::write_gradients_samples(const std::string &prefix, bool append)
{
std::string samples_out_name = prefix + ".count";
std::string gradients_out_name = prefix + ".grad";
std::ios::openmode mode = (append ? std::ios::app : std::ios::out);
std::ostream *samples_os =
cvm::proxy->output_stream(samples_out_name, mode);
if (!samples_os) return;
samples->write_multicol(*samples_os);
cvm::proxy->close_output_stream(samples_out_name);
// In dimension higher than 2, dx is easier to handle and visualize
if (num_variables() > 2) {
std::string samples_dx_out_name = prefix + ".count.dx";
std::ostream *samples_dx_os = cvm::proxy->output_stream(samples_dx_out_name, mode);
if (!samples_os) return;
samples->write_opendx(*samples_dx_os);
*samples_dx_os << std::endl;
cvm::proxy->close_output_stream(samples_dx_out_name);
}
std::ostream *gradients_os =
cvm::proxy->output_stream(gradients_out_name, mode);
if (!gradients_os) return;
gradients->write_multicol(*gradients_os);
cvm::proxy->close_output_stream(gradients_out_name);
if (b_integrate) {
// Do numerical integration (to high precision) and output a PMF
cvm::real err;
pmf->integrate(integrate_initial_iterations, integrate_initial_tol, err);
pmf->set_zero_minimum();
std::string pmf_out_name = prefix + ".pmf";
std::ostream *pmf_os = cvm::proxy->output_stream(pmf_out_name, mode);
if (!pmf_os) return;
pmf->write_multicol(*pmf_os);
// In dimension higher than 2, dx is easier to handle and visualize
if (num_variables() > 2) {
std::string pmf_dx_out_name = prefix + ".pmf.dx";
std::ostream *pmf_dx_os = cvm::proxy->output_stream(pmf_dx_out_name, mode);
if (!pmf_dx_os) return;
pmf->write_opendx(*pmf_dx_os);
*pmf_dx_os << std::endl;
cvm::proxy->close_output_stream(pmf_dx_out_name);
}
*pmf_os << std::endl;
cvm::proxy->close_output_stream(pmf_out_name);
}
if (b_CZAR_estimator) {
// Write eABF CZAR-related quantities
std::string z_samples_out_name = prefix + ".zcount";
std::ostream *z_samples_os =
cvm::proxy->output_stream(z_samples_out_name, mode);
if (!z_samples_os) return;
z_samples->write_multicol(*z_samples_os);
cvm::proxy->close_output_stream(z_samples_out_name);
if (b_czar_window_file) {
std::string z_gradients_out_name = prefix + ".zgrad";
std::ostream *z_gradients_os =
cvm::proxy->output_stream(z_gradients_out_name, mode);
if (!z_gradients_os) return;
z_gradients->write_multicol(*z_gradients_os);
cvm::proxy->close_output_stream(z_gradients_out_name);
}
// Calculate CZAR estimator of gradients
for (std::vector<int> ix = czar_gradients->new_index();
czar_gradients->index_ok(ix); czar_gradients->incr(ix)) {
for (size_t n = 0; n < czar_gradients->multiplicity(); n++) {
czar_gradients->set_value(ix, z_gradients->value_output(ix, n)
- cvm::temperature() * cvm::boltzmann() * z_samples->log_gradient_finite_diff(ix, n), n);
}
}
std::string czar_gradients_out_name = prefix + ".czar.grad";
std::ostream *czar_gradients_os =
cvm::proxy->output_stream(czar_gradients_out_name, mode);
if (!czar_gradients_os) return;
czar_gradients->write_multicol(*czar_gradients_os);
cvm::proxy->close_output_stream(czar_gradients_out_name);
if (b_integrate) {
// Do numerical integration (to high precision) and output a PMF
cvm::real err;
czar_pmf->set_div();
czar_pmf->integrate(integrate_initial_iterations, integrate_initial_tol, err);
czar_pmf->set_zero_minimum();
std::string czar_pmf_out_name = prefix + ".czar.pmf";
std::ostream *czar_pmf_os = cvm::proxy->output_stream(czar_pmf_out_name, mode);
if (!czar_pmf_os) return;
czar_pmf->write_multicol(*czar_pmf_os);
// In dimension higher than 2, dx is easier to handle and visualize
if (num_variables() > 2) {
std::string czar_pmf_dx_out_name = prefix + ".czar.pmf.dx";
std::ostream *czar_pmf_dx_os = cvm::proxy->output_stream(czar_pmf_dx_out_name, mode);
if (!czar_pmf_dx_os) return;
czar_pmf->write_opendx(*czar_pmf_dx_os);
*czar_pmf_dx_os << std::endl;
cvm::proxy->close_output_stream(czar_pmf_dx_out_name);
}
*czar_pmf_os << std::endl;
cvm::proxy->close_output_stream(czar_pmf_out_name);
}
}
return;
}
// For Tcl implementation of selection rules.
/// Give the total number of bins for a given bias.
int colvarbias_abf::bin_num() {
return samples->number_of_points(0);
}
/// Calculate the bin index for a given bias.
int colvarbias_abf::current_bin() {
return samples->current_bin_scalar(0);
}
/// Give the count at a given bin index.
int colvarbias_abf::bin_count(int bin_index) {
if (bin_index < 0 || bin_index >= bin_num()) {
cvm::error("Error: Tried to get bin count from invalid bin index "+cvm::to_str(bin_index));
return -1;
}
std::vector<int> ix(1,(int)bin_index);
return samples->value(ix);
}
void colvarbias_abf::read_gradients_samples()
{
std::string samples_in_name, gradients_in_name, z_samples_in_name, z_gradients_in_name;
for ( size_t i = 0; i < input_prefix.size(); i++ ) {
samples_in_name = input_prefix[i] + ".count";
gradients_in_name = input_prefix[i] + ".grad";
z_samples_in_name = input_prefix[i] + ".zcount";
z_gradients_in_name = input_prefix[i] + ".zgrad";
// For user-provided files, the per-bias naming scheme may not apply
std::ifstream is;
cvm::log("Reading sample count from " + samples_in_name + " and gradient from " + gradients_in_name);
is.open(samples_in_name.c_str());
if (!is.is_open()) cvm::error("Error opening ABF samples file " + samples_in_name + " for reading");
samples->read_multicol(is, true);
is.close();
is.clear();
is.open(gradients_in_name.c_str());
if (!is.is_open()) {
cvm::error("Error opening ABF gradient file " +
gradients_in_name + " for reading", INPUT_ERROR);
} else {
gradients->read_multicol(is, true);
is.close();
}
if (b_CZAR_estimator) {
// Read eABF z-averaged data for CZAR
cvm::log("Reading z-histogram from " + z_samples_in_name + " and z-gradient from " + z_gradients_in_name);
is.clear();
is.open(z_samples_in_name.c_str());
if (!is.is_open()) cvm::error("Error opening eABF z-histogram file " + z_samples_in_name + " for reading");
z_samples->read_multicol(is, true);
is.close();
is.clear();
is.open(z_gradients_in_name.c_str());
if (!is.is_open()) cvm::error("Error opening eABF z-gradient file " + z_gradients_in_name + " for reading");
z_gradients->read_multicol(is, true);
is.close();
}
}
return;
}
std::ostream & colvarbias_abf::write_state_data(std::ostream& os)
{
std::ios::fmtflags flags(os.flags());
os.setf(std::ios::fmtflags(0), std::ios::floatfield); // default floating-point format
os << "\nsamples\n";
samples->write_raw(os, 8);
os.flags(flags);
os << "\ngradient\n";
gradients->write_raw(os, 8);
if (b_CZAR_estimator) {
os.setf(std::ios::fmtflags(0), std::ios::floatfield); // default floating-point format
os << "\nz_samples\n";
z_samples->write_raw(os, 8);
os.flags(flags);
os << "\nz_gradient\n";
z_gradients->write_raw(os, 8);
}
os.flags(flags);
return os;
}
std::istream & colvarbias_abf::read_state_data(std::istream& is)
{
if ( input_prefix.size() > 0 ) {
cvm::error("ERROR: cannot provide both inputPrefix and a colvars state file.\n", INPUT_ERROR);
}
if (! read_state_data_key(is, "samples")) {
return is;
}
if (! samples->read_raw(is)) {
return is;
}
if (! read_state_data_key(is, "gradient")) {
return is;
}
if (! gradients->read_raw(is)) {
return is;
}
if (b_integrate) {
// Update divergence to account for restart data
pmf->set_div();
}
if (b_CZAR_estimator) {
if (! read_state_data_key(is, "z_samples")) {
return is;
}
if (! z_samples->read_raw(is)) {
return is;
}
if (! read_state_data_key(is, "z_gradient")) {
return is;
}
if (! z_gradients->read_raw(is)) {
return is;
}
}
return is;
}
int colvarbias_abf::write_output_files()
{
write_gradients_samples(output_prefix);
return COLVARS_OK;
}
int colvarbias_abf::calc_energy(std::vector<colvarvalue> const *values)
{
if (values) {
return cvm::error("colvarbias_abf::calc_energy() with an argument "
"is currently not implemented.\n",
COLVARS_NOT_IMPLEMENTED);
}
if (num_variables() != 1) return 0.0;
// Get the home bin.
int home0 = gradients->current_bin_scalar(0);
if (home0 < 0) return 0.0;
int gradient_len = (int)(gradients->number_of_points(0));
int home = (home0 < gradient_len) ? home0 : (gradient_len-1);
// Integrate the gradient up to the home bin.
cvm::real sum = 0.0;
for (int i = 0; i < home; i++) {
std::vector<int> ix(1,i);
// Include the full_samples factor if necessary.
unsigned int count = samples->value(ix);
cvm::real fact = 1.0;
if ( count < full_samples ) {
fact = (count < min_samples) ? 0.0 :
(cvm::real(count - min_samples)) / (cvm::real(full_samples - min_samples));
}
if (count > 0) sum += fact*gradients->value(ix)/count*gradients->widths[0];
}
// Integrate the gradient up to the current position in the home interval, a fractional portion of a bin.
std::vector<int> ix(1,home);
cvm::real frac = gradients->current_bin_scalar_fraction(0);
unsigned int count = samples->value(ix);
cvm::real fact = 1.0;
if ( count < full_samples ) {
fact = (count < min_samples) ? 0.0 :
(cvm::real(count - min_samples)) / (cvm::real(full_samples - min_samples));
}
if (count > 0)
sum += fact*gradients->value(ix)/count*gradients->widths[0]*frac;
// The applied potential is the negative integral of force samples.
return -sum;
}
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