colvarbias_abf.cpp
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Created: Mon, Jun 3, 17:59

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 "colvar.h"
	#include "colvarbias_abf.h"


	colvarbias_abf::colvarbias_abf(char const *key)
	: colvarbias(key),
	system_force(NULL),
	gradients(NULL),
	samples(NULL),
	z_gradients(NULL),
	z_samples(NULL),
	czar_gradients(NULL),
	last_gradients(NULL),
	last_samples(NULL)
	{
	}


	int colvarbias_abf::init(std::string const &conf)
	{
	colvarbias::init(conf);

	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 (!cvm::replica_enabled() \|\| cvm::replica_num() <= 1)
	cvm::error("Error: shared ABF requires more than one replica.");
	else
	cvm::log("shared ABF will be applied among "+ cvm::to_str(cvm::replica_num()) + " replicas.\n");

	// If shared_freq is not set, we default to output_freq
	get_keyval(conf, "sharedFreq", shared_freq, output_freq);
	}

	// *********** checking the associated colvars *****************

	if (colvars.size() == 0) {
	cvm::error("Error: no collective variables specified for the ABF bias.\n");
	}

	if (update_bias) {
	// Request calculation of total force (which also checks for availability)
	// TODO - change this to a dependency - needs ABF-specific features
	if(enable(f_cvb_get_total_force)) return cvm::get_error();
	}

	bool b_extended = false;
	for (size_t i = 0; i < colvars.size(); 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);
	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;

	// 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() != colvars.size()) {
	cvm::error("Error: Number of parameters to maxForce does not match number of colvars.");
	}
	for (size_t i = 0; i < colvars.size(); i++) {
	if (max_force[i] < 0.0) {
	cvm::error("Error: maxForce should be non-negative.");
	}
	}
	cap_force = true;
	} else {
	cap_force = false;
	}

	bin.assign(colvars.size(), 0);
	force_bin.assign(colvars.size(), 0);
	system_force = new cvm::real [colvars.size()];

	// 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 eABF z-based estimator
	if (b_extended) {
	// CZAR output files for stratified eABF
	get_keyval(conf, "writeCZARwindowFile", b_czar_window_file, false,
	colvarparse::parse_silent);

	z_bin.assign(colvars.size(), 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 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();
	}

	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 (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;
	}

	// 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);

	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;
	}


	int colvarbias_abf::replica_share() {
	int p;

	if ( !cvm::replica_enabled() ) {
	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 (cvm::replica_index() == 0) {
	// Replica 0 collects the delta gradient and count from the others.
	for (p = 1; p < cvm::replica_num(); p++) {
	// Receive the deltas.
	cvm::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 < cvm::replica_num(); p++) {
	cvm::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]));
	cvm::replica_comm_send(msg_data, msg_total, 0);

	// We now receive the combined gradient from Replica 0.
	cvm::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.
	cvm::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);

	cvm::ofstream samples_os;
	cvm::ofstream gradients_os;

	if (!append) cvm::backup_file(samples_out_name.c_str());
	samples_os.open(samples_out_name.c_str(), mode);
	if (!samples_os.is_open()) {
	cvm::error("Error opening ABF samples file " + samples_out_name + " for writing");
	}
	samples->write_multicol(samples_os);
	samples_os.close();

	if (!append) cvm::backup_file(gradients_out_name.c_str());
	gradients_os.open(gradients_out_name.c_str(), mode);
	if (!gradients_os.is_open()) {
	cvm::error("Error opening ABF gradient file " + gradients_out_name + " for writing");
	}
	gradients->write_multicol(gradients_os);
	gradients_os.close();

	if (colvars.size() == 1) {
	std::string pmf_out_name = prefix + ".pmf";
	if (!append) cvm::backup_file(pmf_out_name.c_str());
	cvm::ofstream pmf_os;
	// Do numerical integration and output a PMF
	pmf_os.open(pmf_out_name.c_str(), mode);
	if (!pmf_os.is_open()) cvm::error("Error opening pmf file " + pmf_out_name + " for writing");
	gradients->write_1D_integral(pmf_os);
	pmf_os << std::endl;
	pmf_os.close();
	}

	if (z_gradients) {
	// Write eABF-related quantities

	std::string z_samples_out_name = prefix + ".zcount";
	cvm::ofstream z_samples_os;

	if (!append) cvm::backup_file(z_samples_out_name.c_str());
	z_samples_os.open(z_samples_out_name.c_str(), mode);
	if (!z_samples_os.is_open()) {
	cvm::error("Error opening eABF z-histogram file " + z_samples_out_name + " for writing");
	}
	z_samples->write_multicol(z_samples_os);
	z_samples_os.close();

	if (b_czar_window_file) {
	std::string z_gradients_out_name = prefix + ".zgrad";
	cvm::ofstream z_gradients_os;

	if (!append) cvm::backup_file(z_gradients_out_name.c_str());
	z_gradients_os.open(z_gradients_out_name.c_str(), mode);
	if (!z_gradients_os.is_open()) {
	cvm::error("Error opening eABF z-gradient file " + z_gradients_out_name + " for writing");
	}
	z_gradients->write_multicol(z_gradients_os);
	z_gradients_os.close();
	}

	// 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";
	cvm::ofstream czar_gradients_os;

	if (!append) cvm::backup_file(czar_gradients_out_name.c_str());
	czar_gradients_os.open(czar_gradients_out_name.c_str(), mode);
	if (!czar_gradients_os.is_open()) {
	cvm::error("Error opening CZAR gradient file " + czar_gradients_out_name + " for writing");
	}
	czar_gradients->write_multicol(czar_gradients_os);
	czar_gradients_os.close();

	if (colvars.size() == 1) {
	std::string czar_pmf_out_name = prefix + ".czar.pmf";
	if (!append) cvm::backup_file(czar_pmf_out_name.c_str());
	cvm::ofstream czar_pmf_os;
	// Do numerical integration and output a PMF
	czar_pmf_os.open(czar_pmf_out_name.c_str(), mode);
	if (!czar_pmf_os.is_open()) cvm::error("Error opening CZAR pmf file " + czar_pmf_out_name + " for writing");
	czar_gradients->write_1D_integral(czar_pmf_os);
	czar_pmf_os << std::endl;
	czar_pmf_os.close();
	}
	}
	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");
	gradients->read_multicol(is, true);
	is.close();

	if (z_gradients) {
	// 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 (z_gradients) {
	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 (z_gradients) {

	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;
	}

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