// -*- 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); 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()); 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."); return COLVARS_ERROR; } cvm::log("shared ABF will be applied among "+ cvm::to_str(cvm::replica_num()) + " 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); 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 UI_lowerboundary; std::vector UI_upperboundary; std::vector UI_width; std::vector 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 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 x(num_variables(),0); std::vector 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::replica_share() { 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) { int p; // 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); 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 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 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; }