This update includes one new feature (neural-network based collective variables), several small enhancements (including an automatic definition of grid boundaries for angle-based CVs, and a normalization option for eigenvector-based CVs), bugfixes and documentation improvements. Usage information for specific features included in the Colvars library (i.e. not just the library as a whole) is now also reported to the screen or LAMMPS logfile (as is done already in other LAMMPS classes). Notable to LAMMPS code development are the removals of duplicated code and of ambiguously-named preprocessor defines in the Colvars headers. Since the last PR, the existing regression tests have also been running automatically via GitHub Actions. The following pull requests in the Colvars repository are relevant to LAMMPS: - 475 Remove fatal error condition https://github.com/Colvars/colvars/pull/475 (@jhenin, @giacomofiorin) - 474 Allow normalizing eigenvector vector components to deal with unit change https://github.com/Colvars/colvars/pull/474 (@giacomofiorin, @jhenin) - 470 Better error handling in the initialization of NeuralNetwork CV https://github.com/Colvars/colvars/pull/470 (@HanatoK) - 468 Add examples of histogram configuration, with and without explicit grid parameters https://github.com/Colvars/colvars/pull/468 (@giacomofiorin) - 464 Fix #463 using more fine-grained features https://github.com/Colvars/colvars/pull/464 (@jhenin, @giacomofiorin) - 447 [RFC] New option "scaledBiasingForce" for colvarbias https://github.com/Colvars/colvars/pull/447 (@HanatoK, @jhenin) - 444 [RFC] Implementation of dense neural network as CV https://github.com/Colvars/colvars/pull/444 (@HanatoK, @giacomofiorin, @jhenin) - 443 Fix explicit gradient dependency of sub-CVs https://github.com/Colvars/colvars/pull/443 (@HanatoK, @jhenin) - 442 Persistent bias count https://github.com/Colvars/colvars/pull/442 (@jhenin, @giacomofiorin) - 437 Return type of bias from scripting interface https://github.com/Colvars/colvars/pull/437 (@giacomofiorin) - 434 More flexible use of boundaries from colvars by grids https://github.com/Colvars/colvars/pull/434 (@jhenin) - 433 Prevent double-free in linearCombination https://github.com/Colvars/colvars/pull/433 (@HanatoK) - 428 More complete documentation for index file format (NDX) https://github.com/Colvars/colvars/pull/428 (@giacomofiorin) - 426 Integrate functional version of backup_file() into base proxy class https://github.com/Colvars/colvars/pull/426 (@giacomofiorin) - 424 Track CVC inheritance when documenting feature usage https://github.com/Colvars/colvars/pull/424 (@giacomofiorin) - 419 Generate citation report while running computations https://github.com/Colvars/colvars/pull/419 (@giacomofiorin, @jhenin) - 415 Rebin metadynamics bias from explicit hills when available https://github.com/Colvars/colvars/pull/415 (@giacomofiorin) - 312 Ignore a keyword if it has content to the left of it (regardless of braces) https://github.com/Colvars/colvars/pull/312 (@giacomofiorin) Authors: @giacomofiorin, @HanatoK, @jhenin
232 lines
6.3 KiB
C++
232 lines
6.3 KiB
C++
// -*- 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_histogram.h"
|
|
|
|
|
|
colvarbias_histogram::colvarbias_histogram(char const *key)
|
|
: colvarbias(key),
|
|
grid(NULL), out_name("")
|
|
{
|
|
provide(f_cvb_bypass_ext_lagrangian); // Allow histograms of actual cv for extended-Lagrangian
|
|
}
|
|
|
|
|
|
int colvarbias_histogram::init(std::string const &conf)
|
|
{
|
|
colvarbias::init(conf);
|
|
cvm::main()->cite_feature("Histogram colvar bias implementation");
|
|
|
|
enable(f_cvb_scalar_variables);
|
|
enable(f_cvb_history_dependent);
|
|
|
|
size_t i;
|
|
|
|
get_keyval(conf, "outputFile", out_name, "");
|
|
// Write DX file by default only in dimension >= 3
|
|
std::string default_name_dx = this->num_variables() > 2 ? "" : "none";
|
|
get_keyval(conf, "outputFileDX", out_name_dx, default_name_dx);
|
|
|
|
/// with VMD, this may not be an error
|
|
// if ( output_freq == 0 ) {
|
|
// cvm::error("User required histogram with zero output frequency");
|
|
// }
|
|
|
|
colvar_array_size = 0;
|
|
{
|
|
bool colvar_array = false;
|
|
get_keyval(conf, "gatherVectorColvars", colvar_array, colvar_array);
|
|
|
|
if (colvar_array) {
|
|
for (i = 0; i < num_variables(); i++) { // should be all vector
|
|
if (colvars[i]->value().type() != colvarvalue::type_vector) {
|
|
cvm::error("Error: used gatherVectorColvars with non-vector colvar.\n", COLVARS_INPUT_ERROR);
|
|
return COLVARS_INPUT_ERROR;
|
|
}
|
|
if (i == 0) {
|
|
colvar_array_size = colvars[i]->value().size();
|
|
if (colvar_array_size < 1) {
|
|
cvm::error("Error: vector variable has dimension less than one.\n", COLVARS_INPUT_ERROR);
|
|
return COLVARS_INPUT_ERROR;
|
|
}
|
|
} else {
|
|
if (colvar_array_size != colvars[i]->value().size()) {
|
|
cvm::error("Error: trying to combine vector colvars of different lengths.\n", COLVARS_INPUT_ERROR);
|
|
return COLVARS_INPUT_ERROR;
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
for (i = 0; i < num_variables(); i++) { // should be all scalar
|
|
if (colvars[i]->value().type() != colvarvalue::type_scalar) {
|
|
cvm::error("Error: only scalar colvars are supported when gatherVectorColvars is off.\n", COLVARS_INPUT_ERROR);
|
|
return COLVARS_INPUT_ERROR;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if (colvar_array_size > 0) {
|
|
weights.assign(colvar_array_size, 1.0);
|
|
get_keyval(conf, "weights", weights, weights);
|
|
}
|
|
|
|
for (i = 0; i < num_variables(); i++) {
|
|
colvars[i]->enable(f_cv_grid); // Could be a child dependency of a f_cvb_use_grids feature
|
|
}
|
|
|
|
grid = new colvar_grid_scalar();
|
|
grid->init_from_colvars(colvars);
|
|
|
|
if (is_enabled(f_cvb_bypass_ext_lagrangian)) {
|
|
grid->request_actual_value();
|
|
}
|
|
|
|
{
|
|
std::string grid_conf;
|
|
if (key_lookup(conf, "histogramGrid", &grid_conf)) {
|
|
grid->parse_params(grid_conf);
|
|
grid->check_keywords(grid_conf, "histogramGrid");
|
|
}
|
|
}
|
|
|
|
return COLVARS_OK;
|
|
}
|
|
|
|
|
|
colvarbias_histogram::~colvarbias_histogram()
|
|
{
|
|
if (grid) {
|
|
delete grid;
|
|
grid = NULL;
|
|
}
|
|
}
|
|
|
|
|
|
int colvarbias_histogram::update()
|
|
{
|
|
int error_code = COLVARS_OK;
|
|
// update base class
|
|
error_code |= colvarbias::update();
|
|
|
|
if (cvm::debug()) {
|
|
cvm::log("Updating histogram bias " + this->name);
|
|
}
|
|
|
|
// assign a valid bin size
|
|
bin.assign(num_variables(), 0);
|
|
|
|
if (out_name.size() == 0) {
|
|
// At the first timestep, we need to assign out_name since
|
|
// output_prefix is unset during the constructor
|
|
if (cvm::step_relative() == 0) {
|
|
out_name = cvm::output_prefix() + "." + this->name + ".dat";
|
|
cvm::log("Histogram " + this->name + " will be written to file \"" + out_name + "\"\n");
|
|
}
|
|
}
|
|
|
|
if (out_name_dx.size() == 0) {
|
|
if (cvm::step_relative() == 0) {
|
|
out_name_dx = cvm::output_prefix() + "." + this->name + ".dx";
|
|
cvm::log("Histogram " + this->name + " will be written to file \"" + out_name_dx + "\"\n");
|
|
}
|
|
}
|
|
|
|
if (colvar_array_size == 0) {
|
|
// update indices for scalar values
|
|
size_t i;
|
|
for (i = 0; i < num_variables(); i++) {
|
|
bin[i] = grid->current_bin_scalar(i);
|
|
}
|
|
|
|
if (can_accumulate_data()) {
|
|
if (grid->index_ok(bin)) {
|
|
grid->acc_value(bin, 1.0);
|
|
}
|
|
}
|
|
} else {
|
|
// update indices for vector/array values
|
|
size_t iv, i;
|
|
for (iv = 0; iv < colvar_array_size; iv++) {
|
|
for (i = 0; i < num_variables(); i++) {
|
|
bin[i] = grid->current_bin_scalar(i, iv);
|
|
}
|
|
|
|
if (grid->index_ok(bin)) {
|
|
grid->acc_value(bin, weights[iv]);
|
|
}
|
|
}
|
|
}
|
|
|
|
error_code |= cvm::get_error();
|
|
return error_code;
|
|
}
|
|
|
|
|
|
int colvarbias_histogram::write_output_files()
|
|
{
|
|
if (!has_data) {
|
|
// nothing to write
|
|
return COLVARS_OK;
|
|
}
|
|
|
|
if (out_name.size() && out_name != "none") {
|
|
cvm::log("Writing the histogram file \""+out_name+"\".\n");
|
|
cvm::backup_file(out_name.c_str());
|
|
std::ostream *grid_os = cvm::proxy->output_stream(out_name);
|
|
if (!grid_os) {
|
|
return cvm::error("Error opening histogram file "+out_name+
|
|
" for writing.\n", COLVARS_FILE_ERROR);
|
|
}
|
|
grid->write_multicol(*grid_os);
|
|
cvm::proxy->close_output_stream(out_name);
|
|
}
|
|
|
|
if (out_name_dx.size() && out_name_dx != "none") {
|
|
cvm::log("Writing the histogram file \""+out_name_dx+"\".\n");
|
|
cvm::backup_file(out_name_dx.c_str());
|
|
std::ostream *grid_os = cvm::proxy->output_stream(out_name_dx);
|
|
if (!grid_os) {
|
|
return cvm::error("Error opening histogram file "+out_name_dx+
|
|
" for writing.\n", COLVARS_FILE_ERROR);
|
|
}
|
|
grid->write_opendx(*grid_os);
|
|
cvm::proxy->close_output_stream(out_name_dx);
|
|
}
|
|
|
|
return COLVARS_OK;
|
|
}
|
|
|
|
|
|
std::istream & colvarbias_histogram::read_state_data(std::istream& is)
|
|
{
|
|
if (! read_state_data_key(is, "grid")) {
|
|
return is;
|
|
}
|
|
if (! grid->read_raw(is)) {
|
|
return is;
|
|
}
|
|
|
|
return is;
|
|
}
|
|
|
|
|
|
std::ostream & colvarbias_histogram::write_state_data(std::ostream& os)
|
|
{
|
|
std::ios::fmtflags flags(os.flags());
|
|
os.setf(std::ios::fmtflags(0), std::ios::floatfield);
|
|
os << "grid\n";
|
|
grid->write_raw(os, 8);
|
|
os.flags(flags);
|
|
return os;
|
|
}
|