Files
lammps/lib/colvars/colvarbias_histogram.cpp
Giacomo Fiorin 1220bea011 Update Colvars to version 2022-05-09
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
2022-05-10 11:24:54 -04:00

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;
}