Files
lammps/lib/colvars/colvargrid.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

956 lines
28 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 "colvarvalue.h"
#include "colvarparse.h"
#include "colvar.h"
#include "colvarcomp.h"
#include "colvargrid.h"
#include <ctime>
colvar_grid_count::colvar_grid_count()
: colvar_grid<size_t>()
{
mult = 1;
}
colvar_grid_count::colvar_grid_count(std::vector<int> const &nx_i,
size_t const &def_count)
: colvar_grid<size_t>(nx_i, def_count, 1)
{}
colvar_grid_count::colvar_grid_count(std::vector<colvar *> &colvars,
size_t const &def_count,
bool margin)
: colvar_grid<size_t>(colvars, def_count, 1, margin)
{}
colvar_grid_scalar::colvar_grid_scalar()
: colvar_grid<cvm::real>(), samples(NULL)
{}
colvar_grid_scalar::colvar_grid_scalar(colvar_grid_scalar const &g)
: colvar_grid<cvm::real>(g), samples(NULL)
{
}
colvar_grid_scalar::colvar_grid_scalar(std::vector<int> const &nx_i)
: colvar_grid<cvm::real>(nx_i, 0.0, 1), samples(NULL)
{
}
colvar_grid_scalar::colvar_grid_scalar(std::vector<colvar *> &colvars, bool margin)
: colvar_grid<cvm::real>(colvars, 0.0, 1, margin), samples(NULL)
{
}
colvar_grid_scalar::~colvar_grid_scalar()
{
}
cvm::real colvar_grid_scalar::maximum_value() const
{
cvm::real max = data[0];
for (size_t i = 0; i < nt; i++) {
if (data[i] > max) max = data[i];
}
return max;
}
cvm::real colvar_grid_scalar::minimum_value() const
{
cvm::real min = data[0];
for (size_t i = 0; i < nt; i++) {
if (data[i] < min) min = data[i];
}
return min;
}
cvm::real colvar_grid_scalar::minimum_pos_value() const
{
cvm::real minpos = data[0];
size_t i;
for (i = 0; i < nt; i++) {
if(data[i] > 0) {
minpos = data[i];
break;
}
}
for (i = 0; i < nt; i++) {
if (data[i] > 0 && data[i] < minpos) minpos = data[i];
}
return minpos;
}
cvm::real colvar_grid_scalar::integral() const
{
cvm::real sum = 0.0;
for (size_t i = 0; i < nt; i++) {
sum += data[i];
}
cvm::real bin_volume = 1.0;
for (size_t id = 0; id < widths.size(); id++) {
bin_volume *= widths[id];
}
return bin_volume * sum;
}
cvm::real colvar_grid_scalar::entropy() const
{
cvm::real sum = 0.0;
for (size_t i = 0; i < nt; i++) {
if (data[i] >0) {
sum += -1.0 * data[i] * cvm::logn(data[i]);
}
}
cvm::real bin_volume = 1.0;
for (size_t id = 0; id < widths.size(); id++) {
bin_volume *= widths[id];
}
return bin_volume * sum;
}
colvar_grid_gradient::colvar_grid_gradient()
: colvar_grid<cvm::real>(),
samples(NULL),
weights(NULL)
{}
colvar_grid_gradient::colvar_grid_gradient(std::vector<int> const &nx_i)
: colvar_grid<cvm::real>(nx_i, 0.0, nx_i.size()),
samples(NULL),
weights(NULL)
{}
colvar_grid_gradient::colvar_grid_gradient(std::vector<colvar *> &colvars)
: colvar_grid<cvm::real>(colvars, 0.0, colvars.size()),
samples(NULL),
weights(NULL)
{}
colvar_grid_gradient::colvar_grid_gradient(std::string &filename)
: colvar_grid<cvm::real>(),
samples(NULL),
weights(NULL)
{
std::ifstream is;
is.open(filename.c_str());
if (!is.is_open()) {
cvm::error("Error opening multicol gradient file " + filename + " for reading.\n");
return;
}
// Data in the header: nColvars, then for each
// xiMin, dXi, nPoints, periodic flag
std::string hash;
size_t i;
if ( !(is >> hash) || (hash != "#") ) {
cvm::error("Error reading grid at position "+
cvm::to_str(static_cast<size_t>(is.tellg()))+
" in stream(read \"" + hash + "\")\n");
return;
}
is >> nd;
if (nd > 50) {
cvm::error("Error: excessive number of dimensions in file \""+
filename+"\". Please ensure that the file is not corrupt.\n",
COLVARS_INPUT_ERROR);
return;
}
mult = nd;
std::vector<cvm::real> lower_in(nd), widths_in(nd);
std::vector<int> nx_in(nd);
std::vector<int> periodic_in(nd);
for (i = 0; i < nd; i++ ) {
if ( !(is >> hash) || (hash != "#") ) {
cvm::error("Error reading grid at position "+
cvm::to_str(static_cast<size_t>(is.tellg()))+
" in stream(read \"" + hash + "\")\n");
return;
}
is >> lower_in[i] >> widths_in[i] >> nx_in[i] >> periodic_in[i];
}
this->setup(nx_in, 0., mult);
widths = widths_in;
for (i = 0; i < nd; i++ ) {
lower_boundaries.push_back(colvarvalue(lower_in[i]));
periodic.push_back(static_cast<bool>(periodic_in[i]));
}
// Reset the istream for read_multicol, which expects the whole file
is.clear();
is.seekg(0);
read_multicol(is);
is.close();
}
void colvar_grid_gradient::write_1D_integral(std::ostream &os)
{
cvm::real bin, min, integral;
std::vector<cvm::real> int_vals;
os << "# xi A(xi)\n";
if (cv.size() != 1) {
cvm::error("Cannot write integral for multi-dimensional gradient grids.");
return;
}
integral = 0.0;
int_vals.push_back(0.0);
min = 0.0;
// correction for periodic colvars, so that the PMF is periodic
cvm::real corr;
if (periodic[0]) {
corr = average();
} else {
corr = 0.0;
}
for (std::vector<int> ix = new_index(); index_ok(ix); incr(ix)) {
if (samples) {
size_t const samples_here = samples->value(ix);
if (samples_here)
integral += (value(ix) / cvm::real(samples_here) - corr) * cv[0]->width;
} else {
integral += (value(ix) - corr) * cv[0]->width;
}
if ( integral < min ) min = integral;
int_vals.push_back(integral);
}
bin = 0.0;
for ( int i = 0; i < nx[0]; i++, bin += 1.0 ) {
os << std::setw(10) << cv[0]->lower_boundary.real_value + cv[0]->width * bin << " "
<< std::setw(cvm::cv_width)
<< std::setprecision(cvm::cv_prec)
<< int_vals[i] - min << "\n";
}
os << std::setw(10) << cv[0]->lower_boundary.real_value + cv[0]->width * bin << " "
<< std::setw(cvm::cv_width)
<< std::setprecision(cvm::cv_prec)
<< int_vals[nx[0]] - min << "\n";
return;
}
integrate_potential::integrate_potential(std::vector<colvar *> &colvars, colvar_grid_gradient * gradients)
: colvar_grid_scalar(colvars, true),
gradients(gradients)
{
// parent class colvar_grid_scalar is constructed with margin option set to true
// hence PMF grid is wider than gradient grid if non-PBC
if (nd > 1) {
cvm::main()->cite_feature("Poisson integration of 2D/3D free energy surfaces");
divergence.resize(nt);
// Compute inverse of Laplacian diagonal for Jacobi preconditioning
// For now all code related to preconditioning is commented out
// until a method better than Jacobi is implemented
// cvm::log("Preparing inverse diagonal for preconditioning...\n");
// inv_lap_diag.resize(nt);
// std::vector<cvm::real> id(nt), lap_col(nt);
// for (int i = 0; i < nt; i++) {
// if (i % (nt / 100) == 0)
// cvm::log(cvm::to_str(i));
// id[i] = 1.;
// atimes(id, lap_col);
// id[i] = 0.;
// inv_lap_diag[i] = 1. / lap_col[i];
// }
// cvm::log("Done.\n");
}
}
integrate_potential::integrate_potential(colvar_grid_gradient * gradients)
: gradients(gradients)
{
nd = gradients->num_variables();
nx = gradients->number_of_points_vec();
widths = gradients->widths;
periodic = gradients->periodic;
// Expand grid by 1 bin in non-periodic dimensions
for (size_t i = 0; i < nd; i++ ) {
if (!periodic[i]) nx[i]++;
// Shift the grid by half the bin width (values at edges instead of center of bins)
lower_boundaries.push_back(gradients->lower_boundaries[i].real_value - 0.5 * widths[i]);
}
setup(nx);
if (nd > 1) {
divergence.resize(nt);
}
}
int integrate_potential::integrate(const int itmax, const cvm::real &tol, cvm::real & err)
{
int iter = 0;
if (nd == 1) {
cvm::real sum = 0.0;
cvm::real corr;
if ( periodic[0] ) {
corr = gradients->average(); // Enforce PBC by subtracting average gradient
} else {
corr = 0.0;
}
std::vector<int> ix;
// Iterate over valid indices in gradient grid
for (ix = new_index(); gradients->index_ok(ix); incr(ix)) {
set_value(ix, sum);
sum += (gradients->value_output(ix) - corr) * widths[0];
}
if (index_ok(ix)) {
// This will happen if non-periodic: then PMF grid has one extra bin wrt gradient grid
set_value(ix, sum);
}
} else if (nd <= 3) {
nr_linbcg_sym(divergence, data, tol, itmax, iter, err);
cvm::log("Integrated in " + cvm::to_str(iter) + " steps, error: " + cvm::to_str(err) + "\n");
} else {
cvm::error("Cannot integrate PMF in dimension > 3\n");
}
return iter;
}
void integrate_potential::set_div()
{
if (nd == 1) return;
for (std::vector<int> ix = new_index(); index_ok(ix); incr(ix)) {
update_div_local(ix);
}
}
void integrate_potential::update_div_neighbors(const std::vector<int> &ix0)
{
std::vector<int> ix(ix0);
int i, j, k;
// If not periodic, expanded grid ensures that neighbors of ix0 are valid grid points
if (nd == 1) {
return;
} else if (nd == 2) {
update_div_local(ix);
ix[0]++; wrap(ix);
update_div_local(ix);
ix[1]++; wrap(ix);
update_div_local(ix);
ix[0]--; wrap(ix);
update_div_local(ix);
} else if (nd == 3) {
for (i = 0; i<2; i++) {
ix[1] = ix0[1];
for (j = 0; j<2; j++) {
ix[2] = ix0[2];
for (k = 0; k<2; k++) {
wrap(ix);
update_div_local(ix);
ix[2]++;
}
ix[1]++;
}
ix[0]++;
}
}
}
void integrate_potential::get_grad(cvm::real * g, std::vector<int> &ix)
{
size_t count, i;
bool edge = gradients->wrap_edge(ix); // Detect edge if non-PBC
if (gradients->samples) {
count = gradients->samples->value(ix);
} else {
count = 1;
}
if (!edge && count) {
cvm::real const *grad = &(gradients->value(ix));
cvm::real const fact = 1.0 / count;
for ( i = 0; i<nd; i++ ) {
g[i] = fact * grad[i];
}
} else {
for ( i = 0; i<nd; i++ ) {
g[i] = 0.0;
}
}
}
void integrate_potential::update_div_local(const std::vector<int> &ix0)
{
const size_t linear_index = address(ix0);
int i, j, k;
std::vector<int> ix = ix0;
if (nd == 2) {
// gradients at grid points surrounding the current scalar grid point
cvm::real g00[2], g01[2], g10[2], g11[2];
get_grad(g11, ix);
ix[0] = ix0[0] - 1;
get_grad(g01, ix);
ix[1] = ix0[1] - 1;
get_grad(g00, ix);
ix[0] = ix0[0];
get_grad(g10, ix);
divergence[linear_index] = ((g10[0]-g00[0] + g11[0]-g01[0]) / widths[0]
+ (g01[1]-g00[1] + g11[1]-g10[1]) / widths[1]) * 0.5;
} else if (nd == 3) {
cvm::real gc[24]; // stores 3d gradients in 8 contiguous bins
int index = 0;
ix[0] = ix0[0] - 1;
for (i = 0; i<2; i++) {
ix[1] = ix0[1] - 1;
for (j = 0; j<2; j++) {
ix[2] = ix0[2] - 1;
for (k = 0; k<2; k++) {
get_grad(gc + index, ix);
index += 3;
ix[2]++;
}
ix[1]++;
}
ix[0]++;
}
divergence[linear_index] =
((gc[3*4]-gc[0] + gc[3*5]-gc[3*1] + gc[3*6]-gc[3*2] + gc[3*7]-gc[3*3])
/ widths[0]
+ (gc[3*2+1]-gc[0+1] + gc[3*3+1]-gc[3*1+1] + gc[3*6+1]-gc[3*4+1] + gc[3*7+1]-gc[3*5+1])
/ widths[1]
+ (gc[3*1+2]-gc[0+2] + gc[3*3+2]-gc[3*2+2] + gc[3*5+2]-gc[3*4+2] + gc[3*7+2]-gc[3*6+2])
/ widths[2]) * 0.25;
}
}
/// Multiplication by sparse matrix representing Laplacian
/// NOTE: Laplacian must be symmetric for solving with CG
void integrate_potential::atimes(const std::vector<cvm::real> &A, std::vector<cvm::real> &LA)
{
if (nd == 2) {
// DIMENSION 2
size_t index, index2;
int i, j;
cvm::real fact;
const cvm::real ffx = 1.0 / (widths[0] * widths[0]);
const cvm::real ffy = 1.0 / (widths[1] * widths[1]);
const int h = nx[1];
const int w = nx[0];
// offsets for 4 reference points of the Laplacian stencil
int xm = -h;
int xp = h;
int ym = -1;
int yp = 1;
// NOTE on performance: this version is slightly sub-optimal because
// it contains two double loops on the core of the array (for x and y terms)
// The slightly faster version is in commit 0254cb5a2958cb2e135f268371c4b45fad34866b
// yet it is much uglier, and probably horrible to extend to dimension 3
// All terms in the matrix are assigned (=) during the x loops, then updated (+=)
// with the y (and z) contributions
// All x components except on x edges
index = h; // Skip first column
// Halve the term on y edges (if any) to preserve symmetry of the Laplacian matrix
// (Long Chen, Finite Difference Methods, UCI, 2017)
fact = periodic[1] ? 1.0 : 0.5;
for (i=1; i<w-1; i++) {
// Full range of j, but factor may change on y edges (j == 0 and j == h-1)
LA[index] = fact * ffx * (A[index + xm] + A[index + xp] - 2.0 * A[index]);
index++;
for (j=1; j<h-1; j++) {
LA[index] = ffx * (A[index + xm] + A[index + xp] - 2.0 * A[index]);
index++;
}
LA[index] = fact * ffx * (A[index + xm] + A[index + xp] - 2.0 * A[index]);
index++;
}
// Edges along x (x components only)
index = 0L; // Follows left edge
index2 = h * static_cast<size_t>(w - 1); // Follows right edge
if (periodic[0]) {
xm = h * (w - 1);
xp = h;
fact = periodic[1] ? 1.0 : 0.5;
LA[index] = fact * ffx * (A[index + xm] + A[index + xp] - 2.0 * A[index]);
LA[index2] = fact * ffx * (A[index2 - xp] + A[index2 - xm] - 2.0 * A[index2]);
index++;
index2++;
for (j=1; j<h-1; j++) {
LA[index] = ffx * (A[index + xm] + A[index + xp] - 2.0 * A[index]);
LA[index2] = ffx * (A[index2 - xp] + A[index2 - xm] - 2.0 * A[index2]);
index++;
index2++;
}
LA[index] = fact * ffx * (A[index + xm] + A[index + xp] - 2.0 * A[index]);
LA[index2] = fact * ffx * (A[index2 - xp] + A[index2 - xm] - 2.0 * A[index2]);
} else {
xm = -h;
xp = h;
fact = periodic[1] ? 1.0 : 0.5; // Halve in corners in full PBC only
// lower corner, "j == 0"
LA[index] = fact * ffx * (A[index + xp] - A[index]);
LA[index2] = fact * ffx * (A[index2 + xm] - A[index2]);
index++;
index2++;
for (j=1; j<h-1; j++) {
// x gradient (+ y term of laplacian, calculated below)
LA[index] = ffx * (A[index + xp] - A[index]);
LA[index2] = ffx * (A[index2 + xm] - A[index2]);
index++;
index2++;
}
// upper corner, j == h-1
LA[index] = fact * ffx * (A[index + xp] - A[index]);
LA[index2] = fact * ffx * (A[index2 + xm] - A[index2]);
}
// Now adding all y components
// All y components except on y edges
index = 1; // Skip first element (in first row)
fact = periodic[0] ? 1.0 : 0.5; // for i == 0
for (i=0; i<w; i++) {
// Factor of 1/2 on x edges if non-periodic
if (i == 1) fact = 1.0;
if (i == w - 1) fact = periodic[0] ? 1.0 : 0.5;
for (j=1; j<h-1; j++) {
LA[index] += fact * ffy * (A[index + ym] + A[index + yp] - 2.0 * A[index]);
index++;
}
index += 2; // skip the edges and move to next column
}
// Edges along y (y components only)
index = 0L; // Follows bottom edge
index2 = h - 1; // Follows top edge
if (periodic[1]) {
fact = periodic[0] ? 1.0 : 0.5;
ym = h - 1;
yp = 1;
LA[index] += fact * ffy * (A[index + ym] + A[index + yp] - 2.0 * A[index]);
LA[index2] += fact * ffy * (A[index2 - yp] + A[index2 - ym] - 2.0 * A[index2]);
index += h;
index2 += h;
for (i=1; i<w-1; i++) {
LA[index] += ffy * (A[index + ym] + A[index + yp] - 2.0 * A[index]);
LA[index2] += ffy * (A[index2 - yp] + A[index2 - ym] - 2.0 * A[index2]);
index += h;
index2 += h;
}
LA[index] += fact * ffy * (A[index + ym] + A[index + yp] - 2.0 * A[index]);
LA[index2] += fact * ffy * (A[index2 - yp] + A[index2 - ym] - 2.0 * A[index2]);
} else {
ym = -1;
yp = 1;
fact = periodic[0] ? 1.0 : 0.5; // Halve in corners in full PBC only
// Left corner
LA[index] += fact * ffy * (A[index + yp] - A[index]);
LA[index2] += fact * ffy * (A[index2 + ym] - A[index2]);
index += h;
index2 += h;
for (i=1; i<w-1; i++) {
// y gradient (+ x term of laplacian, calculated above)
LA[index] += ffy * (A[index + yp] - A[index]);
LA[index2] += ffy * (A[index2 + ym] - A[index2]);
index += h;
index2 += h;
}
// Right corner
LA[index] += fact * ffy * (A[index + yp] - A[index]);
LA[index2] += fact * ffy * (A[index2 + ym] - A[index2]);
}
} else if (nd == 3) {
// DIMENSION 3
int i, j, k;
size_t index, index2;
cvm::real fact = 1.0;
const cvm::real ffx = 1.0 / (widths[0] * widths[0]);
const cvm::real ffy = 1.0 / (widths[1] * widths[1]);
const cvm::real ffz = 1.0 / (widths[2] * widths[2]);
const int h = nx[2]; // height
const int d = nx[1]; // depth
const int w = nx[0]; // width
// offsets for 6 reference points of the Laplacian stencil
int xm = -d * h;
int xp = d * h;
int ym = -h;
int yp = h;
int zm = -1;
int zp = 1;
cvm::real factx = periodic[0] ? 1 : 0.5; // factor to be applied on x edges
cvm::real facty = periodic[1] ? 1 : 0.5; // same for y
cvm::real factz = periodic[2] ? 1 : 0.5; // same for z
cvm::real ifactx = 1 / factx;
cvm::real ifacty = 1 / facty;
cvm::real ifactz = 1 / factz;
// All x components except on x edges
index = d * static_cast<size_t>(h); // Skip left slab
fact = facty * factz;
for (i=1; i<w-1; i++) {
for (j=0; j<d; j++) { // full range of y
if (j == 1) fact *= ifacty;
if (j == d-1) fact *= facty;
LA[index] = fact * ffx * (A[index + xm] + A[index + xp] - 2.0 * A[index]);
index++;
fact *= ifactz;
for (k=1; k<h-1; k++) { // full range of z
LA[index] = fact * ffx * (A[index + xm] + A[index + xp] - 2.0 * A[index]);
index++;
}
fact *= factz;
LA[index] = fact * ffx * (A[index + xm] + A[index + xp] - 2.0 * A[index]);
index++;
}
}
// Edges along x (x components only)
index = 0L; // Follows left slab
index2 = static_cast<size_t>(d) * h * (w - 1); // Follows right slab
if (periodic[0]) {
xm = d * h * (w - 1);
xp = d * h;
fact = facty * factz;
for (j=0; j<d; j++) {
if (j == 1) fact *= ifacty;
if (j == d-1) fact *= facty;
LA[index] = fact * ffx * (A[index + xm] + A[index + xp] - 2.0 * A[index]);
LA[index2] = fact * ffx * (A[index2 - xp] + A[index2 - xm] - 2.0 * A[index2]);
index++;
index2++;
fact *= ifactz;
for (k=1; k<h-1; k++) {
LA[index] = fact * ffx * (A[index + xm] + A[index + xp] - 2.0 * A[index]);
LA[index2] = fact * ffx * (A[index2 - xp] + A[index2 - xm] - 2.0 * A[index2]);
index++;
index2++;
}
fact *= factz;
LA[index] = fact * ffx * (A[index + xm] + A[index + xp] - 2.0 * A[index]);
LA[index2] = fact * ffx * (A[index2 - xp] + A[index2 - xm] - 2.0 * A[index2]);
index++;
index2++;
}
} else {
xm = -d * h;
xp = d * h;
fact = facty * factz;
for (j=0; j<d; j++) {
if (j == 1) fact *= ifacty;
if (j == d-1) fact *= facty;
LA[index] = fact * ffx * (A[index + xp] - A[index]);
LA[index2] = fact * ffx * (A[index2 + xm] - A[index2]);
index++;
index2++;
fact *= ifactz;
for (k=1; k<h-1; k++) {
// x gradient (+ y, z terms of laplacian, calculated below)
LA[index] = fact * ffx * (A[index + xp] - A[index]);
LA[index2] = fact * ffx * (A[index2 + xm] - A[index2]);
index++;
index2++;
}
fact *= factz;
LA[index] = fact * ffx * (A[index + xp] - A[index]);
LA[index2] = fact * ffx * (A[index2 + xm] - A[index2]);
index++;
index2++;
}
}
// Now adding all y components
// All y components except on y edges
index = h; // Skip first column (in front slab)
fact = factx * factz;
for (i=0; i<w; i++) { // full range of x
if (i == 1) fact *= ifactx;
if (i == w-1) fact *= factx;
for (j=1; j<d-1; j++) {
LA[index] += fact * ffy * (A[index + ym] + A[index + yp] - 2.0 * A[index]);
index++;
fact *= ifactz;
for (k=1; k<h-1; k++) {
LA[index] += fact * ffy * (A[index + ym] + A[index + yp] - 2.0 * A[index]);
index++;
}
fact *= factz;
LA[index] += fact * ffy * (A[index + ym] + A[index + yp] - 2.0 * A[index]);
index++;
}
index += 2 * h; // skip columns in front and back slabs
}
// Edges along y (y components only)
index = 0L; // Follows front slab
index2 = h * static_cast<size_t>(d - 1); // Follows back slab
if (periodic[1]) {
ym = h * (d - 1);
yp = h;
fact = factx * factz;
for (i=0; i<w; i++) {
if (i == 1) fact *= ifactx;
if (i == w-1) fact *= factx;
LA[index] += fact * ffy * (A[index + ym] + A[index + yp] - 2.0 * A[index]);
LA[index2] += fact * ffy * (A[index2 - yp] + A[index2 - ym] - 2.0 * A[index2]);
index++;
index2++;
fact *= ifactz;
for (k=1; k<h-1; k++) {
LA[index] += fact * ffy * (A[index + ym] + A[index + yp] - 2.0 * A[index]);
LA[index2] += fact * ffy * (A[index2 - yp] + A[index2 - ym] - 2.0 * A[index2]);
index++;
index2++;
}
fact *= factz;
LA[index] += fact * ffy * (A[index + ym] + A[index + yp] - 2.0 * A[index]);
LA[index2] += fact * ffy * (A[index2 - yp] + A[index2 - ym] - 2.0 * A[index2]);
index++;
index2++;
index += h * static_cast<size_t>(d - 1);
index2 += h * static_cast<size_t>(d - 1);
}
} else {
ym = -h;
yp = h;
fact = factx * factz;
for (i=0; i<w; i++) {
if (i == 1) fact *= ifactx;
if (i == w-1) fact *= factx;
LA[index] += fact * ffy * (A[index + yp] - A[index]);
LA[index2] += fact * ffy * (A[index2 + ym] - A[index2]);
index++;
index2++;
fact *= ifactz;
for (k=1; k<h-1; k++) {
// y gradient (+ x, z terms of laplacian, calculated above and below)
LA[index] += fact * ffy * (A[index + yp] - A[index]);
LA[index2] += fact * ffy * (A[index2 + ym] - A[index2]);
index++;
index2++;
}
fact *= factz;
LA[index] += fact * ffy * (A[index + yp] - A[index]);
LA[index2] += fact * ffy * (A[index2 + ym] - A[index2]);
index++;
index2++;
index += h * static_cast<size_t>(d - 1);
index2 += h * static_cast<size_t>(d - 1);
}
}
// Now adding all z components
// All z components except on z edges
index = 1; // Skip first element (in bottom slab)
fact = factx * facty;
for (i=0; i<w; i++) { // full range of x
if (i == 1) fact *= ifactx;
if (i == w-1) fact *= factx;
for (k=1; k<h-1; k++) {
LA[index] += fact * ffz * (A[index + zm] + A[index + zp] - 2.0 * A[index]);
index++;
}
fact *= ifacty;
index += 2; // skip edge slabs
for (j=1; j<d-1; j++) { // full range of y
for (k=1; k<h-1; k++) {
LA[index] += fact * ffz * (A[index + zm] + A[index + zp] - 2.0 * A[index]);
index++;
}
index += 2; // skip edge slabs
}
fact *= facty;
for (k=1; k<h-1; k++) {
LA[index] += fact * ffz * (A[index + zm] + A[index + zp] - 2.0 * A[index]);
index++;
}
index += 2; // skip edge slabs
}
// Edges along z (z components onlz)
index = 0; // Follows bottom slab
index2 = h - 1; // Follows top slab
if (periodic[2]) {
zm = h - 1;
zp = 1;
fact = factx * facty;
for (i=0; i<w; i++) {
if (i == 1) fact *= ifactx;
if (i == w-1) fact *= factx;
LA[index] += fact * ffz * (A[index + zm] + A[index + zp] - 2.0 * A[index]);
LA[index2] += fact * ffz * (A[index2 - zp] + A[index2 - zm] - 2.0 * A[index2]);
index += h;
index2 += h;
fact *= ifacty;
for (j=1; j<d-1; j++) {
LA[index] += fact * ffz * (A[index + zm] + A[index + zp] - 2.0 * A[index]);
LA[index2] += fact * ffz * (A[index2 - zp] + A[index2 - zm] - 2.0 * A[index2]);
index += h;
index2 += h;
}
fact *= facty;
LA[index] += fact * ffz * (A[index + zm] + A[index + zp] - 2.0 * A[index]);
LA[index2] += fact * ffz * (A[index2 - zp] + A[index2 - zm] - 2.0 * A[index2]);
index += h;
index2 += h;
}
} else {
zm = -1;
zp = 1;
fact = factx * facty;
for (i=0; i<w; i++) {
if (i == 1) fact *= ifactx;
if (i == w-1) fact *= factx;
LA[index] += fact * ffz * (A[index + zp] - A[index]);
LA[index2] += fact * ffz * (A[index2 + zm] - A[index2]);
index += h;
index2 += h;
fact *= ifacty;
for (j=1; j<d-1; j++) {
// z gradient (+ x, y terms of laplacian, calculated above)
LA[index] += fact * ffz * (A[index + zp] - A[index]);
LA[index2] += fact * ffz * (A[index2 + zm] - A[index2]);
index += h;
index2 += h;
}
fact *= facty;
LA[index] += fact * ffz * (A[index + zp] - A[index]);
LA[index2] += fact * ffz * (A[index2 + zm] - A[index2]);
index += h;
index2 += h;
}
}
}
}
/*
/// Inversion of preconditioner matrix (e.g. diagonal of the Laplacian)
void integrate_potential::asolve(const std::vector<cvm::real> &b, std::vector<cvm::real> &x)
{
for (size_t i=0; i<int(nt); i++) {
x[i] = b[i] * inv_lap_diag[i]; // Jacobi preconditioner - little benefit in tests so far
}
return;
}*/
// b : RHS of equation
// x : initial guess for the solution; output is solution
// itol : convergence criterion
void integrate_potential::nr_linbcg_sym(const std::vector<cvm::real> &b, std::vector<cvm::real> &x, const cvm::real &tol,
const int itmax, int &iter, cvm::real &err)
{
cvm::real ak,akden,bk,bkden,bknum,bnrm;
const cvm::real EPS=1.0e-14;
int j;
std::vector<cvm::real> p(nt), r(nt), z(nt);
iter=0;
atimes(x,r);
for (j=0;j<int(nt);j++) {
r[j]=b[j]-r[j];
}
bnrm=l2norm(b);
if (bnrm < EPS) {
return; // Target is zero, will break relative error calc
}
// asolve(r,z); // precon
bkden = 1.0;
while (iter < itmax) {
++iter;
for (bknum=0.0,j=0;j<int(nt);j++) {
bknum += r[j]*r[j]; // precon: z[j]*r[j]
}
if (iter == 1) {
for (j=0;j<int(nt);j++) {
p[j] = r[j]; // precon: p[j] = z[j]
}
} else {
bk=bknum/bkden;
for (j=0;j<int(nt);j++) {
p[j] = bk*p[j] + r[j]; // precon: bk*p[j] + z[j]
}
}
bkden = bknum;
atimes(p,z);
for (akden=0.0,j=0;j<int(nt);j++) {
akden += z[j]*p[j];
}
ak = bknum/akden;
for (j=0;j<int(nt);j++) {
x[j] += ak*p[j];
r[j] -= ak*z[j];
}
// asolve(r,z); // precon
err = l2norm(r)/bnrm;
if (cvm::debug())
std::cout << "iter=" << std::setw(4) << iter+1 << std::setw(12) << err << std::endl;
if (err <= tol)
break;
}
}
cvm::real integrate_potential::l2norm(const std::vector<cvm::real> &x)
{
size_t i;
cvm::real sum = 0.0;
for (i=0;i<x.size();i++)
sum += x[i]*x[i];
return sqrt(sum);
}