// -*- 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 colvar_grid_count::colvar_grid_count() : colvar_grid() { mult = 1; } colvar_grid_count::colvar_grid_count(std::vector const &nx_i, size_t const &def_count) : colvar_grid(nx_i, def_count, 1) {} colvar_grid_count::colvar_grid_count(std::vector &colvars, size_t const &def_count, bool margin) : colvar_grid(colvars, def_count, 1, margin) {} colvar_grid_scalar::colvar_grid_scalar() : colvar_grid(), samples(NULL) {} colvar_grid_scalar::colvar_grid_scalar(colvar_grid_scalar const &g) : colvar_grid(g), samples(NULL) { } colvar_grid_scalar::colvar_grid_scalar(std::vector const &nx_i) : colvar_grid(nx_i, 0.0, 1), samples(NULL) { } colvar_grid_scalar::colvar_grid_scalar(std::vector &colvars, bool margin) : colvar_grid(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(), samples(NULL) {} colvar_grid_gradient::colvar_grid_gradient(std::vector const &nx_i) : colvar_grid(nx_i, 0.0, nx_i.size()), samples(NULL) {} colvar_grid_gradient::colvar_grid_gradient(std::vector &colvars) : colvar_grid(colvars, 0.0, colvars.size()), samples(NULL) {} void colvar_grid_gradient::write_1D_integral(std::ostream &os) { cvm::real bin, min, integral; std::vector 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 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 &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) { 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..."); // inv_lap_diag.resize(nt); // std::vector 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."); } } 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 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)); } else { cvm::error("Cannot integrate PMF in dimension > 3\n"); } return iter; } void integrate_potential::set_div() { if (nd == 1) return; for (std::vector ix = new_index(); index_ok(ix); incr(ix)) { update_div_local(ix); } } void integrate_potential::update_div_neighbors(const std::vector &ix0) { std::vector 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 &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 &ix0) { const int linear_index = address(ix0); int i, j, k; std::vector 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 &A, std::vector &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 &b, std::vector &x) { for (size_t i=0; i &b, std::vector &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 p(nt), r(nt), z(nt); iter=0; atimes(x,r); for (j=0;j