int lua_random_Float32(lua_State *L) {
RandomLib::Random *s = Glue<RandomLib::Random>::checkto(L, 1);
lua::push<double>(L, s->Fixed());
return 1;
}
void WangLandau_Algorithm_bean_2D(float** ptrMatrix, float* Matrix, std::vector<double>& Histogram, std::vector<double>& Histogram2, std::vector<double>& DOS, Lattice lattice, Bean bean, RandomLib::Random& r, double* ptrf, double const InitialDOS, double const end_f, double const average_frac, double const J, double const h, int const mcycle)
{
int k, p, z, g, average;
double H_tot_trial, H_in, H_fin, gE1, gE2;
int M = lattice.get_M();
int N = lattice.get_N();
double H_tot = ising_total_hamiltonian_2D(ptrMatrix,lattice,J,h);
initialiseHistogram_bean(Histogram, DOS);
initialiseHistogram_bean(Histogram2, DOS);
/*
std::cout<<"DEBUG3: DOS size: "<<DOS.size()<< std::endl;
std::cout<<"DEBUG3: Histogram size: "<<Histogram.size()<< std::endl;
*/
while(*ptrf >= end_f)
{
bool LOOP = true;
//std::cout<<"DEBUG Outer while loop"<<std::endl;
while(LOOP == true)
{
//std::cout<<"DEBUG inner while loop"<<std::endl;
for(z = 0; z < mcycle; z++)
{
//the inner loop is necessary for the preconditioning
//select random node on the lattice Matrix
Matrix_samplepoint_2D(lattice, r, &k, &p);
H_in = ising_nodal_hamiltonian_2D(ptrMatrix, p, lattice, J, h);
//flip spin
Matrix[k] = - Matrix[k];
H_fin = ising_nodal_hamiltonian_2D(ptrMatrix, p, lattice, J, h);
H_tot_trial = H_tot - H_in + H_fin;
gE1 = get_DOS_bean(DOS, bean, H_tot);
gE2 = get_DOS_bean(DOS, bean, H_tot_trial);
if(gE2 <= gE1)
{
H_tot = H_tot_trial;
update_DOS_bean(DOS, Histogram, Histogram2, bean, ptrf, H_tot, InitialDOS);
update_EnergyHistogram_bean(Histogram, bean, H_tot);
}
else if (r.Fixed() <= exp( gE1 - gE2 ) )
{
H_tot = H_tot_trial;
update_DOS_bean(DOS, Histogram, Histogram2, bean, ptrf, H_tot, InitialDOS);
update_EnergyHistogram_bean(Histogram, bean, H_tot);
}
else
{
Matrix[k] = - Matrix[k];
update_DOS_bean(DOS, Histogram, Histogram2, bean, ptrf, H_tot, InitialDOS);
update_EnergyHistogram_bean(Histogram, bean, H_tot);
}
}
average = average_EnergyHistogram_bean(Histogram, DOS, InitialDOS, end_f);
LOOP = termination_EnergyHistogram_bean(Histogram, DOS, InitialDOS, average, average_frac, end_f);
}
initialiseHistogram_bean(Histogram, DOS);
evolve_update_function_sqrt(ptrf);
}
}
void sar(char *argv[], unsigned int seed)
{
RandomLib::Random rng;
rng.Reseed();
// List of variables for run conditions:
const int num_species = 50;
const int num_patches = 300;
const int num_step = 5000;
const int search_radius = 5;
// Species characteristics:
const double d = 0.5; // Species mean dispersal distance (shapes dispersal kernel): 1/d
const double m = 0.1; // Disturbance probability (disturbance kills individual)
const double niche_min = 0.1; // Competitive strength of empty cells
const double emi_from_out = 0.001; // Seed bank
array2Ddouble E;
array2Dint occupied;
array2Dint disturbance_counter;
SetArraySize2d(E, num_patches, num_patches);
SetArraySize2d(occupied, num_patches, num_patches);
SetArraySize2d(disturbance_counter, num_patches, num_patches);
// List of global species characteristic parameters (read in from file or defined in main function):
double *h = new double[num_species];
double *u = new double[num_species];
double *c = new double[num_species];
double *sigma = new double[num_species];
// read in species characteristics from file
char buffer[100];
sprintf(buffer, "%s_st.txt", argv[1]);
std::ifstream input_traits(buffer);
for (int i = 0; i < num_species; ++i)
{
input_traits >> sigma[i]; // Niche width
input_traits >> h[i]; // Performance at niche optimum (height, y at maximum)
input_traits >> c[i]; // Seed production
input_traits >> u[i]; // Resource at niche optimum (location, x at maximum)
}
input_traits.close();
sprintf(buffer, "%s_species.txt", argv[1]);
std::ifstream input_species(buffer);
{
int i = 0, j = 0;
while (!input_species.eof())
{
input_species >> i;
input_species >> j;
input_species >> E[i][j];
input_species >> occupied[i][j];
input_species >> disturbance_counter[i][j];
}
}
input_species.close();
for (int block = 0; block < 1; ++block)
{
sprintf(buffer, "%s_dest%d.txt", argv[1], block);
std::ifstream input_dest(buffer);
for (int i = 0; i < num_patches; ++i)
{
for (int j = 0; j < num_patches; ++j)
{
disturbance_counter[i][j] = 0;
// occupied[i][j] = 0;
}
}
// Removed the input_dest as a matrix and replaced by a list (see next loop).
// for (int i = 0; i < num_patches; ++i)
// {
// for (int j = 0; j < num_patches; ++j)
// {
// input_dest >> E[i][j];
// }
// }
// The input file is now x,y,E with 90000 lines
{
int i = 0, j = 0;
while (!input_dest.eof())
{
input_dest >> i;
input_dest >> j;
input_dest >> E[i][j];
}
}
input_dest.close();
// for (int x = 0; x < num_patches; x++)
// {
// for (int y = 0; y < num_patches; y++)
// {
// const int s = (int)(rng.Fixed() * num_species); // Select a species at random
// occupied[x][y] = NICHE_F(h[s], u[s], E[x][y], sigma[s]) < niche_min ? EMPTY : s;
// }
// }
double all_possible_seeds = 0;
for (int dx = -search_radius; dx <= search_radius; dx++)
{
for (int dy = -search_radius; dy <= search_radius; dy++)
{
if ((dx != 0) || (dy != 0))
{
all_possible_seeds += exp(-d * sqrt(dx * dx + dy * dy));
}
}
}
all_possible_seeds += emi_from_out * num_species;
for (int t = 0; t < num_step; ++t)
{
for (int cell = 0; cell < num_patches * num_patches; cell++)
{
const int x = (int)(rng.Fixed() * num_patches);
const int y = (int)(rng.Fixed() * num_patches);
if(rng.Fixed() < m)
{
occupied[x][y] = EMPTY;
++disturbance_counter[x][y]; // BR
}
double Niche_res = niche_min;
if (occupied[x][y] != EMPTY)
{
int i = occupied[x][y];
Niche_res = NICHE_F(h[i], u[i], E[x][y], sigma[i]);
}
std::vector<double> Seed(num_species, 0.0);
const int dx_min = (x - search_radius < 0) ? -x : -search_radius;
const int dx_max = (x + search_radius >= num_patches) ? num_patches - 1 - x : search_radius;
const int dy_min = (y - search_radius < 0) ? -y : -search_radius;
const int dy_max = (y + search_radius >= num_patches) ? num_patches - 1 - y : search_radius;
assert(x + dx_min >= 0 && x + dx_max < num_patches);
assert(y + dy_min >= 0 && y + dy_max < num_patches);
for (int dx = dx_min; dx <= dx_max; ++dx)
{
for (int dy = dy_min; dy <= dy_max; ++dy)
{
if (((dx != 0) || (dy != 0)) && (occupied[x + dx][y + dy] != EMPTY))
{
int i = occupied[x + dx][y + dy];
if (NICHE_F(h[i], u[i], E[x][y], sigma[i]) > Niche_res)
{
Seed[i] += c[i] * (exp(-d * sqrt(dx * dx + dy * dy)));
}
}
}
}
double all_seeds = 0.0;
for (int i = 0; i < num_species; ++i)
{
if (NICHE_F(h[i], u[i], E[x][y], sigma[i]) > Niche_res)
{
Seed[i] += emi_from_out;
}
all_seeds += Seed[i];
}
bool total_colon = false;
if (all_seeds / all_possible_seeds > rng.Fixed())
{
total_colon = true;
}
std::vector<double> Prob_recruit(num_species, 0.0);
if (total_colon == true)
{
//Prob_recruit[0] = Colon[0]/double(total_colon);
Prob_recruit[0] = Seed[0] / all_seeds;
for (int i = 1; i < num_species; ++i)
{
Prob_recruit[i] = Seed[i] / all_seeds + Prob_recruit[i-1];
}
occupied[x][y] = EMPTY;
double randnumb = rng.Fixed();
for (int i = 0; i < num_species; i++)
{
if (randnumb < Prob_recruit[i])
{
assert(NICHE_F(h[i], u[i], E[x][y], sigma[i]) > niche_min);
occupied[x][y] = i;
break;
}
}
}
} // ends loop over cells
} // ends loop t
sprintf(buffer, "%s_out200_%d.txt", argv[1], block);
std::ofstream out(buffer);
for (int x = 0; x < num_patches; ++x)
{
for (int y = 0; y < num_patches; ++y)
{
out << x << " " << y << " " << E[x][y] << " " << occupied[x][y] << " " << disturbance_counter[x][y] << "\n";
}
}
out.close();
}
}
