void QwtHistogram::loadDataFromMatrix() {
if (!d_matrix)
return;
int size = d_matrix->numRows() * d_matrix->numCols();
const double *data = d_matrix->matrixModel()->dataVector();
int n;
gsl_histogram *h;
if (d_autoBin) {
double min, max;
d_matrix->range(&min, &max);
d_begin = floor(min);
d_end = ceil(max);
d_bin_size = 1.0;
n = static_cast<int>(floor((d_end - d_begin) / d_bin_size));
if (!n)
return;
h = gsl_histogram_alloc(n);
if (!h)
return;
gsl_histogram_set_ranges_uniform(h, floor(min), ceil(max));
} else {
n = static_cast<int>((d_end - d_begin) / d_bin_size + 1);
if (!n)
return;
h = gsl_histogram_alloc(n);
if (!h)
return;
double *range = new double[n + 2];
for (int i = 0; i <= n + 1; i++)
range[i] = d_begin + i * d_bin_size;
gsl_histogram_set_ranges(h, range, n + 1);
delete[] range;
}
for (int i = 0; i < size; i++)
gsl_histogram_increment(h, data[i]);
QVarLengthArray<double> X(n), Y(n); // stores ranges (x) and bins (y)
for (int i = 0; i < n; i++) {
Y[i] = gsl_histogram_get(h, i);
double lower, upper;
gsl_histogram_get_range(h, i, &lower, &upper);
X[i] = lower;
}
setData(X.data(), Y.data(), n);
d_mean = gsl_histogram_mean(h);
d_standard_deviation = gsl_histogram_sigma(h);
d_min = gsl_histogram_min_val(h);
d_max = gsl_histogram_max_val(h);
gsl_histogram_free(h);
}
void QwtHistogram::initData(double *Y, int size)
{
if(size<2 || (size == 2 && Y[0] == Y[1]))
{//non valid histogram data
double x[2], y[2];
for (int i = 0; i<2; i++ ){
y[i] = 0;
x[i] = 0;
}
setData(x, y, 2);
return;
}
int n = 10;//default value
QVarLengthArray<double> x(n), y(n);//double x[n], y[n]; //store ranges (x) and bins (y)
gsl_histogram * h = gsl_histogram_alloc (n);
if (!h)
return;
gsl_vector *v;
v = gsl_vector_alloc (size);
for (int i = 0; i<size; i++ )
gsl_vector_set (v, i, Y[i]);
double min, max;
gsl_vector_minmax (v, &min, &max);
gsl_vector_free (v);
d_begin = floor(min);
d_end = ceil(max);
gsl_histogram_set_ranges_uniform (h, floor(min), ceil(max));
for (int i = 0; i<size; i++ )
gsl_histogram_increment (h, Y[i]);
for (int i = 0; i<n; i++ ){
y[i] = gsl_histogram_get (h, i);
double lower, upper;
gsl_histogram_get_range (h, i, &lower, &upper);
x[i] = lower;
}
setData(x.data(), y.data(), n);//setData(x, y, n);
d_bin_size = (d_end - d_begin)/(double)n;
d_autoBin = true;
d_mean = gsl_histogram_mean(h);
d_standard_deviation = gsl_histogram_sigma(h);
d_min = gsl_histogram_min_val(h);
d_max = gsl_histogram_max_val(h);
gsl_histogram_free (h);
}
/* ==== */
static void
output_dos(const gsl_histogram *x, const char T)
{
int i,fnlen;
FILE *dos_fp=NULL;
char *dos_fn=NULL, *lDoS_suffix="lDoS", *sDoS_suffix="sDoS";
char s[50];
double val,lo,hi;
sprintf(s,"%li",steps);
fnlen = strlen(out_prefix)+strlen(lDoS_suffix)+64;
dos_fn = (char*)calloc(fnlen,sizeof(char));
strcpy(dos_fn, out_prefix);
strcat(dos_fn, s);
strcat(dos_fn,".");
switch (T){
case 'l': /* output logarithmic g, usually during the calculation */
strcat(dos_fn, lDoS_suffix);
break;
case 's': /* output scaled g, eg for in-process convergence checks */
strcat(dos_fn, sDoS_suffix);
break;
default:
fprintf (stderr, "%s:%d output_dos(): No handler for type %c",
__FILE__, __LINE__, T);
exit(EXIT_FAILURE);
}
dos_fp = fopen(dos_fn, "w+");
fprintf(dos_fp, "# estimated DOS after %li steps\n",steps);
fprintf(dos_fp, "# sampling range: %6.2f -- %6.2f\n",
gsl_histogram_min(g),gsl_histogram_max(g));
fprintf(dos_fp, "# bin resolution: %g\n",wanglandau_opt.res);
/* loop over histogram g */
for (i=0;i<=maxbin;i++){
val = gsl_histogram_get(x,i);
if (val == 0.){continue;}
gsl_histogram_get_range(x,i,&lo,&hi);
fprintf(dos_fp,"%6.2f\t%20.6f\n",lo+(hi-lo)/2,val);
}
fclose(dos_fp);
free(dos_fn);
return;
}
QVector<QPointF> DistanceToAtom::histogram(int bins) {
QVector<QPointF> histogramVector;
if(!m_isValid) {
qFatal("DistanceToAtom is not valid. Run compute() first.");
exit(1);
}
float minValue = 1e90;
float maxValue = 0;
for(const float &val : m_values) {
if(val >= 0) {
minValue = std::min(minValue, (float)sqrt(val));
maxValue = std::max(maxValue, (float)sqrt(val));
}
}
gsl_histogram *hist = gsl_histogram_alloc (bins);
gsl_histogram_set_ranges_uniform (hist, minValue, maxValue);
for(const float &value : m_values) {
if(value >= 0) {
gsl_histogram_increment (hist, sqrt(value));
}
}
histogramVector.resize(bins);
for(int i=0; i<bins; i++) {
double upper, lower;
gsl_histogram_get_range(hist, i, &lower, &upper);
float middle = 0.5*(upper+lower);
histogramVector[i].setX(middle);
histogramVector[i].setY(gsl_histogram_get(hist,i));
}
gsl_histogram_free (hist);
return histogramVector;
}
bool FrequencyCountDialog::apply()
{
if (!d_col_values){
QMessageBox::critical(this, tr("QtiPlot - Error"),
tr("Not enough data points, operation aborted!"));
return false;
}
double from = boxStart->value();
double to = boxEnd->value();
if (from >= to){
QMessageBox::critical(this, tr("QtiPlot - Frequency input error"),
tr("Please enter frequency limits that satisfy: From < To !"));
boxEnd->setFocus();
return false;
}
int old_bins = d_bins;
double bin_size = boxStep->value();
d_bins = int((to - from)/bin_size + 1);
if (!d_bins)
return false;
ApplicationWindow *app = (ApplicationWindow *)parent();
if (!app)
return false;
if (!d_result_table){
d_result_table = app->newTable(30, 4, app->generateUniqueName(tr("Count"), true),
tr("Frequency count of %1").arg(d_col_name));
d_result_table->setColName(0, tr("BinCtr"));
d_result_table->setColName(1, tr("Count"));
d_result_table->setColName(2, tr("BinEnd"));
d_result_table->setColName(3, tr("Sum"));
d_result_table->showMaximized();
}
gsl_histogram *h = gsl_histogram_alloc(d_bins);
if (!h)
return false;
double *range = (double *) malloc((d_bins + 2)*sizeof(double));
if (!range)
return false;
for (int i = 0; i <= d_bins + 1; i++)
range[i] = from + i*bin_size;
gsl_histogram_set_ranges (h, range, d_bins + 1);
free(range);
int dataSize = d_col_values->size;
for (int i = 0; i < dataSize; i++ )
gsl_histogram_increment (h, gsl_vector_get(d_col_values, i));
if (d_bins > d_result_table->numRows())
d_result_table->setNumRows(d_bins);
for(int i = d_bins; i < old_bins; i++){
d_result_table->setText(i, 0, "");
d_result_table->setText(i, 1, "");
d_result_table->setText(i, 2, "");
d_result_table->setText(i, 3, "");
}
double sum = 0.0;
for (int i = 0; i<d_bins; i++ ){
double aux = gsl_histogram_get (h, i);
sum += aux;
double lower, upper;
gsl_histogram_get_range (h, i, &lower, &upper);
d_result_table->setCell(i, 0, 0.5*(lower + upper));
d_result_table->setCell(i, 1, aux);
d_result_table->setCell(i, 2, upper);
d_result_table->setCell(i, 3, sum);
}
return true;
}
void compute_global_autocorrelations(struct State *S)
/* Compute population rate autocorrelation */
{
struct Network *ntw = &S->ntw;
int n_neurons_sample = 100;
const size_t n_bins = 201;
const double max_lag = 100; /* maximal lag in ms */
double bin_width = 2 * max_lag / (double) n_bins; /* 2*max_lag */
char filename[100];
gsl_histogram *h = gsl_histogram_alloc(n_bins);
gsl_histogram_set_ranges_uniform(h, -max_lag, max_lag);
FILE *f;
struct Dynamic_Array poptrain;
size_t num_spikes_total = 0;
for (int i = 0; i < n_neurons_sample; i++)
num_spikes_total += ntw->cell[i].spike_train.n;
poptrain.data = emalloc(num_spikes_total * sizeof(double));
poptrain.n = 0;
poptrain.size = num_spikes_total;
report("Computing global (population rate) autocorrelation...\n");
fill_population_spike_train(S, &poptrain, n_neurons_sample);
gsl_sort(poptrain.data, 1, num_spikes_total);
sprintf(filename, "global_autocorrelation_%s", S->sim.suffix);
double t_sp;
f = fopen(filename, "w");
size_t l = 0;
size_t r = 0;
for (size_t j = 0; j < poptrain.n; j++) {
t_sp = poptrain.data[j];
/* look for left index */
while (l < poptrain.n && poptrain.data[l] < t_sp - max_lag)
l++;
/* look for right index */
while (r < poptrain.n && poptrain.data[r] < t_sp + max_lag)
r++;
/* And feed the histogram with the relevant spikes */
for (size_t k = l; k < r; k++)
gsl_histogram_increment(h, t_sp - poptrain.data[k]);
}
/* gsl_histogram_fprintf(f, h, "%g", "%g"); */
/* correct for boundary effects and substract mean */
double w;
double T = S->sim.total_time - S->sim.offset;
/* double nu = num_spikes_total / (T * (double) n_neurons_sample); */
double lower, upper, ac;
int status;
for (size_t j = 0; j < n_bins; j++) {
w = T - fabs( (n_bins - 1.0) / 2.0 - j ) * bin_width;
ac = gsl_histogram_get(h, j) / (w * n_neurons_sample);
ac -= (pow(num_spikes_total / (T * n_neurons_sample), 2) * bin_width);
/* ac /= pow(nu * bin_width, 2); [> Normalization <] */
status = gsl_histogram_get_range(h, j, &lower, &upper);
if (status==0) {
fprintf(f, "% 9.4f % 9.4f % 9.6f\n", lower, upper, ac);
} else {
report("Somehting wrong here.\n");
exit(2);
}
}
fclose(f);
free(poptrain.data);
gsl_histogram_free(h);
}
void compute_average_autocorrelations(struct State *S)
/* Compute spike-time autocorrelation */
{
struct Network *ntw = &S->ntw;
struct Dynamic_Array *nrn_train;
int n_neurons_sample = 1000;
size_t num_spikes_total = 0;
const size_t n_bins = 201;
const double max_lag = 50; /* in ms */
double bin_width = 2 * max_lag / (double) n_bins;
char filename[100];
gsl_histogram *h = gsl_histogram_alloc(n_bins);
gsl_histogram_set_ranges_uniform(h, -max_lag, max_lag);
FILE *f;
report("Computing average autocorrelation...\n");
sprintf(filename, "autocorrelation_%s", S->sim.suffix);
double t_sp;
size_t l, r;
f = fopen(filename, "w");
for (int i = 0; i < n_neurons_sample; i++) {
l = 0;
r = 0;
nrn_train = &ntw->cell[i].spike_train;
num_spikes_total += nrn_train->n;
for (size_t j = 0; j < nrn_train->n; j++) {
t_sp = nrn_train->data[j];
/* look for left index */
while (l < nrn_train->n && nrn_train->data[l] < t_sp - max_lag)
l++;
/* look for right index */
while (r < nrn_train->n && nrn_train->data[r] < t_sp + max_lag)
r++;
/* And feed the histogram with the relevant spikes */
for (size_t k = l; k < r; k++)
gsl_histogram_increment(h, t_sp - nrn_train->data[k]);
}
}
/* gsl_histogram_fprintf(f, h, "%g", "%g"); */
/* correct for boundary effects and substract mean */
double w;
double T = S->sim.total_time - S->sim.offset;
/* double nu = num_spikes_total / (T * (double) n_neurons_sample); */
double lower, upper, ac;
int status;
for (size_t j = 0; j < n_bins; j++) {
w = T - fabs( (n_bins - 1.0) / 2.0 - j ) * bin_width;
ac = gsl_histogram_get(h, j) / (w * n_neurons_sample);
ac -= (pow(num_spikes_total / (T * n_neurons_sample), 2) * bin_width);
/* ac /= pow(nu * bin_width, 2); [> Normalization <] */
status = gsl_histogram_get_range(h, j, &lower, &upper);
if (status==0) {
fprintf(f, "% 9.4f % 9.4f % 9.7f\n", lower, upper, ac);
} else {
report("Something wrong here.\n");
exit(2);
}
}
fclose(f);
gsl_histogram_free(h);
}
void
test1d_resample (void)
{
size_t i;
int status = 0;
gsl_histogram *h;
gsl_ieee_env_setup ();
h = gsl_histogram_calloc_uniform (10, 0.0, 1.0);
gsl_histogram_increment (h, 0.1);
gsl_histogram_increment (h, 0.2);
gsl_histogram_increment (h, 0.2);
gsl_histogram_increment (h, 0.3);
{
gsl_histogram_pdf *p = gsl_histogram_pdf_alloc (10);
gsl_histogram *hh = gsl_histogram_calloc_uniform (100, 0.0, 1.0);
gsl_histogram_pdf_init (p, h);
for (i = 0; i < 100000; i++)
{
double u = urand();
double x = gsl_histogram_pdf_sample (p, u);
gsl_histogram_increment (hh, x);
}
for (i = 0; i < 100; i++)
{
double y = gsl_histogram_get (hh, i) / 2500;
double x, xmax;
size_t k;
double ya;
gsl_histogram_get_range (hh, i, &x, &xmax);
gsl_histogram_find (h, x, &k);
ya = gsl_histogram_get (h, k);
if (ya == 0)
{
if (y != 0)
{
printf ("%d: %g vs %g\n", (int) i, y, ya);
status = 1;
}
}
else
{
double err = 1 / sqrt (gsl_histogram_get (hh, i));
double sigma = fabs ((y - ya) / (ya * err));
if (sigma > 3)
{
status = 1;
printf ("%g vs %g err=%g sigma=%g\n", y, ya, err, sigma);
}
}
}
gsl_histogram_pdf_free (p) ;
gsl_histogram_free (hh);
gsl_test (status, "gsl_histogram_pdf_sample within statistical errors");
}
gsl_histogram_free (h);
}
void test_normal()
{
gen g_uni[] = {builtin, devurandom, kalkulator, mersenne,
#if N <= (1 << 12)
randorg,
#endif
0};
test t[] = {chisq, kolsmir, 0};
task* prob = task_new(0, g_uni, t, 0 );
gsl_histogram* h = gsl_histogram_alloc(B);
gsl_histogram_set_ranges_uniform(h, 0, 1);
gsl_histogram_shift(h, AVG);
prob->density = h;
gsl_histogram2d* hd = gsl_histogram2d_alloc(B2, B2);
gsl_histogram2d_set_ranges_uniform(hd, 0, 1, 0, 1);
gsl_histogram2d_shift(hd, AVG2/2);
prob->density2d = hd;
prob->min = 0;
prob->max = 1;
task_perform(prob);
task_free(prob);
gen g_gauss[] = {gauss_bm, gauss_zig, gauss_ratio, 0};
prob = task_new(2, g_gauss, t, 0);
prob->max = 3;
prob->min = -prob->max;
h = gsl_histogram_alloc(B);
gsl_histogram_set_ranges_uniform(h, prob->min, prob->max);
int j;
for (j=0; j<B; ++j)
{
double min, max;
gsl_histogram_get_range(h, j, &min, &max);
double x = (max + min)/2;
gsl_histogram_accumulate(h, x, N*gsl_ran_ugaussian_pdf(x) / B * (prob->max - prob->min));
}
hd = gsl_histogram2d_alloc(B2, B2);
gsl_histogram2d_set_ranges_uniform(hd, prob->min, prob->max, prob->min, prob->max);
int k;
for (j=0; j < B2; ++j)
for (k=0; k<B2; ++k)
{
double min, max;
gsl_histogram2d_get_xrange(hd, j, &min, &max);
double x = (max + min)/2;
gsl_histogram2d_get_yrange(hd, k, &min, &max);
double y = (max + min)/2;
gsl_histogram2d_accumulate(hd, x, y, gsl_ran_ugaussian_pdf(x) * gsl_ran_ugaussian_pdf(y));
}
gsl_histogram2d_scale( hd, N/2/gsl_histogram2d_sum(hd) );
prob->density = h;
prob->density2d = hd;
task_perform(prob);
task_free(prob);
}
void QwtHistogram::loadData()
{
if (d_matrix){
loadDataFromMatrix();
return;
}
int r = abs(d_end_row - d_start_row) + 1;
QVarLengthArray<double> Y(r);
int ycol = d_table->colIndex(title().text());
int size = 0;
for (int i = 0; i<r; i++ ){
QString yval = d_table->text(i, ycol);
if (!yval.isEmpty()){
bool valid_data = true;
Y[size] = ((Graph *)plot())->locale().toDouble(yval, &valid_data);
if (valid_data)
size++;
}
}
if(size < 2 || (size==2 && Y[0] == Y[1])){//non valid histogram
double X[2];
Y.resize(2);
for (int i = 0; i<2; i++ ){
Y[i] = 0;
X[i] = 0;
}
setData(X, Y.data(), 2);
return;
}
int n;
gsl_histogram *h;
if (d_autoBin){
n = 10;
h = gsl_histogram_alloc (n);
if (!h)
return;
gsl_vector *v = gsl_vector_alloc (size);
for (int i = 0; i<size; i++ )
gsl_vector_set (v, i, Y[i]);
double min, max;
gsl_vector_minmax (v, &min, &max);
gsl_vector_free (v);
d_begin = floor(min);
d_end = ceil(max);
d_bin_size = (d_end - d_begin)/(double)n;
gsl_histogram_set_ranges_uniform (h, floor(min), ceil(max));
} else {
n = int((d_end - d_begin)/d_bin_size + 1);
h = gsl_histogram_alloc (n);
if (!h)
return;
double *range = new double[n+2];
for (int i = 0; i<= n+1; i++ )
range[i] = d_begin + i*d_bin_size;
gsl_histogram_set_ranges (h, range, n+1);
delete[] range;
}
for (int i = 0; i<size; i++ )
gsl_histogram_increment (h, Y[i]);
#ifdef Q_CC_MSVC
QVarLengthArray<double> X(n); //stores ranges (x) and bins (y)
#else
double X[n]; //stores ranges (x) and bins (y)
#endif
Y.resize(n);
for (int i = 0; i<n; i++ ){
Y[i] = gsl_histogram_get (h, i);
double lower, upper;
gsl_histogram_get_range (h, i, &lower, &upper);
X[i] = lower;
}
#ifdef Q_CC_MSVC
setData(X.data(), Y.data(), n);
#else
setData(X, Y.data(), n);
#endif
d_mean = gsl_histogram_mean(h);
d_standard_deviation = gsl_histogram_sigma(h);
d_min = gsl_histogram_min_val(h);
d_max = gsl_histogram_max_val(h);
gsl_histogram_free (h);
}
void
test1d_trap (void)
{
gsl_histogram *h;
double result, lower, upper;
size_t i;
gsl_set_error_handler (&my_error_handler);
gsl_ieee_env_setup ();
status = 0;
h = gsl_histogram_calloc (0);
gsl_test (!status, "gsl_histogram_calloc traps zero-length histogram");
gsl_test (h != 0,
"gsl_histogram_calloc returns NULL for zero-length histogram");
status = 0;
h = gsl_histogram_calloc_uniform (0, 0.0, 1.0);
gsl_test (!status,
"gsl_histogram_calloc_uniform traps zero-length histogram");
gsl_test (h != 0,
"gsl_histogram_calloc_uniform returns NULL for zero-length histogram");
status = 0;
h = gsl_histogram_calloc_uniform (10, 1.0, 1.0);
gsl_test (!status,
"gsl_histogram_calloc_uniform traps equal endpoints");
gsl_test (h != 0,
"gsl_histogram_calloc_uniform returns NULL for equal endpoints");
status = 0;
h = gsl_histogram_calloc_uniform (10, 2.0, 1.0);
gsl_test (!status,
"gsl_histogram_calloc_uniform traps invalid range");
gsl_test (h != 0,
"gsl_histogram_calloc_uniform returns NULL for invalid range");
h = gsl_histogram_calloc_uniform (N, 0.0, 1.0);
status = gsl_histogram_accumulate (h, 1.0, 10.0);
gsl_test (status != GSL_EDOM, "gsl_histogram_accumulate traps x at xmax");
status = gsl_histogram_accumulate (h, 2.0, 100.0);
gsl_test (status != GSL_EDOM, "gsl_histogram_accumulate traps x above xmax");
status = gsl_histogram_accumulate (h, -1.0, 1000.0);
gsl_test (status != GSL_EDOM, "gsl_histogram_accumulate traps x below xmin");
status = gsl_histogram_increment (h, 1.0);
gsl_test (status != GSL_EDOM, "gsl_histogram_increment traps x at xmax");
status = gsl_histogram_increment (h, 2.0);
gsl_test (status != GSL_EDOM, "gsl_histogram_increment traps x above xmax");
status = gsl_histogram_increment (h, -1.0);
gsl_test (status != GSL_EDOM, "gsl_histogram_increment traps x below xmin");
result = gsl_histogram_get (h, N);
gsl_test (result != 0, "gsl_histogram_get traps index at n");
result = gsl_histogram_get (h, N + 1);
gsl_test (result != 0, "gsl_histogram_get traps index above n");
status = gsl_histogram_get_range (h, N, &lower, &upper);
gsl_test (status != GSL_EDOM,
"gsl_histogram_get_range traps index at n");
status = gsl_histogram_get_range (h, N + 1, &lower, &upper);
gsl_test (status != GSL_EDOM,
"gsl_histogram_get_range traps index above n");
status = 0;
gsl_histogram_find (h, -0.01, &i);
gsl_test (status != GSL_EDOM, "gsl_histogram_find traps x below xmin");
status = 0;
gsl_histogram_find (h, 1.0, &i);
gsl_test (status != GSL_EDOM, "gsl_histogram_find traps x at xmax");
status = 0;
gsl_histogram_find (h, 1.1, &i);
gsl_test (status != GSL_EDOM, "gsl_histogram_find traps x above xmax");
gsl_histogram_free (h);
}
main (int argc,char *argv[])
{
int ia,ib,ic,id,it,inow,ineigh,icont;
int in,ia2,ia3,irun,icurrent,ORTOGONALFLAG;
int RP, P,L,N,NRUNS,next,sweep,SHOWFLAG;
double u,field1,field2,field0,q,aux1,aux2;
double alfa,aux,Q1,Q2,QZ,RZQ,rho,R;
double pm,D,wmax,mQ,wx,wy,h_sigma,h_mean;
double TOL,MINLOGF,E;
double DELTA;
double E_new,Ex,DeltaE,ER;
double EW,meanhist,hvalue,wE,aratio;
double logG_old,logG_new,lf;
size_t i_old,i_new;
long seed;
double lGvR,lGv,DlG;
size_t iL,iR,i1,i2;
int I_endpoint[NBINS];
double lower,upper;
size_t i0;
FILE * wlsrange;
FILE * dos;
FILE * thermodynamics;
FILE * canonical;
FILE * logfile;
//FILE * pajek;
//***********************************
// Help
//***********************************
if (argc<15){
help();
return(1);
}
else{
DELTA = atof(argv[1]);
P = atoi(argv[2]);
RP = atoi(argv[3]);
L = atoi(argv[4]);
N = atoi(argv[5]);
TOL = atof(argv[6]);
MINLOGF = atof(argv[7]);
}
wlsrange=fopen(argv[8],"w");
dos=fopen(argv[9],"w");
thermodynamics=fopen(argv[10],"w");
canonical=fopen(argv[11],"w");
logfile=fopen(argv[12],"w");
SHOWFLAG = atoi(argv[13]);
ORTOGONALFLAG = atoi(argv[14]);
if ((ORTOGONALFLAG==1) && (P>L)) P=L;
//maximum number of orthogonal issues
if (SHOWFLAG==1){
printf("# parameters are DELTA=%1.2f P=%d ",DELTA,P);
printf("D=%d L=%d M=%d TOL=%1.2f MINLOGF=%g \n",L,N,RP,TOL,MINLOGF);
}
fprintf(logfile,"# parameters are DELTA=%1.2f P=%d D=%d",DELTA,P,L);
fprintf(logfile,"L=%d M=%d TOL=%1.2f MINLOGF=%g\n",L,RP,TOL,MINLOGF);
//**********************************************************************
// Alocate matrices
//**********************************************************************
gsl_matrix * sociedade = gsl_matrix_alloc(SIZE,L);
gsl_matrix * issue = gsl_matrix_alloc(P,L);
gsl_vector * current_issue = gsl_vector_alloc(L);
gsl_vector * v0 = gsl_vector_alloc(L);
gsl_vector * v1 = gsl_vector_alloc(L);
gsl_vector * Z = gsl_vector_alloc(L);
gsl_vector * E_borda = gsl_vector_alloc(NBINS);
//**********************************************************************
// Inicialization
//**********************************************************************
const gsl_rng_type * T;
gsl_rng * r;
gsl_rng_env_setup();
T = gsl_rng_default;
r=gsl_rng_alloc (T);
seed = time (NULL) * getpid();
//seed = 13188839657852;
gsl_rng_set(r,seed);
igraph_t graph;
igraph_vector_t neighbors;
igraph_vector_t result;
igraph_vector_t dim_vector;
igraph_real_t res;
igraph_bool_t C;
igraph_vector_init(&neighbors,1000);
igraph_vector_init(&result,0);
igraph_vector_init(&dim_vector,DIMENSION);
for(ic=0;ic<DIMENSION;ic++) VECTOR(dim_vector)[ic]=N;
gsl_histogram * HE = gsl_histogram_alloc (NBINS);
gsl_histogram * logG = gsl_histogram_alloc (NBINS);
gsl_histogram * LG = gsl_histogram_alloc (NBINS);
//********************************************************************
// Social Graph
//********************************************************************
//Barabasi-Alberts network
igraph_barabasi_game(&graph,SIZE,RP,&result,1,0);
/* for (inow=0;inow<SIZE;inow++){
igraph_neighbors(&graph,&neighbors,inow,IGRAPH_OUT);
printf("%d ",inow);
for(ic=0;ic<igraph_vector_size(&neighbors);ic++)
{
ineigh=(int)VECTOR(neighbors)[ic];
printf("%d ",ineigh);
}
printf("\n");
}*/
//pajek=fopen("graph.xml","w");
// igraph_write_graph_graphml(&graph,pajek);
//igraph_write_graph_pajek(&graph, pajek);
//fclose(pajek);
//**********************************************************************
//Quenched issues set and Zeitgeist
//**********************************************************************
gsl_vector_set_zero(Z);
gera_config(Z,issue,P,L,r,1.0);
if (ORTOGONALFLAG==1) gsl_matrix_set_identity(issue);
for (ib=0;ib<P;ib++)
{
gsl_matrix_get_row(current_issue,issue,ib);
gsl_blas_ddot(current_issue,current_issue,&Q1);
gsl_vector_scale(current_issue,1/sqrt(Q1));
gsl_vector_add(Z,current_issue);
}
gsl_blas_ddot(Z,Z,&QZ);
gsl_vector_scale(Z,1/sqrt(QZ));
//**********************************************************************
// Ground state energy
//**********************************************************************
double E0;
gera_config(Z,sociedade,SIZE,L,r,0);
E0 = hamiltoneana(sociedade,issue,SIZE,L,P,DELTA,graph);
double EMIN=E0;
double EMAX=-E0;
double E_old=E0;
gsl_histogram_set_ranges_uniform (HE,EMIN,EMAX);
gsl_histogram_set_ranges_uniform (logG,EMIN,EMAX);
if (SHOWFLAG==1) printf("# ground state: %3.0f\n",E0);
fprintf(logfile,"# ground state: %3.0f\n",E0);
//**********************************************************************
// Find sampling interval
//**********************************************************************
//printf("#finding the sampling interval...\n");
lf=1;
sweep=0;
icont=0;
int iflag=0;
int TMAX=NSWEEPS;
while(sweep<=TMAX){
if (icont==10000) {
//printf("%d sweeps\n",sweep);
icont=0;
}
for(it=0;it<SIZE;it++){
igraph_vector_init(&neighbors,SIZE);
//choose a random site
do{
inow=gsl_rng_uniform_int(r,SIZE);
}while((inow<0)||(inow>=SIZE));
gsl_matrix_get_row(v1,sociedade,inow);
igraph_neighbors(&graph,&neighbors,inow,IGRAPH_OUT);
//generates a random vector v1
gsl_vector_memcpy(v0,v1);
gera_vetor(v1,L,r);
//calculates energy change when v0->v1
// in site inow
DeltaE=variacaoE(v0,v1,inow,sociedade,
issue,N,L,P,DELTA,graph,neighbors);
E_new=E_old+DeltaE;
//WL: accepts in [EMIN,EMAX]
if ((E_new>EMIN) && (E_new<EMAX))
{
gsl_histogram_find(logG,E_old,&i_old);
logG_old=gsl_histogram_get(logG,i_old);
gsl_histogram_find(logG,E_new,&i_new);
logG_new=gsl_histogram_get(logG,i_new);
wE = GSL_MIN(exp(logG_old-logG_new),1);
if (gsl_rng_uniform(r)<wE){
E_old=E_new;
gsl_matrix_set_row(sociedade,inow,v1);
}
}
//WL: update histograms
gsl_histogram_increment(HE,E_old);
gsl_histogram_accumulate(logG,E_old,lf);
igraph_vector_destroy(&neighbors);
}
sweep++;
icont++;
}
gsl_histogram_fprintf(wlsrange,HE,"%g","%g");
double maxH=gsl_histogram_max_val(HE);
//printf("ok\n");
Ex=0;
hvalue=maxH;
while((hvalue>TOL*maxH)&&(Ex>EMIN)){
gsl_histogram_find(HE,Ex,&i0);
hvalue=gsl_histogram_get(HE,i0);
Ex-=1;
if(Ex<=EMAX)TMAX+=10000;
}
EMIN=Ex;
Ex=0;
hvalue=maxH;
while((hvalue>TOL*maxH)&&(Ex<EMAX)) {
gsl_histogram_find(HE,Ex,&i0);
hvalue=gsl_histogram_get(HE,i0);
Ex+=1;
if(Ex>=EMAX)TMAX+=10000;
}
EMAX=Ex;
EMAX=GSL_MIN(10.0,Ex);
if (SHOWFLAG==1)
printf("# the sampling interval is [%3.0f,%3.0f] found in %d sweeps \n\n"
,EMIN,EMAX,sweep);
fprintf(logfile,
"# the sampling interval is [%3.0f,%3.0f] found in %d sweeps \n\n"
,EMIN,EMAX,sweep);
gsl_histogram_set_ranges_uniform (HE,EMIN-1,EMAX+1);
gsl_histogram_set_ranges_uniform (logG,EMIN-1,EMAX+1);
gsl_histogram_set_ranges_uniform (LG,EMIN-1,EMAX+1);
//**********************************************************************
// WLS
//**********************************************************************
int iE,itera=0;
double endpoints[NBINS];
double w = WINDOW; //(EMAX-EMIN)/10.0;
//printf("W=%f\n",w);
lf=1;
//RESOLUTION ----> <------RESOLUTION*****
do{
int iverify=0,iborda=0,flat=0;
sweep=0;
Ex=EMAX;
EW=EMAX;
E_old=EMAX+1;
iE=0;
endpoints[iE]=EMAX;
iE++;
gsl_histogram_reset(LG);
//WINDOWS --> <--WINDOWS*******
while((Ex>EMIN)&&(sweep<MAXSWEEPS)){
//initial config
gera_config(Z,sociedade,SIZE,L,r,0);
E_old = hamiltoneana(sociedade,issue,SIZE,L,P,DELTA,graph);
while( (E_old<EMIN+1)||(E_old>Ex) ){
//printf("%d %3.1f\n",E_old);
do{
inow=gsl_rng_uniform_int(r,SIZE);
}while((inow<0)||(inow>=SIZE));
gsl_matrix_get_row(v0,sociedade,inow);
gera_vetor(v1,L,r);
gsl_matrix_set_row(sociedade,inow,v1);
E_old = hamiltoneana(sociedade,issue,SIZE,L,P,DELTA,graph);
if (E_old>Ex){
gsl_matrix_set_row(sociedade,inow,v0);
E_old = hamiltoneana(sociedade,issue,SIZE,L,P,DELTA,graph);
}
//printf("%3.1f %3.1f %3.1f\n",EMIN+1,E_old, Ex);
}
if (SHOWFLAG==1){
printf("# sampling [%f,%f]\n",EMIN,Ex);
printf("# walking from E=%3.0f\n",E_old);
}
fprintf(logfile,"# sampling [%f,%f]\n",EMIN,Ex);
fprintf(logfile,"# walking from E=%3.0f\n",E_old);
do{ //FLAT WINDOW------> <------FLAT WINDOW*****
//MC sweep ----> <------MC sweep********
for(it=0;it<SIZE;it++){
igraph_vector_init(&neighbors,SIZE);
//escolhe sítio aleatoriamente
do{
inow=gsl_rng_uniform_int(r,SIZE);
}while((inow<0)||(inow>=SIZE));
gsl_matrix_get_row(v1,sociedade,inow);
igraph_neighbors(&graph,&neighbors,inow,IGRAPH_OUT);
//gera vetor aleatorio v1
gsl_vector_memcpy(v0,v1);
gera_vetor(v1,L,r);
//calculates energy change when
//v0->v1 in site inow
DeltaE=variacaoE(v0,v1,inow,sociedade,issue,
N,L,P,DELTA,graph,neighbors);
E_new=E_old+DeltaE;
//WL: accepts in [EMIN,Ex]
if ((E_new>EMIN) && (E_new<Ex))
{
gsl_histogram_find(logG,E_old,&i_old);
logG_old=gsl_histogram_get(logG,i_old);
gsl_histogram_find(logG,E_new,&i_new);
logG_new=gsl_histogram_get(logG,i_new);
wE = GSL_MIN(exp(logG_old-logG_new),1);
if (gsl_rng_uniform(r)<wE){
E_old=E_new;
gsl_matrix_set_row(sociedade,inow,v1);
}
}
//WL: updates histograms
gsl_histogram_increment(HE,E_old);
gsl_histogram_accumulate(logG,E_old,lf);
itera++;
igraph_vector_destroy(&neighbors);
}
//MC sweep ----> <--------MC sweep****
sweep++; iverify++;
if( (EMAX-EMIN)<NDE*DE ) {
EW=EMIN;
}else{
EW=GSL_MAX(Ex-w,EMIN);
}
if (iverify==CHECK){//Verify flatness
if (SHOWFLAG==1)
printf(" #verificando flatness em [%f,%f]\n",EW,Ex);
fprintf(logfile," #verificando flatness em [%f,%f]\n"
,EW,Ex);
iverify=0;
flat=flatness(HE,EW,Ex,TOL,itera,meanhist,hvalue);
if (SHOWFLAG==1)
printf("#minH= %8.0f\t k<H>=%8.0f\t %d sweeps\t ",
hvalue,TOL*meanhist,sweep,flat);
fprintf(logfile,
"#minH= %8.0f\t k<H>=%8.0f\t %d sweeps\t ",
hvalue,TOL*meanhist,sweep,flat);
}
}while(flat==0);// <------FLAT WINDOW******
flat=0;
//Find ER
//printf("# EMAX=%f EMIN = %f Ex =%f\n",EMAX, EMIN, Ex);
if( (EMAX-EMIN)<NDE*DE ) {
Ex=EMIN;
endpoints[iE]=EMIN;
}
else {
if (EW>EMIN){
ER=flatwindow(HE,EW,TOL,meanhist);
if (SHOWFLAG==1)
printf("# extending flatness to[%f,%f]\n",ER,Ex);
fprintf(logfile,
"# extending flatness to [%f,%f]\n",ER,Ex);
if((ER-EMIN)<1){
ER=EMIN;
Ex=EMIN;
endpoints[iE]=EMIN;
}else{
endpoints[iE]=GSL_MIN(ER+DE,EMAX);
Ex=GSL_MIN(ER+2*DE,EMAX);
}
}
else{
endpoints[iE]=EMIN;
Ex=EMIN;
ER=EMIN;
}
}
if (SHOWFLAG==1)
printf("# window %d [%3.0f,%3.0f] is flat after %d sweeps \n",
iE,endpoints[iE],endpoints[iE-1],sweep);
fprintf(logfile,"# window %d [%3.0f,%3.0f] is flat after %d sweeps\n",
iE,endpoints[iE],endpoints[iE-1],sweep);
//saves histogram
if (iE==1){
gsl_histogram_find(logG,endpoints[iE],&i1);
gsl_histogram_find(logG,endpoints[iE-1],&i2);
for(i0=i1;i0<=i2;i0++){
lGv=gsl_histogram_get(logG,i0);
gsl_histogram_get_range(logG,i0,&lower,&upper);
E=0.5*(upper+lower);
gsl_histogram_accumulate(LG,E,lGv);
}
}else{
gsl_histogram_find(logG,endpoints[iE],&i1);
gsl_histogram_find(logG,endpoints[iE-1],&i2);
lGv=gsl_histogram_get(logG,i2);
lGvR=gsl_histogram_get(LG,i2);
DlG=lGvR-lGv;
//printf("i1=%d i2=%d lGv=%f lGvR=%f DlG=%f\n",i1,i2,lGv,lGvR,DlG);
for(i0=i1;i0<i2;i0++){
lGv=gsl_histogram_get(logG,i0);
lGv=lGv+DlG;
gsl_histogram_get_range(logG,i0,&lower,&upper);
E=(upper+lower)*0.5;
//printf("i0=%d E=%f lGv=%f\n",i0,E,lGv);
gsl_histogram_accumulate(LG,E,lGv);
}
}
//printf("#########################################\n");
//gsl_histogram_fprintf(stdout,LG,"%g","%g");
//printf("#########################################\n");
iE++;
if((Ex-EMIN)>NDE*DE) {
if (SHOWFLAG==1)
printf("# random walk is now restricted to [%3.0f,%3.0f]\n"
,EMIN,Ex);
fprintf(logfile,"# random walk is now restricted to [%3.0f,%3.0f]\n"
,EMIN,Ex);
}
gsl_histogram_reset(HE);
}
//WINDOWS -->
if(sweep<MAXSWEEPS){
if (SHOWFLAG==1)
printf("# log(f)=%f converged within %d sweeps\n\n",lf,sweep);
fprintf(logfile,"# log(f)=%f converged within %d sweeps\n\n",lf,sweep);
lf=lf/2.0;
gsl_histogram_reset(HE);
gsl_histogram_memcpy(logG,LG);
}else {
if (SHOWFLAG==1)
printf("# FAILED: no convergence has been attained.");
fprintf(logfile,
"# FAILED: no convergence has been attained. Simulation ABANDONED.");
return(1);
}
}while(lf>MINLOGF);
//RESOLUTION --> <-----RESOLUTION****
//*****************************************************************
//Density of states
//*****************************************************************
double minlogG=gsl_histogram_min_val(logG);
gsl_histogram_shift(logG,-minlogG);
gsl_histogram_fprintf(dos,logG,"%g","%g");
//*****************************************************************
//Thermodynamics
//*****************************************************************
double beta,A,wT,Zmin_beta;
double lGvalue,maxA,betaC,CTMAX=0;
double Z_beta,U,U2,CT,F,S;
for (beta=0.01;beta<=30;beta+=0.01)
{
//******************************************************************
//Energy, free-energy, entropy, specific heat and Tc
//******************************************************************
maxA=0;
for (ia2=0; ia2<NBINS;ia2++)
{
lGvalue=gsl_histogram_get(logG,ia2);
gsl_histogram_get_range(logG,ia2,&lower,&upper);
E=(lower+upper)/2;
A=lGvalue-beta*E;
if (A>maxA) maxA=A;
}
gsl_histogram_find(logG,EMIN,&i0);
Z_beta=0;U=0;U2=0;
for (ia2=0; ia2<NBINS;ia2++)
{
lGvalue=gsl_histogram_get(logG,ia2);
gsl_histogram_get_range(logG,ia2,&lower,&upper);
E=(lower+upper)/2;
A=lGvalue-beta*E-maxA;
Z_beta+=exp(A);
U+=E*exp(A);
U2+=E*E*exp(A);
if(ia2==i0) Zmin_beta=exp(A);
}
wT=Zmin_beta/Z_beta;
F=-log(Z_beta)/beta - maxA/beta;
U=U/Z_beta;
S= (U-F)*beta;
U2=U2/Z_beta;
CT=(U2-U*U)*beta*beta;
if(CT>CTMAX){
CTMAX=CT;
betaC=beta;
}
fprintf(thermodynamics,"%f %f %f %f %f %f %f \n"
,beta,1/beta,F/(double)(SIZE),S/(double)(SIZE),
U/(double)(SIZE),CT/(double)(SIZE),wT);
}
if (SHOWFLAG==1) printf("# BETAc: %f Tc:%f \n",betaC,1/betaC);
fprintf(logfile,"# BETAc: %f Tc:%f \n",betaC,1/betaC);
//******************************************************************
//canonical distribuition at Tc
//******************************************************************
beta=betaC;
double distr_canonica;
maxA=0;
for (ia2=0; ia2<NBINS;ia2++)
{
lGvalue=gsl_histogram_get(logG,ia2);
gsl_histogram_get_range(logG,ia2,&lower,&upper);
E=(lower+upper)/2;
A=lGvalue-beta*E;
if (A>maxA) maxA=A;
}
for (ia2=0; ia2<NBINS;ia2++)
{
lGvalue=gsl_histogram_get(logG,ia2);
gsl_histogram_get_range(logG,ia2,&lower,&upper);
E=(lower+upper)/2;
A=lGvalue-beta*E-maxA;
distr_canonica=exp(A);
fprintf(canonical,"%f %f %f\n",
E/(double)(SIZE),distr_canonica,A);
}
//*****************************************************************
// Finalization
//*****************************************************************
igraph_destroy(&graph);
igraph_vector_destroy(&neighbors);
igraph_vector_destroy(&result);
gsl_matrix_free(issue);
gsl_vector_free(current_issue);
gsl_vector_free(v1);
gsl_vector_free(v0);
gsl_matrix_free(sociedade);
gsl_rng_free(r);
fclose(wlsrange);
fclose(dos);
fclose(thermodynamics);
fclose(canonical);
fclose(logfile);
return(0);
}
/* ==== */
static void
initialize_wl(void)
{
int bins,fnlen=512;
char *res_string=NULL;
double low,high,lo,hi,hmin,hmax,erange,*range = NULL;
srand(time(NULL));
if(wanglandau_opt.verbose){
printf("[[initialize_wl()]]\n");
}
/* assign function pointers */
initialize_model = initialize_RNA; /* for RNA */
pre_process_model = pre_process_RNA;
post_process_model = post_process_RNA;
/* set energy paramters for current model; compute mfe */
initialize_model(wanglandau_opt.sequence);
range = (double*)calloc((wanglandau_opt.bins+1), sizeof(double));
assert(range!=NULL);
/* initialize histograms */
hmin=mfe;
if(wanglandau_opt.res_given){ /* determine histogram ranges manually */
int i;
range[0]=mfe;
for(i=1;i<=wanglandau_opt.bins;i++){
range[i]=range[i-1]+wanglandau_opt.res;
}
if(wanglandau_opt.verbose){
/* info output */
fprintf(stderr,"#allocating %lu bins of width %g\n",
wanglandau_opt.bins,wanglandau_opt.res);
/* fprintf(stderr,"#histogram ranges:\n #");
for(i=0;i<=wanglandau_opt.bins;i++){
fprintf(stderr, "%6.2f ",range[i]);
}
fprintf(stderr,"\n");
*/
}
if(wanglandau_opt.max_given){
hmax=wanglandau_opt.max;
}
else{
wanglandau_opt.max=range[wanglandau_opt.bins]; /* the last element */
hmax=wanglandau_opt.max;
}
h = gsl_histogram_alloc(wanglandau_opt.bins);
g = gsl_histogram_alloc(wanglandau_opt.bins);
s = gsl_histogram_alloc(wanglandau_opt.bins);
gsl_histogram_set_ranges(h,range,(wanglandau_opt.bins+1));
gsl_histogram_set_ranges(g,range,(wanglandau_opt.bins+1));
gsl_histogram_set_ranges(s,range,(wanglandau_opt.bins+1));
}
else{ /* determine histogram ranges automatically */
if(wanglandau_opt.max_given){
hmax = wanglandau_opt.max;
}
else{
hmax=20*fabs(mfe);
}
h = ini_histogram_uniform(wanglandau_opt.bins,hmin,hmax);
g = ini_histogram_uniform(wanglandau_opt.bins,hmin,hmax);
s = ini_histogram_uniform(wanglandau_opt.bins,hmin,hmax);
}
fprintf (stderr, "# sampling energy range is %6.2f - %6.2f\n",
hmin,hmax);
free(range);
/* get the energy range up to which we will compute true DOS via
RNAsubopt */
gsl_histogram_get_range(s,0,&lo,&hi);
low = lo;
gsl_histogram_get_range(s,(wanglandau_opt.truedosbins-1),&lo,&hi);
high = hi;
wanglandau_opt.erange=(float)fabs(mfe-high+0.01);
if(wanglandau_opt.verbose){
printf("Using true DOS for bins 0-%d: (%6.3g -- %6.3g) wl_opt.erange=%6.3f\n",
(wanglandau_opt.truedosbins-1),low,high,wanglandau_opt.erange);
}
/* prepare gsl random-number generation */
(void) clock_gettime(CLOCK_REALTIME, &ts);
if(wanglandau_opt.seed_given){
seed = wanglandau_opt.seed;
}
else {
seed = ts.tv_sec ^ ts.tv_nsec;
}
fprintf(stderr, "initializing random seed: %d\n",seed);
gsl_rng_env_setup();
r = gsl_rng_alloc (gsl_rng_mt19937);
gsl_rng_set( r, seed );
/* end gsl */
/* make prefix for output */
out_prefix = (char*)calloc(fnlen, sizeof(char));
res_string = (char*)calloc(16, sizeof(char));
sprintf(res_string,"%3.1f", wanglandau_opt.res);
strcpy(out_prefix, wanglandau_opt.basename); strcat(out_prefix, ".res");
strcat(out_prefix, res_string); strcat(out_prefix, ".");
free(res_string);
return;
}
double InformacionMalla::getLimiteMayorHistograma(int bin) {
double resultado;
double aux;
gsl_histogram_get_range(h,bin,&aux,&resultado);
return resultado;
}
