void average_bin(double* array_x, double* array_y, double* bins,
double* binned_array, double *error_array, int bin_size, int array_size)
{
#ifdef PRINT_INFO
fprintf(stdout, "\nBinning and averaging into %d bins an array of size:%d...", bin_size, array_size);
#endif
int i=0, j=0;
gsl_histogram *h = gsl_histogram_alloc (bin_size-1);
gsl_histogram *g = gsl_histogram_alloc (bin_size-1);
gsl_histogram_set_ranges (h, bins, bin_size);
gsl_histogram_set_ranges (g, bins, bin_size);
for(i=0; i<array_size; i++)
{
gsl_histogram_increment (h, array_x[i]);
gsl_histogram_accumulate (g, array_x[i], array_y[i]);
}
for(j=0; j<bin_size-1; j++)
{
binned_array[j] = g->bin[j]/h->bin[j];
if(h->bin[j]>0) // Assuming poissonian error
error_array[j] = g->bin[j] / sqrt(h->bin[j]);
}
gsl_histogram_free(h);
gsl_histogram_free(g);
}
void AlexSim::writeHist(const QString filename) const
{
long ii;
long nbins=(long)((t-tStart)/burstDuration*10);
if(nbins==0||photons.size()==0) qWarning()<<"writeHist: no photon records ";
gsl_histogram * histDonor = gsl_histogram_alloc (nbins);
gsl_histogram_set_ranges_uniform (histDonor, tStart, t); // change t to tEnd if the latter is implemented
gsl_histogram * histAcceptor = gsl_histogram_alloc (nbins);
gsl_histogram_set_ranges_uniform (histAcceptor, tStart, t); // change t to tEnd if the latter is implemented
for (ii=0;ii<photons.size();ii++) {
#ifdef PHOTONMACRO
if(isChannel(photons.at(ii),DonorEm)) gsl_histogram_increment (histDonor, photons.at(ii).time);
#else
if(photons.at(ii).isChannel(DonorEm)) gsl_histogram_increment (histDonor, photons.at(ii).time);
#endif
else gsl_histogram_increment (histAcceptor, photons.at(ii).time);
}
QFile file(filename);
if(!file.open(QIODevice::WriteOnly | QIODevice::Text)) QMessageBox::warning(0, "error", file.errorString());
QTextStream out(&file);
out <<"# Simulation data. photon count over time. parameters are:\n";
out <<"# rateDonor="<<rateDemDex<<"\trateAcceptor="<<rateAemAex<<"\trateBackground="<<rateBackground<<"\tburstDuration="<<burstDuration<<"\tsigma="<<sigma<<"\ttStart="<<tStart<<"\ttEnd="<<tEnd()<<"\n";
out.setRealNumberPrecision(11);
out <<"# time in ms \tdonor channel \tacceptor channel\n";
for(ii=0;ii<histDonor->n;ii++) {
out << histDonor->range[ii]*1e3 << "\t" << gsl_histogram_get(histDonor,ii) << "\t" << gsl_histogram_get(histAcceptor,ii) << "\n";
}
file.close();
gsl_histogram_free (histDonor);
gsl_histogram_free (histAcceptor);
qDebug()<<nbins<<"histogram entries written to file "<<filename;
}
void ALEXHistogramsWidget::plotHistogramS()
{
// pr histogram
plot=ui->widPlotHistS;
JKQTPdatastore* ds=plot->get_plotter()->getDatastore();
plot->get_plotter()->clearGraphs(true);
ds->clear();
plot->get_plotter()->getYAxis()->set_axisLabel("S");
plot->get_plotter()->getXAxis()->set_axisLabel("frequency");
int nbins=ui->spinBoxBins->value();
gsl_histogram * histS=gsl_histogram_calloc_uniform(nbins,ad->rangeALEX.minP,ad->rangeALEX.maxP);
gsl_histogram * histSFiltered=gsl_histogram_calloc_uniform(nbins,ad->rangeALEX.minP,ad->rangeALEX.maxP);
for(int i=0;i<ad->burstVectorDual.size();++i) {
gsl_histogram_increment(histS,ad->burstVectorDual.at(i).stoichiometryRatio);
if(!ad->burstVectorDual.at(i).isMasked(ad->FRETparams))
gsl_histogram_increment(histSFiltered,ad->burstVectorDual.at(i).stoichiometryRatio);
}
qDebug("plot S histogram");
unsigned long long nrows=histS->n;
size_t yColumn=ds->addCopiedColumn(histS->range, nrows, "s"); // adds column and returns column ID
size_t xColumn=ds->addCopiedColumn(histS->bin, nrows, "all");
size_t xColumnFiltered=ds->addCopiedColumn(histSFiltered->bin, nrows, "selected");
gsl_histogram_free(histS);
gsl_histogram_free(histSFiltered);
JKQTPbarVerticalGraph* g=new JKQTPbarVerticalGraph(plot->get_plotter()); // g->set_title("S");
g->set_xColumn(xColumn);
g->set_yColumn(yColumn);
g->set_shift(0);
g->set_width(1);
g->set_style(Qt::NoPen);
g->set_fillColor(QColor(DISTRIBUTION_COLOR));
plot->addGraph(g);
g=new JKQTPbarVerticalGraph(plot->get_plotter());
g->set_xColumn(xColumnFiltered);
g->set_yColumn(yColumn);
g->set_shift(0);
g->set_width(1);
g->set_style(Qt::NoPen);
g->set_fillColor(QColor(DISTRIBUTION_COLOR_SELECTED));
plot->addGraph(g);
plot->get_plotter()->zoomToFit();
// JKQTPhorizontalRange *gr;
// gr = new JKQTPhorizontalRange(plot->get_plotter());
// gr->set_rangeMin(ad->FRETparams.MinS);
// gr->set_rangeMax(ad->FRETparams.MaxS);
// gr->set_invertedRange(true);
// QColor col = QColor(Qt::lightGray);
// col.setAlpha(ui->spinBoxAlpha->value());
// gr->set_fillColor(col);
// gr->set_style(Qt::NoPen);
// gr->set_plotCenterLine(false);
// plot->addGraph(gr);
}
/* Histogram a PETSC vector
*
* x is the vector
* nbins -- number of bins
* xmin, xmax -- histogram xmin, xmax -- assume uniform bins
* hh -- output vector -- assumed to be defined.
*/
void VecHist(const Vec& x, int nbins, double xmin, double xmax, vector<double>& hh) {
gsl_histogram *h1;
double *_x, x1;
PetscInt lo, hi;
vector<double> tmp(nbins);
// Set up the histogram struct
h1 = gsl_histogram_alloc(nbins);
gsl_histogram_set_ranges_uniform(h1, xmin, xmax);
// Get the array
VecGetOwnershipRange(x, &lo, &hi);
hi -= lo;
VecGetArray(x, &_x);
for (PetscInt ii=0; ii < hi; ++ii) {
x1 = _x[ii];
if (x1 < xmin) x1 = xmin;
if (x1 >= xmax) x1 = xmax - 1.e-10;
gsl_histogram_increment(h1, x1);
}
VecRestoreArray(x, &_x);
// Fill the temporary output vector
for (int ii =0; ii<nbins; ++ii)
tmp[ii] = gsl_histogram_get(h1, ii);
// MPI Allreduce
MPI_Allreduce(&tmp[0], &hh[0], nbins, MPI_DOUBLE, MPI_SUM, PETSC_COMM_WORLD);
// Clean up
gsl_histogram_free(h1);
}
int
main (void)
{
struct data ntuple_row;
int i;
gsl_ntuple *ntuple = gsl_ntuple_open ("test.dat", &ntuple_row,
sizeof (ntuple_row));
gsl_histogram *h = gsl_histogram_calloc_uniform (100, 0., 10.);
gsl_ntuple_select_fn S;
gsl_ntuple_value_fn V;
double scale = 1.5;
S.function = &sel_func;
S.params = &scale;
V.function = &val_func;
V.params = 0;
gsl_ntuple_project (h, ntuple, &V, &S);
gsl_histogram_fprintf (stdout, h, "%f", "%f");
gsl_histogram_free (h);
gsl_ntuple_close (ntuple);
}
void AlexSim::simTestLognormal()
{
int n=10000; // number of samples
int nbins=100; // number of bins for the histogram
QFile file("./AlexSimTestLognormal.txt");
if(!file.open(QIODevice::WriteOnly | QIODevice::Text)) QMessageBox::information(0, "error", file.errorString());
QTextStream out(&file);
out <<"# Test lognormal distribution. Parameters are: burstDuration="<<burstDuration<<"\tvariance="<<burstDurationVar<<"\n";
out <<"# burst duration in ms\tfrequency\n";
out.setRealNumberPrecision(11);
gsl_histogram * hist = gsl_histogram_alloc (nbins);
gsl_histogram_set_ranges_uniform (hist, 0, 10*burstDuration);
for (int ii=0;ii<n;ii++) {
gsl_histogram_increment (hist,gsl_ran_lognormal(r,mu,sigma));
}
for(unsigned int ii=0;ii<hist->n;ii++) {
out << hist->range[ii]*1e3 << "\t" << gsl_histogram_get(hist,ii) << "\n";
}
file.close();
gsl_histogram_free (hist);
}
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);
}
bool myHistogram::Convert(vector<double> &x)
{
gsl_histogram *r;
size_t n=x.size();
if(n<=2) return false;
double *res;
res=new double[n];
size_t i;
for(i=0;i<n;i++) res[i]=x[i];
double std=gsl_stats_sd(res,1,n);
double bin=3.49*std/pow(n*1.0,1.0/3);//Scott's ruler
if(bin<=0)
{
delete []res;
return false;
}
double a=gsl_stats_min(res,1,n);
double b=gsl_stats_max(res,1,n);
int num=(int)((b-a)/bin);
r=gsl_histogram_alloc(num);
gsl_histogram_set_ranges_uniform(r,a,b);
for(i=0;i<n;i++)
{
gsl_histogram_increment(r,res[i]);
}
Convert(r,n);
gsl_histogram_free(r);
delete []res;
return true;
}
int histc(double *data, int length, double *xmesh, int n , double *bins)
{
XML_IN;
printf("-1\n");
gsl_histogram * h = gsl_histogram_alloc(n-1);
printf("0\n");
gsl_histogram_set_ranges (h, xmesh, n);
double h_max = gsl_histogram_max(h);
double h_min = gsl_histogram_min(h);
printf("h_min: %g h_max: %g\n",h_min,h_max);
for (int i=0; i<length; i++)
gsl_histogram_increment (h, data[i]);
printf("2\n");
for (int i=0;i<n-1;i++)
bins[i] = gsl_histogram_get (h, i);
printf("3\n");
gsl_histogram_fprintf (stdout, h, "%g", "%g");
/*...*/
gsl_histogram_free(h);
XML_OUT;
return 0;
}
int main(void) {
struct data ntuple_row;
gsl_ntuple *ntuple
= gsl_ntuple_open ("test.dat", &ntuple_row,
sizeof (ntuple_row));
double lower = 1.5;
gsl_ntuple_select_fn S;
gsl_ntuple_value_fn V;
gsl_histogram *h = gsl_histogram_alloc (100);
gsl_histogram_set_ranges_uniform(h, 0.0, 10.0);
S.function = &sel_func;
S.params = &lower;
V.function = &val_func;
V.params = 0;
gsl_ntuple_project (h, ntuple, &V, &S);
gsl_histogram_fprintf (stdout, h, "%f", "%f");
gsl_histogram_free (h);
gsl_ntuple_close (ntuple);
return 0;
}
/* ==== */
void
wanglandau_free_memory(void)
{
gsl_histogram_free(h);
gsl_histogram_free(g);
gsl_rng_free(r);
free(pt);
free(s0);
free(s1);
free(wanglandau_opt.sequence);
free(wanglandau_opt.structure);
free(wanglandau_opt.basename);
free(P);
free(out_prefix);
dealloc_gengetopt();
return;
}
int
main (int argc, char **argv)
{
double a = 0.0, b = 1.0;
size_t n = 10;
if (argc != 3 && argc !=4)
{
printf ("Usage: gsl-histogram xmin xmax [n]\n");
printf (
"Computes a histogram of the data on stdin using n bins from xmin to xmax.\n"
"If n is unspecified then bins of integer width are used.\n");
exit (0);
}
a = atof (argv[1]);
b = atof (argv[2]);
if (argc == 4)
{
n = atoi (argv[3]);
}
else
{
n = (int)(b - a) ;
}
{
double x;
gsl_histogram *h = gsl_histogram_alloc (n);
gsl_histogram_set_ranges_uniform (h, a, b);
while (fscanf(stdin, "%lg", &x) == 1)
{
gsl_histogram_increment(h, x);
}
#ifdef DISPLAY_STATS
{
double mean = gsl_histogram_mean (h);
double sigma = gsl_histogram_sigma (h);
fprintf (stdout, "# mean = %g\n", mean);
fprintf (stdout, "# sigma = %g\n", sigma);
}
#endif
gsl_histogram_fprintf (stdout, h, "%g", "%g");
gsl_histogram_free (h);
}
return 0;
}
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);
}
void i2c_destroy(struct plugin *p)
{
unsigned int i;
DECL_ASSIGN_I2C(i2c,p->data);
if(i2c->oio_f)
fclose(i2c->oio_f);
if(i2c->oio_hist_f)
fclose(i2c->oio_hist_f);
for (i = 0; i <= i2c->maxouts; i++) {
gsl_histogram_free(i2c->oio[i].op[READ]);
gsl_histogram_free(i2c->oio[i].op[WRITE]);
}
free(i2c->oio);
g_free(p->data);
}
void DistToPA(gsl_histogram * h, double mag, double sigma)
{
double m1 = 0.5*mag + 0.5;
double s1 = 0.25*sigma;
double n1 = m1*(1. - m1)/s1 - 1.;
double a1 = m1*n1;
double b1 = (1. - m1)*n1;
for (int pa = 1; pa < 8; pa++) {
char line[80];
double x;
double data[6000];
int size = 0;
int nbins = h->n;
gsl_histogram * hpa = gsl_histogram_alloc( nbins);
gsl_histogram_set_ranges_uniform( hpa, 0.0, 1.0);
char fname[50];
sprintf(fname,"/home/jonatas/mzanlz/mZ_pa%d.dat",pa);
FILE * fp;
fp = fopen(fname,"rt");
while(fgets(line,80,fp) != NULL){
x = atof(line);
size++;
data[size] = x;
gsl_histogram_increment( hpa, x);
}
double m2 = 0.5*gsl_stats_mean( data, 1, size ) + 0.5;
double s2 = 0.25*gsl_stats_variance( data, 1, size );
double n2 = m2*(1. - m2)/s2 - 1.;
double a2 = m2*n2;
double b2 = (1. - m2)*n2;
NormalizaGSLHistograma( hpa );
//char hname[100];
//sprintf(hname,"pa%d",pa);
//PrintGSLHistogram( hpa, hname );
gsl_histogram_sub( hpa, h);
double D = 0;
for (size_t i = 0; i < nbins; i++) {
D += gsl_histogram_get( hpa , i )*gsl_histogram_get( hpa , i );
}
// printf("%g %g ", sqrt(D), KLbetas(a1,b1,a2,b2));
fclose(fp);
gsl_histogram_free( hpa );
}
}
/* Number the entries per bin in a given array */
void lin_bin(double* array, double* bins, int bin_size, int array_size, int* binned_array)
{
int i=0, j=0;
#ifdef PRINT_INFO
fprintf(stdout, "\nBinning into %d bins an array of size:%d...", bin_size, array_size);
#endif
gsl_histogram *h = gsl_histogram_alloc (bin_size-1);
gsl_histogram_set_ranges (h, bins, bin_size);
for(i=0; i<array_size; i++)
gsl_histogram_increment(h, array[i]);
for(j=0; j<bin_size-1; j++)
binned_array[j] = (int) h->bin[j];
gsl_histogram_free(h);
}
bool HistList::AddHist(double *res,int n,double m,double sd,CString raw)
{
gsl_histogram *r;
if(n<=0) return false;
double std=gsl_stats_sd(res,1,n);
double bin=3.49*std/pow(n*1.0,1.0/3);//Scott's ruler
if(bin<=0) return false;
double a=gsl_stats_min(res,1,n);
double b=gsl_stats_max(res,1,n);
int num=(int)((b-a)/bin);
r=gsl_histogram_alloc(num);
gsl_histogram_set_ranges_uniform(r,a,b);
for(int i=0;i<n;i++)
{
gsl_histogram_increment(r,res[i]);
}
bool bResult=AddHist(r,m,sd,raw);
gsl_histogram_free(r);
return bResult;
}
bool myHistogram::Convert(double *res,int n)
{
gsl_histogram *r;
if(n<=2) return false;
double std=gsl_stats_sd(res,1,n);
double bin=3.49*std/pow(n*1.0,1.0/3);//Scott's ruler
if(bin<=0) return false;
double a=gsl_stats_min(res,1,n);
double b=gsl_stats_max(res,1,n);
int num=(int)((b-a)/bin);
r=gsl_histogram_alloc(num);
gsl_histogram_set_ranges_uniform(r,a,b);
for(int i=0;i<n;i++)
{
gsl_histogram_increment(r,res[i]);
}
Convert(r,n);
gsl_histogram_free(r);
return true;
}
/** @short Markov Chain Monte Carlo
@param rng the GSL random number generator
@param p the vector of parameters being searched over
@param x the vector of observations */
void mcmc(const gsl_rng *rng, gsl_vector *p, const gsl_vector *x)
{
gsl_vector * p_test = gsl_vector_alloc(p->size);
size_t i;
gsl_histogram * hist = gsl_histogram_alloc(nbins);
gsl_histogram_set_ranges_uniform(hist, min, max);
for(i=0; i < chain_length; i++)
{
gsl_vector_memcpy(p_test,p);
propose(rng, p_test);
double lnLikelihoodRatio = ln_lik_fn(p_test, x) - ln_lik_fn(p, x);
if (take_step(rng, lnLikelihoodRatio))
p = p_test;
gsl_histogram_increment(hist, p->data[1]);
}
gsl_vector_free(p_test);
gsl_histogram_fprintf(stdout, hist, "%g", "%g");
gsl_histogram_free(hist);
}
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;
}
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);
}
/* ==== */
static void
wl_montecarlo(char *struc)
{
short *pt=NULL;
move_str m;
int e,enew,emove,eval_me,status,debug=1;
long int crosscheck=1000000; /* used for convergence checks */
long int crosscheck_limit = 100000000000000000;
double g_b1,g_b2,prob,lnf = 1.; /* log modification parameter f */
size_t b1,b2; /* indices in g/h corresponding to
old/new energies */
gsl_histogram *gcp=NULL; /* clone of g used during crosscheck output */
eval_me = 1; /* paranoid checking of neighbors against RNAeval */
if (wanglandau_opt.verbose){
printf("[[wl_montecarlo()]]\n");
}
pt = vrna_pt_get(struc);
//mtw_dump_pt(pt);
//char *str = vrna_pt_to_db(pt);
//printf(">%s<\n",str);
e = vrna_eval_structure_pt(wanglandau_opt.sequence,pt,P);
/* determine bin where the start structure goes */
status = gsl_histogram_find(g,(float)e/100,&b1);
if (status) {
if (status == GSL_EDOM){
printf ("error: %s\n", gsl_strerror (status));
}
else {fprintf(stderr, "GSL error: gsl_errno=%d\n",status);}
exit(EXIT_FAILURE);
}
printf("%s\n", wanglandau_opt.sequence);
print_str(stderr,pt);
printf(" (%6.2f) bin:%d\n",(float)e/100,b1);
if (wanglandau_opt.verbose){
fprintf(stderr,"\nStarting MC loop ...\n");
}
while (lnf > wanglandau_opt.ffinal) {
if(wanglandau_opt.debug){
fprintf(stderr,"\n==================\n");
fprintf(stderr,"in while: lnf=%8.6f\n",lnf);
fprintf(stderr,"steps: %d\n",steps);
fprintf(stderr,"current histogram g:\n");
gsl_histogram_fprintf(stderr,g,"%6.2f","%30.6f");
fprintf(stderr,"\n");
print_str(stderr,pt);
fprintf(stderr, " (%6.2f) bin:%d\n",(float)e/100,b1);
/* mtw_dump_pt(pt); */
}
/* make a random move */
m = get_random_move_pt(wanglandau_opt.sequence,pt);
/* compute energy difference for this move */
emove = vrna_eval_move_pt(pt,s0,s1,m.left,m.right,P);
/* evaluate energy of the new structure */
enew = e + emove;
if(wanglandau_opt.debug){
fprintf(stderr,
"random move: left %i right %i enew(%6.4f)=e(%6.4f)+emove(%6.4f)\n",
m.left,m.right,(float)enew/100,(float)e/100,(float)emove/100);
}
/* ensure the new energy is within sampling range */
if ((float)enew/100 >= wanglandau_opt.max){
fprintf(stderr,
"New structure has energy %6.2f >= %6.2f (upper energy bound)\n",
(float)enew/100,wanglandau_opt.max);
fprintf(stderr,"Please increase --bins or adjust --max! Exiting ...\n");
exit(EXIT_FAILURE);
}
/* determine bin where the new structure goes */
status = gsl_histogram_find(g,(float)enew/100,&b2);
if (status) {
if (status == GSL_EDOM){
printf ("error: %s\n", gsl_strerror (status));
}
else {fprintf(stderr, "GSL error: gsl_errno=%d\n",status);}
exit(EXIT_FAILURE);
}
steps++; /* # of MC steps performed so far */
/* lookup current values for bins b1 and b2 */
g_b1 = gsl_histogram_get(g,b1);
g_b2 = gsl_histogram_get(g,b2);
/* core MC steps */
prob = MIN2(exp(g_b1 - g_b2), 1.0);
rnum = gsl_rng_uniform (r);
if ((prob == 1 || (rnum <= prob)) ) { /* accept & apply the move */
apply_move_pt(pt,m);
if(wanglandau_opt.debug){
print_str(stderr,pt);
fprintf(stderr, " %6.2f bin:%d [A]\n", (float)enew/100,b2);
}
b1 = b2;
e = enew;
}
else { /* reject the move */
if(wanglandau_opt.debug){
print_str(stderr,pt);
fprintf(stderr, " (%6.2f) bin:%d [R]\n", (float)enew/100,b2);
}
}
/* update histograms g and h */
if(wanglandau_opt.truedosbins_given && b2 <= wanglandau_opt.truedosbins){
/* do not update if b2 <= truedosbins, i.e. keep true DOS values
in those bins */
if (wanglandau_opt.debug){
fprintf(stderr, "NOT UPDATING bin %d\n",b1);
}
} else{
if(wanglandau_opt.debug){
fprintf(stderr, "UPDATING bin %d\n",b1);
}
status = gsl_histogram_increment(h,(float)e/100);
status = gsl_histogram_accumulate(g,(float)e/100,lnf);
}
maxbin = MAX2(maxbin,(int)b1);
// stuff that can be skipped
/*
printf ("performed move l:%4d r:%4d\t Energy +/- %6.2f\n",m.left,m.right,(float)emove/100);
print_str(stderr,pt);printf(" %6.2f bin:%d\n",(float)enew/100,b2);
e = vrna_eval_structure_pt(wanglandau_opt.sequence,pt,P);
if (eval_me == 1 && e != enew){
fprintf(stderr, "energy evaluation against vrna_eval_structure_pt() mismatch... HAVE %6.2f != %6.2f (SHOULD BE)\n",(float)enew/100, (float)e/100);
exit(EXIT_FAILURE);
}
print_str(stderr,pt);printf(" %6.2f\n",(float)e/100);
*/
// end of stuff that can be skipped
/* output DoS every x*10^(1/4) steps, starting with x=10^6 (we
used this fopr comparing perfomance and convergence of
different DoS sampling methods */
if((steps % crosscheck == 0) && (crosscheck <= crosscheck_limit)){
fprintf(stderr,"# crosscheck reached %li steps ",crosscheck);
gcp = gsl_histogram_clone(g);
if(wanglandau_opt.verbose){
fprintf(stderr,"## gcp before scaling\n");
gsl_histogram_fprintf(stderr,gcp,"%6.2f","%30.6f");
}
scale_dos(gcp); /* scale estimated g; make ln(g[0])=0 */
if(wanglandau_opt.verbose){
fprintf(stderr,"## gcp after scaling\n");
gsl_histogram_fprintf(stderr,gcp,"%6.2f","%30.6f");
}
double Z = partition_function(gcp);
output_dos(gcp,'s');
fprintf(stderr, "Z=%10.4g\n", Z);
crosscheck *= (pow(10, 1.0/4.0));
gsl_histogram_free(gcp);
fprintf(stderr,"-> new crosscheck will be performed at %li steps\n", crosscheck);
}
if(steps % wanglandau_opt.checksteps == 0) {
if( histogram_is_flat(h) ) {
lnf /= 2;
fprintf(stderr,"# steps=%20li | f=%12g | histogram is FLAT\n",
steps,lnf);
gsl_histogram_reset(h);
}
else {
fprintf(stderr, "# steps=%20li | f=%12g | histogram is NOT FLAT\n",
steps,lnf);
}
output_dos(g,'l');
}
/* stop criterion */
if(steps % wanglandau_opt.steplimit == 0){
fprintf(stderr,"maximun number of MC steps (%li) reached, exiting ...",
wanglandau_opt.steplimit);
break;
}
} /* end while */
free(pt);
return;
}
void ALEXHistogramsWidget::plotHistogramPR()
{
// pr histogram
plot=ui->widPlotPrHist;
JKQTPdatastore* ds=plot->get_plotter()->getDatastore();
plot->get_plotter()->clearGraphs(true);
ds->clear();
plot->get_plotter()->getXAxis()->set_axisLabel("P");
plot->get_plotter()->getYAxis()->set_axisLabel("frequency");
int nbins=ui->spinBoxBins->value();
gsl_histogram * histPr=gsl_histogram_calloc_uniform(nbins,ad->rangeALEX.minP,ad->rangeALEX.maxP);
gsl_histogram * histPrSelected=gsl_histogram_calloc_uniform(nbins,ad->rangeALEX.minP,ad->rangeALEX.maxP);
for(int i=0;i<ad->burstVectorDual.size();++i) {
gsl_histogram_increment(histPr,ad->burstVectorDual.at(i).proximityRatio);
if(!ad->burstVectorDual.at(i).isMasked(ad->FRETparams))
gsl_histogram_increment(histPrSelected,ad->burstVectorDual.at(i).proximityRatio);
}
qDebug("plot PR histogram");
unsigned long long nrows=histPr->n;
size_t xColumn=ds->addCopiedColumn(histPr->range, nrows, "P"); // adds column and returns column ID
size_t yColumn=ds->addCopiedColumn(histPr->bin, nrows, "all");
size_t yColumnFiltered=ds->addCopiedColumn(histPrSelected->bin, nrows, "selected");
gsl_histogram_free(histPr);
gsl_histogram_free(histPrSelected);
JKQTPbarHorizontalGraph* g=new JKQTPbarHorizontalGraph(plot->get_plotter()); // g->set_title("pr");
g->set_xColumn(xColumn);
g->set_yColumn(yColumn);
g->set_shift(0);
g->set_width(1);
g->set_style(Qt::NoPen);
g->set_fillColor(QColor(DISTRIBUTION_COLOR));
plot->addGraph(g);
g=new JKQTPbarHorizontalGraph(plot->get_plotter());
g->set_xColumn(xColumn);
g->set_yColumn(yColumnFiltered);
g->set_shift(0);
g->set_width(1);
g->set_style(Qt::NoPen);
g->set_fillColor(QColor(DISTRIBUTION_COLOR_SELECTED));
plot->addGraph(g);
// double area=0;
// for(int i=0;i<histPrSelected->n;++i) {
// area+=fabs(histPrSelected->range[i+1]-histPrSelected->range[i])*histPrSelected->bin[i];
// }
// QVector<double> gauss,gauss_x;
// gauss.reserve(nbins);
// int k=0;
// double mu=model.getGMM()->getMu(k,0);
// double sigma=model.getGMM()->getGauss(k).getSigma()(0,0);
// double norm=area/sqrt(2*M_PI)/sigma;
// qDebug()<<"plot gauss\nmu="<<mu<<" sigma="<<sigma<<"norm="<<norm<<"area="<<area;
// double x_min=histPrSelected->range[0];
// double x_max=histPrSelected->range[histPrSelected->n];
// int npoints=200;
// double step=(x_max-x_min)/(npoints-1);
// for(int i=0;i<npoints;++i) {
// gauss_x.append(x_min+i*step);
// }
// for(int i=0;i<npoints;++i) {
// gauss.append(norm*exp(-pow((gauss_x.at(i)-mu),2)/sigma/sigma/2));
// }
// size_t xColumnGauss=ds->addCopiedColumn(gauss_x.data(), gauss_x.size(), "gauss P");
// size_t yColumnGauss=ds->addCopiedColumn(gauss.data(), gauss.size(), "gauss fit");
// JKQTPxyLineGraph* gg=new JKQTPxyLineGraph(plot->get_plotter());
// gg->set_xColumn(xColumnGauss);
// gg->set_yColumn(yColumnGauss);
// gg->set_style(Qt::SolidLine);
// gg->set_color(QColor("black"));
// plot->addGraph(gg);
plot->get_plotter()->zoomToFit();
// JKQTPverticalRange *gr;
// gr = new JKQTPverticalRange(plot->get_plotter());
// gr->set_rangeMin(ad->FRETparams.MinP);
// gr->set_rangeMax(ad->FRETparams.MaxP);
// gr->set_invertedRange(true);
// QColor col = QColor(Qt::lightGray);
// col.setAlpha(ui->spinBoxAlpha->value());
// gr->set_fillColor(col);
// gr->set_style(Qt::NoPen);
// gr->set_plotCenterLine(false);
// plot->addGraph(gr);
}
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 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 histo_maker(int n, double* omega, double hmax)
{
//Histogramme
int i;
int ncolumns = 2;
int nbhist = 5000;
double hmin = 5;
double *histogram = new double [nbhist];
double *N = new double [nbhist];
double **outtable = new double* [nbhist];
for( i=0 ; i < nbhist ; i++ )
outtable[i] = new double[ncolumns]; //2:nb de colonnes
size_t nhist = size_t(nbhist);
gsl_histogram *h = gsl_histogram_alloc(nhist);
gsl_histogram_set_ranges_uniform (h, hmin, hmax);
for (i = 0 ; i < n ; i++) {
gsl_histogram_increment (h, omega[i]);
}
for (i = 0 ; i < nbhist ; i++) {
histogram[i] = gsl_histogram_get (h, i);
//N[i] = (-(double(nbhist) - 1) / 2 + i) * hmax * 2 / nbhist;
N[i] = hmin + (hmax - hmin) * (i + 1/2.) / nbhist ;
outtable[i][0]=N[i];
outtable[i][1]=histogram[i];
}
gsl_histogram_free (h);
int *size = new int [2];
size[0] = nbhist; // lignes
size[1] = ncolumns; // colonnes
string filename="histo_result.csv";
//écriture dans les fichiers (taille, pointeur vers un tableau, nom fichier)
file_maker(size, outtable, filename);
/*
size[0] = n;
size[1] = 1;
double **outtable2 = new double* [n];
for( i=0 ; i < n ; i++ )
outtable2[i] = new double[2]; //1:nb de colonnes
filename = "raw_result.csv";
for ( i = 0 ; i < n ; i++) {
outtable2[i][0] = omega[i];
outtable2[i][1] = 0;
}
file_maker(size, outtable2, filename);
// ménage
for( i=0 ; i < n ; i++ )
delete [] outtable2[i];
delete [] outtable2;
*/
for( i=0 ; i < nbhist ; i++ )
delete [] outtable[i];
delete [] outtable;
delete [] size;
delete [] N;
delete [] histogram;
}
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 BurstSearchWidget::on_pushButtonBurstView_clicked()
{
qDebug("on_pushButtonBurstView_clicked");
if(ad->isEmpty()) return;
JKQtPlotter* plot = new JKQtPlotter(true,NULL);
getParameters();
double binwidth=5e-6, alternationPeriod=ad->getExcitationPeriodDonor();
int nbins=(int)(alternationPeriod/binwidth+1);
gsl_histogram * histCh1 = gsl_histogram_alloc (nbins);
gsl_histogram * histCh2 = gsl_histogram_alloc (nbins);
gsl_histogram_set_ranges_uniform (histCh1, 0, alternationPeriod*1.1);
gsl_histogram_set_ranges_uniform (histCh2, 0, alternationPeriod*1.1);
int last=0;
int lastExc=0;
while(ad->photons.at(last).time<ad->markerTimeDexc.at(lastExc)) ++last;
uint ch1=ui->comboBoxChannel1->channel();
uint ch2=ui->comboBoxChannel2->channel();
while((lastExc<1e4)&&(lastExc<ad->markerTimeDexc.size()+1)) { // average first 10000 periods
while ((last<ad->photons.size()) && ad->photons[last].time<ad->markerTimeDexc.at(lastExc+1)) {
// qDebug()<<QString::number(ad->photons[last].flags,2)+"\t"<<ch1<<ch2<<"\t"<<ad->photons[last].isPhotonFlag(ch1)<<ad->photons[last].isPhotonFlag(ch2);
#ifdef PHOTONMACRO
if(isPhotonFlag(ad->photons[last],ch1)) gsl_histogram_increment (histCh1, ad->photons[last].time-ad->markerTimeDexc[lastExc]);
if(isPhotonFlag(ad->photons[last],ch2)) gsl_histogram_increment (histCh2, ad->photons[last].time-ad->markerTimeDexc[lastExc]);
#else
if(ad->photons[last].isPhotonFlag(ch1)) gsl_histogram_increment (histCh1, ad->photons[last].time-ad->markerTimeDexc[lastExc]);
if(ad->photons[last].isPhotonFlag(ch2)) gsl_histogram_increment (histCh2, ad->photons[last].time-ad->markerTimeDexc[lastExc]);
#endif
last++;
}
// end of excitation period
lastExc++;
}
JKQTPdatastore* ds=plot->get_plotter()->getDatastore();
ds->clear();
plot->get_plotter()->clearGraphs(true);
plot->get_plotter()->setGrid(false);
qDebug("plotHist DA");
unsigned long long nrows=(unsigned long long)histCh1->n;
for(size_t i=0;i<histCh1->n;++i) histCh1->range[i] *= 1e6;
plot->get_xAxis()->set_axisLabel("time in us");
plot->get_yAxis()->set_axisLabel("countrate in Hz");
gsl_histogram_scale(histCh1,(1.0)/(lastExc*binwidth)); // convert bins to countrate in Hz (counts averaged over lastDex excitation periods in bin of width binwidth)
gsl_histogram_scale(histCh2,(1.0)/(lastExc*binwidth));
size_t xColumn=ds->addCopiedColumn(histCh1->range, nrows, "time"); // adds column (copies values!) and returns column ID
QVector<size_t> yColumns;
yColumns.push_back(ds->addCopiedColumn(histCh1->bin, nrows, "Channel 1"));
yColumns.push_back(ds->addCopiedColumn(histCh2->bin, nrows, "Channel 2"));
QList<QColor> colors;
colors.append(QColor(COUNTRATE_COLOR_CH1));
colors.append(QColor(COUNTRATE_COLOR_CH2));
gsl_histogram_free(histCh1);
gsl_histogram_free(histCh2);
// plot
double width=0.5;
double s=-0.5+width/2.0;
for (int i=0; i<yColumns.size(); ++i) {
JKQTPbarHorizontalGraph* g=new JKQTPbarHorizontalGraph(plot->get_plotter());
g->set_xColumn(xColumn);
g->set_yColumn(yColumns[i]);
g->set_shift(s);
g->set_width(width);
g->set_style(Qt::NoPen);
g->set_fillColor(colors.at(i));
s=s+width;
plot->addGraph(g);
}
plot->get_plotter()->set_keyPosition(JKQTPkeyInsideTopRight); // set legend position
plot->get_plotter()->set_showKey(true);
plot->zoomToFit();
plot->show();
// old:
// if(ad->burstVectorDual.isEmpty())
// runBurstSearch();
// analysisFRET(ad->burstVectorDual,ad->FRETparams);
// BurstView *burstView= new BurstView(this, ad);
// burstView->exec();
}
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);
}
population::~population(){
if (VERBOSE)
cout << "Destructing Population...\n";
gsl_histogram_free(blockHist); //Remove usage of histogram!
}
