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;
}
//calls the Point(int) constructor, creating Point object parameters, before calling MCMC's
MCMC::MCMC() : parameters(DIMENSION), proposal_mean(DIMENSION,0.0), proposal_var(DIMENSION,PROPOSAL_VAR) {
rng = gsl_rng_alloc(gsl_rng_ranlxs0); //assigning a RNG to an MCMC object
simulationLength = 5000000;
accepts = 0;
rejects = 0;
bin_num = 50;
range_min = 0;
range_max = 5.0;
thin_factor = 25;
h = gsl_histogram_alloc(bin_num);
gsl_histogram_set_ranges_uniform(h, range_min, range_max);
bin_num_X = 50;
bin_num_Y = 50;
X_min = 0;
X_max = 2;
Y_min = 0;
Y_max = 2;
h2d = gsl_histogram2d_alloc (bin_num_X, bin_num_Y);
gsl_histogram2d_set_ranges_uniform (h2d, X_min, X_max, Y_min, Y_max);
}
//Overloaded Constructors
population::population(int N_in, int L_in, double r_in, int seed_in, gsl_rng* rng_in) { //Neutral Evolution!
try {
if (r_in < 0 || r_in > 1 || N_in < 0 || L_in <0) {throw "Bad inputs. Check arguments.";}
else {
N = N_in;
L = L_in;
r = r_in;
gen_pop = 0;
neutral = true;
avgFit = 0;
selective_weight = vector<double>(N); //All zeros
genetic_weight = vector< vector<int> >(N,vector<int>(L));
fixations = vector< vector<fixation_event> > (N);
seed = seed_in ? seed_in:get_random_seed();
rng = rng_in;
gsl_rng_set(rng,seed);
blockHist = gsl_histogram_alloc(L);
gsl_histogram_set_ranges_uniform(blockHist,1,L+1);
for(int i=0;i<N;i++) {
pop.push_back(genome(i,L,rng));
}
updateBlockSizes();
}
}
catch(const char* Message) {cout << "Error: " << Message << "\n";}
}
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);
}
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;
}
population::population(int N_in, int L_in, double r_in, int seed_in, gsl_rng* rng_in, vector< vector<double> >& fit) {
try {
if (fit.size() != N_in || fit[0].size() != L_in) {throw "Incorrect Fitness Landscape dimensions!";}
else if (r_in < 0 || r_in > 1 || N_in < 0 || L_in <0) {throw "Bad inputs. Check arguments.";}
else {
N = N_in;
L = L_in;
r = r_in;
gen_pop = 0;
neutral = false;
avgFit = 0;
selective_weight = vector<double>(N);
genetic_weight = vector< vector<int> >(N,vector<int>(L));
fixations = vector< vector<fixation_event> > (N);
seed = seed_in ? seed_in : get_random_seed();
rng = rng_in;
gsl_rng_set(rng,seed);
blockHist = gsl_histogram_alloc(L);
gsl_histogram_set_ranges_uniform(blockHist,1,L+1); //Questionable range choice. As it stands now, the bins are partitioned as follows {[1,2),[2,3)......[L,L+1)}. Thus if all have length L, then average will come out to be (L+.5). Will have to rescale.
map<int,double> genome_fit;
for(int i=0;i<N;i++) {
genome_fit = convert_vector_toMap(fit[i]);
if (!genome_fit.empty()) {pop.push_back(genome(i, L, rng, genome_fit));}
else {pop.push_back(genome(i,L,rng));}
avgFit += pop[i].get_fit();
}
avgFit/=N;
update_selection_weights();
updateBlockSizes();
}
}
catch (const char* Message) {
cout << "Error:" << Message << "\n";
}
}
gsl_histogram * calc_hist(const gsl_vector * v, int nbins) {
double max;
double min;
unsigned int i;
double binwidth;
double sum = 0;
double val;
gsl_histogram * h;
gsl_vector_minmax(v, &min, &max);
binwidth = (max - min) / nbins;
dump_d("min", min);
dump_d("max", max);
debug("allocating the histogram");
h = gsl_histogram_alloc(v->size);
debug("setting range");
require(gsl_histogram_set_ranges_uniform (h, min, max));
/* with out the following, the max element doesn't fall in the last bin */
h->range[h->n] += 1;
debug("summing up");
for (i = 0; i < v->size; i++) {
val = gsl_vector_get(v, i);
sum += val;
require(gsl_histogram_increment (h, val));
}
debug("scaling");
/* double gsl_histogram_sum (const gsl_histogram * h) */
require(gsl_histogram_scale (h, 1/sum));
debug("done");
return h;
}
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 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);
}
/* 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);
}
/* ==== */
static gsl_histogram *
ini_histogram_uniform(const int n,
const double min,
const double max)
{
gsl_histogram *a = gsl_histogram_alloc(n);
gsl_histogram_set_ranges_uniform(a,min,max);
return a;
}
CAMLprim value ml_gsl_histogram_set_ranges_uniform(value vh,
value xmin, value xmax)
{
gsl_histogram h;
histo_of_val(&h, vh);
gsl_histogram_set_ranges_uniform(&h, Double_val(xmin),
Double_val(xmax));
return Val_unit;
}
gsl_histogram *AlexData::burstDurationDistribution(size_t nbins)
{
gsl_histogram *hist = gsl_histogram_alloc(nbins);
gsl_histogram_set_ranges_uniform(hist,0,burstSearchParamsGlobal.MaxDuration*1e3);
for(int i=0;i<burstVectorDual.size();++i)
gsl_histogram_increment(hist, burstVectorDual.at(i).duration*1e3);
return hist;
}
gsl_histogram * create_hist(int nbins, double min, double max) {
gsl_histogram * h;
h = gsl_histogram_alloc(nbins);
gsl_histogram_set_ranges_uniform(h, min, max);
/* with out the following, the max element doesn't fall in the last bin */
h->range[h->n] += (max - min) / 10000;
return h;
}
gsl_histogram *AlexData::burstRateDistribution(size_t nbins)
{
gsl_histogram *hist = gsl_histogram_alloc(nbins);
gsl_histogram_set_ranges_uniform(hist,burstSearchParamsGlobal.MinBurstrate*1e-3,burstSearchParamsGlobal.MaxBurstrate*1e-3);
for(int i=0;i<burstVectorDual.size();++i)
gsl_histogram_increment(hist, burstVectorDual.at(i).burstrate()*1e-3);
return hist;
}
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);
}
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;
}
gsl_histogram *AlexData::burstSizeDistribution(size_t nbins, double maxBurstSize)
{
if(maxBurstSize==0) maxBurstSize = burstSearchParamsGlobal.MaxBurstrate*burstSearchParamsGlobal.MaxDuration;
gsl_histogram *hist = gsl_histogram_alloc(nbins);
gsl_histogram_set_ranges_uniform(hist,burstSearchParamsGlobal.MinBurstSize,maxBurstSize);
for(int i=0;i<burstVectorDual.size();++i)
gsl_histogram_increment(hist, burstVectorDual.at(i).numberOfPhotons);
return hist;
}
gsl_histogram* population::get_cumulant_fit_hist(int l, double sigma) {
//Initialize histogram.
gsl_histogram* fit_hist = gsl_histogram_alloc(1000); //Think more about bin size.
gsl_histogram_set_ranges_uniform(fit_hist, -10*l*sigma, 10*l*sigma);
for (int n=0; n<N; n++) { //Iterate over the entire population.
for (int i=0; i<L-l; i+l/2) { //Center Locus @ i+l/2
gsl_histogram_increment(fit_hist,pop[n].sub_cumulant_fit(i,l)); //Histogram delta_f values.
}
}
return fit_hist;
}
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 );
}
}
static gsl_histogram *new_hist()
{
gsl_histogram *h;
/* allocate bins and set ranges uniformally */
h = gsl_histogram_alloc(N_BINS);
gsl_histogram_set_ranges_uniform(h,0,N_BINS*BINS_SEP);
/* last bin should hold everything greater
* than (N_BINS-1)*BINS_SEP */
h->range[h->n] = DBL_MAX;
return h;
}
size_t histogram (size_t N, double *data, size_t nbins, double bin_min, double bin_max, gsl_histogram **hist) {
size_t i;
/* prepare the gsl_histogram structure */
*hist = gsl_histogram_alloc (nbins);
gsl_histogram_set_ranges_uniform (*hist, bin_min, bin_max);
/* populate the bins in the histogram */
for (i=0; i<N; i++)
gsl_histogram_increment (*hist, data [i]);
/* success */
return MYLIB_SUCCESS;
}
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;
}
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;
}
/** @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 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 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 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);
}
