void exportHistograms(){
TreeReader reader;
reader.openSeries(257,"D");
reader.openSeries(258,"D");
reader.openSeries(260,"D");
reader.openSeries(261,"D");
reader.openSeries(262,"D");
TH1F hist1("hist1","hist1",100,0,30000);
TH1F hist2("hist2","hist2",100,0,30000);
TH1F hist3("hist3","hist3",100,0,30000);
for(Long64_t i=0;i<reader.tree->GetEntries();i++){
reader.tree->GetEntry(i);
if(fabs(reader.recon.t_gamma[2]-600)<10){
hist1.Fill(reader.recon.E_gamma[2]);
}
if(fabs(reader.recon.t_gamma[6]-600)<10){
hist2.Fill(reader.recon.E_gamma[6]);
}
if(fabs(reader.recon.t_gamma[13]-600)<10){
hist3.Fill(reader.recon.E_gamma[13]);
}
}
ofstream output(Form("%s/output.csv",RDKANAL));
output<<"bin center,det02,det06,det16"<<endl;
for(Long64_t i=1;i<=100;i++){
output<<hist1.GetBinCenter(i)<<","<<hist1.GetBinContent(i)<<",";
output<<hist2.GetBinContent(i)<<","<<hist3.GetBinContent(i)<<endl;
}
output.close();
}
TEST(Histogram, General)
{
Histogram hist1(-1.0, 1.0, 10);
EXPECT_EQ(10, hist1.numBins());
EXPECT_EQ(-1.0, hist1.minValue());
EXPECT_EQ(1.0, hist1.maxValue());
EXPECT_EQ(7, hist1.valueToBin(hist1.binToValue(7)));
EXPECT_EQ(8, hist1.valueToBin(R(0.79)));
EXPECT_EQ(9, hist1.valueToBin(R(0.81)));
EXPECT_EQ(9, hist1.valueToBin(R(0.99)));
EXPECT_EQ(9, hist1.valueToBin(R(1.0)));
for (size_t i = 0; i < 10; ++i)
EXPECT_EQ(0, hist1[i]);
hist1.insert(R(1e-6));
EXPECT_EQ(1, hist1[5]);
EXPECT_EQ(1, hist1.numEntries());
hist1.insert(R( 0.1));
hist1.insert(R(-0.1));
EXPECT_EQ(3, hist1.numEntries());
EXPECT_EQ(5, hist1.mode());
EXPECT_DOUBLE_EQ(0.1 / 3.0, hist1.mean());
}
int KMeansClustering::GetClusterID( std::vector<float> cur ) const {
int curC = -1;
float curD = FLT_MAX;
for(int j = 0; j < clusterN; j++){
float d = 0;
if( dist_type == KM_L2Norm ){
d = SCI::L2Norm( cur, cluster[j] );
}
if( dist_type == KM_Pearson ){
d = 1.0f - SCI::PearsonCorrelation( cur, cluster[j] );
}
if( dist_type == KM_Histogram ){
SCI::SimpleHistogram hist0( 5, histogram_min, histogram_max );
SCI::SimpleHistogram hist1( 5, histogram_min, histogram_max );
hist0.AddValues(cur);
hist1.AddValues(cluster[j]);
d = 0;
for(int i = 0; i < 5; i++){
d += (float)abs( hist0.GetBin(i)-hist1.GetBin(i) );
}
return d;
}
if( d < curD ){
curD = d;
curC = j;
}
}
return curC;
}
void CompareChiDialog::startMining()
{
QString logsDir = medianFilter->isChecked() ? "/CleanLogs" : "/Logs";
QDir logs_dir = QApplication::applicationDirPath();
logs_dir = logs_dir.path() + logsDir;
logs_dir = logs_dir.path() + "/" + this->userNameEdit1->text();
if (this->userNameEdit1->text().isEmpty() || this->userNameEdit2->text().isEmpty())
{
QMessageBox::critical(this, tr("Error"), tr("Usernames cannot be empty!"));
return;
}
if (!logs_dir.exists()) {
QMessageBox::critical(this, tr("Error"),
tr("Can't find logs for user \"") +
this->userNameEdit1->text() +
tr("\" in the directory ") +
logs_dir.path());
return;
}
logs_dir = QApplication::applicationDirPath();
logs_dir = logs_dir.path() + logsDir;
logs_dir = logs_dir.path() + "/" + this->userNameEdit2->text();
if (!logs_dir.exists()) {
QMessageBox::critical(this, tr("Error"),
tr("Can't find logs for user \"") +
this->userNameEdit2->text() +
tr("\" in the directory ") +
logs_dir.path());
return;
}
if (theresoldCut->isChecked()){
Histogram hist1 (this->steps->value(), this->maxValue->value(), this->userNameEdit1->text(), medianFilter->isChecked());
int end1 = hist1.findMinGoodEnd();
Histogram hist2 (this->steps->value(), this->maxValue->value(), this->userNameEdit2->text(), medianFilter->isChecked());
int end2 = hist2.findMinGoodEnd();
this->maxValue->setValue(end1 < end2 ? end2 : end1);
}
long long int info = Mining::useChiSquareApprox(this->userNameEdit1->text(),
this->userNameEdit2->text(),
this->steps->value(),
this->maxValue->value(),
medianFilter->isChecked());
QMessageBox::information(this, "Mining complete", "The probability that it is one person is " + QString::number(info) + "%");
}
template<class Storage1, class Storage2> static int foaOperations(Storage1, Storage2)
{
const int SIZE = 720;
typedef Storage1::value_type Val;
typedef Storage1::idx_type Idx;
CVHistogram<Storage1> hist1( SIZE );
CVHistogram<Storage2> hist2( SIZE );
int Errors = 0;
for( Idx i = 0; i < SIZE; i++ )
{
hist1[i] = (Val)(i % 10);
hist2[i] = (Val)(9 - hist1[i]);
}
if( calc_histogram_intersection( hist1, hist2 ) != SIZE * 2 )
{
Errors++;
trsWrite( ATS_CON | ATS_LST, "Error Intersection function\n" );
}
double res;
if( (res = fabs(calc_histogram_chi_square( hist1, hist2 ) - SIZE * 330 / 90)) > 0.0001 )
{
Errors++;
trsWrite( ATS_CON | ATS_LST, "Error ChiSqr function\n" );
}
for( i = 0; i < SIZE; i++ )
{
hist1[i] = (Val)(i % 9);
hist2[i] = (Val)(8 - hist1[i]);
}
if( fabs(calc_histogram_correlation( hist1, hist2 ) + 1 ) >= 0.0001 )
{
Errors++;
trsWrite( ATS_CON | ATS_LST, "Error Correl function\n" );
}
return Errors == 0 ? TRS_OK : trsResult( TRS_FAIL, "Fixed %d errors", Errors );
}
float KMeansClustering::GetDistance( int id ) const {
std::vector<float> cur = pdata->GetElement( id );
std::vector<float> clu = cluster[ clusterID[id] ];
if( dist_type == KM_L2Norm ){
return SCI::L2Norm( cur, clu );
}
if( dist_type == KM_Pearson ){
return 1.0f - SCI::PearsonCorrelation( cur, clu );
}
if( dist_type == KM_Histogram ){
SCI::SimpleHistogram hist0( 5, histogram_min, histogram_max );
SCI::SimpleHistogram hist1( 5, histogram_min, histogram_max );
hist0.AddValues(cur);
hist1.AddValues(clu);
float diff = 0;
for(int i = 0; i < 5; i++){
diff += (float)abs( hist0.GetBin(i)-hist1.GetBin(i) );
}
return diff;
}
return 0;
}
int test_calculate_histogram()
{
// Blog: http://blog.csdn.net/fengbingchun/article/details/76417598
const int length{ 10 * 1024 * 1024 }; // 100MB
std::unique_ptr<unsigned char[]> data(new unsigned char[length]);
generator_random_number<unsigned char>(data.get(), length, 0, 255);
const int hist_size{ 256 };
std::unique_ptr<unsigned int[]> hist1(new unsigned int[hist_size]), hist2(new unsigned int[hist_size]);
std::for_each(hist1.get(), hist1.get() + hist_size, [](unsigned int& n) {n = 0; });
std::for_each(hist2.get(), hist2.get() + hist_size, [](unsigned int& n) {n = 0; });
float elapsed_time1{ 0.f }, elapsed_time2{ 0.f }; // milliseconds
unsigned int value1{ 0 }, value2{ 0 };
int ret = calculate_histogram_cpu(data.get(), length, hist1.get(), value1, &elapsed_time1);
if (ret != 0) PRINT_ERROR_INFO(calculate_histogram_cpu);
ret = calculate_histogram_gpu(data.get(), length, hist2.get(), value2, &elapsed_time2);
if (ret != 0) PRINT_ERROR_INFO(calculate_histogram_gpu);
if (value1 != value2) {
fprintf(stderr, "their values are different: val1: %d, val2: %d\n", value1, value2);
return -1;
}
for (int i = 0; i < hist_size; ++i) {
if (hist1[i] != hist2[i]) {
fprintf(stderr, "their values are different at: %d, val1: %d, val2: %d\n",
i, hist1[i], hist2[i]);
return -1;
}
}
fprintf(stderr, "test calculate histogram: cpu run time: %f ms, gpu run time: %f ms\n", elapsed_time1, elapsed_time2);
return 0;
}
void whichone(char **arg,int len)
{
int i;
int bg = 0;
int pipe = 0;
int redir = 0;
if(arg[0] == NULL)
{
fprintf(stderr,"Invalid Command\n");
}
for(i=0; i<len; i++)
if((strcmp(arg[i],"<") == 0) || (strcmp(arg[i],">") == 0))
redir = 1;
for(i=0; i<len; i++)
if(strcmp(arg[i],"|") == 0)
pipe = 1;
if(arg[len-1][strlen(arg[len-1])-1] == '&')
{
bg = 1;
if(arg[len-1][0] == '&')
arg[len-1] = NULL;
else
arg[len-1][strlen(arg[len-1])-1] = '\0';
}
if(pipe && !redir)
{
pipe_func(arg,len);
}
else if(!pipe && redir)
{
redir_func(arg,len);
}
else if(pipe && redir)
{
redir_pipe_func(arg,len);
}
else if(strcmp(arg[0],"cd") == 0)
{
cd(arg,len);
}
else if(strcmp(arg[0],"pid") == 0)
{
pidx(arg,len);
}
else if(arg[0][0]=='h' && arg[0][1]=='i' && arg[0][2]=='s' && arg[0][3]=='t')
{
hist(arg,len);
}
else if(arg[0][0]=='!' && arg[0][1]=='h' && arg[0][2]=='i' && arg[0][3]=='s' && arg[0][4]=='t')
{
hist1(arg,len);
}
else
{
other(arg,len,bg);
}
}
// MOD3<-function(Ng.out,Ng.in,STin,STout,max.con,Cg=0)
MATRICEi *mod3(MATRICEi *ris, int Ng_out, int Ng_in, const VETTOREd *STin, const VETTOREd *STout, int max_con, double Cg)
{
int N, j, n_reg, L, Ls, ns;
double S, tmpd;
int aus;
#ifdef MDEBUG
GString *tmp = NULL;
#endif
_Intestazione("\n***mod3***\n");
#ifdef DET
fprintf(fp_det, "input:\n");
fprintf(fp_det, "\tNg_out = %d\n", Ng_out);
fprintf(fp_det, "\tNg_in = %d\n", Ng_in);
fprintf(fp_det, "\tSTin = ");
_StampaRawVett_d(STin);
fprintf(fp_det, "\tSTout = ");
_StampaRawVett_d(STout);
fprintf(fp_det, "\tmax_con = %d\n", max_con);
fprintf(fp_det, "\tCg = %.16g\n", Cg);
#endif
/*
#ifdef FDEBUG
fprintf(fp_fdbg, "Ng_out = %d\n", Ng_out);
fprintf(fp_fdbg, "Ng_in = %d\n", Ng_in);
_StampaVett_d(STin);
_StampaVett_d(STout);
fprintf(fp_fdbg, "max_con = %d\nCg = %.5e\n", max_con, Cg);
fprintf(fp_fdbg, "\n");
fprintf(fp_fdbg, "*****************************************\n\n");
#endif*/
CREAv_d(g_scalare_d, 1);
CREAv_i(g_scalare_i, 1);
// ris<-matrix(0,Ng.out+Ng.in,Ng.out+Ng.in)
CREAm_i(ris, Ng_out + Ng_in, Ng_out + Ng_in);
InitMatr_i(ris, 0);
// N<-Ng.in+Ng.out
N = Ng_in + Ng_out;
// S<-sum(STout[2:(Ng.in+1)],na.rm=TRUE)
g_tmp1_d = segmento_v_d(g_tmp1_d, STout, 2, Ng_in + 1);
S = somma_v_d(g_tmp1_d, true);
// if (S>0) g_p<-STout[2:(Ng.in+1)]/S
if (S > 0.0) {
g_p = segmento_v_d(g_p, STout, 2, Ng_in + 1);
dividi1_vs_d(g_p, S);
}
// else g_p<-rep(1/Ng.in,Ng.in)
else {
#ifdef MDEBUG
if (Ng_in == 0) {
CREAstr(tmp, "");
g_string_printf(tmp, "ATTENZIONE (mod3.c, linea 53): divisione per zero!\n");
warning(tmp->str);
fprintf(fp_fdbg, tmp->str);
CANCELLAstr(tmp);
}
#endif
CREAv_d(g_p, Ng_in);
InitVett_d(g_p, (double) 1.0 / Ng_in);
}
// M.out<-sampleB(seq(1,Ng.in,1),Ng.out,prob=g_p,replace=TRUE)
g_tmp1_i = seq_i(g_tmp1_i, 1, Ng_in, 1);
g_M_out = sampleB_p(g_M_out, g_tmp1_i, Ng_out, 1, g_p);
g_tmpSTin = copia_v_d(g_tmpSTin, STin, 1, LENGTHv_d(STin));
g_tmpSTout = copia_v_d(g_tmpSTout, STout, 1, LENGTHv_d(STout));
// while (sum(M.out)<(N-1)) # rm.na == FALSE
while (somma_v_i(g_M_out, false) < N - 1) {
// {if (S>0)
if (S > 0.0) {
// {STout<-(STout[1:(Ng.in+1)]/S)*Ng.in
segmento1_v_d(g_tmpSTout, 1, Ng_in + 1);
#ifdef MDEBUG
if (Ng_in == 0) {
CREAstr(tmp, "");
g_string_printf(tmp, "ATTENZIONE (mod3.c, linea 74): divisione per zero!\n");
warning(tmp->str);
fprintf(fp_fdbg, tmp->str);
CANCELLAstr(tmp);
}
#endif
dividi1_vs_d(g_tmpSTout, (double) S / Ng_in);
// aus<-max(M.out)+1
aus = max_v_i(g_M_out) + 1;
// Freq.out<-hist(M.out,breaks=seq(0,Ng.in+1,1),right=FALSE,plot=FALSE)$counts/N
g_tmp2_i = seq_i(g_tmp2_i, 0, Ng_in + 1, 1);
g_tmp1_i = hist1(g_tmp1_i, g_M_out, g_tmp2_i, 0, 1, 0);
g_Freq_out = dividi_vs_i(g_Freq_out, g_tmp1_i, (double) N);
// g_Sc<-Score(S=M.out,ST=STout,Freq=Freq.out,n=1,toll=rep(Inf,(Ng.out+1)))
CREAv_d(g_tmp3_d, Ng_out + 1);
InitVett_d(g_tmp3_d, R_PosInf);
g_Sc = score1(g_Sc, g_M_out, g_tmpSTout, g_Freq_out, 1, g_tmp3_d);
// g_indok<-which(g_Sc!=-Inf)
g_indok = which_v_indxne_d(g_indok, g_Sc, R_NegInf);
// g_indInf <- setdiff(seq(1, Ng.out, 1), g_indok)
g_tmp3_i = seq_i(g_tmp3_i, 1, Ng_out, 1);
g_indInf = setdiff_i(g_indInf, g_tmp3_i, g_indok);
// g_Sc[g_indInf]<-min(c(0,g_Sc[g_indok]))-1
g_tmp1_d = assegna_v_indx_d(g_tmp1_d, g_Sc, g_indok);
tmpd = min_v_d(g_tmp1_d);
if (tmpd < 0.0)
tmpd = 0.0;
assegna1_v_indx_d(g_Sc, g_indInf, tmpd - 1);
// g_p<-g_Sc/sum(g_Sc)
g_p = dividi_vs_d(g_p, g_Sc, somma_v_d(g_Sc, false));
// }
}
// else g_p<-rep(1/Ng.in,Ng.in)
else {
#ifdef MDEBUG
if (Ng_in == 0) {
CREAstr(tmp, "");
g_string_printf(tmp, "ATTENZIONE (mod3.c, linea 111): divisione per zero!\n");
warning(tmp->str);
fprintf(fp_fdbg, tmp->str);
CANCELLAstr(tmp);
}
#endif
CREAv_d(g_p, Ng_in);
InitVett_d(g_p, (double) 1.0 / Ng_in);
}
// g_ind.M<-sampleB(seq(1,Ng.out,1),1,prob=g_p)
g_tmp2_i = seq_i(g_tmp2_i, 1, Ng_out, 1);
g_ind_M = sampleB_p(g_ind_M, g_tmp2_i, 1, 0, g_p);
// M.out[g_ind.M]<-M.out[g_ind.M]+1
ASSEGNAv_i(g_M_out, ACCEDIv_i(g_ind_M, 1), ACCEDIv_i(g_M_out, ACCEDIv_i(g_ind_M, 1)) + 1);
}
// for (j in (1:Ng.out))
for (j = 1; j <= Ng_out; j++) {
// {n.reg<-M.out[j]
n_reg = ACCEDIv_i(g_M_out, j);
// if (j==1)
if (j == 1) {
// {ris[1:n.reg,(j+Ng.in)]<-1
g_tmp1_i = seq_i(g_tmp1_i, 1, n_reg, 1);
ASSEGNAv_i(g_scalare_i, 1, j + Ng_in);
assegna1_m_vv_i(ris, g_tmp1_i, g_scalare_i, 1);
// g_indS<-1:n.reg
g_indS = seq_i(g_indS, 1, n_reg, 1);
// g_indBS<-setdiff(1:Ng.in,g_indS)
g_tmp2_i = seq_i(g_tmp2_i, 1, Ng_in, 1);
g_indBS = setdiff_i(g_indBS, g_tmp2_i, g_indS);
// }
}
// else
else {
// {L<-length(g_indBS)
L = LENGTHv_i(g_indBS);
// Ls<-min(L,n.reg-1)
Ls = min_s_i(L, n_reg - 1);
// ns<-n.reg-Ls
ns = n_reg - Ls;
// S<-sum(STin[1:(Ng.out+1)],na.rm=TRUE)
g_tmp1_d = segmento_v_d(g_tmp1_d, g_tmpSTin, 1, Ng_out + 1);
S = somma_v_d(g_tmp1_d, true);
// g_Sin<-apply(ris,1,sum)
g_Sin = somma_righe_i(g_Sin, ris);
// if (S>0)
if (S > 0.0) {
// {STin<-(STin[1:(Ng.out+1)]/S)*Ng.out
segmento1_v_d(g_tmpSTin, 1, Ng_out + 1);
#ifdef MDEBUG
if (Ng_out == 0) {
CREAstr(tmp, "");
g_string_printf(tmp, "ATTENZIONE (mod3.c, linea 163): divisione per zero!\n");
warning(tmp->str);
fprintf(fp_fdbg, tmp->str);
CANCELLAstr(tmp);
}
#endif
dividi1_vs_d(g_tmpSTin, (double) S / Ng_out);
// aus<-max(g_Sin)+1
aus = max_v_i(g_Sin) + 1;
// Freq.in<-hist(g_Sin,breaks=seq(0,aus,1),right=FALSE,plot=FALSE)$counts/N
g_tmp2_i = seq_i(g_tmp2_i, 0, aus, 1);
g_tmp1_i = hist1(g_tmp1_i, g_Sin, g_tmp2_i, 0, 1, 0);
g_Freq_in = dividi_vs_i(g_Freq_in, g_tmp1_i, (double) N);
// g_Sc<-Score(S=g_Sin[g_indS],ST=STin,Freq=Freq.in,n=1,toll=rep(Inf,(Ng.out+1)))
g_tmp2_i = assegna_v_indx_i(g_tmp2_i, g_Sin, g_indS);
CREAv_d(g_tmp3_d, Ng_out + 1);
InitVett_d(g_tmp3_d, R_PosInf);
g_Sc = score1(g_Sc, g_tmp2_i, g_tmpSTin, g_Freq_in, 1, g_tmp3_d);
// g_indok<-which(g_Sc!=-Inf)
g_indok = which_v_indxne_d(g_indok, g_Sc, R_NegInf) ;
// g_indInf <- setdiff(seq(1, length(g_indS), 1), g_indok)
g_tmp1_i = seq_i(g_tmp1_i, 1, LENGTHv_i(g_indS), 1);
g_indInf = setdiff_i(g_indInf, g_tmp1_i, g_indok);
// g_Sc[g_indInf]<-min(c(0,g_Sc[g_indok]))-1
g_tmp1_d = assegna_v_indx_d(g_tmp1_d, g_Sc, g_indok);
tmpd = min_v_d(g_tmp1_d);
if (tmpd < 0.0)
tmpd = 0.0;
assegna1_v_indx_d(g_Sc, g_indInf, tmpd - 1);
// g_p.sc<-g_Sc/sum(g_Sc)
g_p_sc = dividi_vs_d(g_p_sc, g_Sc, somma_v_d(g_Sc, false));
// }
}
// else g_p.sc<-rep(1/length(g_indS),length(g_indS))
else {
#ifdef MDEBUG
if (LENGTHv_i(g_indS) == 0) {
CREAstr(tmp, "");
g_string_printf(tmp, "ATTENZIONE (mod3.c, linea 201): divisione per zero!\n");
warning(tmp->str);
fprintf(fp_fdbg, tmp->str);
CANCELLAstr(tmp);
}
#endif
CREAv_d(g_p_sc, LENGTHv_i(g_indS));
InitVett_d(g_p_sc, (double) 1.0 / LENGTHv_i(g_indS));
}
//
// g_ind<-which(g_Sin[g_indS]==max.con)
g_ind = which_v_indxeq_i(g_ind, g_tmp1_i, max_con);
// g_p.sc[g_ind]<-0
assegna1_vs_indx_d(g_p_sc, g_ind, 0.0);
// g_p.sc<-g_p.sc/sum(g_p.sc)
g_p = dividi_vs_d(g_p, g_p_sc, somma_v_d(g_p_sc, false));
// g_ind1<-sampleB(g_indS,ns,prob=g_p.sc)
g_ind1 = sampleB_p(g_ind1, g_indS, ns, 0, g_p_sc);
// if (Ls>0)
if (Ls > 0) {
// {g_ind<-g_indBS[1:Ls]
g_ind = segmento_v_i(g_ind, g_indBS, 1, Ls);
// g_indS<-c(g_indS,g_ind)
g_indS = accoda1_vv_i(g_indS, g_ind);
// g_indBS<-setdiff(1:Ng.in,g_indS)
g_tmp1_i = seq_i(g_tmp1_i, 1, Ng_in, 1);
g_indBS = setdiff_i(g_indBS, g_tmp1_i, g_indS);
// g_ind1<-c(g_ind1,g_ind)
g_ind1 = accoda1_vv_i(g_ind1, g_ind);
// }
}
// m[g_ind1,(j+Ng.in)]<-1
ASSEGNAv_i(g_scalare_i, 1, j + Ng_in);
assegna1_m_vv_i(ris, g_ind1, g_scalare_i, 1);
// }
}
// }
}
// if ( (Cg>0)&((Ng.in+Ng.out)>2) ) ris<-triangola(M=ris,Cg=Cg,max.con)
if (Cg > 0.0 && Ng_in + Ng_out > 2)
ris = triangola(ris, Cg, max_con);
// return(ris)
//~ CANCELLAv_i(g_M_out);
//~ CANCELLAv_i(g_indok);
//~ CANCELLAv_i(g_tmp3_i);
//~ CANCELLAv_d(g_tmp1_d);
//~ CANCELLAv_d(g_Freq_out);
//~ CANCELLAv_d(g_tmp3_d);
//~ CANCELLAv_d(g_p);
//~ CANCELLAv_d(g_Sc);
//~ CANCELLAv_i(g_tmp2_i);
//~ CANCELLAv_i(g_ind_M);
//~ CANCELLAv_i(g_ind1);
//~ CANCELLAv_i(g_indS);
//~ CANCELLAv_i(g_indBS);
//~ CANCELLAv_i(g_Sin);
//~ CANCELLAv_i(g_ind);
//~ CANCELLAv_i(g_indInf);
//~ CANCELLAv_i(g_tmp1_i);
//~ CANCELLAv_i(g_scalare_i);
//~ CANCELLAv_d(g_scalare_d);
//~ CANCELLAv_d(g_p_sc);
#ifdef FDEBUG
fprintf(fp_fdbg, "\n\n");
fprintf(fp_fdbg, "*****************************************\n");
fprintf(fp_fdbg, "*****************************************\n\n");
#endif
StrBilanciam();
#ifdef DET
fprintf(fp_det, "mod3 output:\n");
fprintf(fp_det, "\tm = ");
_StampaRawMatr_i(ris);
#endif
return ris;
// }
}
bool brightRGB::getMedian(const image& img,dvector& dest) const{
// image empty?
if (img.empty()) {
setStatusString("image empty");
dest.resize(0);
return false;
}
const rgbPixel transColor = getParameters().transColor;
dest.resize(3);
ivector hist0(256,0);
ivector hist1(256,0);
ivector hist2(256,0);
image::const_iterator it = img.begin();
if(getParameters().transparent) {
while(it != img.end()) {
if(*it != transColor) {
++hist0.at((*it).getRed());
++hist1.at((*it).getGreen());
++hist2.at((*it).getBlue());
}
it++;
}
const int counterHalf = hist0.sumOfElements()/2;
// check for complete image transparent
if (counterHalf==0) {
setStatusString("only transparent pixels");
dest.resize(0);
return false;
}
int i,s;
i=-1,s=0;
while(++i<256 && s<counterHalf) {
s += hist0.at(i);
}
dest.at(0) = i-1;
i=-1,s=0;
while(++i<256 && s<counterHalf) {
s += hist1.at(i);
}
dest.at(1) = i-1;
i=-1,s=0;
while(++i<256 && s<counterHalf) {
s += hist2.at(i);
}
dest.at(2) = i-1;
} else { // no transparent color
while(it != img.end()) {
++hist0.at((*it).getRed());
++hist1.at((*it).getGreen());
++hist2.at((*it).getBlue());
it++;
}
const int counterHalf = img.columns()*img.rows()/2;
int i,s;
i=-1,s=0;
while(++i<256 && s<counterHalf) {
s += hist0.at(i);
}
dest.at(0) = i-1;
i=-1,s=0;
while(++i<256 && s<counterHalf) {
s += hist1.at(i);
}
dest.at(1) = i-1;
i=-1,s=0;
while(++i<256 && s<counterHalf) {
s += hist2.at(i);
}
dest.at(2) = i-1;
}
// normalize to 0..1
dest.divide(255);
return true;
};
int main( int argc, char* argv[])
{
if( argc != 3)
{
std::cerr << "Usage: "<<argv[0]<<" [input.nc] [output.nc]\n";
return -1;
}
std::cout << argv[1]<< " -> "<<argv[2]<<std::endl;
//----------------
const unsigned Nhist = 50;
const unsigned nhist = 1;
const unsigned Ninput =100;
const double Nsigma =4.;
std::vector<double> input1(Ninput,0.);
std::vector<double> input2(Ninput,0.);
thrust::random::minstd_rand generator;
thrust::random::normal_distribution<double> d1;
thrust::random::normal_distribution<double> d2;
std::vector<double> rand1(Ninput,0.);
std::vector<double> rand2(Ninput,0.);
for (unsigned i=0;i<rand1.size();i++) { rand1[i] = d1(generator); }
for (unsigned i=0;i<rand2.size();i++) { rand2[i] = d2(generator); }
for (unsigned i=0;i<input1.size();i++) {
double t = (double)(i/(input1.size()-1));
double omega1 =2.*M_PI* 20.;
input1[i] = (rand1[i]*0.1*cos( omega1*t)+1.);
}
for (unsigned i=0;i<input2.size();i++) {
double t = (double)(i/(input2.size()-1));
//double omega1 = 2.*M_PI*20.;
double omega2= 2.*M_PI*30.;
//double phase = 0.5*M_PI;
// input2[i] =input1[i]; //perfectly correlated
// input2[i] = (-rand1[i]*0.1*cos(omega1*t)+1.);//perfectly anticorrelated
// input2[i] = (rand2[i]*0.001*cos(omega2*t)+3.);//perfectly uncorrelated
input2[i] = (rand2[i]*0.001*cos(omega2*t)+3.);//uncorrelated
}
//normalize grid and compute sigma
NormalizeToFluc(input1);
NormalizeToFluc(input2);
dg::Grid1d g1d1(-Nsigma,Nsigma, nhist, Nhist,dg::DIR);
dg::Grid1d g1d2(-Nsigma,Nsigma, nhist, Nhist,dg::DIR);
dg::Grid2d g2d( -Nsigma,Nsigma,-Nsigma,Nsigma, nhist, Nhist,Nhist,dg::DIR,dg::DIR);
dg::Histogram<dg::HVec> hist1(g1d1,input1);
dg::Histogram<dg::HVec> hist2(g1d2,input2);
dg::Histogram2D<dg::HVec> hist12(g2d,input1,input2);
dg::HVec PA1 = dg::evaluate(hist1,g1d1);
dg::HVec A1 = dg::evaluate(dg::cooX1d,g1d1);
dg::HVec PA2= dg::evaluate(hist2,g1d2);
dg::HVec A2 = dg::evaluate(dg::cooX1d,g1d2);
dg::HVec PA1A2= dg::evaluate(hist12,g2d);
//-----------------NC output start
int dataIDs1[2],dataIDs2[2],dataIDs12[1];
int dim_ids1[1],dim_ids2[1],dim_ids12[2];
int ncid;
file::NC_Error_Handle err;
err = nc_create(argv[2],NC_NETCDF4|NC_CLOBBER, &ncid);
//plot 1
err = file::define_dimension( ncid,"A1_", &dim_ids1[0], g1d1);
err = nc_def_var( ncid, "P(A1)", NC_DOUBLE, 1, &dim_ids1[0], &dataIDs1[0]);
err = nc_def_var( ncid, "A1", NC_DOUBLE, 1, &dim_ids1[0], &dataIDs1[1]);
err = nc_enddef( ncid);
err = nc_put_var_double( ncid, dataIDs1[0], PA1.data() );
err = nc_put_var_double( ncid, dataIDs1[1], A1.data() );
err = nc_redef(ncid);
//plot 2
err = file::define_dimension( ncid,"A2_", &dim_ids2[0], g1d2);
err = nc_def_var( ncid, "P(A2)", NC_DOUBLE, 1, &dim_ids2[0], &dataIDs2[0]);
err = nc_def_var( ncid, "A2", NC_DOUBLE, 1, &dim_ids2[0], &dataIDs2[1]);
err = nc_enddef( ncid);
err = nc_put_var_double( ncid, dataIDs2[0], PA2.data() );
err = nc_put_var_double( ncid, dataIDs2[1], A2.data() );
err = nc_redef(ncid);
//plot12
// dim_ids12[0]=dim_ids1[0];
// dim_ids12[1]=dim_ids2[0];
dim_ids12[0]=dataIDs1[0];
dim_ids12[1]=dataIDs2[0];
err = file::define_dimensions( ncid, &dim_ids12[0], g2d);
err = nc_def_var( ncid, "P(A1,A2)", NC_DOUBLE, 2, &dim_ids12[0], &dataIDs12[0]);
err = nc_enddef( ncid);
err = nc_put_var_double( ncid, dataIDs12[0], PA1A2.data() );
err = nc_redef(ncid);
nc_close( ncid);
return 0;
}
