void NtupleChecker(){
TString path = "/uscms_data/d2/uplegger/CMSSW/CMSSW_3_8_0_pre7/src/RecoVertex/BeamSpotProducer/test/scripts/Ntuples/";
TSystemDirectory sourceDir("fileDir",path);
TList* fileList = sourceDir.GetListOfFiles();
TIter next(fileList);
TSystemFile* fileName;
int fileNumber = 1;
int maxFiles = 1000;
BeamSpotTreeData aData;
while ((fileName = (TSystemFile*)next()) && fileNumber <= maxFiles){
if(TString(fileName->GetName()) == "." || TString(fileName->GetName()) == ".." ){
continue;
}
TTree* aTree = 0;
TFile file(path+fileName->GetName(),"READ");//STARTUP
cout << "Opening file: " << path+fileName->GetName() << endl;
file.cd();
// aTree = (TTree*)file.Get("PrimaryVertices");
aTree = (TTree*)file.Get("BeamSpotTree");
cout << (100*fileNumber)/(fileList->GetSize()-2) << "% of files done." << endl;
++fileNumber;
if(aTree == 0){
cout << "Can't find the tree" << endl;
continue;
}
aData.setBranchAddress(aTree);
for(unsigned int entry=0; entry<aTree->GetEntries(); entry++){
aTree->GetEntry(entry);
cout << aData.getRun() << endl;
}
}
}
void create_file(const char *dirname, TString filename, TString histoname, TString Prefix){
TSystemDirectory dir(dirname, dirname);
TList *fileslist = dir.GetListOfFiles();
if (fileslist) {
TSystemFile *systemfile;
TString fname;
TIter next(fileslist);
while ((systemfile=(TSystemFile*)next())) {
fname = systemfile->GetName();
//cout<<fname<<endl;
if (!systemfile->IsDirectory() && fname.Contains(filename+".root")) {
cout<<Prefix<<endl;
TFile* file = new TFile(dirname+fname,"READ");
TH1F* h1 = (TH1F*)file->Get(histoname);
//if(!fname.Contains("Bpb_TW")) h1->Scale(0.25);
//h1->Scale(15);
TFile* file2 = new TFile("/nfs/dust/cms/user/multh/RunII_76X_v1/Limit/Tstar_comb.root","UPDATE");
TString histname = h1->GetName();
h1->SetName(Prefix+"__"+filename);
h1->Write();
file->Close();
file2->Close();
}
}
}
}
void KVSimDir::AnalyseDirectory()
{
// Read contents of directory given to ctor.
// Each ROOT file will be analysed by AnalyseFile().
Info("AnalyseDirectory", "Analysing %s...", GetDirectory());
fSimData.Clear();
fFiltData.Clear();
//loop over files in current directory
TSystemDirectory thisDir(".", GetDirectory());
TList* fileList = thisDir.GetListOfFiles();
TIter nextFile(fileList);
TSystemFile* aFile = 0;
while ((aFile = (TSystemFile*)nextFile())) {
if (aFile->IsDirectory()) continue;
KVString fileName = aFile->GetName();
if (!fileName.EndsWith(".root")) continue; /* skip non-ROOT files */
AnalyseFile(fileName);
}
delete fileList;
}
void runTree(char* dir="hist_pAu2", char* filter="16142010", unsigned int neventsIn = 0){
// If nEvents is negative, reset to the maximum possible value for an Int_t
if( neventsIn <= 0 ) neventsIn = 1<<31-1;
TSystemDirectory DIR(dir, dir);
TList *files = DIR.GetListOfFiles();
TChain* trees = new TChain();
int nfile=0;
if (files) {
TSystemFile *file;
TString fname;
TIter next(files);
while ((file=(TSystemFile*)next())) {
fname = file->GetName();
if (!file->IsDirectory() && fname.BeginsWith(filter) && fname.EndsWith("tree.root")) {
cout << Form("Adding %s/%s to TChain",dir,fname.Data())<<endl;
trees->AddFile(Form("%s/%s/dipi0",dir,fname.Data()));
nfile++;
}
}
}
cout << Form("%d files added",nfile) << endl;
// load the shared libraries
std::cout << "***** Loading libraries *****" << endl;
LoadLibs();
// Create the analysis chain
analysisChain = new StChain("dipi0Chain");
//cout << "Constructing StFmsDbMaker" << endl;
//StFmsDbMaker* fmsDbMkr = new StFmsDbMaker( "fmsDb" );
//fmsDbMkrName = fgtDbMkr->GetName();
gSystem->Load("StFmsDiPi0");
StFmsDiPi0* dipi0=new StFmsDiPi0;
TString filenameDiPi0(Form("%s/%s.dipi0.root",dir,filter));
cout << "DiPi0 outfile name = " << filenameDiPi0.Data()<<endl;
dipi0->setFileName(filenameDiPi0.Data());
dipi0->setReadTree(trees);
analysisChain->Init();
Int_t ierr = kStOK; // err flag
Int_t nevents = 0; // cumulative number of events in
for( ; nevents < neventsIn && !ierr; ++nevents ){
if(nevents%10000==0) cout <<"event: "<< nevents <<endl;
analysisChain->Clear();
ierr = analysisChain->Make();
}
analysisChain->Finish();
analysisChain->Delete();
return;
};
void runSelector(TString runNumber = "30496", TString myPath = "/sciclone/data10/jrstevens01/RunPeriod-2017-01/analysis/ver08/tree_pi0pi0pimpip__B3/merged/")
{
// Load DSelector library
gROOT->ProcessLine(".x $(ROOT_ANALYSIS_HOME)/scripts/Load_DSelector.C");
int Proof_Nthreads = 8;
// process signal
TString sampleDir = myPath;
//sampleDir += Form("0%s/", runNumber.Data());
cout<<"running selector on files in: "<<sampleDir.Data()<<endl;
TChain *chain = new TChain("pi0pi0pimpip__B3_Tree");
TSystemDirectory dir(sampleDir, sampleDir);
TList *files = dir.GetListOfFiles();
int ifile = 0;
if(files) {
TSystemFile *file;
TString fileName;
TIter next(files);
// loop over files
while ((file=(TSystemFile*)next())) {
fileName = file->GetName();
if(fileName.Contains(runNumber)) {
cout<<fileName.Data()<<endl;
// check if file corrupted
TFile f(sampleDir+fileName);
if(f.TestBit(TFile::kRecovered)) {
cout<<"file corrupted -> skipping"<<endl;
continue;
}
if(f.IsZombie()) {
cout<<"file is a Zombie -> skipping"<<endl;
continue;
}
// add file to chain
chain->Add(sampleDir+fileName);
ifile++;
}
}
cout<<"total entries in TChain = "<<chain->GetEntries()<<" from "<<ifile<<" files"<<endl;
DPROOFLiteManager::Process_Chain(chain, "DSelector_pomegapi.C+", Proof_Nthreads, Form("hist_pomegapi_%s.acc.root", runNumber.Data()));
}
return;
}
void get_filenames(vector<TString> &names, const char *dirname=".", const char *ext=".root", const char *prefix = "uhh2")
{
TSystemDirectory dir(dirname, dirname);
TList *files = dir.GetListOfFiles();
if (files) {
TSystemFile *file;
TString fname;
TIter next(files);
while ((file=(TSystemFile*)next())) {
fname = file->GetName();
if (!file->IsDirectory() && fname.EndsWith(ext) && fname.BeginsWith(prefix)) {
names.push_back(TString(fname.Data()));
}
}
}
}
void BrowseHistograms(const char* histname) {
fHistName=histname;
TSystemDirectory dir(".",".");
TList *files = dir.GetListOfFiles();
if (!files) {
cerr << "Error: No files found in " << fFileDir << endl;
return;
}
files->Sort();
histograms = new TList();
saved = new TList();
c = new TCanvas("canvas","Browse Histograms");
TSystemFile *file;
TIter next(files);
Int_t n=0;
TString fname;
while ((file=(TSystemFile*)next())) {
fname = file->GetName();
if (!file->IsDirectory() && fname.EndsWith(".root")) {
histograms->Add(new TObjString(fname));
n++;
//if(n>10)
// break;
}
}
DrawCurrHistogram();
TControlBar *bar = new TControlBar("vertical", "Control", 10, 10);
bar->AddButton("Next","ProcessClick(1);", "Show next run");
bar->AddButton("Next And Save","NextAndSave();", "Show previous run");
bar->AddButton("Next And Forget","NextAndForget();", "Show previous run");
bar->AddButton("Prev","ProcessClick(-1);", "Show previous run");
bar->AddButton("Print saved","PrintSaved();", "Show previous run");
bar->SetButtonWidth(90);
bar->Show();
}
vector<TString> list_files(const char *dirname, const char *exp=".*HiForestAOD.*\\.root")
{
vector<TString> names;
TSystemDirectory dir(dirname, dirname);
TList *files = dir.GetListOfFiles();
if (files) {
TSystemFile *file;
TString fname;
TIter next(files);
while ((file=(TSystemFile*)next())) {
fname = file->GetName();
if (!file->IsDirectory() && fname.Contains(TRegexp(exp))) {
names.push_back(TString(dirname)+"/"+fname);
}
}
}
if (names.size()==0) return {dirname};
return names;
}
void addfiles(TChain *ch, const TString dirname=".", const TString ext=".root")
{
bool verbose(false);
TSystemDirectory dir(dirname, dirname);
TList *files = dir.GetListOfFiles();
if (files) {
if (verbose) std::cout << "Found files" << std::endl;
TSystemFile *file;
TString fname;
TIter next(files);
while ((file=(TSystemFile*)next())) {
fname = file->GetName();
if (verbose) std::cout << "found fname " << fname << std::endl;
if (!file->IsDirectory() && fname.BeginsWith(ext)) {
if (verbose) std::cout << "adding fname " << fname << std::endl;
ch->Add(fname);
}
}
}
}
// Returns list of directorites or files in folder
vector<TString> dirlist(const TString &folder,
const TString &inname,
const TString &tag) {
TString pwd(gSystem->pwd());
vector<TString> v_dirs;
TSystemDirectory dir(folder, folder);
TList *files = dir.GetListOfFiles();
if (files) {
TSystemFile *file;
TString fname;
TIter next(files);
while ((file=static_cast<TSystemFile*>(next()))) {
fname = file->GetName();
if (inname=="dir") {
if ((file->IsDirectory() && !fname.Contains(".") && fname.Contains(tag))) v_dirs.push_back(fname);
} else if(fname.Contains(inname)) v_dirs.push_back(fname);
}
} // if(files)
gSystem->cd(pwd); // The TSystemDirectory object seems to change current folder
return v_dirs;
}
void add_periods(const char *newname, const char *perioddir)
{
int i, j, K, N, L;
char str[1024];
TList *keys;
TFile *fIn;
TFile *f0;
TSystemFile *fSys;
char prefix[128];
char *ptr;
TNamed *obj;
TH1D *h;
TH1D *hist[MAXHIST];
TSystemDirectory *dir = new TSystemDirectory("MyDir", perioddir);
TList *files = dir->GetListOfFiles();
if (!files) {
printf("%s - nothing to do(files)\n", perioddir);
delete dir;
return;
}
N = files->GetEntries() - 2;
if (N <= 1) {
printf("%s - nothing to do\n", perioddir);
delete dir;
return;
}
TFile *fNew = new TFile(newname, "RECREATE");
if (!fNew->IsOpen()) {
delete dir;
delete files;
return;
}
fSys = (TSystemFile *) files->At(2);
if (!fSys) {
printf("Can not open the first file\n");
delete dir;
delete files;
return;
}
sprintf(str, "%s/%s", perioddir, fSys->GetName());
f0 = new TFile(str);
if (!f0->IsOpen()) {
printf("Can not open the first file\n");
delete dir;
delete files;
return;
}
keys = f0->GetListOfKeys();
K = keys->GetEntries();
if (K <= 0) {
printf("Nothing to do: K=0\n");
delete dir;
delete files;
return;
}
L = 0;
for (j=0; j<K; j++) {
obj = (TNamed *) keys->At(j);
if (!obj) continue;
obj = (TNamed *) f0->Get(obj->GetName());
if (!obj) continue;
if (strcmp(obj->ClassName(), "TH1D")) continue;
hist[L] = (TH1D *)obj;
L++;
}
if (!L) {
printf("Nothing to do: L=0\n");
f0->Close();
delete dir;
delete files;
return;
}
for (i=1; i<N; i++) {
fSys = (TSystemFile *) files->At(i+2); // skip . and ..
if (!fSys) continue;
sprintf(str, "%s/%s", perioddir, fSys->GetName());
fIn = new TFile(str);
if (!fIn->IsOpen()) continue;
for (j=0; j<L; j++) {
h = (TH1D *) fIn->Get(hist[j]->GetName());
if (!h) continue;
hist[j]->Add(h);
}
// printf("%s\n", fSys->GetName());
fIn->Close();
}
fNew->cd();
for (j=0; j<L; j++) hist[j]->Write();
fNew->Close();
f0->Close();
delete files;
delete dir;
}
int eff(){
cout << "Starting Efficency Script" << endl;
cout << "Usage: eff( module_name_string , starting_hr_file_string )" << endl;
cout << "for defaults enter \"hr\" for starting hr file designator. " << endl;
char chpath[256];
getcwd(chpath, 255);
std::string path = chpath;
std::string mod("paxxx");//<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
//if( newmod != "" ) mod = newmod;
// <<<<<< change folder/module name to run in
//std::string mod("yhc691015sn3p35");
std::string dataPath = path + "/" + mod + "data";
//std::string measurementFolder = mod + "data";
//std::string configPath = path + "/" + mod;
std::string configPath = path;
std::string HighRateSaveFileName( "Results_Hr" );
std::string HighRateFileName( "hr" );//<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
//if( fileDesg != "" ) HighRateFileName = fileDesg;
int namelength = HighRateFileName.length();
// <<<<<<<<<<<<<<<<<<< change Highrate File name to use
// assumes something like hr08ma_pa225_082715.root
// 10 or 08 or 06 or 04 or 02 required after hr
// -- looks for a root file with "HighRateFileName" followed by 10 or 08 or ect....
// -- so will parse hr10****.root and hr08**********.root ect... with above settings
std::string moduleName = mod;
std::string maskFileName("defaultMaskFile.dat");
//phrun files here
std::string phLowName("dc05_mn325_0503.root");
std::string phHighName("dc15_mn325_0503.root");
//
const bool FIDUCIAL_ONLY = true; // don't change
const bool VERBOSE = true;
int nTrigPerPixel = 50; // will be read from testParameters.dat
int nPixels = 4160;
int nTrig = nTrigPerPixel * nPixels;
float pixelArea = 0.01 * 0.015; // cm^2
float triggerDuration = 25e-9; //s
const int nRocs = 16;
const int nDCol = 25;
double worstDCol[nRocs];
for( int i = 0; i<nRocs; i++) worstDCol[i] = -1;
double worstDColEff[nRocs];
for( int i = 0; i<nRocs; i++) worstDColEff[i] = 10;
double bestDCol[nRocs];
for( int i = 0; i<nRocs; i++) bestDCol[i] = -1;
double bestDColEff[nRocs];
for( int i = 0; i<nRocs; i++) bestDColEff[i] = 10;
double lowestdceff = 10;
int lowestdc = -1;
int lowestroc = 25;
double highdceff = -10;
int highdc = -1;
int highroc = 25;
std::string directoryList = mod;
std::string outFileName = dataPath + "/" + HighRateFileName + "Efficiency.log";
std::ofstream log(outFileName.c_str());
cout << "search for HREfficiency folders in elComandante folder structure" << endl;
log << "High Rate Efficency Log File Module: "<< mod << endl << endl;
TSystemDirectory dir(dataPath.c_str(), dataPath.c_str());
TList *files = dir.GetListOfFiles();
std::vector<std::string> fileList;
if (files) {
TSystemFile *file;
TString fname;
TIter next(files);
while (file=(TSystemFile*)next()) {
fname = file->GetName();
std::cout << fname << endl;
std::string filename = fname.Data();
if (filename.substr(0,namelength) == HighRateFileName ) {
if( filename.substr(( filename.length() - 4 ), 4) == "root" ){
fileList.push_back(filename);
std::cout << "---Added to fileList" << endl;
}
}
}
}
std::vector<double> ylist;
ylist.push_back(0.05);
ylist.push_back(0.10);
ylist.push_back(0.15);
// ylist.push_back(0.15);
// ylist.push_back(0.15);
std::cout << " Declaring vectors" << endl;
std::vector< std::vector< std::pair< int,int > > > maskedPixels;
std::vector< std::vector< double > > efficiencies;
std::vector< std::vector< double > > efficiencyErrors;
std::vector< std::vector< double > > rates;
std::vector< std::vector< double > > rateErrors;
std::vector< double > hitslow;
std::vector< double > hitshigh;
std::vector< double > efflow;
std::vector< double > effhigh;
std::vector< double > DCUni;
std::vector< double > DCUniNum;
// std::vector< std::vector< std::vector< double > > > byAmpEfficiencies;
// std::vector< std::vector< std::vector< double > > > byAmpEfficiencyErrors;
// std::vector< std::vector< std::vector< double > > > byAmpRates;
// std::vector< std::vector< std::vector< double > > > byAmpRateErrors;
std::vector< std::vector< std::vector< double > > > dcolHits;
std::vector< std::vector< std::vector< double > > > dcolHitErrors;
std::vector< std::vector< std::vector< double > > > dcolRates;
std::vector< std::vector< std::vector< double > > > dcolRateErrors;
std::vector< std::vector< std::vector< double > > > dcolEff;
std::vector< std::vector< std::vector< double > > > dcolEffErrors;
std::vector< std::vector< double > > lineList;
std::vector<std::vector<double>> dclineList;
std::vector< std::vector< double > > bigempty;
std::vector< double > empty;
std::cout << " Line Lists" << endl;
for( int i=0; i <4; i++ ){
lineList.push_back(empty);
dclineList.push_back(empty);
}
for( int i = 0; i<201;i++){
lineList[1].push_back(.98);
lineList[0].push_back(i);
}
for( int i = 0; i<= 100; i++ ){
lineList[3].push_back(i/100);
lineList[2].push_back(120.0);
}
for( int i = 0; i <= 450; i++){
dclineList[1].push_back(1.5);
dclineList[0].push_back(i);
dclineList[3].push_back(0.6);
dclineList[2].push_back(i);
}
// std::cout << "byamp inits" << endl;
/* for (int i=0;i<=5;i++){
byAmpEfficiencies.push_back(bigempty);
byAmpEfficiencyErrors.push_back(bigempty);
byAmpRates.push_back(bigempty);
byAmpRateErrors.push_back(bigempty);
}
*/
// std::cout << "initilizing 2 tier" << endl;
/* for( int i=0; i<=5; i++){
for( int j=0;j<=nRocs;j++){
byAmpEfficiencies[i].push_back(empty);
byAmpEfficiencyErrors[i].push_back(empty);
byAmpRates[i].push_back(empty);
byAmpRateErrors[i].push_back(empty);
}
}
*/
//std::cout << "initilizing vectors" << endl;
for (int i=0;i<=nRocs;i++) {
efficiencies.push_back(empty);
efficiencyErrors.push_back(empty);
rates.push_back(empty);
rateErrors.push_back(empty);
dcolHits.push_back(bigempty);
dcolHitErrors.push_back(bigempty);
dcolRates.push_back(bigempty);
dcolRateErrors.push_back(bigempty);
dcolEff.push_back(bigempty);
dcolEffErrors.push_back(bigempty);
}
int dc98count[nRocs][nDCol];
int dc95count[nRocs][nDCol];
int totdc98 = 0;
int totdc95 = 0;
int dc08count[nRocs][nDCol];
int dc12count[nRocs][nDCol];
int totdc08 = 0;
int totdc12 = 0;
int dcbothcount[nRocs][nDCol];
int totdcboth = 0;
for( int i=0; i<nRocs; i++){
for( int j=0; j<nDCol; j++){
dc98count[i][j] = 0;
dc95count[i][j] = 0;
dc08count[i][j] = 0;
dc12count[i][j] = 0;
dcbothcount[i][j] = 0;
}
}
for( int i=0; i<=nRocs; i++){
for( int j=0; j<=nDCol; j++){
dcolHits[i].push_back(empty);
dcolHitErrors[i].push_back(empty);
dcolRates[i].push_back(empty);
dcolRateErrors[i].push_back(empty);
dcolEff[i].push_back(empty);
dcolEffErrors[i].push_back(empty);
}
}
std::cout << "loop over all commander_HREfficiency.root root files" << endl;
for (int i=0;i<1;++i) {
chdir(path.c_str());
std::cout << "looking in directory <" << directoryList << ">" << std::endl;
std::cout << "From " << path << endl;
std::string parmFile = directoryList + "/testParameters.dat";
cout << "For " << parmFile << endl;
std::ifstream testParameters(parmFile.c_str());
std::string line2;
cout << "Getting line" << endl;
while (getline(testParameters, line2)) {
cout << line2 << endl;
if (line2.find("HighRate") != std::string::npos) {
while (getline(testParameters, line2)) {
if (line2.size() < 1) break;
size_t pos = line2.find("Ntrig");
std::cout << line2 << " " << pos << endl;
if (pos != std::string::npos) {
nTrigPerPixel = atoi(line2.substr(pos+6).c_str());
nTrig = nTrigPerPixel * nPixels;
std::cout << ">" << line2 << "< pos:" << pos << std::endl;
std::cout << "number of triggers per pixel: " << nTrigPerPixel << std::endl;
}
}
}
}
testParameters.close();
// read masked pixels
maskedPixels.clear();
for (int j=0;j<nRocs;j++) {
std::vector< std::pair<int,int> > rocMaskedPixels;
maskedPixels.push_back(rocMaskedPixels);
}
std::ifstream maskFile;
char maskFilePath[256];
sprintf(maskFilePath, "%s/%s/%s", path.c_str(), directoryList.c_str(), maskFileName.c_str());
maskFile.open(maskFilePath, std::ifstream::in);
if (!maskFile) {
std::cout << "ERROR: mask file <" << maskFilePath << "> can't be opened!"<<std::endl;
}
std::string line;
std::vector< std::string > tokens;
while(getline(maskFile, line)) {
if (line[0] != '#') {
std::stringstream ss(line);
std::string buf;
tokens.clear();
while (ss >> buf) {
tokens.push_back(buf);
}
std::cout << "tok0 <" << tokens[0] << "> ";
if (tokens[0] == "pix" && tokens.size() >= 4) {
int roc = atoi(tokens[1].c_str());
int col = atoi(tokens[2].c_str());
int row = atoi(tokens[3].c_str());
std::cout << "mask pixel " << roc << " " << col << " " << row << std::endl;
maskedPixels[roc].push_back(std::make_pair(col, row));
}
}
}
maskFile.close();
}
TChain* CreateESDChain(const char* aDataDir = "ESDfiles.txt", Int_t aRuns = 20, Int_t offset = 0, Bool_t addFileName = kFALSE, Bool_t addFriend = kFALSE, const char* check = 0)
{
// creates chain of files in a given directory or file containing a list.
// In case of directory the structure is expected as:
// <aDataDir>/<dir0>/AliESDs.root
// <aDataDir>/<dir1>/AliESDs.root
// ...
//
// if addFileName is true the list only needs to contain the directories that contain the AliESDs.root files
// if addFriend is true a file AliESDfriends.root is expected in the same directory and added to the chain as friend
// if check is != 0 the files that work are written back into the textfile with the name check
if (!aDataDir)
return 0;
Long_t id, size, flags, modtime;
if (gSystem->GetPathInfo(aDataDir, &id, &size, &flags, &modtime))
{
printf("%s not found.\n", aDataDir);
return 0;
}
TChain* chain = new TChain("esdTree");
TChain* chainFriend = 0;
if (addFriend)
chainFriend = new TChain("esdFriendTree");
if (flags & 2)
{
TString execDir(gSystem->pwd());
TSystemDirectory* baseDir = new TSystemDirectory(".", aDataDir);
TList* dirList = baseDir->GetListOfFiles();
Int_t nDirs = dirList->GetEntries();
gSystem->cd(execDir);
Int_t count = 0;
for (Int_t iDir=0; iDir<nDirs; ++iDir)
{
TSystemFile* presentDir = (TSystemFile*) dirList->At(iDir);
if (!presentDir || !presentDir->IsDirectory() || strcmp(presentDir->GetName(), ".") == 0 || strcmp(presentDir->GetName(), "..") == 0)
continue;
if (offset > 0)
{
--offset;
continue;
}
if (count++ == aRuns)
break;
TString presentDirName(aDataDir);
presentDirName += "/";
presentDirName += presentDir->GetName();
chain->Add(presentDirName + "/AliESDs.root/esdTree");
}
}
else
{
// Open the input stream
ifstream in;
in.open(aDataDir);
ofstream outfile;
if (check)
outfile.open(check);
Int_t count = 0;
// Read the input list of files and add them to the chain
TString line;
while (in.good())
{
in >> line;
if (line.Length() == 0)
continue;
if (offset > 0)
{
offset--;
continue;
}
if (count++ == aRuns)
break;
TString esdFile(line);
if (addFileName)
esdFile += "/AliESDs.root";
TString esdFileFriend(esdFile);
esdFileFriend.ReplaceAll("AliESDs.root", "AliESDfriends.root");
if (check)
{
TFile* file = TFile::Open(esdFile);
if (!file)
continue;
file->Close();
if (chainFriend)
{
TFile* file = TFile::Open(esdFileFriend);
if (!file)
continue;
file->Close();
}
outfile << line.Data() << endl;
printf("%s\n", line.Data());
}
// add esd file
chain->Add(esdFile);
// add file
if (chainFriend)
chainFriend->Add(esdFileFriend);
}
in.close();
if (check)
outfile.close();
}
if (chainFriend)
chain->AddFriend(chainFriend);
return chain;
}
//void TMVAClassification( TString myMethodList = "" )
void tmvaClassifier( TString myMethodList = "", TString inputDir="~/work/ewkzp2j_5311/ll/", bool minimalTrain=false, bool useQG=false)
{
gSystem->ExpandPathName(inputDir);
TString pf("base_weights");
if(!minimalTrain){
if(useQG) pf="full_weights";
else pf="weights";
}
TMVA::gConfig().GetIONames().fWeightFileDir = inputDir + pf;
// The explicit loading of the shared libTMVA is done in TMVAlogon.C, defined in .rootrc
// if you use your private .rootrc, or run from a different directory, please copy the
// corresponding lines from .rootrc
// methods to be processed can be given as an argument; use format:
//
// mylinux~> root -l TMVAClassification.C\(\"myMethod1,myMethod2,myMethod3\"\)
//
// if you like to use a method via the plugin mechanism, we recommend using
//
// mylinux~> root -l TMVAClassification.C\(\"P_myMethod\"\)
// (an example is given for using the BDT as plugin (see below),
// but of course the real application is when you write your own
// method based)
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
// --- Cut optimisation
Use["Cuts"] = 0;
Use["CutsD"] = 0;
Use["CutsPCA"] = 0;
Use["CutsGA"] = 0;
Use["CutsSA"] = 0;
//
// --- 1-dimensional likelihood ("naive Bayes estimator")
Use["Likelihood"] = 0;
Use["LikelihoodD"] = 1; // the "D" extension indicates decorrelated input variables (see option strings)
Use["LikelihoodPCA"] = 0; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
Use["LikelihoodKDE"] = 0;
Use["LikelihoodMIX"] = 0;
//
// --- Mutidimensional likelihood and Nearest-Neighbour methods
Use["PDERS"] = 0;
Use["PDERSD"] = 0;
Use["PDERSPCA"] = 0;
Use["PDEFoam"] = 0;
Use["PDEFoamBoost"] = 0; // uses generalised MVA method boosting
Use["KNN"] = 0; // k-nearest neighbour method
//
// --- Linear Discriminant Analysis
Use["LD"] = 0; // Linear Discriminant identical to Fisher
Use["Fisher"] = 1;
Use["FisherCat"] = 0;//added by loic
Use["FisherG"] = 0;
Use["BoostedFisher"] = 0; // uses generalised MVA method boosting
Use["HMatrix"] = 0;
//
// --- Function Discriminant analysis
Use["FDA_GA"] = 0; // minimisation of user-defined function using Genetics Algorithm
Use["FDA_SA"] = 0;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 0;
Use["FDA_GAMT"] = 0;
Use["FDA_MCMT"] = 0;
//
// --- Neural Networks (all are feed-forward Multilayer Perceptrons)
Use["MLP"] = 0; // Recommended ANN
Use["MLPBFGS"] = 0; // Recommended ANN with optional training method
Use["MLPBNN"] = 0; // Recommended ANN with BFGS training method and bayesian regulator
Use["CFMlpANN"] = 0; // Depreciated ANN from ALEPH
Use["TMlpANN"] = 0; // ROOT's own ANN
//
// --- Support Vector Machine
Use["SVM"] = 0;
//
// --- Boosted Decision Trees
Use["BDT"] = 0; // uses Adaptive Boost
Use["BDTG"] = 0; // uses Gradient Boost
Use["BDTB"] = 0; // uses Bagging
Use["BDTD"] = 1; // decorrelation + Adaptive Boost
Use["BDTF"] = 0; // allow usage of fisher discriminant for node splitting
//
// --- Friedman's RuleFit method, ie, an optimised series of cuts ("rules")
Use["RuleFit"] = 0;
// ---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVAClassification" << std::endl;
// Select methods (don't look at this code - not of interest)
if (myMethodList != "") {
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;
std::vector<TString> mlist = TMVA::gTools().SplitString( myMethodList, ',' );
for (UInt_t i=0; i<mlist.size(); i++) {
std::string regMethod(mlist[i]);
if (Use.find(regMethod) == Use.end()) {
std::cout << "Method \"" << regMethod << "\" not known in TMVA under this name. Choose among the following:" << std::endl;
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) std::cout << it->first << " ";
std::cout << std::endl;
return;
}
Use[regMethod] = 1;
}
}
// --------------------------------------------------------------------------------------------------
// --- Here the preparation phase begins
// Create a ROOT output file where TMVA will store ntuples, histograms, etc.
TString outfileName( "TMVA.root" );
TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
// Create the factory object. Later you can choose the methods
// whose performance you'd like to investigate. The factory is
// the only TMVA object you have to interact with
//
// The first argument is the base of the name of all the
// weightfiles in the directory weight/
//
// The second argument is the output file for the training results
// All TMVA output can be suppressed by removing the "!" (not) in
// front of the "Silent" argument in the option string
TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" );
// You can add so-called "Spectator variables", which are not used in the MVA training,
// but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the
// input variables, the response values of all trained MVAs, and the spectator variables
// factory->AddSpectator( "spec1 := var1*2", "Spectator 1", "units", 'F' );
// factory->AddSpectator( "spec2 := var1*3", "Spectator 2", "units", 'F' );
// Read training and test data
// (it is also possible to use ASCII format as input -> see TMVA Users Guide)
// TString fname = "./tmva_class_example.root";
//if (gSystem->AccessPathName( fname )) // file does not exist in local directory
// gSystem->Exec("wget http://root.cern.ch/files/tmva_class_example.root");
// std::cout << "--- TMVAClassification : Using input file: " << input->GetName() << std::endl;
// --- Register the training and test trees
TChain *signal = new TChain("ewkzp2j");
TChain *background = new TChain("ewkzp2j");
TSystemDirectory dir(inputDir,inputDir);
TList *files = dir.GetListOfFiles();
if (files) {
TSystemFile *file;
TString fname;
TIter next(files);
while ((file=(TSystemFile*)next())) {
fname = file->GetName();
if(!fname.EndsWith("_summary.root")) continue;
if(fname.Contains("Data")) continue;
if(!fname.Contains("DY")) continue;
bool isSignal(false);
if(fname.Contains("JJ")) { signal->Add(fname); isSignal=true; }
else if(fname.Contains("50toInf") && fname.Contains("DY")) background->Add(fname);
cout << fname << " added as " << (isSignal ? "signal" : "background") << endl;
}
}else{
cout << "[Error] no files found in " << inputDir << endl;
}
cout << "Signal has " << signal->GetEntries() << " raw events" << endl
<< "Background has " << background->GetEntries() << " raw events"<< endl;
// global event weights per tree
Double_t signalWeight = 1.0;
Double_t backgroundWeight = 1.0;
factory->AddSignalTree ( signal, signalWeight );
factory->AddBackgroundTree( background, backgroundWeight );
// event-per-event weights per tree
factory->SetBackgroundWeightExpression( "weight/cnorm" );
factory->SetSignalWeightExpression( "weight/cnorm" );
//define variables for the training
if(minimalTrain)
{
factory->AddVariable( "mjj", "M_{jj}" "GeV", 'F' );
factory->AddVariable( "detajj", "#Delta#eta_{jj}", "", 'F' );
factory->AddVariable( "spt", "#Delta_{rel}", "GeV", 'F' );
}
else
{
factory->AddVariable( "mjj", "M_{jj}" "GeV", 'F' );
factory->AddVariable( "detajj", "#Delta#eta_{jj}", "", 'F' );
factory->AddVariable( "setajj", "#Sigma#eta_{j}", "", 'F' );
factory->AddVariable( "eta1", "#eta(1)", "", 'F' );
factory->AddVariable( "eta2", "#eta(2)", "", 'F' );
factory->AddVariable( "pt1", "p_{T}(1)", "GeV", 'F' );
factory->AddVariable( "pt2", "p_{T}(2)", "GeV", 'F' );
factory->AddVariable( "spt", "#Delta_{rel}", "GeV", 'F' );
if(useQG) factory->AddVariable( "qg1", "q/g(1)", "", 'F' );
if(useQG) factory->AddVariable( "qg2", "q/g(2)", "", 'F' );
}
// Apply additional cuts on the signal and background samples (can be different)
TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5";
factory->PrepareTrainingAndTestTree( mycuts, mycutb,
"nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );
// ---- Book MVA methods
//
// Please lookup the various method configuration options in the corresponding cxx files, eg:
// src/MethoCuts.cxx, etc, or here: http://tmva.sourceforge.net/optionRef.html
// it is possible to preset ranges in the option string in which the cut optimisation should be done:
// "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable
// Cut optimisation
if (Use["Cuts"])
factory->BookMethod( TMVA::Types::kCuts, "Cuts",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" );
if (Use["CutsD"])
factory->BookMethod( TMVA::Types::kCuts, "CutsD",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=Decorrelate" );
if (Use["CutsPCA"])
factory->BookMethod( TMVA::Types::kCuts, "CutsPCA",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=PCA" );
if (Use["CutsGA"])
factory->BookMethod( TMVA::Types::kCuts, "CutsGA",
"H:!V:FitMethod=GA:CutRangeMin[0]=-10:CutRangeMax[0]=10:VarProp[1]=FMax:EffSel:Steps=30:Cycles=3:PopSize=400:SC_steps=10:SC_rate=5:SC_factor=0.95" );
if (Use["CutsSA"])
factory->BookMethod( TMVA::Types::kCuts, "CutsSA",
"!H:!V:FitMethod=SA:EffSel:MaxCalls=150000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );
// Likelihood ("naive Bayes estimator")
if (Use["Likelihood"])
factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood",
"H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" );
// Decorrelated likelihood
if (Use["LikelihoodD"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodD",
"!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=Decorrelate" );
// PCA-transformed likelihood
if (Use["LikelihoodPCA"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodPCA",
"!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=PCA" );
// Use a kernel density estimator to approximate the PDFs
if (Use["LikelihoodKDE"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodKDE",
"!H:!V:!TransformOutput:PDFInterpol=KDE:KDEtype=Gauss:KDEiter=Adaptive:KDEFineFactor=0.3:KDEborder=None:NAvEvtPerBin=50" );
// Use a variable-dependent mix of splines and kernel density estimator
if (Use["LikelihoodMIX"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodMIX",
"!H:!V:!TransformOutput:PDFInterpolSig[0]=KDE:PDFInterpolBkg[0]=KDE:PDFInterpolSig[1]=KDE:PDFInterpolBkg[1]=KDE:PDFInterpolSig[2]=Spline2:PDFInterpolBkg[2]=Spline2:PDFInterpolSig[3]=Spline2:PDFInterpolBkg[3]=Spline2:KDEtype=Gauss:KDEiter=Nonadaptive:KDEborder=None:NAvEvtPerBin=50" );
// Test the multi-dimensional probability density estimator
// here are the options strings for the MinMax and RMS methods, respectively:
// "!H:!V:VolumeRangeMode=MinMax:DeltaFrac=0.2:KernelEstimator=Gauss:GaussSigma=0.3" );
// "!H:!V:VolumeRangeMode=RMS:DeltaFrac=3:KernelEstimator=Gauss:GaussSigma=0.3" );
if (Use["PDERS"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERS",
"!H:!V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600" );
if (Use["PDERSD"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERSD",
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=Decorrelate" );
if (Use["PDERSPCA"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERSPCA",
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=PCA" );
// Multi-dimensional likelihood estimator using self-adapting phase-space binning
if (Use["PDEFoam"])
factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoam",
"!H:!V:SigBgSeparate=F:TailCut=0.001:VolFrac=0.0666:nActiveCells=500:nSampl=2000:nBin=5:Nmin=100:Kernel=None:Compress=T" );
if (Use["PDEFoamBoost"])
factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoamBoost",
"!H:!V:Boost_Num=30:Boost_Transform=linear:SigBgSeparate=F:MaxDepth=4:UseYesNoCell=T:DTLogic=MisClassificationError:FillFoamWithOrigWeights=F:TailCut=0:nActiveCells=500:nBin=20:Nmin=400:Kernel=None:Compress=T" );
// K-Nearest Neighbour classifier (KNN)
if (Use["KNN"])
factory->BookMethod( TMVA::Types::kKNN, "KNN",
"H:nkNN=20:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim" );
// H-Matrix (chi2-squared) method
if (Use["HMatrix"])
factory->BookMethod( TMVA::Types::kHMatrix, "HMatrix", "!H:!V:VarTransform=None" );
// Linear discriminant (same as Fisher discriminant)
if (Use["LD"])
factory->BookMethod( TMVA::Types::kLD, "LD", "H:!V:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
// Fisher discriminant (same as LD)
if (Use["Fisher"])
factory->BookMethod( TMVA::Types::kFisher, "Fisher", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
if (Use["FisherCat"]){
TMVA::MethodBase* fiCat = factory->BookMethod( TMVA::Types::kCategory, "FisherCat","" );
TMVA::MethodCategory* mcategory = dynamic_cast<TMVA::MethodCategory*>(fiCat);
mcategory->AddMethod( "mjj<250", "mjj:detajj:spt:", TMVA::Types::kFisher, "Fisher_Cat1", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
mcategory->AddMethod( "mjj>=250&&mjj<350" , "mjj:detajj:spt:", TMVA::Types::kFisher, "Fisher_Cat0000", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
mcategory->AddMethod( "mjj>=350&&mjj<450" , "mjj:detajj:spt:", TMVA::Types::kFisher, "Fisher_Cat0350", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
mcategory->AddMethod( "mjj>=450&&mjj<550" , "mjj:detajj:spt:", TMVA::Types::kFisher, "Fisher_Cat0450", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
mcategory->AddMethod( "mjj>=550&&mjj<750" , "mjj:detajj:spt:", TMVA::Types::kFisher, "Fisher_Cat0550", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
mcategory->AddMethod( "mjj>=750&&mjj<1000", "mjj:detajj:spt:", TMVA::Types::kFisher, "Fisher_Cat0750", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
mcategory->AddMethod( "mjj>=1000" , "mjj:detajj:spt:", TMVA::Types::kFisher, "Fisher_Cat1000", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
}
// Fisher with Gauss-transformed input variables
if (Use["FisherG"])
factory->BookMethod( TMVA::Types::kFisher, "FisherG", "H:!V:VarTransform=Gauss" );
// Composite classifier: ensemble (tree) of boosted Fisher classifiers
if (Use["BoostedFisher"])
factory->BookMethod( TMVA::Types::kFisher, "BoostedFisher",
"H:!V:Boost_Num=20:Boost_Transform=log:Boost_Type=AdaBoost:Boost_AdaBoostBeta=0.2:!Boost_DetailedMonitoring" );
// Function discrimination analysis (FDA) -- test of various fitters - the recommended one is Minuit (or GA or SA)
if (Use["FDA_MC"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MC",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:SampleSize=100000:Sigma=0.1" );
if (Use["FDA_GA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options])
factory->BookMethod( TMVA::Types::kFDA, "FDA_GA",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:PopSize=300:Cycles=3:Steps=20:Trim=True:SaveBestGen=1" );
if (Use["FDA_SA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options])
factory->BookMethod( TMVA::Types::kFDA, "FDA_SA",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=SA:MaxCalls=15000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );
if (Use["FDA_MT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=2:UseImprove:UseMinos:SetBatch" );
if (Use["FDA_GAMT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_GAMT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:Cycles=1:PopSize=5:Steps=5:Trim" );
if (Use["FDA_MCMT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MCMT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:SampleSize=20" );
// TMVA ANN: MLP (recommended ANN) -- all ANNs in TMVA are Multilayer Perceptrons
if (Use["MLP"])
factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:!UseRegulator" );
if (Use["MLPBFGS"])
factory->BookMethod( TMVA::Types::kMLP, "MLPBFGS", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:!UseRegulator" );
if (Use["MLPBNN"])
factory->BookMethod( TMVA::Types::kMLP, "MLPBNN", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:UseRegulator" ); // BFGS training with bayesian regulators
// CF(Clermont-Ferrand)ANN
if (Use["CFMlpANN"])
factory->BookMethod( TMVA::Types::kCFMlpANN, "CFMlpANN", "!H:!V:NCycles=2000:HiddenLayers=N+1,N" ); // n_cycles:#nodes:#nodes:...
// Tmlp(Root)ANN
if (Use["TMlpANN"])
factory->BookMethod( TMVA::Types::kTMlpANN, "TMlpANN", "!H:!V:NCycles=200:HiddenLayers=N+1,N:LearningMethod=BFGS:ValidationFraction=0.3" ); // n_cycles:#nodes:#nodes:...
// Support Vector Machine
if (Use["SVM"])
factory->BookMethod( TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=Norm" );
// Boosted Decision Trees
if (Use["BDTG"]) // Gradient Boost
factory->BookMethod( TMVA::Types::kBDT, "BDTG",
"!H:!V:NTrees=1000:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedGrad:GradBaggingFraction=0.5:nCuts=20:MaxDepth=2" );
if (Use["BDT"]) // Adaptive Boost
factory->BookMethod( TMVA::Types::kBDT, "BDT",
"!H:!V:NTrees=850:MinNodeSize=2.5%:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20" );
if (Use["BDTB"]) // Bagging
factory->BookMethod( TMVA::Types::kBDT, "BDTB",
"!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20" );
if (Use["BDTD"]) // Decorrelation + Adaptive Boost
factory->BookMethod( TMVA::Types::kBDT, "BDTD",
"!H:!V:NTrees=400:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=25:PruneMethod=CostComplexity:PruneStrength=25.0:VarTransform=Decorrelate");
//"!H:!V:NTrees=400:MinNodeSize=5%:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:VarTransform=Decorrelate" );
if (Use["BDTF"]) // Allow Using Fisher discriminant in node splitting for (strong) linearly correlated variables
factory->BookMethod( TMVA::Types::kBDT, "BDTMitFisher",
"!H:!V:NTrees=50:MinNodeSize=2.5%:UseFisherCuts:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20" );
// RuleFit -- TMVA implementation of Friedman's method
if (Use["RuleFit"])
factory->BookMethod( TMVA::Types::kRuleFit, "RuleFit",
"H:!V:RuleFitModule=RFTMVA:Model=ModRuleLinear:MinImp=0.001:RuleMinDist=0.001:NTrees=20:fEventsMin=0.01:fEventsMax=0.5:GDTau=-1.0:GDTauPrec=0.01:GDStep=0.01:GDNSteps=10000:GDErrScale=1.02" );
// For an example of the category classifier usage, see: TMVAClassificationCategory
// --------------------------------------------------------------------------------------------------
// ---- Now you can optimize the setting (configuration) of the MVAs using the set of training events
// factory->OptimizeAllMethods("SigEffAt001","Scan");
// factory->OptimizeAllMethods("ROCIntegral","FitGA");
// --------------------------------------------------------------------------------------------------
// ---- Now you can tell the factory to train, test, and evaluate the MVAs
// Train MVAs using the set of training events
factory->TrainAllMethods();
// ---- Evaluate all MVAs using the set of test events
factory->TestAllMethods();
// ----- Evaluate and compare performance of all configured MVAs
factory->EvaluateAllMethods();
// --------------------------------------------------------------
// Save the output
outputFile->Close();
std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
std::cout << "==> TMVAClassification is done!" << std::endl;
std::cout << " ==> Weights are stored in " << TMVA::gConfig().GetIONames().fWeightFileDir << std::endl;
delete factory;
// Launch the GUI for the root macros
// if (!gROOT->IsBatch()) TMVAGui( outfileName );
}
void TMVAClassification( TString myMethodList = "" )
{
// The explicit loading of the shared libTMVA is done in TMVAlogon.C, defined in .rootrc
// if you use your private .rootrc, or run from a different directory, please copy the
// corresponding lines from .rootrc
// methods to be processed can be given as an argument; use format:
//
// mylinux~> root -l TMVAClassification.C\(\"myMethod1,myMethod2,myMethod3\"\)
//
// if you like to use a method via the plugin mechanism, we recommend using
//
// mylinux~> root -l TMVAClassification.C\(\"P_myMethod\"\)
// (an example is given for using the BDT as plugin (see below),
// but of course the real application is when you write your own
// method based)
// this loads the library
TMVA::Tools::Instance();
//---------------------------------------------------------------
// default MVA methods to be trained + tested
std::map<std::string,int> Use;
Use["Cuts"] = 1;
Use["CutsD"] = 1;
Use["CutsPCA"] = 1;
Use["CutsGA"] = 1;
Use["CutsSA"] = 1;
// ---
Use["Likelihood"] = 1;
Use["LikelihoodD"] = 1; // the "D" extension indicates decorrelated input variables (see option strings)
Use["LikelihoodPCA"] = 1; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
Use["LikelihoodKDE"] = 1;
Use["LikelihoodMIX"] = 1;
// ---
Use["PDERS"] = 1;
Use["PDERSD"] = 1;
Use["PDERSPCA"] = 1;
Use["PDERSkNN"] = 1; // depreciated until further notice
Use["PDEFoam"] = 1;
// --
Use["KNN"] = 1;
// ---
Use["HMatrix"] = 1;
Use["Fisher"] = 1;
Use["FisherG"] = 1;
Use["BoostedFisher"] = 1;
Use["LD"] = 1;
// ---
Use["FDA_GA"] = 1;
Use["FDA_SA"] = 1;
Use["FDA_MC"] = 1;
Use["FDA_MT"] = 1;
Use["FDA_GAMT"] = 1;
Use["FDA_MCMT"] = 1;
// ---
Use["MLP"] = 1; // this is the recommended ANN
Use["MLPBFGS"] = 1; // recommended ANN with optional training method
Use["CFMlpANN"] = 1; // *** missing
Use["TMlpANN"] = 1;
// ---
Use["SVM"] = 1;
// ---
Use["BDT"] = 1;
Use["BDTD"] = 0;
Use["BDTG"] = 1;
Use["BDTB"] = 0;
// ---
Use["RuleFit"] = 1;
// ---
Use["Plugin"] = 0;
// ---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVAClassification" << std::endl;
if (myMethodList != "") {
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;
std::vector<TString> mlist = TMVA::gTools().SplitString( myMethodList, ',' );
for (UInt_t i=0; i<mlist.size(); i++) {
std::string regMethod(mlist[i]);
if (Use.find(regMethod) == Use.end()) {
std::cout << "Method \"" << regMethod << "\" not known in TMVA under this name. Choose among the following:" << std::endl;
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) std::cout << it->first << " ";
std::cout << std::endl;
return;
}
Use[regMethod] = 1;
}
}
// Create a new root output file.
TString outfileName = "TMVA";
int toAppNum = 1;
char name[1024];
TSystemDirectory dir("",".");
TList *files = dir.GetListOfFiles();
vector<string> vfname;
if (files) {
TIter next(files);
TSystemFile *file;
TString fname;
while ((file=(TSystemFile*)next())) {
fname = file->GetName();
if (!file->IsDirectory() && fname.EndsWith(".root") && fname.BeginsWith("TMVA")) {
vfname.push_back(string(fname));
}
}
delete files;
if (vfname.size()>0) {
std::sort(vfname.begin(),vfname.end());
TString num = TString(vfname[vfname.size()-1]);
num.ReplaceAll(".root","");
num.ReplaceAll("TMVA","");
toAppNum = num.Atoi()+1;
}
}
sprintf(name,"%d",toAppNum);
outfileName = outfileName + name + ".root";
//TString outfileName( "TMVA.root" );
TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
// Create the factory object. Later you can choose the methods
// whose performance you'd like to investigate. The factory will
// then run the performance analysis for you.
//
// The first argument is the base of the name of all the
// weightfiles in the directory weight/
//
// The second argument is the output file for the training results
// All TMVA output can be suppressed by removing the "!" (not) in
// front of the "Silent" argument in the option string
TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D" );
// If you wish to modify default settings
// (please check "src/Config.h" to see all available global options)
// (TMVA::gConfig().GetVariablePlotting()).fTimesRMS = 8.0;
// (TMVA::gConfig().GetIONames()).fWeightFileDir = "myWeightDirectory";
// Define the input variables that shall be used for the MVA training
factory->AddVariable("tEventProbSig := tEventProb[0]+tEventProb[1]+tEventProb[2]","F",0,0);
factory->AddVariable("tEventProbBkg := tEventProb","F",0,0);
// You can add so-called "Spectator variables", which are not used in the MVA training,
// but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the
// input variables, the response values of all trained MVAs, and the spectator variables
factory->AddSpectator("run := m_run", "I");
factory->AddSpectator("event := m_event", "I");
// read training and test data
TFile *input1 = TFile::Open( "/uscms/home/ilyao/nobackup/Spring12ME7TeV/MEResults/microNtuples/microWW_EPDv01.root" );
TFile *input2 = TFile::Open( "/uscms/home/ilyao/nobackup/Spring12ME7TeV/MEResults/microNtuples/microWZ_EPDv01.root" );
TFile *input3 = TFile::Open( "/uscms/home/ilyao/nobackup/Spring12ME7TeV/MEResults/microNtuples/microWJets_EPDv01.root" );
TFile *input4 = TFile::Open( "/uscms/home/ilyao/nobackup/Spring12ME7TeV/MEResults/microNtuples/microZJets_EPDv01.root" );
TFile *input5 = TFile::Open( "/uscms/home/ilyao/nobackup/Spring12ME7TeV/MEResults/microNtuples/microTTbar_EPDv01.root" );
TFile *input6 = TFile::Open( "/uscms/home/ilyao/nobackup/Spring12ME7TeV/MEResults/microNtuples/microHWWMH150_EPDv01.root" );
std::cout << "--- TMVAClassification : Using input file: " << input1->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input file: " << input2->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input file: " << input3->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input file: " << input4->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input file: " << input5->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input file: " << input6->GetName() << std::endl;
//TTree *signal = (TTree*)input1->Get("WJet");
//TTree *background = (TTree*)input3->Get("WJet");
TChain *signal = new TChain("METree");
signal->Add("/uscms/home/ilyao/nobackup/Spring12ME7TeV/MEResults/microNtuples/microHWWMH150_EPDv01.root");
TChain *background = new TChain("METree");
background->Add("/uscms/home/ilyao/nobackup/Spring12ME7TeV/MEResults/microNtuples/microWW_EPDv01.root");
background->Add("/uscms/home/ilyao/nobackup/Spring12ME7TeV/MEResults/microNtuples/microWZ_EPDv01.root");
background->Add("/uscms/home/ilyao/nobackup/Spring12ME7TeV/MEResults/microNtuples/microWJets_EPDv01.root");
background->Add("/uscms/home/ilyao/nobackup/Spring12ME7TeV/MEResults/microNtuples/microZJets_EPDv01.root");
background->Add("/uscms/home/ilyao/nobackup/Spring12ME7TeV/MEResults/microNtuples/microTTbar_EPDv01.root");
// global event weights per tree (see below for setting event-wise weights)
Double_t signalWeight = 1.0;
Double_t backgroundWeight = 1.0;
// ====== register trees ====================================================
//
// the following method is the prefered one:
// you can add an arbitrary number of signal or background trees
factory->AddSignalTree ( signal, signalWeight );
factory->AddBackgroundTree( background, backgroundWeight );
// To give different trees for training and testing, do as follows:
// factory->AddSignalTree( signalTrainingTree, signalTrainWeight, "Training" );
// factory->AddSignalTree( signalTestTree, signalTestWeight, "Test" );
// Use the following code instead of the above two or four lines to add signal and background
// training and test events "by hand"
// NOTE that in this case one should not give expressions (such as "var1+var2") in the input
// variable definition, but simply compute the expression before adding the event
//
// // --- begin ----------------------------------------------------------
// std::vector<Double_t> vars( 4 ); // vector has size of number of input variables
// Float_t treevars[4];
// for (Int_t ivar=0; ivar<4; ivar++) signal->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
// for (Int_t i=0; i<signal->GetEntries(); i++) {
// signal->GetEntry(i);
// for (Int_t ivar=0; ivar<4; ivar++) vars[ivar] = treevars[ivar];
// // add training and test events; here: first half is training, second is testing
// // note that the weight can also be event-wise
// if (i < signal->GetEntries()/2) factory->AddSignalTrainingEvent( vars, signalWeight );
// else factory->AddSignalTestEvent ( vars, signalWeight );
// }
//
// for (Int_t ivar=0; ivar<4; ivar++) background->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
// for (Int_t i=0; i<background->GetEntries(); i++) {
// background->GetEntry(i);
// for (Int_t ivar=0; ivar<4; ivar++) vars[ivar] = treevars[ivar];
// // add training and test events; here: first half is training, second is testing
// // note that the weight can also be event-wise
// if (i < background->GetEntries()/2) factory->AddBackgroundTrainingEvent( vars, backgroundWeight );
// else factory->AddBackgroundTestEvent ( vars, backgroundWeight );
// }
// // --- end ------------------------------------------------------------
//
// ====== end of register trees ==============================================
// This would set individual event weights (the variables defined in the
// expression need to exist in the original TTree)
// for signal : factory->SetSignalWeightExpression("weight1*weight2");
// for background: factory->SetBackgroundWeightExpression("weight1*weight2");
// factory->SetBackgroundWeightExpression("weight");
// Apply additional cuts on the signal and background samples (can be different)
// TCut mycuts = "abs(eta)>1.5"; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
// TCut mycutb = "abs(eta)>1.5"; // for example: TCut mycutb = "abs(var1)<0.5";
//Acceptance/Base cuts
//TCut goodW("W_electron_et>30. && TMath::Abs(W_electron_eta)<2.5 && event_met_pfmet>25.");
TCut goodW("W_muon_pt>25. && TMath::Abs(W_muon_eta)<2.5 && event_met_pfmet>25.");
TCut twojets("numPFCorJets==2");
TCut jetPt("JetPFCor_Pt[0]>30. && JetPFCor_Pt[1]>30.");
TCut jetEta("TMath::Abs(JetPFCor_Eta[0])<2.5 && TMath::Abs(JetPFCor_Eta[1])<2.5");
TCut deltaR1("TMath::Sqrt(TMath::Power(TMath::Abs(TMath::Abs(TMath::Abs(JetPFCor_Phi[0]-W_muon_phi)-TMath::Pi())-TMath::Pi()),2)+TMath::Power(JetPFCor_Eta[0]-W_muon_eta,2))>0.5");
TCut deltaR2("TMath::Sqrt(TMath::Power(TMath::Abs(TMath::Abs(TMath::Abs(JetPFCor_Phi[1]-W_muon_phi)-TMath::Pi())-TMath::Pi()),2)+TMath::Power(JetPFCor_Eta[1]-W_muon_eta,2))>0.5");
TCut noBJets("numPFCorJetBTags==0");
TCut null("");
//TCut mycuts (goodW && twojets && jetPt && jetEta && deltaR1 && deltaR2 && noBJets);
TCut mycuts (null);
// tell the factory to use all remaining events in the trees after training for testing:
factory->PrepareTrainingAndTestTree( mycuts, mycuts,
"nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );
// If no numbers of events are given, half of the events in the tree are used for training, and
// the other half for testing:
// factory->PrepareTrainingAndTestTree( mycut, "SplitMode=random:!V" );
// To also specify the number of testing events, use:
// factory->PrepareTrainingAndTestTree( mycut,
// "NSigTrain=3000:NBkgTrain=3000:NSigTest=3000:NBkgTest=3000:SplitMode=Random:!V" );
// ---- Book MVA methods
//
// please lookup the various method configuration options in the corresponding cxx files, eg:
// src/MethoCuts.cxx, etc, or here: http://tmva.sourceforge.net/optionRef.html
// it is possible to preset ranges in the option string in which the cut optimisation should be done:
// "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable
// Cut optimisation
if (Use["Cuts"])
factory->BookMethod( TMVA::Types::kCuts, "Cuts",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" );
if (Use["CutsD"])
factory->BookMethod( TMVA::Types::kCuts, "CutsD",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=Decorrelate" );
if (Use["CutsPCA"])
factory->BookMethod( TMVA::Types::kCuts, "CutsPCA",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=PCA" );
if (Use["CutsGA"])
factory->BookMethod( TMVA::Types::kCuts, "CutsGA",
"H:!V:FitMethod=GA:CutRangeMin[0]=-10:CutRangeMax[0]=10:VarProp[1]=FMax:EffSel:Steps=30:Cycles=3:PopSize=400:SC_steps=10:SC_rate=5:SC_factor=0.95" );
if (Use["CutsSA"])
factory->BookMethod( TMVA::Types::kCuts, "CutsSA",
"!H:!V:FitMethod=SA:EffSel:MaxCalls=150000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );
// Likelihood
if (Use["Likelihood"])
factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood",
"H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" );
// test the decorrelated likelihood
if (Use["LikelihoodD"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodD",
"!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=Decorrelate" );
if (Use["LikelihoodPCA"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodPCA",
"!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=PCA" );
// test the new kernel density estimator
if (Use["LikelihoodKDE"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodKDE",
"!H:!V:!TransformOutput:PDFInterpol=KDE:KDEtype=Gauss:KDEiter=Adaptive:KDEFineFactor=0.3:KDEborder=None:NAvEvtPerBin=50" );
// test the mixed splines and kernel density estimator (depending on which variable)
if (Use["LikelihoodMIX"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodMIX",
"!H:!V:!TransformOutput:PDFInterpolSig[0]=KDE:PDFInterpolBkg[0]=KDE:PDFInterpolSig[1]=KDE:PDFInterpolBkg[1]=KDE:PDFInterpolSig[2]=Spline2:PDFInterpolBkg[2]=Spline2:PDFInterpolSig[3]=Spline2:PDFInterpolBkg[3]=Spline2:KDEtype=Gauss:KDEiter=Nonadaptive:KDEborder=None:NAvEvtPerBin=50" );
// test the multi-dimensional probability density estimator
// here are the options strings for the MinMax and RMS methods, respectively:
// "!H:!V:VolumeRangeMode=MinMax:DeltaFrac=0.2:KernelEstimator=Gauss:GaussSigma=0.3" );
// "!H:!V:VolumeRangeMode=RMS:DeltaFrac=3:KernelEstimator=Gauss:GaussSigma=0.3" );
if (Use["PDERS"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERS",
"!H:!V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600" );
if (Use["PDERSkNN"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERSkNN",
"!H:!V:VolumeRangeMode=kNN:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600" );
if (Use["PDERSD"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERSD",
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=Decorrelate" );
if (Use["PDERSPCA"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERSPCA",
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=PCA" );
/*
// Multi-dimensional likelihood estimator using self-adapting phase-space binning
if (Use["PDEFoam"])
factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoam",
"H:!V:SigBgSeparate=F:TailCut=0.001:VolFrac=0.0333:nActiveCells=500:nSampl=2000:nBin=5:CutNmin=T:Nmin=100:Kernel=None:Compress=T" );
*/
// K-Nearest Neighbour classifier (KNN)
if (Use["KNN"])
factory->BookMethod( TMVA::Types::kKNN, "KNN",
"H:nkNN=20:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim" );
// H-Matrix (chi2-squared) method
if (Use["HMatrix"])
factory->BookMethod( TMVA::Types::kHMatrix, "HMatrix", "!H:!V" );
// Fisher discriminant
if (Use["Fisher"])
factory->BookMethod( TMVA::Types::kFisher, "Fisher", "H:!V:Fisher:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=60:NsmoothMVAPdf=10" );
// Fisher with Gauss-transformed input variables
if (Use["FisherG"])
factory->BookMethod( TMVA::Types::kFisher, "FisherG", "H:!V:VarTransform=Gauss" );
// Composite classifier: ensemble (tree) of boosted Fisher classifiers
if (Use["BoostedFisher"])
factory->BookMethod( TMVA::Types::kFisher, "BoostedFisher", "H:!V:Boost_Num=20:Boost_Transform=log:Boost_Type=AdaBoost:Boost_AdaBoostBeta=0.2");
/*
// Linear discriminant (same as Fisher)
if (Use["LD"])
factory->BookMethod( TMVA::Types::kLD, "LD", "H:!V:VarTransform=None" );
*/
// Function discrimination analysis (FDA) -- test of various fitters - the recommended one is Minuit (or GA or SA)
if (Use["FDA_MC"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MC",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:SampleSize=100000:Sigma=0.1" );
if (Use["FDA_GA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options])
factory->BookMethod( TMVA::Types::kFDA, "FDA_GA",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:PopSize=300:Cycles=3:Steps=20:Trim=True:SaveBestGen=1" );
if (Use["FDA_SA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options])
factory->BookMethod( TMVA::Types::kFDA, "FDA_SA",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=SA:MaxCalls=15000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );
if (Use["FDA_MT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=2:UseImprove:UseMinos:SetBatch" );
if (Use["FDA_GAMT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_GAMT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:Cycles=1:PopSize=5:Steps=5:Trim" );
if (Use["FDA_MCMT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MCMT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:SampleSize=20" );
// TMVA ANN: MLP (recommended ANN) -- all ANNs in TMVA are Multilayer Perceptrons
if (Use["MLP"])
factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=500:HiddenLayers=N+5:TestRate=10:EpochMonitoring" );
if (Use["MLPBFGS"])
factory->BookMethod( TMVA::Types::kMLP, "MLPBFGS", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=500:HiddenLayers=N+5:TestRate=10:TrainingMethod=BFGS:!EpochMonitoring" );
// CF(Clermont-Ferrand)ANN
if (Use["CFMlpANN"])
factory->BookMethod( TMVA::Types::kCFMlpANN, "CFMlpANN", "!H:!V:NCycles=2000:HiddenLayers=N+1,N" ); // n_cycles:#nodes:#nodes:...
// Tmlp(Root)ANN
if (Use["TMlpANN"])
factory->BookMethod( TMVA::Types::kTMlpANN, "TMlpANN", "!H:!V:NCycles=200:HiddenLayers=N+1,N:LearningMethod=BFGS:ValidationFraction=0.3" ); // n_cycles:#nodes:#nodes:...
// Support Vector Machine
if (Use["SVM"])
factory->BookMethod( TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=Norm" );
// Boosted Decision Trees
if (Use["BDTG"]) // Gradient Boost
factory->BookMethod( TMVA::Types::kBDT, "BDTG",
"!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.6:SeparationType=GiniIndex:nCuts=20:NNodesMax=5" );
if (Use["BDT"]) // Adaptive Boost
factory->BookMethod( TMVA::Types::kBDT, "BDT",
"!H:!V:NTrees=400:nEventsMin=400:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );
if (Use["BDTB"]) // Bagging
factory->BookMethod( TMVA::Types::kBDT, "BDTB",
"!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );
if (Use["BDTD"]) // Decorrelation + Adaptive Boost
factory->BookMethod( TMVA::Types::kBDT, "BDTD",
"!H:!V:NTrees=400:nEventsMin=400:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning:VarTransform=Decorrelate" );
// RuleFit -- TMVA implementation of Friedman's method
if (Use["RuleFit"])
factory->BookMethod( TMVA::Types::kRuleFit, "RuleFit",
"H:!V:RuleFitModule=RFTMVA:Model=ModRuleLinear:MinImp=0.001:RuleMinDist=0.001:NTrees=20:fEventsMin=0.01:fEventsMax=0.5:GDTau=-1.0:GDTauPrec=0.01:GDStep=0.01:GDNSteps=10000:GDErrScale=1.02" );
// For an example of the category classifier, see: TMVAClassificationCategory
// --------------------------------------------------------------------------------------------------
// As an example how to use the ROOT plugin mechanism, book BDT via
// plugin mechanism
if (Use["Plugin"]) {
//
// first the plugin has to be defined, which can happen either through the following line in the local or global .rootrc:
//
// # plugin handler plugin name(regexp) class to be instanciated library constructor format
// Plugin.TMVA@@MethodBase: ^BDT TMVA::MethodBDT TMVA.1 "MethodBDT(TString,TString,DataSet&,TString)"
//
// or by telling the global plugin manager directly
gPluginMgr->AddHandler("TMVA@@MethodBase", "BDT", "TMVA::MethodBDT", "TMVA.1", "MethodBDT(TString,TString,DataSet&,TString)");
factory->BookMethod( TMVA::Types::kPlugins, "BDT",
"!H:!V:NTrees=400:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=CostComplexity:PruneStrength=50" );
}
// --------------------------------------------------------------------------------------------------
// ---- Now you can tell the factory to train, test, and evaluate the MVAs
// Train MVAs using the set of training events
factory->TrainAllMethods();
// ---- Evaluate all MVAs using the set of test events
factory->TestAllMethods();
// ----- Evaluate and compare performance of all configured MVAs
factory->EvaluateAllMethods();
// --------------------------------------------------------------
// Save the output
outputFile->Close();
std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
std::cout << "==> TMVAClassification is done!" << std::endl;
delete factory;
// Launch the GUI for the root macros
if (!gROOT->IsBatch()) TMVAGui( outfileName );
}
int efficiency(char* path0) {
char the_path[256];
getcwd(the_path, 255);
std::string path(path0);
unsigned found = path.find_last_of("/\\", path.size()-2);
std::string dataPath(path.substr(0,found));
std::string measurementFolder(path.substr(found+1));
std::stringstream ss1(measurementFolder);
std::string moduleName;
std::getline(ss1, moduleName, '_');
std::string rootFileName("commander_HREfficiency.root");
std::string maskFileName("defaultMaskFile.dat");
const bool FIDUCIAL_ONLY = true;
const bool VERBOSE = true;
int nTrigPerPixel = 50;
int nPixels = 4160;
int nTrig = nTrigPerPixel * nPixels;
float pixelArea = 0.01 * 0.015; // cm^2
float triggerDuration = 25e-9; //s
int nRocs = 16;
//search for XrarSpectrum folders in elComandante folder structure
TSystemDirectory dir(path.c_str(), path.c_str());
TList *files = dir.GetListOfFiles();
std::vector<std::string> directoryList;
if (files) {
TSystemFile *file;
TString fname;
TIter next(files);
while (file=(TSystemFile*)next()) {
fname = file->GetName();
if (file->IsDirectory()) {
std::string dirname(fname.Data());
if (dirname.find("HREfficiency") != std::string::npos) {
directoryList.push_back(dirname);
}
}
}
}
std::vector< std::vector< std::pair< int,int > > > maskedPixels;
//sort!
std::sort(directoryList.begin(), directoryList.end());
std::vector< std::vector< double > > efficiencies;
std::vector< std::vector< double > > efficiencyErrors;
std::vector< std::vector< double > > rates;
std::vector< std::vector< double > > rateErrors;
for (int i=0;i<nRocs;i++) {
std::vector< double > empty;
efficiencies.push_back(empty);
efficiencyErrors.push_back(empty);
rates.push_back(empty);
rateErrors.push_back(empty);
}
// loop over all commander_HREfficiency.root root files
for (int i=0;i<directoryList.size();++i) {
chdir(path.c_str());
std::cout << "looking in directory <" << directoryList[i] << ">" << std::endl;
std::ifstream testParameters(Form("%s/testParameters.dat", directoryList[i].c_str()));
std::string line2;
while (getline(testParameters, line2)) {
std::transform(line2.begin(), line2.end(), line2.begin(), ::tolower);
if (line2.find("highrate") != std::string::npos) {
while (getline(testParameters, line2)) {
if (line2.size() < 1) break;
std::transform(line2.begin(), line2.end(), line2.begin(), ::tolower);
size_t pos = line2.find("ntrig");
if (pos != std::string::npos) {
nTrigPerPixel = atoi(line2.substr(pos+6).c_str());
nTrig = nTrigPerPixel * nPixels;
std::cout << ">" << line2 << "< pos:" << pos << std::endl;
std::cout << "number of triggers per pixel: " << nTrigPerPixel << std::endl;
}
}
}
}
testParameters.close();
// read masked pixels
maskedPixels.clear();
for (int j=0;j<nRocs;j++) {
std::vector< std::pair<int,int> > rocMaskedPixels;
maskedPixels.push_back(rocMaskedPixels);
}
std::ifstream maskFile;
char maskFilePath[256];
sprintf(maskFilePath, "%s/%s/%s", path.c_str(), directoryList[i].c_str(), maskFileName.c_str());
maskFile.open(maskFilePath, std::ifstream::in);
if (!maskFile) {
std::cout << "ERROR: mask file <" << maskFilePath << "> can't be opened!"<<std::endl;
}
std::string line;
std::vector< std::string > tokens;
while(getline(maskFile, line)) {
if (line[0] != '#') {
std::stringstream ss(line);
std::string buf;
tokens.clear();
while (ss >> buf) {
tokens.push_back(buf);
}
std::cout << "tok0 <" << tokens[0] << "> ";
if (tokens[0] == "pix" && tokens.size() >= 4) {
int roc = atoi(tokens[1].c_str());
int col = atoi(tokens[2].c_str());
int row = atoi(tokens[3].c_str());
std::cout << "mask pixel " << roc << " " << col << " " << row << std::endl;
maskedPixels[roc].push_back(make_pair(col, row));
}
}
}
maskFile.close();
chdir(directoryList[i].c_str());
TFile f(rootFileName.c_str());
if (f.IsZombie()) {
std::cout << "could not read: " << rootFileName << " .";
exit(0);
}
std::cout << "list keys:" << std::endl;
TIter next(f.GetListOfKeys());
bool highRateFound = false;
while (TKey *obj = (TKey*)next()) {
if (strcmp(obj->GetTitle(),"HighRate") == 0) highRateFound = true;
if (VERBOSE) {
std::cout << obj->GetTitle() << std::endl;
}
}
if (highRateFound) {
std::cout << "highRate test found, reading data..." << std::endl;
TH2D* xraymap;
TH2D* calmap;
char calmapName[256];
char xraymapName[256];
std::ofstream output;
for (int iRoc=0;iRoc<nRocs;iRoc++) {
std::cout << "ROC" << iRoc << std::endl;
sprintf(xraymapName, "HighRate/highRate_xraymap_C%d_V0;1", iRoc);
f.GetObject(xraymapName, xraymap);
if (xraymap == 0) {
std::cout << "ERROR: x-ray hitmap not found!" << std::endl;
}
int nBinsX = xraymap->GetXaxis()->GetNbins();
int nBinsY = xraymap->GetYaxis()->GetNbins();
sprintf(calmapName, "HighRate/highRate_C%d_V0;1", iRoc);
f.GetObject(calmapName, calmap);
if (calmap == 0) {
sprintf(calmapName, "HighRate/highRate_calmap_C%d_V0;1", iRoc);
f.GetObject(calmapName, calmap);
if (calmap == 0) {
std::cout << "ERROR: calibration hitmap not found!" << std::endl;
}
}
//std::cout << nBinsX << "x" << nBinsY << std::endl;
float rate, efficiency;
for (int doubleColumn = 1; doubleColumn < 25; doubleColumn++) {
//std::cout << "reading dc " << doubleColumn << std::endl;
std::vector<double> hits;
std::vector<double> xray_hits;
for (int y = 0; y < 160; y++) {
bool masked = false;
for (int iMaskedPixels=0; iMaskedPixels < maskedPixels[iRoc].size(); iMaskedPixels++) {
if (maskedPixels[iRoc][iMaskedPixels].first == doubleColumn * 2 + (int)(y / 80) && maskedPixels[iRoc][iMaskedPixels].second == (y % 80)) {
masked = true;
break;
}
}
if ((!FIDUCIAL_ONLY || ((y % 80) > 0 && (y % 80) < 79)) && !masked) {
//std::cout << " get " << (doubleColumn * 2 + (int)(y / 80) + 1) << " / " << ((y % 80) + 1) << std::endl;
hits.push_back( calmap->GetBinContent(doubleColumn * 2 + (int)(y / 80) + 1, (y % 80) + 1) );
xray_hits.push_back( xraymap->GetBinContent(doubleColumn * 2 + (int)(y / 80) + 1, (y % 80) + 1) );
}
}
//std::cout << "calculating rates and efficiencies" << std::endl;
int nPixelsDC = hits.size();
if (nPixelsDC < 1) nPixelsDC = 1;
double rate = TMath::Mean(nPixelsDC, &xray_hits[0]) / (nTrig * triggerDuration * pixelArea) * 1.0e-6;
double efficiency = TMath::Mean(nPixelsDC, &hits[0]) / nTrigPerPixel;
double rateError = TMath::RMS(nPixelsDC, &xray_hits[0]) / std::sqrt(nPixelsDC) / (nTrig * triggerDuration * pixelArea) * 1.0e-6;
double efficiencyError = TMath::RMS(nPixelsDC, &hits[0]) / std::sqrt(nPixelsDC) / nTrigPerPixel;
efficiencies[iRoc].push_back(efficiency);
efficiencyErrors[iRoc].push_back(efficiencyError);
rates[iRoc].push_back(rate);
rateErrors[iRoc].push_back(rateError);
if (VERBOSE) {
std::cout << "dc " << doubleColumn << " nPixelsDC: " << nPixelsDC << " rate: " << rate << " " << efficiency << std::endl;
}
}
}
//output.open (Form("%s/output_%d.txt",the_path,i), std::ofstream::out);
//output.close();
} else {
std::cout << "high rate test not found";
return 1;
}
}
void PValueCorrection::readFiles(TString name, int id, bool isPlugin){
// read coverage test files
name = "root/"+name;
TSystemDirectory dir(name,name);
TList *files = dir.GetListOfFiles();
TChain *fChain = new TChain("tree");
int foundFiles=0;
if (files){
TSystemFile *file;
TString fname;
TIter next(files);
while ((file=(TSystemFile*)next())) {
fname = file->GetName();
if (!file->IsDirectory() && fname.Contains(Form("id%d",id)) && fname.EndsWith(".root")) {
//cout << fname << endl;
fChain->Add(name+"/"+fname);
foundFiles++;
}
}
}
cout << "PValueCorrector::readFiles() -- found " << foundFiles << " files in " << dir.GetName() << " with total of " << fChain->GetEntries() << " entries" << endl;
cout << "PValueCorrector::readFiles() -- will apply on the fly correction of type ";
isPlugin ? cout << " plugin"<< endl : cout << " prob" << endl;
// fill histogram from tree
h_pvalue_before = new TH1F("h_pvalue_before","p-value",50,0.,1.);
h_pvalue_after = new TH1F("h_pvalue_after","p-value",50,0.,1.);
// set up root tree for reading
float tSol = 0.0;
float tChi2free = 0.0;
float tChi2scan = 0.0;
float tSolGen = 0.0;
float tPvalue = 0.0;
fChain->SetBranchAddress("sol", &tSol);
fChain->SetBranchAddress("chi2free", &tChi2free);
fChain->SetBranchAddress("chi2scan", &tChi2scan);
fChain->SetBranchAddress("solGen", &tSolGen);
fChain->SetBranchAddress("pvalue", &tPvalue);
// initialize loop variables
Long64_t nentries = fChain->GetEntries();
Long64_t nfailed = 0;
float n68 =0.;
float n95 =0.;
float n99 =0.;
for (Long64_t i = 0; i < nentries; i++)
{
fChain->GetEntry(i);
// apply cuts (we'll count the failed ones later)
if ( ! (tChi2free > -1e10 && tChi2scan > -1e10
&& tChi2scan-tChi2free>0
&& tSol != 0.0 ///< exclude some pathological jobs from when tSol wasn't set yet
&& tChi2free < 500 && tChi2scan < 500
)){
nfailed++;
continue;
}
float pvalue = -999.;
if (isPlugin) {
pvalue = tPvalue;
}
else {
pvalue = TMath::Prob(tChi2scan-tChi2free,1);
}
if ( pvalue > TMath::Prob(1,1) ) n68++;
if ( pvalue > TMath::Prob(4,1) ) n95++;
if ( pvalue > TMath::Prob(9,1) ) n99++;
h_pvalue_before->Fill(pvalue);
}
fitHist(h_pvalue_before);
if (verbose) printCoverage(n68,n95,n99,float(nentries-nfailed),"Before Correction");
n68=0.;
n95=0.;
n99=0.;
for (Long64_t i = 0; i < nentries; i++)
{
fChain->GetEntry(i);
// apply cuts (we'll count the failed ones later)
if ( ! (tChi2free > -1e10 && tChi2scan > -1e10
&& tChi2scan-tChi2free>0
&& tSol != 0.0 ///< exclude some pathological jobs from when tSol wasn't set yet
&& tChi2free < 500 && tChi2scan < 500
)){
continue;
}
float pvalue = -999.;
if (isPlugin) {
pvalue = transform(tPvalue);
}
else {
pvalue = transform(TMath::Prob(tChi2scan-tChi2free,1));
}
if ( pvalue > TMath::Prob(1,1) ) n68++;
if ( pvalue > TMath::Prob(4,1) ) n95++;
if ( pvalue > TMath::Prob(9,1) ) n99++;
h_pvalue_after->Fill(pvalue);
}
if (verbose) printCoverage(n68,n95,n99,float(nentries-nfailed),"After Correction");
checkParams();
}
void recupThePlots(){
// TDirectory *theDr = (TDirectory*) myFile->Get("eleIDdir");///denom_pt/fit_eff_plots");
//theDr->ls();
cout << "coucou" << theOutFileName << endl;
TFile *myOutFile = new TFile(theOutFileName,"RECREATE");
TSystemDirectory dir(thePath, thePath);
TSystemFile *file;
TString fname;
TIter next(dir.GetListOfFiles());
while (((file=(TSystemFile*)next()))) {
fname = file->GetName();
if ((fname.BeginsWith("TnP"))&&fname.Contains("data")) {
cout << "--------------------"<< "\n";
cout << fname << "\n";
TFile *myFile = new TFile(fname);
TIter nextkey(myFile->GetListOfKeys());
TKey *key;
while ((key = (TKey*)nextkey())) {
// cout << key << "\n";
TString theTypeClasse = key->GetClassName();
TString theNomClasse = key->GetTitle();
cout << "theTypeClasse: "<< theTypeClasse << " , " << theNomClasse << "\n";
if ( theTypeClasse == "TDirectoryFile" ){
// cout << "we are here 1" << "\n";
TDirectory *theDr = (TDirectory*) myFile->Get(theNomClasse);
TIter nextkey2(theDr->GetListOfKeys());
TKey *key2;
while ((key2 = (TKey*)nextkey2())) {
TString theTypeClasse2 = key2->GetClassName();
TString theNomClasse2 = key2->GetTitle();
if ( theTypeClasse == "TDirectoryFile" || theTypeClasse == "TGraphAsymmErrors" ){
TDirectory *theDr2 = (TDirectory*) myFile->Get(theNomClasse+"/"+theNomClasse2+"/fit_eff_plots");
TIter nextkey3(theDr2->GetListOfKeys());
TKey *key3;
while ((key3 = (TKey*)nextkey3())) {
TString theTypeClasse3 = key3->GetClassName();
TString theNomClasse3 = key3->GetName();
cout << "type = " << theTypeClasse3 << " nom = " << theNomClasse3 << endl;
TCanvas *theCanvas = (TCanvas*) myFile->Get(theNomClasse+"/"+theNomClasse2+"/fit_eff_plots/"+theNomClasse3);
TIter nextObject(theCanvas->GetListOfPrimitives());
TObject *obj;
while ((obj = (TObject*)nextObject())) {
if (obj->InheritsFrom("TGraphAsymmErrors")) {
cout << "histo: " << obj->GetName() << endl;
myOutFile->cd();
obj->Write(theNomClasse2+"_"+theNomClasse3);
myFile->cd();
}
if (obj->InheritsFrom("TH2F")) {
cout << "the TH2F = " << obj->GetName() << endl;
myOutFile->cd();
obj->Write(theNomClasse2+"_"+theNomClasse3);
myFile->cd();
}
}
}
}
}
}
}
delete myFile;
}
}
myOutFile->Close();
}
TChain* CreateAODChain(const char* aDataDir, Int_t aRuns, Int_t offset)
{
// creates chain of files in a given directory or file containing a list.
// In case of directory the structure is expected as:
// <aDataDir>/<dir0>/AliAOD.root
// <aDataDir>/<dir1>/AliAOD.root
// ...
if (!aDataDir)
return 0;
Long_t id, size, flags, modtime;
if (gSystem->GetPathInfo(aDataDir, &id, &size, &flags, &modtime))
{
printf("%s not found.\n", aDataDir);
return 0;
}
TChain* chain = new TChain("aodTree");
TChain* chaingAlice = 0;
if (flags & 2)
{
TString execDir(gSystem->pwd());
TSystemDirectory* baseDir = new TSystemDirectory(".", aDataDir);
TList* dirList = baseDir->GetListOfFiles();
Int_t nDirs = dirList->GetEntries();
gSystem->cd(execDir);
Int_t count = 0;
for (Int_t iDir=0; iDir<nDirs; ++iDir)
{
TSystemFile* presentDir = (TSystemFile*) dirList->At(iDir);
if (!presentDir || !presentDir->IsDirectory() || strcmp(presentDir->GetName(), ".") == 0 || strcmp(presentDir->GetName(), "..") == 0)
continue;
if (offset > 0)
{
--offset;
continue;
}
if (count++ == aRuns)
break;
TString presentDirName(aDataDir);
presentDirName += "/";
presentDirName += presentDir->GetName();
chain->Add(presentDirName + "/AliAOD.root/aodTree");
// cerr<<presentDirName<<endl;
}
}
else
{
// Open the input stream
ifstream in;
in.open(aDataDir);
Int_t count = 0;
// Read the input list of files and add them to the chain
TString aodfile;
while(in.good())
{
in >> aodfile;
if (!aodfile.Contains("root")) continue; // protection
if (offset > 0)
{
--offset;
continue;
}
if (count++ == aRuns)
break;
// add aod file
chain->Add(aodfile);
}
in.close();
}
return chain;
} // end of TChain* CreateAODChain(const char* aDataDir, Int_t aRuns, Int_t offset)
prepDataFiles(){
// TDirectory *theDr = (TDirectory*) myFile->Get("eleIDdir");///denom_pt/fit_eff_plots");
//theDr->ls();
int myIndex;
TSystemDirectory dir(thePath, thePath);
TSystemFile *file;
TString fname;
TIter next(dir.GetListOfFiles());
while ((file=(TSystemFile*)next())) {
fname = file->GetName();
if (fname.BeginsWith("TnP")&& fname.Contains("mc")) {
ofstream myfile;
TFile *myFile = new TFile(fname);
TIter nextkey(myFile->GetListOfKeys());
TKey *key;
while (key = (TKey*)nextkey()) {
TString theTypeClasse = key->GetClassName();
TString theNomClasse = key->GetTitle();
if ( theTypeClasse == "TDirectoryFile"){
TDirectory *theDr = (TDirectory*) myFile->Get(theNomClasse);
TIter nextkey2(theDr->GetListOfKeys());
TKey *key2;
while (key2 = (TKey*)nextkey2()) {
TString theTypeClasse2 = key2->GetClassName();
TString theNomClasse2 = key2->GetTitle();
myfile.open (theNomClasse2+".info");
if ( theTypeClasse == "TDirectoryFile"){
cout << "avant " << endl;
TDirectory *theDr2 = (TDirectory*) myFile->Get(theNomClasse+"/"+theNomClasse2);
cout << "apres " << endl;
TIter nextkey3(theDr2->GetListOfKeys());
TKey *key3;
while (key3 = (TKey*)nextkey3()) {
TString theTypeClasse3 = key3->GetClassName();
TString theNomClasse3 = key3->GetTitle();
if ((theNomClasse3.Contains("FromMC"))) {
TString localClasse3 = theNomClasse3;
localClasse3.ReplaceAll("__","%");
cout << "apres " << localClasse3 << endl;
TObjArray* listBin = localClasse3.Tokenize('%');
TString first = ((TObjString*)listBin->At(0))->GetString();
TString second = ((TObjString*)listBin->At(2))->GetString();
myfile << first;
myfile << " " << second << " ";
cout << "coucou la on va récupérer le rooFitResult " << endl;
RooFitResult *theResults = (RooFitResult*) myFile->Get(theNomClasse+"/"+theNomClasse2+"/"+theNomClasse3+"/fitresults");
theResults->Print();
RooArgList theParam = theResults->floatParsFinal();
int taille = theParam.getSize();
for (int m = 0 ; m < taille ; m++){
cout << "m=" << m << endl;
RooAbsArg *theArg = (RooAbsArg*) theParam.at(m);
RooAbsReal *theReal = (RooAbsReal*) theArg;
myfile << theReal->getVal() << " " ;
}
myfile << "\n";
}
}
}
myfile.close();
}
}
}
delete myFile;
}
}
}
recupTheZpeak(){
// TDirectory *theDr = (TDirectory*) myFile->Get("eleIDdir");///denom_pt/fit_eff_plots");
//theDr->ls();
int myIndex;
TSystemDirectory dir(thePath, thePath);
TSystemFile *file;
TString fname;
TIter next(dir.GetListOfFiles());
while ((file=(TSystemFile*)next())) {
fname = file->GetName();
if (fname.BeginsWith("TnP")) {
if (fname.Contains("data")) kind = "data";
else kind = "mc";
bool pass = false;
for (int p = 0 ; p< nbOfIds ; p++){
if (fname.Contains(thePlotToDraw[p])) {
pass = true;
theIndex = p;
break;
}
}
if (!(pass)) continue;
TFile *myFile = new TFile(fname);
TIter nextkey(myFile->GetListOfKeys());
TKey *key;
while (key = (TKey*)nextkey()) {
TString theTypeClasse = key->GetClassName();
TString theNomClasse = key->GetTitle();
if ( theTypeClasse == "TDirectoryFile"){
TDirectory *theDr = (TDirectory*) myFile->Get(theNomClasse);
TIter nextkey2(theDr->GetListOfKeys());
TKey *key2;
while (key2 = (TKey*)nextkey2()) {
TString theTypeClasse2 = key2->GetClassName();
TString theNomClasse2 = key2->GetTitle();
if ( theTypeClasse == "TDirectoryFile"){
TDirectory *theDr2 = (TDirectory*) myFile->Get(theNomClasse+"/"+theNomClasse2);
TIter nextkey3(theDr2->GetListOfKeys());
TKey *key3;
while (key3 = (TKey*)nextkey3()) {
TString theTypeClasse3 = key3->GetClassName();
TString theNomClasse3 = key3->GetTitle();
if (((theNomClasse3.Contains("vpvPlusExpo"))||(theNomClasse3.Contains("higgsSF")))&&(!(theNomClasse3.Contains("all")))) {
TCanvas *theCanvas = (TCanvas*) myFile->Get(theNomClasse+"/"+theNomClasse2+"/"+theNomClasse3+"/fit_canvas");
theCanvas->Print(outputDir+"/"+kind+"_"+theName[theIndex]+"__"+theNomClasse3+".png");
}
}
}
}
}
}
delete myFile;
}
}
}
