diff --git a/src/Routines/ComparisonRoutines.cxx b/src/Routines/ComparisonRoutines.cxx index 7b4a191..ec725fd 100755 --- a/src/Routines/ComparisonRoutines.cxx +++ b/src/Routines/ComparisonRoutines.cxx @@ -1,642 +1,650 @@ // Copyright 2016-2021 L. Pickering, P Stowell, R. Terri, C. Wilkinson, C. Wret /******************************************************************************* * This file is part of NUISANCE. * * NUISANCE is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * NUISANCE is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with NUISANCE. If not, see . *******************************************************************************/ #include "ComparisonRoutines.h" /* Constructor/Destructor */ //************************ void ComparisonRoutines::Init() { //************************ fOutputFile = ""; fOutputRootFile = NULL; fStrategy = "Compare"; fRoutines.clear(); fCardFile = ""; fFakeDataInput = ""; fSampleFCN = NULL; fAllowedRoutines = ("Compare"); }; //************************************* ComparisonRoutines::~ComparisonRoutines() { //************************************* delete fOutputRootFile; }; /* Input Functions */ //************************************* ComparisonRoutines::ComparisonRoutines(int argc, char *argv[]) { //************************************* // Initialise Defaults Init(); nuisconfig configuration = Config::Get(); // Default containers std::string cardfile = ""; std::string maxevents = "-1"; std::string skipevents = "0"; int errorcount = 0; int verbocount = 0; std::vector xmlcmds; std::vector configargs; // Make easier to handle arguments. std::vector args = GeneralUtils::LoadCharToVectStr(argc, argv); ParserUtils::ParseArgument(args, "-c", fCardFile, true); ParserUtils::ParseArgument(args, "-o", fOutputFile, false, false); ParserUtils::ParseArgument(args, "-n", maxevents, false, false); ParserUtils::ParseArgument(args, "-f", fStrategy, false, false); ParserUtils::ParseArgument(args, "-d", fFakeDataInput, false, false); ParserUtils::ParseArgument(args, "-i", xmlcmds); ParserUtils::ParseArgument(args, "-q", configargs); ParserUtils::ParseArgument(args, "-s", skipevents); ParserUtils::ParseCounter(args, "e", errorcount); ParserUtils::ParseCounter(args, "v", verbocount); ParserUtils::CheckBadArguments(args); // Add extra defaults if none given if (fCardFile.empty() and xmlcmds.empty()) { NUIS_ABORT("No input supplied!"); } if (fOutputFile.empty() and !fCardFile.empty()) { fOutputFile = fCardFile + ".root"; NUIS_ERR(WRN, "No output supplied so saving it to: " << fOutputFile); } else if (fOutputFile.empty()) { NUIS_ABORT("No output file or cardfile supplied!"); } // Configuration Setup ============================= // Check no comp key is available nuiskey fCompKey; if (Config::Get().GetNodes("nuiscomp").empty()) { fCompKey = Config::Get().CreateNode("nuiscomp"); } else { fCompKey = Config::Get().GetNodes("nuiscomp")[0]; } if (!fCardFile.empty()) fCompKey.Set("cardfile", fCardFile); if (!fOutputFile.empty()) fCompKey.Set("outputfile", fOutputFile); if (!fStrategy.empty()) fCompKey.Set("strategy", fStrategy); // Load XML Cardfile configuration.LoadSettings(fCompKey.GetS("cardfile"), ""); // Add Config Args for (size_t i = 0; i < configargs.size(); i++) { configuration.OverrideConfig(configargs[i]); } if (maxevents.compare("-1")) { configuration.OverrideConfig("MAXEVENTS=" + maxevents); } configuration.OverrideConfig("NSKIPEVENTS=" + skipevents); // Finish configuration XML configuration.FinaliseSettings(fCompKey.GetS("outputfile") + ".xml"); // Add Error Verbo Lines verbocount += Config::GetParI("VERBOSITY"); errorcount += Config::GetParI("ERROR"); bool trace = Config::GetParB("TRACE"); std::cout << "[ NUISANCE ]: Setting VERBOSITY=" << verbocount << std::endl; std::cout << "[ NUISANCE ]: Setting ERROR=" << errorcount << std::endl; SETVERBOSITY(verbocount); SETTRACE(trace); // Comparison Setup ======================================== // Proper Setup fOutputRootFile = new TFile(fCompKey.GetS("outputfile").c_str(), "RECREATE"); SetupComparisonsFromXML(); SetupRWEngine(); SetupFCN(); return; }; //************************************* void ComparisonRoutines::SetupComparisonsFromXML() { //************************************* NUIS_LOG(FIT, "Setting up nuiscomp"); // Setup Parameters ------------------------------------------ std::vector parkeys = Config::QueryKeys("parameter"); if (!parkeys.empty()) { NUIS_LOG(FIT, "Number of parameters : " << parkeys.size()); } for (size_t i = 0; i < parkeys.size(); i++) { nuiskey key = parkeys.at(i); // Check for type,name,nom if (!key.Has("type")) { NUIS_ERR(FTL, "No type given for parameter " << i); NUIS_ABORT("type='PARAMETER_TYPE'"); } else if (!key.Has("name")) { NUIS_ERR(FTL, "No name given for parameter " << i); NUIS_ABORT("name='SAMPLE_NAME'"); } else if (!key.Has("nominal")) { NUIS_ERR(FTL, "No nominal given for parameter " << i); NUIS_ABORT("nominal='NOMINAL_VALUE'"); } // Get Inputs std::string partype = key.GetS("type"); std::string parname = key.GetS("name"); double parnom = key.GetD("nominal"); double parlow = parnom - 1; double parhigh = parnom + 1; double parstep = 1; // override if state not given if (!key.Has("state")) { key.SetS("state", "FIX"); } std::string parstate = key.GetS("state"); // Check for incomplete limtis int limdef = ((int)key.Has("low") + (int)key.Has("high") + (int)key.Has("step")); if (limdef > 0 and limdef < 3) { NUIS_ERR(FTL, "Incomplete limit set given for parameter : " << parname); NUIS_ABORT( "Requires: low='LOWER_LIMIT' high='UPPER_LIMIT' step='STEP_SIZE' "); } // Extra limits if (key.Has("low")) { parlow = key.GetD("low"); parhigh = key.GetD("high"); parstep = key.GetD("step"); NUIS_LOG(FIT, "Read " << partype << " : " << parname << " = " << parnom << " : " << parlow << " < p < " << parhigh << " : " << parstate); } else { NUIS_LOG(FIT, "Read " << partype << " : " << parname << " = " << parnom << " : " << parstate); } bool ismirr = false; if (key.Has("mirror_point")) { ismirr = true; mirror_param mir; mir.mirror_value = key.GetD("mirror_point"); mir.mirror_above = key.GetB("mirror_above"); fMirroredParams[parname] = mir; NUIS_LOG(FIT, "\t\t" << parname << " is mirrored at " << mir.mirror_value << " " << (mir.mirror_above ? "from above" : "from below")); } // Convert if required if (parstate.find("ABS") != std::string::npos) { if (ismirr) { NUIS_ABORT("Cannot mirror parameters with ABS state!"); } parnom = FitBase::RWAbsToSigma(partype, parname, parnom); parlow = FitBase::RWAbsToSigma(partype, parname, parlow); parhigh = FitBase::RWAbsToSigma(partype, parname, parhigh); parstep = FitBase::RWAbsToSigma(partype, parname, parstep); } else if (parstate.find("FRAC") != std::string::npos) { if (ismirr) { NUIS_ABORT("Cannot mirror parameters with FRAC state!"); } parnom = FitBase::RWFracToSigma(partype, parname, parnom); parlow = FitBase::RWFracToSigma(partype, parname, parlow); parhigh = FitBase::RWFracToSigma(partype, parname, parhigh); parstep = FitBase::RWFracToSigma(partype, parname, parstep); } // Push into vectors fParams.push_back(parname); fTypeVals[parname] = FitBase::ConvDialType(partype); fCurVals[parname] = parnom; fStateVals[parname] = parstate; } // Setup Samples ---------------------------------------------- std::vector samplekeys = Config::QueryKeys("sample"); if (!samplekeys.empty()) { NUIS_LOG(FIT, "Number of samples : " << samplekeys.size()); } for (size_t i = 0; i < samplekeys.size(); i++) { nuiskey key = samplekeys.at(i); // Get Sample Options std::string samplename = key.GetS("name"); std::string samplefile = key.GetS("input"); - std::string sampletype = key.Has("type") ? key.GetS("type") : "DEFAULT"; - double samplenorm = key.Has("norm") ? key.GetD("norm") : 1.0; + // Handle the samplefile name + std::string mc_type = GeneralUtils::ParseToStr(samplefile, ":")[0]; + std::string input_samples = GeneralUtils::ParseToStr(samplefile, ":")[1]; + input_samples = GeneralUtils::ReplaceAll(input_samples, "(", ""); + input_samples = GeneralUtils::ReplaceAll(input_samples, ")", ""); + std::vector sample_vect = GeneralUtils::ParseToStr(input_samples, ";"); + // Print out NUIS_LOG(FIT, "Read Sample " << i << ". : " << samplename << " (" << sampletype << ") [Norm=" << samplenorm << "]"); - NUIS_LOG(FIT, " |-> Input="<< samplefile); + NUIS_LOG(FIT, " |-> Input MC type = "<< mc_type <<" with " << sample_vect.size() << " input files"); + for (uint j = 0; j < sample_vect.size(); ++j){ + NUIS_LOG(FIT, " |-> Input file #" << j << " = " << sample_vect[j]); + } // If FREE add to parameters otherwise continue if (sampletype.find("FREE") == std::string::npos) { if (samplenorm != 1.0) { NUIS_ERR(FTL, "You provided a sample normalisation but did not specify " "that the sample is free"); NUIS_ABORT("Change so sample contains type=\"FREE\" and re-run"); } continue; } // Form norm dial from samplename + sampletype + "_norm"; std::string normname = samplename + "_norm"; // Check normname not already present if (fTypeVals.find("normname") != fTypeVals.end()) { continue; } // Add new norm dial to list if its passed above checks fParams.push_back(normname); fTypeVals[normname] = kNORM; fStateVals[normname] = sampletype; fCurVals[normname] = samplenorm; } // Setup Fake Parameters ----------------------------- std::vector fakekeys = Config::QueryKeys("fakeparameter"); if (!fakekeys.empty()) { NUIS_LOG(FIT, "Number of fake parameters : " << fakekeys.size()); } for (size_t i = 0; i < fakekeys.size(); i++) { nuiskey key = fakekeys.at(i); // Check for type,name,nom if (!key.Has("name")) { NUIS_ABORT("No name given for fakeparameter " << i); } else if (!key.Has("nominal")) { NUIS_ABORT("No nominal given for fakeparameter " << i); } // Get Inputs std::string parname = key.GetS("name"); double parnom = key.GetD("nominal"); // Push into vectors fFakeVals[parname] = parnom; } } //************************************* void ComparisonRoutines::SetupRWEngine() { //************************************* NUIS_LOG(FIT, "Setting up FitWeight Engine"); for (UInt_t i = 0; i < fParams.size(); i++) { std::string name = fParams[i]; FitBase::GetRW()->IncludeDial(name, fTypeVals.at(name)); } return; } //************************************* void ComparisonRoutines::SetupFCN() { //************************************* NUIS_LOG(FIT, "Building the SampleFCN"); if (fSampleFCN) delete fSampleFCN; Config::Get().out = fOutputRootFile; fOutputRootFile->cd(); fSampleFCN = new JointFCN(fOutputRootFile); SetFakeData(); return; } //************************************* void ComparisonRoutines::SetFakeData() { //************************************* if (fFakeDataInput.empty()) return; if (fFakeDataInput.compare("MC") == 0) { NUIS_LOG(FIT, "Setting fake data from MC starting prediction."); UpdateRWEngine(fFakeVals); FitBase::GetRW()->Reconfigure(); fSampleFCN->ReconfigureAllEvents(); fSampleFCN->SetFakeData("MC"); std::map CurVals_wmirr; for (size_t i = 0; i < fParams.size(); ++i) { std::string const &pname = fParams[i]; if (fMirroredParams.count(pname)) { if (!fMirroredParams[pname].mirror_above && (fCurVals[pname] < fMirroredParams[pname].mirror_value)) { double xabove = fMirroredParams[pname].mirror_value - fCurVals[pname]; CurVals_wmirr[pname] = fMirroredParams[pname].mirror_value + xabove; std::cout << "\t--Parameter " << pname << " mirrored from " << fCurVals[pname] << " -> " << CurVals_wmirr[pname] << std::endl; } else if (fMirroredParams[pname].mirror_above && (fCurVals[pname] >= fMirroredParams[pname].mirror_value)) { double xabove = fCurVals[pname] - fMirroredParams[pname].mirror_value; CurVals_wmirr[pname] = fMirroredParams[pname].mirror_value - xabove; std::cout << "\t--Parameter " << pname << " mirrored from " << fCurVals[pname] << " -> " << CurVals_wmirr[pname] << std::endl; } else { CurVals_wmirr[pname] = fCurVals[pname]; } } else { CurVals_wmirr[pname] = fCurVals[pname]; } } UpdateRWEngine(CurVals_wmirr); NUIS_LOG(FIT, "Set all data to fake MC predictions."); } else { NUIS_LOG(FIT, "Setting fake data from: " << fFakeDataInput); fSampleFCN->SetFakeData(fFakeDataInput); } return; } /* Fitting Functions */ //************************************* void ComparisonRoutines::UpdateRWEngine( std::map &updateVals) { //************************************* for (UInt_t i = 0; i < fParams.size(); i++) { std::string name = fParams[i]; if (updateVals.find(name) == updateVals.end()) continue; FitBase::GetRW()->SetDialValue(name, updateVals.at(name)); } FitBase::GetRW()->Reconfigure(); return; } //************************************* void ComparisonRoutines::Run() { //************************************* NUIS_LOG(FIT, "Running ComparisonRoutines : " << fStrategy); if (FitPar::Config().GetParB("save_nominal")) { SaveNominal(); } // Parse given routines fRoutines = GeneralUtils::ParseToStr(fStrategy, ","); if (fRoutines.empty()) { NUIS_ABORT("Trying to run ComparisonRoutines with no routines given!"); } for (UInt_t i = 0; i < fRoutines.size(); i++) { std::string routine = fRoutines.at(i); // Mostly this is unnecessary information, so don't always print if (fRoutines.size() > 1){ NUIS_LOG(FIT, "Routine: " << routine); } if (!routine.compare("Compare")) { std::map CurVals_wmirr; for (size_t i = 0; i < fParams.size(); ++i) { std::string const &pname = fParams[i]; if (fMirroredParams.count(pname)) { std::cout << "MA? " << fMirroredParams[pname].mirror_above << ", OVal: " << fCurVals[pname] << ", MPoint: " << fMirroredParams[pname].mirror_value << std::endl; if (!fMirroredParams[pname].mirror_above && (fCurVals[pname] < fMirroredParams[pname].mirror_value)) { double xabove = fMirroredParams[pname].mirror_value - fCurVals[pname]; CurVals_wmirr[pname] = fMirroredParams[pname].mirror_value + xabove; std::cout << "\t--Parameter " << pname << " mirrored from " << fCurVals[pname] << " -> " << CurVals_wmirr[pname] << std::endl; } else if (fMirroredParams[pname].mirror_above && (fCurVals[pname] >= fMirroredParams[pname].mirror_value)) { double xabove = fCurVals[pname] - fMirroredParams[pname].mirror_value; CurVals_wmirr[pname] = fMirroredParams[pname].mirror_value - xabove; std::cout << "\t--Parameter " << pname << " mirrored from " << fCurVals[pname] << " -> " << CurVals_wmirr[pname] << std::endl; } else { CurVals_wmirr[pname] = fCurVals[pname]; } } else { CurVals_wmirr[pname] = fCurVals[pname]; } std::cout << "~~~~~~~" << pname << " : " << fCurVals[pname] << " -> " << CurVals_wmirr[pname] << std::endl; } UpdateRWEngine(CurVals_wmirr); GenerateComparison(); PrintState(); SaveCurrentState(); } } return; } //************************************* void ComparisonRoutines::GenerateComparison() { //************************************* NUIS_LOG(FIT, "Generating Comparison."); // Main Event Loop from event Manager fSampleFCN->ReconfigureAllEvents(); return; } //************************************* void ComparisonRoutines::PrintState() { //************************************* // Count max size int maxcount = 0; for (UInt_t i = 0; i < fParams.size(); i++) { maxcount = max(int(fParams[i].size()), maxcount); } // Header if (fParams.size()){ NUIS_LOG(FIT, "------------"); NUIS_LOG(FIT, " # " << left << setw(maxcount) << "Parameter " << " = " << setw(10) << "Value" << " +- " << setw(10) << "Error" << " " << setw(8) << "(Units)"); // << " " << setw(10) << "Conv. Val" // << " +- " << setw(10) << "Conv. Err" // << " " << setw(8) << "(Units)"); } std::map CurVals_wmirr; for (size_t i = 0; i < fParams.size(); ++i) { std::string const &pname = fParams[i]; if (fMirroredParams.count(pname)) { if (!fMirroredParams[pname].mirror_above && (fCurVals[pname] < fMirroredParams[pname].mirror_value)) { double xabove = fMirroredParams[pname].mirror_value - fCurVals[pname]; CurVals_wmirr[pname] = fMirroredParams[pname].mirror_value + xabove; std::cout << "\t--Parameter " << pname << " mirrored from " << fCurVals[pname] << " -> " << CurVals_wmirr[pname] << std::endl; } else if (fMirroredParams[pname].mirror_above && (fCurVals[pname] >= fMirroredParams[pname].mirror_value)) { double xabove = fCurVals[pname] - fMirroredParams[pname].mirror_value; CurVals_wmirr[pname] = fMirroredParams[pname].mirror_value - xabove; std::cout << "\t--Parameter " << pname << " mirrored from " << fCurVals[pname] << " -> " << CurVals_wmirr[pname] << std::endl; } else { CurVals_wmirr[pname] = fCurVals[pname]; } } else { CurVals_wmirr[pname] = fCurVals[pname]; } } // Parameters for (UInt_t i = 0; i < fParams.size(); i++) { std::string syst = fParams.at(i); std::string typestr = FitBase::ConvDialType(fTypeVals[syst]); std::string curunits = "(sig.)"; double curval = fCurVals[syst]; double curerr = 0.0; if (fStateVals[syst].find("ABS") != std::string::npos) { curval = FitBase::RWSigmaToAbs(typestr, syst, curval); curerr = (FitBase::RWSigmaToAbs(typestr, syst, curerr) - FitBase::RWSigmaToAbs(typestr, syst, 0.0)); curunits = "(Abs.)"; } else if (fStateVals[syst].find("FRAC") != std::string::npos) { curval = FitBase::RWSigmaToFrac(typestr, syst, curval); curerr = (FitBase::RWSigmaToFrac(typestr, syst, curerr) - FitBase::RWSigmaToFrac(typestr, syst, 0.0)); curunits = "(Frac)"; } // std::string convunits = "(" + FitBase::GetRWUnits(typestr, syst) + ")"; // double convval = FitBase::RWSigmaToAbs(typestr, syst, curval); // double converr = (FitBase::RWSigmaToAbs(typestr, syst, curerr) - // FitBase::RWSigmaToAbs(typestr, syst, 0.0)); std::ostringstream curparstring; curparstring << " " << setw(3) << left << i << " " << setw(maxcount) << syst << " = " << setw(10) << Form("%.7lf", curval) << " +- " << setw(10) << Form("%.7lf", curerr) << " " << setw(8) << curunits; // << " " << setw(10); // << convval << " +- " << setw(10) << converr << " " << setw(8) // << convunits; NUIS_LOG(FIT, curparstring.str()); if (fMirroredParams.count(syst)) { NUIS_LOG(FIT, "\t\t--> Mirrored at " << fMirroredParams[syst].mirror_value << " to effective value " << CurVals_wmirr[syst]); } } NUIS_LOG(FIT, "------------"); double like = fSampleFCN->GetLikelihood(); int ndof = fSampleFCN->GetNDOF(); NUIS_LOG(FIT, std::left << std::setw(55) << "Likelihood for JointFCN" << ": " << like << "/" << ndof); NUIS_LOG(FIT, "------------"); } /* Write Functions */ //************************************* void ComparisonRoutines::SaveCurrentState(std::string subdir) { //************************************* NUIS_LOG(FIT, "Saving current full FCN predictions"); // Setup DIRS TDirectory *curdir = gDirectory; if (!subdir.empty()) { TDirectory *newdir = (TDirectory *)gDirectory->mkdir(subdir.c_str()); newdir->cd(); } fSampleFCN->Write(); // Change back to current DIR curdir->cd(); return; } //************************************* void ComparisonRoutines::SaveNominal() { //************************************* fOutputRootFile->cd(); NUIS_LOG(FIT, "Saving Nominal Predictions (be cautious with this)"); FitBase::GetRW()->Reconfigure(); GenerateComparison(); SaveCurrentState("nominal"); }; diff --git a/src/Routines/MinimizerRoutines.cxx b/src/Routines/MinimizerRoutines.cxx index b46ba32..148b6d3 100755 --- a/src/Routines/MinimizerRoutines.cxx +++ b/src/Routines/MinimizerRoutines.cxx @@ -1,1563 +1,1569 @@ // Copyright 2016-2021 L. Pickering, P Stowell, R. Terri, C. Wilkinson, C. Wret /******************************************************************************* * This file is part of NUISANCE. * * NUISANCE is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * NUISANCE is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with NUISANCE. If not, see . *******************************************************************************/ #include "MinimizerRoutines.h" #include "Simple_MH_Sampler.h" /* Constructor/Destructor */ //************************ void MinimizerRoutines::Init() { //************************ fInputFile = ""; fInputRootFile = NULL; fOutputFile = ""; fOutputRootFile = NULL; fCovar = NULL; fCovFree = NULL; fCorrel = NULL; fCorFree = NULL; fDecomp = NULL; fDecFree = NULL; fStrategy = "Migrad,FixAtLimBreak,Migrad"; fRoutines.clear(); fCardFile = ""; fFakeDataInput = ""; fSampleFCN = NULL; fMinimizer = NULL; fMinimizerFCN = NULL; fCallFunctor = NULL; fAllowedRoutines = ("Migrad,Simplex,Combined," "Brute,Fumili,ConjugateFR," "ConjugatePR,BFGS,BFGS2," "SteepDesc,GSLSimAn,FixAtLim,FixAtLimBreak," "Chi2Scan1D,Chi2Scan2D,Contours,ErrorBands," "DataToys,MCMC"); }; //************************************* MinimizerRoutines::~MinimizerRoutines(){ //************************************* }; /* Input Functions */ //************************************* MinimizerRoutines::MinimizerRoutines() { //************************************* Init(); } //************************************* MinimizerRoutines::MinimizerRoutines(int argc, char *argv[]) { //************************************* // Initialise Defaults Init(); nuisconfig configuration = Config::Get(); // Default containers std::string cardfile = ""; std::string maxevents = "-1"; int errorcount = 0; int verbocount = 0; std::vector xmlcmds; std::vector configargs; // Make easier to handle arguments. std::vector args = GeneralUtils::LoadCharToVectStr(argc, argv); ParserUtils::ParseArgument(args, "-c", fCardFile, true); ParserUtils::ParseArgument(args, "-o", fOutputFile, false, false); ParserUtils::ParseArgument(args, "-n", maxevents, false, false); ParserUtils::ParseArgument(args, "-f", fStrategy, false, false); ParserUtils::ParseArgument(args, "-d", fFakeDataInput, false, false); ParserUtils::ParseArgument(args, "-i", xmlcmds); ParserUtils::ParseArgument(args, "-q", configargs); ParserUtils::ParseCounter(args, "e", errorcount); ParserUtils::ParseCounter(args, "v", verbocount); ParserUtils::CheckBadArguments(args); // Add extra defaults if none given if (fCardFile.empty() and xmlcmds.empty()) { NUIS_ABORT("No input supplied!"); } if (fOutputFile.empty() and !fCardFile.empty()) { fOutputFile = fCardFile + ".root"; NUIS_ERR(WRN, "No output supplied so saving it to: " << fOutputFile); } else if (fOutputFile.empty()) { NUIS_ABORT("No output file or cardfile supplied!"); } // Configuration Setup ============================= // Check no comp key is available nuiskey fCompKey; if (Config::Get().GetNodes("nuiscomp").empty()) { fCompKey = Config::Get().CreateNode("nuiscomp"); } else { fCompKey = Config::Get().GetNodes("nuiscomp")[0]; } if (!fCardFile.empty()) fCompKey.Set("cardfile", fCardFile); if (!fOutputFile.empty()) fCompKey.Set("outputfile", fOutputFile); if (!fStrategy.empty()) fCompKey.Set("strategy", fStrategy); // Load XML Cardfile configuration.LoadSettings(fCompKey.GetS("cardfile"), ""); // Add Config Args for (size_t i = 0; i < configargs.size(); i++) { configuration.OverrideConfig(configargs[i]); } if (maxevents.compare("-1")) { configuration.OverrideConfig("MAXEVENTS=" + maxevents); } // Finish configuration XML configuration.FinaliseSettings(fCompKey.GetS("outputfile") + ".xml"); // Sort out the printout verbocount += Config::GetParI("VERBOSITY"); errorcount += Config::GetParI("ERROR"); bool trace = Config::GetParB("TRACE"); std::cout << "[ NUISANCE ]: Setting VERBOSITY=" << verbocount << std::endl; std::cout << "[ NUISANCE ]: Setting ERROR=" << errorcount << std::endl; SETVERBOSITY(verbocount); SETTRACE(trace); // Minimizer Setup ======================================== fOutputRootFile = new TFile(fCompKey.GetS("outputfile").c_str(), "RECREATE"); SetupMinimizerFromXML(); SetupCovariance(); SetupRWEngine(); SetupFCN(); return; }; //************************************* void MinimizerRoutines::SetupMinimizerFromXML() { //************************************* NUIS_LOG(FIT, "Setting up nuismin"); // Setup Parameters ------------------------------------------ std::vector parkeys = Config::QueryKeys("parameter"); if (!parkeys.empty()) { NUIS_LOG(FIT, "Number of parameters : " << parkeys.size()); } for (size_t i = 0; i < parkeys.size(); i++) { nuiskey key = parkeys.at(i); // Check for type,name,nom if (!key.Has("type")) { NUIS_ABORT("No type given for parameter " << i); } else if (!key.Has("name")) { NUIS_ABORT("No name given for parameter " << i); } else if (!key.Has("nominal")) { NUIS_ABORT("No nominal given for parameter " << i); } // Get Inputs std::string partype = key.GetS("type"); std::string parname = key.GetS("name"); double parnom = key.GetD("nominal"); double parlow = parnom - 1; double parhigh = parnom + 1; double parstep = 1; // Override state if none given if (!key.Has("state")) { key.SetS("state", "FIX"); } std::string parstate = key.GetS("state"); // Extra limits if (key.Has("low")) { parlow = key.GetD("low"); parhigh = key.GetD("high"); parstep = key.GetD("step"); NUIS_LOG(FIT, "Read " << partype << " : " << parname << " = " << parnom << " : " << parlow << " < p < " << parhigh << " : " << parstate); } else { NUIS_LOG(FIT, "Read " << partype << " : " << parname << " = " << parnom << " : " << parstate); } bool ismirr = false; if (key.Has("mirror_point")) { ismirr=true; mirror_param mir; mir.mirror_value = key.GetD("mirror_point"); mir.mirror_above = key.GetB("mirror_above"); fMirroredParams[parname] = mir; NUIS_LOG(FIT, "\t\t" << parname << " is mirrored at " << mir.mirror_value << " " << (mir.mirror_above ? "from above" : "from below")); } // Run Parameter Conversion if needed if (parstate.find("ABS") != std::string::npos) { if(ismirr){ NUIS_ABORT("Cannot mirror parameters with ABS state!"); } double opnom = parnom; double oparstep = parstep; parnom = FitBase::RWAbsToSigma(partype, parname, parnom); parlow = FitBase::RWAbsToSigma(partype, parname, parlow); parhigh = FitBase::RWAbsToSigma(partype, parname, parhigh); parstep = FitBase::RWAbsToSigma(partype, parname, opnom + parstep) - parnom; NUIS_LOG(FIT, "ParStep: " << parstep << " (" << oparstep << ")."); } else if (parstate.find("FRAC") != std::string::npos) { if(ismirr){ NUIS_ABORT("Cannot mirror parameters with FRAC state!"); } parnom = FitBase::RWFracToSigma(partype, parname, parnom); parlow = FitBase::RWFracToSigma(partype, parname, parlow); parhigh = FitBase::RWFracToSigma(partype, parname, parhigh); parstep = FitBase::RWFracToSigma(partype, parname, parstep); } // Push into vectors fParams.push_back(parname); fTypeVals[parname] = FitBase::ConvDialType(partype); fStartVals[parname] = parnom; fCurVals[parname] = parnom; fErrorVals[parname] = 0.0; fStateVals[parname] = parstate; bool fixstate = parstate.find("FIX") != std::string::npos; fFixVals[parname] = fixstate; fStartFixVals[parname] = fFixVals[parname]; fMinVals[parname] = parlow; fMaxVals[parname] = parhigh; fStepVals[parname] = parstep; } // Setup Samples ---------------------------------------------- std::vector samplekeys = Config::QueryKeys("sample"); if (!samplekeys.empty()) { NUIS_LOG(FIT, "Number of samples : " << samplekeys.size()); } for (size_t i = 0; i < samplekeys.size(); i++) { nuiskey key = samplekeys.at(i); // Get Sample Options std::string samplename = key.GetS("name"); std::string samplefile = key.GetS("input"); - std::string sampletype = key.Has("type") ? key.GetS("type") : "DEFAULT"; - double samplenorm = key.Has("norm") ? key.GetD("norm") : 1.0; + // Handle the samplefile name + std::string mc_type = GeneralUtils::ParseToStr(samplefile, ":")[0]; + std::string input_samples = GeneralUtils::ParseToStr(samplefile, ":")[1]; + input_samples = GeneralUtils::ReplaceAll(input_samples, "(", ""); + input_samples = GeneralUtils::ReplaceAll(input_samples, ")", ""); + std::vector sample_vect = GeneralUtils::ParseToStr(input_samples, ";"); + // Print out - NUIS_LOG(FIT, "Read sample info " - << i << " : " << samplename << std::endl - << "\t\t|-> input -> " << samplefile << std::endl - << "\t\t|-> state -> " << sampletype << std::endl - << "\t\t|-> norm -> " << samplenorm); + NUIS_LOG(FIT, "Read sample " << i << ". : " << samplename << " (" + << sampletype << ") [Norm=" << samplenorm << "]"); + NUIS_LOG(FIT, " |-> Input MC type = "<< mc_type <<" with " << sample_vect.size() << " input files"); + for (uint j=0; j < sample_vect.size(); ++j){ + NUIS_LOG(FIT, " |-> Input file #" << j << " = " << sample_vect[j]); + } // If FREE add to parameters otherwise continue if (sampletype.find("FREE") == std::string::npos) { if (samplenorm != 1.0) { NUIS_ERR(FTL, "You provided a sample normalisation but did not specify " "that the sample is free"); NUIS_ABORT("Change so sample contains type=\"FREE\" and re-run"); } continue; } // Form norm dial from samplename + sampletype + "_norm"; std::string normname = samplename + "_norm"; // Check normname not already present if (fTypeVals.find(normname) != fTypeVals.end()) { continue; } // Add new norm dial to list if its passed above checks fParams.push_back(normname); fTypeVals[normname] = kNORM; fStateVals[normname] = sampletype; fStartVals[normname] = samplenorm; fCurVals[normname] = samplenorm; fErrorVals[normname] = 0.0; fMinVals[normname] = 0.1; fMaxVals[normname] = 10.0; fStepVals[normname] = 0.5; bool state = sampletype.find("FREE") == std::string::npos; fFixVals[normname] = state; fStartFixVals[normname] = state; } // Setup Fake Parameters ----------------------------- std::vector fakekeys = Config::QueryKeys("fakeparameter"); if (!fakekeys.empty()) { NUIS_LOG(FIT, "Number of fake parameters : " << fakekeys.size()); } for (size_t i = 0; i < fakekeys.size(); i++) { nuiskey key = fakekeys.at(i); // Check for type,name,nom if (!key.Has("name")) { NUIS_ABORT("No name given for fakeparameter " << i); } else if (!key.Has("nom")) { NUIS_ABORT("No nominal given for fakeparameter " << i); } // Get Inputs std::string parname = key.GetS("name"); double parnom = key.GetD("nom"); // Push into vectors fFakeVals[parname] = parnom; } } /* Setup Functions */ //************************************* void MinimizerRoutines::SetupRWEngine() { //************************************* for (UInt_t i = 0; i < fParams.size(); i++) { std::string name = fParams[i]; FitBase::GetRW()->IncludeDial(name, fTypeVals.at(name)); } UpdateRWEngine(fStartVals); return; } //************************************* void MinimizerRoutines::SetupFCN() { //************************************* NUIS_LOG(FIT, "Making the jointFCN"); if (fSampleFCN) delete fSampleFCN; // fSampleFCN = new JointFCN(fCardFile, fOutputRootFile); fSampleFCN = new JointFCN(fOutputRootFile); SetFakeData(); fMinimizerFCN = new MinimizerFCN(fSampleFCN); fCallFunctor = new ROOT::Math::Functor(*fMinimizerFCN, fParams.size()); fSampleFCN->CreateIterationTree("fit_iterations", FitBase::GetRW()); return; } //****************************************** void MinimizerRoutines::SetupFitter(std::string routine) { //****************************************** // Make the fitter std::string fitclass = ""; std::string fittype = ""; bool UseMCMC = false; // Get correct types if (!routine.compare("Migrad")) { fitclass = "Minuit2"; fittype = "Migrad"; } else if (!routine.compare("Simplex")) { fitclass = "Minuit2"; fittype = "Simplex"; } else if (!routine.compare("Combined")) { fitclass = "Minuit2"; fittype = "Combined"; } else if (!routine.compare("Brute")) { fitclass = "Minuit2"; fittype = "Scan"; } else if (!routine.compare("Fumili")) { fitclass = "Minuit2"; fittype = "Fumili"; } else if (!routine.compare("ConjugateFR")) { fitclass = "GSLMultiMin"; fittype = "ConjugateFR"; } else if (!routine.compare("ConjugatePR")) { fitclass = "GSLMultiMin"; fittype = "ConjugatePR"; } else if (!routine.compare("BFGS")) { fitclass = "GSLMultiMin"; fittype = "BFGS"; } else if (!routine.compare("BFGS2")) { fitclass = "GSLMultiMin"; fittype = "BFGS2"; } else if (!routine.compare("SteepDesc")) { fitclass = "GSLMultiMin"; fittype = "SteepestDescent"; // } else if (!routine.compare("GSLMulti")) { fitclass = "GSLMultiFit"; // fittype = ""; // Doesn't work out of the box } else if (!routine.compare("GSLSimAn")) { fitclass = "GSLSimAn"; fittype = ""; } else if (!routine.compare("MCMC")) { UseMCMC = true; } // make minimizer if (fMinimizer) delete fMinimizer; if (UseMCMC) { fMinimizer = new Simple_MH_Sampler(); } else { fMinimizer = ROOT::Math::Factory::CreateMinimizer(fitclass, fittype); } fMinimizer->SetMaxFunctionCalls(FitPar::Config().GetParI("MAXCALLS")); if (!routine.compare("Brute")) { fMinimizer->SetMaxFunctionCalls(fParams.size() * fParams.size() * 4); fMinimizer->SetMaxIterations(fParams.size() * fParams.size() * 4); } fMinimizer->SetMaxIterations(FitPar::Config().GetParI("MAXITERATIONS")); fMinimizer->SetTolerance(FitPar::Config().GetParD("TOLERANCE")); fMinimizer->SetStrategy(FitPar::Config().GetParI("STRATEGY")); fMinimizer->SetFunction(*fCallFunctor); int ipar = 0; // Add Fit Parameters for (UInt_t i = 0; i < fParams.size(); i++) { std::string syst = fParams.at(i); bool fixed = true; double vstart, vstep, vlow, vhigh; vstart = vstep = vlow = vhigh = 0.0; if (fCurVals.find(syst) != fCurVals.end()) vstart = fCurVals.at(syst); if (fMinVals.find(syst) != fMinVals.end()) vlow = fMinVals.at(syst); if (fMaxVals.find(syst) != fMaxVals.end()) vhigh = fMaxVals.at(syst); if (fStepVals.find(syst) != fStepVals.end()) vstep = fStepVals.at(syst); if (fFixVals.find(syst) != fFixVals.end()) fixed = fFixVals.at(syst); // fix for errors if (vhigh == vlow) vhigh += 1.0; fMinimizer->SetVariable(ipar, syst, vstart, vstep); fMinimizer->SetVariableLimits(ipar, vlow, vhigh); if (fMirroredParams.count(syst)) { fSampleFCN->SetVariableMirrored(ipar, fMirroredParams[syst].mirror_value, fMirroredParams[syst].mirror_above); } if (fixed) { fMinimizer->FixVariable(ipar); NUIS_LOG(FIT, "Fixed Param: " << syst); } else { NUIS_LOG(FIT, "Free Param: " << syst << " Start:" << vstart << " Range:" << vlow << " to " << vhigh << " Step:" << vstep); } ipar++; } fSampleFCN->SetNParams(ipar); NUIS_LOG(FIT, "Setup Minimizer: " << fMinimizer->NDim() << "(NDim) " << fMinimizer->NFree() << "(NFree)"); return; } //************************************* // Set fake data from user input void MinimizerRoutines::SetFakeData() { //************************************* // If the fake data input field (-d) isn't provided, return to caller if (fFakeDataInput.empty()) return; // If user specifies -d MC we set the data to the MC // User can also specify fake data parameters to reweight by doing // "fake_parameter" in input card file // "fake_parameter" gets read in ReadCard function (reads to fFakeVals) if (fFakeDataInput.compare("MC") == 0) { NUIS_LOG(FIT, "Setting fake data from MC starting prediction."); // fFakeVals get read in in ReadCard UpdateRWEngine(fFakeVals); // Reconfigure the reweight engine FitBase::GetRW()->Reconfigure(); // Reconfigure all the samples to the new reweight fSampleFCN->ReconfigureAllEvents(); // Feed on and set the fake-data in each measurement class fSampleFCN->SetFakeData("MC"); // Changed the reweight engine values back to the current values // So we start the fit at a different value than what we set the fake-data // to UpdateRWEngine(fCurVals); NUIS_LOG(FIT, "Set all data to fake MC predictions."); } else { fSampleFCN->SetFakeData(fFakeDataInput); } return; } /* Fitting Functions */ //************************************* void MinimizerRoutines::UpdateRWEngine( std::map &updateVals) { //************************************* for (UInt_t i = 0; i < fParams.size(); i++) { std::string name = fParams[i]; if (updateVals.find(name) == updateVals.end()) continue; FitBase::GetRW()->SetDialValue(name, updateVals.at(name)); } FitBase::GetRW()->Reconfigure(); return; } //************************************* void MinimizerRoutines::Run() { //************************************* NUIS_LOG(FIT, "Running MinimizerRoutines : " << fStrategy); if (FitPar::Config().GetParB("save_nominal")) { SaveNominal(); } // Parse given routines fRoutines = GeneralUtils::ParseToStr(fStrategy, ","); if (fRoutines.empty()) { NUIS_ABORT("Trying to run MinimizerRoutines with no routines given!"); } for (UInt_t i = 0; i < fRoutines.size(); i++) { std::string routine = fRoutines.at(i); int fitstate = kFitUnfinished; NUIS_LOG(FIT, "Running Routine: " << routine); // Try Routines if (routine.find("LowStat") != std::string::npos) LowStatRoutine(routine); else if (routine == "FixAtLim") FixAtLimit(); else if (routine == "FixAtLimBreak") fitstate = FixAtLimit(); else if (routine.find("ErrorBands") != std::string::npos) GenerateErrorBands(); else if (routine.find("DataToys") != std::string::npos) ThrowDataToys(); else if (!routine.compare("Chi2Scan1D")) Create1DScans(); else if (!routine.compare("Chi2Scan2D")) Chi2Scan2D(); else fitstate = RunFitRoutine(routine); // If ending early break here if (fitstate == kFitFinished || fitstate == kNoChange) { NUIS_LOG(FIT, "Ending fit routines loop."); break; } } return; } //************************************* int MinimizerRoutines::RunFitRoutine(std::string routine) { //************************************* int endfits = kFitUnfinished; // set fitter at the current start values fOutputRootFile->cd(); SetupFitter(routine); // choose what to do with the minimizer depending on routine. if (!routine.compare("Migrad") or !routine.compare("Simplex") or !routine.compare("Combined") or !routine.compare("Brute") or !routine.compare("Fumili") or !routine.compare("ConjugateFR") or !routine.compare("ConjugatePR") or !routine.compare("BFGS") or !routine.compare("BFGS2") or !routine.compare("SteepDesc") or // !routine.compare("GSLMulti") or !routine.compare("GSLSimAn") or !routine.compare("MCMC")) { if (fMinimizer->NFree() > 0) { fMinimizer->Minimize(); GetMinimizerState(); } } // other otptions else if (!routine.compare("Contour")) { CreateContours(); } return endfits; } //************************************* void MinimizerRoutines::PrintState() { //************************************* NUIS_LOG(FIT, "------------"); // Count max size int maxcount = 0; for (UInt_t i = 0; i < fParams.size(); i++) { maxcount = max(int(fParams[i].size()), maxcount); } // Header NUIS_LOG(FIT, " # " << left << setw(maxcount) << "Parameter " << " = " << setw(10) << "Value" << " +- " << setw(10) << "Error" << " " << setw(8) << "(Units)"); // << " " << setw(10) << "Conv. Val" // << " +- " << setw(10) << "Conv. Err" // << " " << setw(8) << "(Units)"); // Parameters for (UInt_t i = 0; i < fParams.size(); i++) { std::string syst = fParams.at(i); std::string typestr = FitBase::ConvDialType(fTypeVals[syst]); std::string curunits = "(sig.)"; double curval = fCurVals[syst]; double curerr = fErrorVals[syst]; if (fStateVals[syst].find("ABS") != std::string::npos) { curval = FitBase::RWSigmaToAbs(typestr, syst, curval); curerr = (FitBase::RWSigmaToAbs(typestr, syst, curerr) - FitBase::RWSigmaToAbs(typestr, syst, 0.0)); curunits = "(Abs.)"; } else if (fStateVals[syst].find("FRAC") != std::string::npos) { curval = FitBase::RWSigmaToFrac(typestr, syst, curval); curerr = (FitBase::RWSigmaToFrac(typestr, syst, curerr) - FitBase::RWSigmaToFrac(typestr, syst, 0.0)); curunits = "(Frac)"; } // std::string convunits = "(" + FitBase::GetRWUnits(typestr, syst) + ")"; // double convval = FitBase::RWSigmaToAbs(typestr, syst, curval); // double converr = (FitBase::RWSigmaToAbs(typestr, syst, curerr) - // FitBase::RWSigmaToAbs(typestr, syst, 0.0)); std::ostringstream curparstring; curparstring << " " << setw(3) << left << i << " " << setw(maxcount) << syst << " = " << setw(10) << Form("%.7lf", curval) << " +- " << setw(10) << Form("%.7lf", curerr) << " " << setw(8) << curunits; // << " " << setw(10) // << convval << " +- " << setw(10) << converr << " " << setw(8) // << convunits; NUIS_LOG(FIT, curparstring.str()); } NUIS_LOG(FIT, "------------"); double like = fSampleFCN->GetLikelihood(); int ndof = fSampleFCN->GetNDOF(); NUIS_LOG(FIT, std::left << std::setw(55) << "Likelihood for JointFCN" << ": " << like << "/" << ndof) NUIS_LOG(FIT, "------------"); } //************************************* void MinimizerRoutines::GetMinimizerState() { //************************************* NUIS_LOG(DEB, "Minimizer State: "); // Get X and Err const double *values = fMinimizer->X(); const double *errors = fMinimizer->Errors(); // int ipar = 0; for (UInt_t i = 0; i < fParams.size(); i++) { std::string syst = fParams.at(i); fCurVals[syst] = values[i]; fErrorVals[syst] = errors[i]; } PrintState(); // Covar SetupCovariance(); if (fMinimizer->CovMatrixStatus() > 0) { // Fill Full Covar NUIS_LOG(FIT, "Filling covariance"); for (int i = 0; i < fCovar->GetNbinsX(); i++) { for (int j = 0; j < fCovar->GetNbinsY(); j++) { fCovar->SetBinContent(i + 1, j + 1, fMinimizer->CovMatrix(i, j)); } } int freex = 0; int freey = 0; for (int i = 0; i < fCovar->GetNbinsX(); i++) { if (fMinimizer->IsFixedVariable(i)) continue; freey = 0; for (int j = 0; j < fCovar->GetNbinsY(); j++) { if (fMinimizer->IsFixedVariable(j)) continue; fCovFree->SetBinContent(freex + 1, freey + 1, fMinimizer->CovMatrix(i, j)); freey++; } freex++; } fCorrel = PlotUtils::GetCorrelationPlot(fCovar, "correlation"); fDecomp = PlotUtils::GetDecompPlot(fCovar, "decomposition"); if (fMinimizer->NFree() > 0) { fCorFree = PlotUtils::GetCorrelationPlot(fCovFree, "correlation_free"); fDecFree = PlotUtils::GetDecompPlot(fCovFree, "decomposition_free"); } } return; }; //************************************* void MinimizerRoutines::LowStatRoutine(std::string routine) { //************************************* NUIS_LOG(FIT, "Running Low Statistics Routine: " << routine); int lowstatsevents = FitPar::Config().GetParI("LOWSTATEVENTS"); int maxevents = FitPar::Config().GetParI("MAXEVENTS"); int verbosity = FitPar::Config().GetParI("VERBOSITY"); std::string trueroutine = routine; std::string substring = "LowStat"; trueroutine.erase(trueroutine.find(substring), substring.length()); // Set MAX EVENTS=1000 Config::SetPar("MAXEVENTS", lowstatsevents); Config::SetPar("VERBOSITY", 3); SetupFCN(); RunFitRoutine(trueroutine); Config::SetPar("MAXEVENTS", maxevents); SetupFCN(); Config::SetPar("VERBOSITY", verbosity); return; } //************************************* void MinimizerRoutines::Create1DScans() { //************************************* // 1D Scan Routine // Steps through all free parameters about nominal using the step size // Creates a graph for each free parameter // At the current point create a 1D Scan for all parametes (Uncorrelated) for (UInt_t i = 0; i < fParams.size(); i++) { if (fFixVals[fParams[i]]) continue; NUIS_LOG(FIT, "Running 1D Scan for " << fParams[i]); fSampleFCN->CreateIterationTree(fParams[i] + "_scan1D_iterations", FitBase::GetRW()); double scanmiddlepoint = fCurVals[fParams[i]]; // Determine N points needed double limlow = fMinVals[fParams[i]]; double limhigh = fMaxVals[fParams[i]]; double step = fStepVals[fParams[i]]; int npoints = int(fabs(limhigh - limlow) / (step + 0.)); TH1D *contour = new TH1D(("Chi2Scan1D_" + fParams[i]).c_str(), ("Chi2Scan1D_" + fParams[i] + ";" + fParams[i]).c_str(), npoints, limlow, limhigh); // Fill bins for (int x = 0; x < contour->GetNbinsX(); x++) { // Set X Val fCurVals[fParams[i]] = contour->GetXaxis()->GetBinCenter(x + 1); // Run Eval double *vals = FitUtils::GetArrayFromMap(fParams, fCurVals); double chi2 = fSampleFCN->DoEval(vals); delete vals; // Fill Contour contour->SetBinContent(x + 1, chi2); } // Save contour contour->Write(); // Reset Parameter fCurVals[fParams[i]] = scanmiddlepoint; // Save TTree fSampleFCN->WriteIterationTree(); } return; } //************************************* void MinimizerRoutines::Chi2Scan2D() { //************************************* // Chi2 Scan 2D // Creates a 2D chi2 scan by stepping through all free parameters // Works for all pairwise combos of free parameters // Scan I for (UInt_t i = 0; i < fParams.size(); i++) { if (fFixVals[fParams[i]]) continue; // Scan J for (UInt_t j = 0; j < i; j++) { if (fFixVals[fParams[j]]) continue; fSampleFCN->CreateIterationTree(fParams[i] + "_" + fParams[j] + "_" + "scan2D_iterations", FitBase::GetRW()); double scanmid_i = fCurVals[fParams[i]]; double scanmid_j = fCurVals[fParams[j]]; double limlow_i = fMinVals[fParams[i]]; double limhigh_i = fMaxVals[fParams[i]]; double step_i = fStepVals[fParams[i]]; double limlow_j = fMinVals[fParams[j]]; double limhigh_j = fMaxVals[fParams[j]]; double step_j = fStepVals[fParams[j]]; int npoints_i = int(fabs(limhigh_i - limlow_i) / (step_i + 0.)) + 1; int npoints_j = int(fabs(limhigh_j - limlow_j) / (step_j + 0.)) + 1; TH2D *contour = new TH2D( ("Chi2Scan2D_" + fParams[i] + "_" + fParams[j]).c_str(), ("Chi2Scan2D_" + fParams[i] + "_" + fParams[j] + ";" + fParams[i] + ";" + fParams[j]) .c_str(), npoints_i, limlow_i, limhigh_i, npoints_j, limlow_j, limhigh_j); // Begin Scan NUIS_LOG(FIT, "Running scan for " << fParams[i] << " " << fParams[j]); // Fill bins for (int x = 0; x < contour->GetNbinsX(); x++) { // Set X Val fCurVals[fParams[i]] = contour->GetXaxis()->GetBinCenter(x + 1); // Loop Y for (int y = 0; y < contour->GetNbinsY(); y++) { // Set Y Val fCurVals[fParams[j]] = contour->GetYaxis()->GetBinCenter(y + 1); // Run Eval double *vals = FitUtils::GetArrayFromMap(fParams, fCurVals); double chi2 = fSampleFCN->DoEval(vals); delete vals; // Fill Contour contour->SetBinContent(x + 1, y + 1, chi2); fCurVals[fParams[j]] = scanmid_j; } fCurVals[fParams[i]] = scanmid_i; fCurVals[fParams[j]] = scanmid_j; } // Save contour contour->Write(); // Save Iterations fSampleFCN->WriteIterationTree(); } } return; } //************************************* void MinimizerRoutines::CreateContours() { //************************************* // Use MINUIT for this if possible NUIS_ABORT("Contours not yet implemented as it is really slow!"); return; } //************************************* int MinimizerRoutines::FixAtLimit() { //************************************* bool fixedparam = false; for (UInt_t i = 0; i < fParams.size(); i++) { std::string syst = fParams.at(i); if (fFixVals[syst]) continue; double curVal = fCurVals.at(syst); double minVal = fMinVals.at(syst); double maxVal = fMinVals.at(syst); if (fabs(curVal - minVal) < 0.0001) { fCurVals[syst] = minVal; fFixVals[syst] = true; fixedparam = true; } if (fabs(maxVal - curVal) < 0.0001) { fCurVals[syst] = maxVal; fFixVals[syst] = true; fixedparam = true; } } if (!fixedparam) { NUIS_LOG(FIT, "No dials needed fixing!"); return kNoChange; } else return kStateChange; } /* Write Functions */ //************************************* void MinimizerRoutines::SaveResults() { //************************************* fOutputRootFile->cd(); if (fMinimizer) { SetupCovariance(); SaveMinimizerState(); } SaveCurrentState(); } //************************************* void MinimizerRoutines::SaveMinimizerState() { //************************************* NUIS_LOG(FIT, "Saving Minimizer State"); if (!fMinimizer) { NUIS_ABORT("Can't save minimizer state without min object"); } // Save main fit tree fSampleFCN->WriteIterationTree(); // Get Vals and Errors GetMinimizerState(); // Save tree with fit status std::vector nameVect; std::vector valVect; std::vector errVect; std::vector minVect; std::vector maxVect; std::vector startVect; std::vector endfixVect; std::vector startfixVect; // int NFREEPARS = fMinimizer->NFree(); int NPARS = fMinimizer->NDim(); int ipar = 0; // Dial Vals for (UInt_t i = 0; i < fParams.size(); i++) { std::string name = fParams.at(i); nameVect.push_back(name); valVect.push_back(fCurVals.at(name)); errVect.push_back(fErrorVals.at(name)); minVect.push_back(fMinVals.at(name)); maxVect.push_back(fMaxVals.at(name)); startVect.push_back(fStartVals.at(name)); endfixVect.push_back(fFixVals.at(name)); startfixVect.push_back(fStartFixVals.at(name)); ipar++; } int NFREE = fMinimizer->NFree(); int NDIM = fMinimizer->NDim(); double CHI2 = fSampleFCN->GetLikelihood(); int NBINS = fSampleFCN->GetNDOF(); int NDOF = NBINS - NFREE; // Write fit results TTree *fit_tree = new TTree("fit_result", "fit_result"); fit_tree->Branch("parameter_names", &nameVect); fit_tree->Branch("parameter_values", &valVect); fit_tree->Branch("parameter_errors", &errVect); fit_tree->Branch("parameter_min", &minVect); fit_tree->Branch("parameter_max", &maxVect); fit_tree->Branch("parameter_start", &startVect); fit_tree->Branch("parameter_fix", &endfixVect); fit_tree->Branch("parameter_startfix", &startfixVect); fit_tree->Branch("CHI2", &CHI2, "CHI2/D"); fit_tree->Branch("NDOF", &NDOF, "NDOF/I"); fit_tree->Branch("NBINS", &NBINS, "NBINS/I"); fit_tree->Branch("NDIM", &NDIM, "NDIM/I"); fit_tree->Branch("NFREE", &NFREE, "NFREE/I"); fit_tree->Fill(); fit_tree->Write(); // Make dial variables TH1D dialvar = TH1D("fit_dials", "fit_dials", NPARS, 0, NPARS); TH1D startvar = TH1D("start_dials", "start_dials", NPARS, 0, NPARS); TH1D minvar = TH1D("min_dials", "min_dials", NPARS, 0, NPARS); TH1D maxvar = TH1D("max_dials", "max_dials", NPARS, 0, NPARS); TH1D dialvarfree = TH1D("fit_dials_free", "fit_dials_free", NFREE, 0, NFREE); TH1D startvarfree = TH1D("start_dials_free", "start_dials_free", NFREE, 0, NFREE); TH1D minvarfree = TH1D("min_dials_free", "min_dials_free", NFREE, 0, NFREE); TH1D maxvarfree = TH1D("max_dials_free", "max_dials_free", NFREE, 0, NFREE); int freecount = 0; for (UInt_t i = 0; i < nameVect.size(); i++) { std::string name = nameVect.at(i); dialvar.SetBinContent(i + 1, valVect.at(i)); dialvar.SetBinError(i + 1, errVect.at(i)); dialvar.GetXaxis()->SetBinLabel(i + 1, name.c_str()); startvar.SetBinContent(i + 1, startVect.at(i)); startvar.GetXaxis()->SetBinLabel(i + 1, name.c_str()); minvar.SetBinContent(i + 1, minVect.at(i)); minvar.GetXaxis()->SetBinLabel(i + 1, name.c_str()); maxvar.SetBinContent(i + 1, maxVect.at(i)); maxvar.GetXaxis()->SetBinLabel(i + 1, name.c_str()); if (NFREE > 0) { if (!startfixVect.at(i)) { freecount++; dialvarfree.SetBinContent(freecount, valVect.at(i)); dialvarfree.SetBinError(freecount, errVect.at(i)); dialvarfree.GetXaxis()->SetBinLabel(freecount, name.c_str()); startvarfree.SetBinContent(freecount, startVect.at(i)); startvarfree.GetXaxis()->SetBinLabel(freecount, name.c_str()); minvarfree.SetBinContent(freecount, minVect.at(i)); minvarfree.GetXaxis()->SetBinLabel(freecount, name.c_str()); maxvarfree.SetBinContent(freecount, maxVect.at(i)); maxvarfree.GetXaxis()->SetBinLabel(freecount, name.c_str()); } } } // Save Dial Plots dialvar.Write(); startvar.Write(); minvar.Write(); maxvar.Write(); if (NFREE > 0) { dialvarfree.Write(); startvarfree.Write(); minvarfree.Write(); maxvarfree.Write(); } // Save fit_status plot TH1D statusplot = TH1D("fit_status", "fit_status", 8, 0, 8); std::string fit_labels[8] = {"status", "cov_status", "maxiter", "maxfunc", "iter", "func", "precision", "tolerance"}; double fit_vals[8]; fit_vals[0] = fMinimizer->Status() + 0.; fit_vals[1] = fMinimizer->CovMatrixStatus() + 0.; fit_vals[2] = fMinimizer->MaxIterations() + 0.; fit_vals[3] = fMinimizer->MaxFunctionCalls() + 0.; fit_vals[4] = fMinimizer->NIterations() + 0.; fit_vals[5] = fMinimizer->NCalls() + 0.; fit_vals[6] = fMinimizer->Precision() + 0.; fit_vals[7] = fMinimizer->Tolerance() + 0.; for (int i = 0; i < 8; i++) { statusplot.SetBinContent(i + 1, fit_vals[i]); statusplot.GetXaxis()->SetBinLabel(i + 1, fit_labels[i].c_str()); } statusplot.Write(); // Save Covars if (fCovar) fCovar->Write(); if (fCovFree) fCovFree->Write(); if (fCorrel) fCorrel->Write(); if (fCorFree) fCorFree->Write(); if (fDecomp) fDecomp->Write(); if (fDecFree) fDecFree->Write(); return; } //************************************* void MinimizerRoutines::SaveCurrentState(std::string subdir) { //************************************* NUIS_LOG(FIT, "Saving current full FCN predictions"); // Setup DIRS TDirectory *curdir = gDirectory; if (!subdir.empty()) { TDirectory *newdir = (TDirectory *)gDirectory->mkdir(subdir.c_str()); newdir->cd(); } FitBase::GetRW()->Reconfigure(); fSampleFCN->ReconfigureAllEvents(); fSampleFCN->Write(); // Change back to current DIR curdir->cd(); return; } //************************************* void MinimizerRoutines::SaveNominal() { //************************************* fOutputRootFile->cd(); NUIS_LOG(FIT, "Saving Nominal Predictions (be cautious with this)"); FitBase::GetRW()->Reconfigure(); SaveCurrentState("nominal"); }; //************************************* void MinimizerRoutines::SavePrefit() { //************************************* fOutputRootFile->cd(); NUIS_LOG(FIT, "Saving Prefit Predictions"); UpdateRWEngine(fStartVals); SaveCurrentState("prefit"); UpdateRWEngine(fCurVals); }; /* MISC Functions */ //************************************* int MinimizerRoutines::GetStatus() { //************************************* return 0; } //************************************* void MinimizerRoutines::SetupCovariance() { //************************************* // Remove covares if they exist if (fCovar) delete fCovar; if (fCovFree) delete fCovFree; if (fCorrel) delete fCorrel; if (fCorFree) delete fCorFree; if (fDecomp) delete fDecomp; if (fDecFree) delete fDecFree; NUIS_LOG(FIT, "Building covariance matrix..."); int NFREE = 0; int NDIM = 0; // Get NFREE from min or from vals (for cases when doing throws) if (fMinimizer) { NFREE = fMinimizer->NFree(); NDIM = fMinimizer->NDim(); } else { NDIM = fParams.size(); for (UInt_t i = 0; i < fParams.size(); i++) { NUIS_LOG(FIT, "Getting Param " << fParams[i]); if (!fFixVals[fParams[i]]) NFREE++; } } if (NDIM == 0) return; NUIS_LOG(DEB, "NFREE == " << NFREE); fCovar = new TH2D("covariance", "covariance", NDIM, 0, NDIM, NDIM, 0, NDIM); if (NFREE > 0) { fCovFree = new TH2D("covariance_free", "covariance_free", NFREE, 0, NFREE, NFREE, 0, NFREE); } else { fCovFree = NULL; } // Set Bin Labels int countall = 0; int countfree = 0; for (UInt_t i = 0; i < fParams.size(); i++) { fCovar->GetXaxis()->SetBinLabel(countall + 1, fParams[i].c_str()); fCovar->GetYaxis()->SetBinLabel(countall + 1, fParams[i].c_str()); countall++; if (!fFixVals[fParams[i]] and NFREE > 0) { fCovFree->GetXaxis()->SetBinLabel(countfree + 1, fParams[i].c_str()); fCovFree->GetYaxis()->SetBinLabel(countfree + 1, fParams[i].c_str()); countfree++; } } fCorrel = PlotUtils::GetCorrelationPlot(fCovar, "correlation"); fDecomp = PlotUtils::GetDecompPlot(fCovar, "decomposition"); if (NFREE > 0) { fCorFree = PlotUtils::GetCorrelationPlot(fCovFree, "correlation_free"); fDecFree = PlotUtils::GetDecompPlot(fCovFree, "decomposition_free"); } else { fCorFree = NULL; fDecFree = NULL; } return; }; //************************************* void MinimizerRoutines::ThrowCovariance(bool uniformly) { //************************************* std::vector rands; if (!fDecFree) { NUIS_ERR(WRN, "Trying to throw 0 free parameters"); return; } // Generate Random Gaussians for (Int_t i = 0; i < fDecFree->GetNbinsX(); i++) { rands.push_back(gRandom->Gaus(0.0, 1.0)); } // Reset Thrown Values for (UInt_t i = 0; i < fParams.size(); i++) { fThrownVals[fParams[i]] = fCurVals[fParams[i]]; } // Loop and get decomp for (Int_t i = 0; i < fDecFree->GetNbinsX(); i++) { std::string parname = std::string(fDecFree->GetXaxis()->GetBinLabel(i + 1)); double mod = 0.0; if (!uniformly) { for (Int_t j = 0; j < fDecFree->GetNbinsY(); j++) { mod += rands[j] * fDecFree->GetBinContent(j + 1, i + 1); } } if (fCurVals.find(parname) != fCurVals.end()) { if (uniformly) fThrownVals[parname] = gRandom->Uniform(fMinVals[parname], fMaxVals[parname]); else { fThrownVals[parname] = fCurVals[parname] + mod; } } } // Check Limits for (UInt_t i = 0; i < fParams.size(); i++) { std::string syst = fParams[i]; if (fFixVals[syst]) continue; if (fThrownVals[syst] < fMinVals[syst]) fThrownVals[syst] = fMinVals[syst]; if (fThrownVals[syst] > fMaxVals[syst]) fThrownVals[syst] = fMaxVals[syst]; } return; }; //************************************* void MinimizerRoutines::GenerateErrorBands() { //************************************* TDirectory *errorDIR = (TDirectory *)fOutputRootFile->mkdir("error_bands"); errorDIR->cd(); // Make a second file to store throws std::string tempFileName = fOutputFile; if (tempFileName.find(".root") != std::string::npos) tempFileName.erase(tempFileName.find(".root"), 5); tempFileName += ".throws.root"; TFile *tempfile = new TFile(tempFileName.c_str(), "RECREATE"); tempfile->cd(); int nthrows = FitPar::Config().GetParI("error_throws"); UpdateRWEngine(fCurVals); fSampleFCN->ReconfigureAllEvents(); TDirectory *nominal = (TDirectory *)tempfile->mkdir("nominal"); nominal->cd(); fSampleFCN->Write(); TDirectory *outnominal = (TDirectory *)fOutputRootFile->mkdir("nominal_throw"); outnominal->cd(); fSampleFCN->Write(); errorDIR->cd(); TTree *parameterTree = new TTree("throws", "throws"); double chi2; for (UInt_t i = 0; i < fParams.size(); i++) parameterTree->Branch(fParams[i].c_str(), &fThrownVals[fParams[i]], (fParams[i] + "/D").c_str()); parameterTree->Branch("chi2", &chi2, "chi2/D"); bool uniformly = FitPar::Config().GetParB("error_uniform"); // Run Throws and save for (Int_t i = 0; i < nthrows; i++) { TDirectory *throwfolder = (TDirectory *)tempfile->mkdir(Form("throw_%i", i)); throwfolder->cd(); // Generate Random Parameter Throw ThrowCovariance(uniformly); // Run Eval double *vals = FitUtils::GetArrayFromMap(fParams, fThrownVals); chi2 = fSampleFCN->DoEval(vals); delete vals; // Save the FCN fSampleFCN->Write(); parameterTree->Fill(); } errorDIR->cd(); fDecFree->Write(); fCovFree->Write(); parameterTree->Write(); delete parameterTree; // Now go through the keys in the temporary file and look for TH1D, and TH2D // plots TIter next(nominal->GetListOfKeys()); TKey *key; while ((key = (TKey *)next())) { TClass *cl = gROOT->GetClass(key->GetClassName()); if (!cl->InheritsFrom("TH1D") and !cl->InheritsFrom("TH2D")) continue; TH1D *baseplot = (TH1D *)key->ReadObj(); std::string plotname = std::string(baseplot->GetName()); int nbins = baseplot->GetNbinsX() * baseplot->GetNbinsY(); // Setup TProfile with RMS option TProfile *tprof = new TProfile((plotname + "_prof").c_str(), (plotname + "_prof").c_str(), nbins, 0, nbins, "S"); // Setup The TTREE double *bincontents; bincontents = new double[nbins]; double *binlowest; binlowest = new double[nbins]; double *binhighest; binhighest = new double[nbins]; errorDIR->cd(); TTree *bintree = new TTree((plotname + "_tree").c_str(), (plotname + "_tree").c_str()); for (Int_t i = 0; i < nbins; i++) { bincontents[i] = 0.0; binhighest[i] = 0.0; binlowest[i] = 0.0; bintree->Branch(Form("content_%i", i), &bincontents[i], Form("content_%i/D", i)); } for (Int_t i = 0; i < nthrows; i++) { TH1 *newplot = (TH1 *)tempfile->Get(Form(("throw_%i/" + plotname).c_str(), i)); for (Int_t j = 0; j < nbins; j++) { tprof->Fill(j + 0.5, newplot->GetBinContent(j + 1)); bincontents[j] = newplot->GetBinContent(j + 1); if (bincontents[j] < binlowest[j] or i == 0) binlowest[j] = bincontents[j]; if (bincontents[j] > binhighest[j] or i == 0) binhighest[j] = bincontents[j]; } errorDIR->cd(); bintree->Fill(); delete newplot; } errorDIR->cd(); for (Int_t j = 0; j < nbins; j++) { if (!uniformly) { baseplot->SetBinError(j + 1, tprof->GetBinError(j + 1)); } else { baseplot->SetBinContent(j + 1, (binlowest[j] + binhighest[j]) / 2.0); baseplot->SetBinError(j + 1, (binhighest[j] - binlowest[j]) / 2.0); } } errorDIR->cd(); baseplot->Write(); tprof->Write(); bintree->Write(); delete baseplot; delete tprof; delete bintree; delete[] bincontents; } return; }; void MinimizerRoutines::ThrowDataToys() { NUIS_LOG(FIT, "Generating Toy Data Throws"); int verb = Config::GetParI("VERBOSITY"); SETVERBOSITY(FIT); int nthrows = FitPar::Config().GetParI("NToyThrows"); double maxlike = -1.0; double minlike = -1.0; std::vector values; for (int i = 0; i < 1.E4; i++) { fSampleFCN->ThrowDataToy(); double like = fSampleFCN->GetLikelihood(); values.push_back(like); if (maxlike == -1.0 or like > maxlike) maxlike = like; if (minlike == -1.0 or like < minlike) minlike = like; } SETVERBOSITY(verb); // Fill Histogram TH1D *likes = new TH1D("toydatalikelihood", "toydatalikelihood", int(sqrt(nthrows)), minlike, maxlike); for (size_t i = 0; i < values.size(); i++) { likes->Fill(values[i]); } // Save to file NUIS_LOG(FIT, "Writing toy data throws"); fOutputRootFile->cd(); likes->Write(); }