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diff --git a/src/FitBase/Measurement1D.cxx b/src/FitBase/Measurement1D.cxx
index 26a6d18..b87e8a6 100644
--- a/src/FitBase/Measurement1D.cxx
+++ b/src/FitBase/Measurement1D.cxx
@@ -1,1896 +1,1896 @@
// Copyright 2016 L. Pickering, P. Stowell, R. Terri, C. Wilkinson, C. Wret
/*******************************************************************************
* This ile 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 <http://www.gnu.org/licenses/>.
*******************************************************************************/
#include "Measurement1D.h"
//********************************************************************
Measurement1D::Measurement1D(void) {
//********************************************************************
// XSec Scalings
fScaleFactor = -1.0;
fCurrentNorm = 1.0;
// Histograms
fDataHist = NULL;
fDataTrue = NULL;
fMCHist = NULL;
fMCFine = NULL;
fMCWeighted = NULL;
fMaskHist = NULL;
// Covar
covar = NULL;
fFullCovar = NULL;
fShapeCovar = NULL;
fCovar = NULL;
fInvert = NULL;
fDecomp = NULL;
// Fake Data
fFakeDataInput = "";
fFakeDataFile = NULL;
// Options
fDefaultTypes = "FIX/FULL/CHI2";
fAllowedTypes =
"FIX,FREE,SHAPE/FULL,DIAG/CHI2/NORM/ENUCORR/Q2CORR/ENU1D/MASK/NOWIDTH";
fIsFix = false;
fIsShape = false;
fIsFree = false;
fIsDiag = false;
fIsFull = false;
fAddNormPen = false;
fIsMask = false;
fIsChi2SVD = false;
fIsRawEvents = false;
fIsNoWidth = false;
fIsDifXSec = false;
fIsEnu1D = false;
// Inputs
fInput = NULL;
fRW = NULL;
// Extra Histograms
fMCHist_Modes = NULL;
}
//********************************************************************
Measurement1D::~Measurement1D(void) {
//********************************************************************
if (fDataHist) delete fDataHist;
if (fDataTrue) delete fDataTrue;
if (fMCHist) delete fMCHist;
if (fMCFine) delete fMCFine;
if (fMCWeighted) delete fMCWeighted;
if (fMaskHist) delete fMaskHist;
if (covar) delete covar;
if (fFullCovar) delete fFullCovar;
if (fShapeCovar) delete fShapeCovar;
if (fCovar) delete fCovar;
if (fInvert) delete fInvert;
if (fDecomp) delete fDecomp;
}
//********************************************************************
void Measurement1D::FinaliseSampleSettings() {
//********************************************************************
MeasurementBase::FinaliseSampleSettings();
// Setup naming + renaming
fName = fSettings.GetName();
fSettings.SetS("originalname", fName);
if (fSettings.Has("rename")) {
fName = fSettings.GetS("rename");
fSettings.SetS("name", fName);
}
// Setup all other options
LOG(SAM) << "Finalising Sample Settings: " << fName << std::endl;
if ((fSettings.GetS("originalname").find("Evt") != std::string::npos)) {
fIsRawEvents = true;
LOG(SAM) << "Found event rate measurement but using poisson likelihoods."
<< std::endl;
}
if (fSettings.GetS("originalname").find("XSec_1DEnu") != std::string::npos) {
fIsEnu1D = true;
LOG(SAM) << "::" << fName << "::" << std::endl;
LOG(SAM) << "Found XSec Enu measurement, applying flux integrated scaling, "
<< "not flux averaged!" << std::endl;
}
if (fIsEnu1D && fIsRawEvents) {
LOG(SAM) << "Found 1D Enu XSec distribution AND fIsRawEvents, is this "
"really correct?!"
<< std::endl;
LOG(SAM) << "Check experiment constructor for " << fName
<< " and correct this!" << std::endl;
LOG(SAM) << "I live in " << __FILE__ << ":" << __LINE__ << std::endl;
exit(-1);
}
if (!fRW) fRW = FitBase::GetRW();
if (!fInput and !fIsJoint) SetupInputs(fSettings.GetS("input"));
// Setup options
SetFitOptions(fDefaultTypes); // defaults
SetFitOptions(fSettings.GetS("type")); // user specified
EnuMin = GeneralUtils::StrToDbl(fSettings.GetS("enu_min"));
EnuMax = GeneralUtils::StrToDbl(fSettings.GetS("enu_max"));
if (fAddNormPen) {
if (fNormError <= 0.0) {
ERR(WRN) << "Norm error for class " << fName << " is 0.0!" << std::endl;
ERR(WRN) << "If you want to use it please add fNormError=VAL" << std::endl;
throw;
}
}
}
//********************************************************************
void Measurement1D::CreateDataHistogram(int dimx, double* binx) {
//********************************************************************
if (fDataHist) delete fDataHist;
fDataHist = new TH1D( (fSettings.GetName() + "_data").c_str(), (fSettings.GetFullTitles()).c_str(),
dimx, binx) ;
}
//********************************************************************
void Measurement1D::SetDataFromTextFile(std::string datafile) {
//********************************************************************
LOG(SAM) << "Reading data from text file: " << datafile << std::endl;
fDataHist = PlotUtils::GetTH1DFromFile(datafile,
fSettings.GetName() + "_data",
fSettings.GetFullTitles());
}
//********************************************************************
void Measurement1D::SetDataFromRootFile(std::string datafile,
std::string histname) {
//********************************************************************
LOG(SAM) << "Reading data from root file: " << datafile << ";" << histname << std::endl;
fDataHist = PlotUtils::GetTH1DFromRootFile(datafile, histname);
fDataHist->SetNameTitle((fSettings.GetName() + "_data").c_str(),
(fSettings.GetFullTitles()).c_str());
return;
};
//********************************************************************
void Measurement1D::SetEmptyData(){
//********************************************************************
fDataHist = new TH1D("EMPTY_DATA","EMPTY_DATA",1,0.0,1.0);
}
//********************************************************************
void Measurement1D::SetPoissonErrors() {
//********************************************************************
if (!fDataHist) {
ERR(FTL) << "Need a data hist to setup possion errors! " << std::endl;
ERR(FTL) << "Setup Data First!" << std::endl;
throw;
}
for (int i = 0; i < fDataHist->GetNbinsX() + 1; i++) {
fDataHist->SetBinError(i + 1, sqrt(fDataHist->GetBinContent(i + 1)));
}
}
//********************************************************************
void Measurement1D::SetCovarFromDiagonal(TH1D* data) {
//********************************************************************
if (!data and fDataHist) {
data = fDataHist;
}
if (data) {
LOG(SAM) << "Setting diagonal covariance for: " << data->GetName() << std::endl;
fFullCovar = StatUtils::MakeDiagonalCovarMatrix(data);
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
} else {
ERR(FTL) << "No data input provided to set diagonal covar from!" << std::endl;
}
// if (!fIsDiag) {
// ERR(FTL) << "SetCovarMatrixFromDiag called for measurement "
// << "that is not set as diagonal." << std::endl;
// throw;
// }
}
//********************************************************************
void Measurement1D::SetCovarFromTextFile(std::string covfile, int dim) {
//********************************************************************
if (dim == -1) {
dim = fDataHist->GetNbinsX();
}
LOG(SAM) << "Reading covariance from text file: " << covfile << std::endl;
fFullCovar = StatUtils::GetCovarFromTextFile(covfile, dim);
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
//********************************************************************
void Measurement1D::SetCovarFromMultipleTextFiles(std::string covfiles, int dim) {
//********************************************************************
if (dim == -1) {
dim = fDataHist->GetNbinsX();
}
std::vector<std::string> covList = GeneralUtils::ParseToStr(covfiles, ";");
fFullCovar = new TMatrixDSym(dim);
for (uint i = 0; i < covList.size(); ++i){
LOG(SAM) << "Reading covariance from text file: " << covList[i] << std::endl;
TMatrixDSym* temp_cov = StatUtils::GetCovarFromTextFile(covList[i], dim);
(*fFullCovar) += (*temp_cov);
delete temp_cov;
}
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
//********************************************************************
void Measurement1D::SetCovarFromRootFile(std::string covfile, std::string histname) {
//********************************************************************
LOG(SAM) << "Reading covariance from text file: " << covfile << ";" << histname << std::endl;
fFullCovar = StatUtils::GetCovarFromRootFile(covfile, histname);
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
//********************************************************************
void Measurement1D::SetCovarInvertFromTextFile(std::string covfile, int dim) {
//********************************************************************
if (dim == -1) {
dim = fDataHist->GetNbinsX();
}
LOG(SAM) << "Reading inverted covariance from text file: " << covfile << std::endl;
covar = StatUtils::GetCovarFromTextFile(covfile, dim);
fFullCovar = StatUtils::GetInvert(covar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
//********************************************************************
void Measurement1D::SetCovarInvertFromRootFile(std::string covfile, std::string histname) {
//********************************************************************
LOG(SAM) << "Reading inverted covariance from text file: " << covfile << ";" << histname << std::endl;
covar = StatUtils::GetCovarFromRootFile(covfile, histname);
fFullCovar = StatUtils::GetInvert(covar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
//********************************************************************
void Measurement1D::SetCorrelationFromTextFile(std::string covfile, int dim) {
//********************************************************************
if (dim == -1) dim = fDataHist->GetNbinsX();
LOG(SAM) << "Reading data correlations from text file: " << covfile << ";" << dim << std::endl;
TMatrixDSym* correlation = StatUtils::GetCovarFromTextFile(covfile, dim);
if (!fDataHist) {
ERR(FTL) << "Trying to set correlations from text file but there is no data to build it from. \n"
<< "In constructor make sure data is set before SetCorrelationFromTextFile is called. \n" << std::endl;
throw;
}
// Fill covar from data errors and correlations
fFullCovar = new TMatrixDSym(dim);
for (int i = 0; i < fDataHist->GetNbinsX(); i++) {
for (int j = 0; j < fDataHist->GetNbinsX(); j++) {
(*fFullCovar)(i, j) = (*correlation)(i, j) * fDataHist->GetBinError(i + 1) * fDataHist->GetBinError(j + 1) * 1.E76;
}
}
// Fill other covars.
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
delete correlation;
}
//********************************************************************
void Measurement1D::SetCorrelationFromMultipleTextFiles(std::string corrfiles, int dim) {
//********************************************************************
if (dim == -1) {
dim = fDataHist->GetNbinsX();
}
std::vector<std::string> corrList = GeneralUtils::ParseToStr(corrfiles, ";");
fFullCovar = new TMatrixDSym(dim);
for (uint i = 0; i < corrList.size(); ++i){
LOG(SAM) << "Reading covariance from text file: " << corrList[i] << std::endl;
TMatrixDSym* temp_cov = StatUtils::GetCovarFromTextFile(corrList[i], dim);
for (int i = 0; i < fDataHist->GetNbinsX(); i++) {
for (int j = 0; j < fDataHist->GetNbinsX(); j++) {
(*temp_cov)(i, j) = (*temp_cov)(i, j) * fDataHist->GetBinError(i + 1) * fDataHist->GetBinError(j + 1) * 1.E76;
}
}
(*fFullCovar) += (*temp_cov);
delete temp_cov;
}
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
//********************************************************************
void Measurement1D::SetCorrelationFromRootFile(std::string covfile, std::string histname) {
//********************************************************************
LOG(SAM) << "Reading data correlations from text file: " << covfile << ";" << histname << std::endl;
TMatrixDSym* correlation = StatUtils::GetCovarFromRootFile(covfile, histname);
if (!fDataHist) {
ERR(FTL) << "Trying to set correlations from text file but there is no data to build it from. \n"
<< "In constructor make sure data is set before SetCorrelationFromTextFile is called. \n" << std::endl;
throw;
}
// Fill covar from data errors and correlations
fFullCovar = new TMatrixDSym(fDataHist->GetNbinsX());
for (int i = 0; i < fDataHist->GetNbinsX(); i++) {
for (int j = 0; j < fDataHist->GetNbinsX(); j++) {
(*fFullCovar)(i, j) = (*correlation)(i, j) * fDataHist->GetBinError(i + 1) * fDataHist->GetBinError(j + 1) * 1.E76;
}
}
// Fill other covars.
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
delete correlation;
}
//********************************************************************
void Measurement1D::SetCholDecompFromTextFile(std::string covfile, int dim) {
//********************************************************************
if (dim == -1) {
dim = fDataHist->GetNbinsX();
}
LOG(SAM) << "Reading cholesky from text file: " << covfile << std::endl;
TMatrixD* temp = StatUtils::GetMatrixFromTextFile(covfile, dim, dim);
TMatrixD* trans = (TMatrixD*)temp->Clone();
trans->T();
(*trans) *= (*temp);
fFullCovar = new TMatrixDSym(dim, trans->GetMatrixArray(), "");
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
delete temp;
delete trans;
}
//********************************************************************
void Measurement1D::SetCholDecompFromRootFile(std::string covfile, std::string histname) {
//********************************************************************
LOG(SAM) << "Reading cholesky decomp from root file: " << covfile << ";" << histname << std::endl;
TMatrixD* temp = StatUtils::GetMatrixFromRootFile(covfile, histname);
TMatrixD* trans = (TMatrixD*)temp->Clone();
trans->T();
(*trans) *= (*temp);
fFullCovar = new TMatrixDSym(temp->GetNrows(), trans->GetMatrixArray(), "");
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
delete temp;
delete trans;
}
void Measurement1D::SetShapeCovar(){
// Return if this is missing any pre-requisites
if (!fFullCovar) return;
if (!fDataHist) return;
// Also return if it's bloody stupid under the circumstances
if (fIsDiag) return;
fShapeCovar = StatUtils::ExtractShapeOnlyCovar(fFullCovar, fDataHist);
return;
}
//********************************************************************
void Measurement1D::ScaleData(double scale) {
//********************************************************************
fDataHist->Scale(scale);
}
//********************************************************************
void Measurement1D::ScaleDataErrors(double scale) {
//********************************************************************
for (int i = 0; i < fDataHist->GetNbinsX(); i++) {
fDataHist->SetBinError(i + 1, fDataHist->GetBinError(i + 1) * scale);
}
}
//********************************************************************
void Measurement1D::ScaleCovar(double scale) {
//********************************************************************
(*fFullCovar) *= scale;
(*covar) *= 1.0 / scale;
(*fDecomp) *= sqrt(scale);
}
//********************************************************************
void Measurement1D::SetBinMask(std::string maskfile) {
//********************************************************************
if (!fIsMask) return;
LOG(SAM) << "Reading bin mask from file: " << maskfile << std::endl;
// Create a mask histogram with dim of data
int nbins = fDataHist->GetNbinsX();
fMaskHist =
new TH1I((fSettings.GetName() + "_BINMASK").c_str(),
(fSettings.GetName() + "_BINMASK; Bin; Mask?").c_str(), nbins, 0, nbins);
std::string line;
std::ifstream mask(maskfile.c_str(), ifstream::in);
if (!mask.is_open()) {
LOG(FTL) << " Cannot find mask file." << std::endl;
throw;
}
while (std::getline(mask >> std::ws, line, '\n')) {
std::vector<int> entries = GeneralUtils::ParseToInt(line, " ");
// Skip lines with poorly formatted lines
if (entries.size() < 2) {
LOG(WRN) << "Measurement1D::SetBinMask(), couldn't parse line: " << line
<< std::endl;
continue;
}
// The first index should be the bin number, the second should be the mask
// value.
int val = 0;
if (entries[1] > 0) val = 1;
fMaskHist->SetBinContent(entries[0], val);
}
// Apply masking by setting masked data bins to zero
PlotUtils::MaskBins(fDataHist, fMaskHist);
return;
}
//********************************************************************
void Measurement1D::FinaliseMeasurement() {
//********************************************************************
LOG(SAM) << "Finalising Measurement: " << fName << std::endl;
if (fSettings.GetB("onlymc")){
if (fDataHist) delete fDataHist;
fDataHist = new TH1D("empty_data","empty_data",1,0.0,1.0);
}
// Make sure data is setup
if (!fDataHist) {
ERR(FTL) << "No data has been setup inside " << fName << " constructor!" << std::endl;
throw;
}
// Make sure covariances are setup
if (!fFullCovar) {
fIsDiag = true;
SetCovarFromDiagonal(fDataHist);
}
if (!covar) {
covar = StatUtils::GetInvert(fFullCovar);
}
if (!fDecomp) {
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
// Push the diagonals of fFullCovar onto the data histogram
// Comment this out until the covariance/data scaling is consistent!
StatUtils::SetDataErrorFromCov(fDataHist, fFullCovar, 1E-38);
// If shape only, set covar and fDecomp using the shape-only matrix (if set)
- if (fIsShape && fShapeCovar and FitPar::GetParB("UseShapeCovar")){
+ if (fIsShape && fShapeCovar and FitPar::Config().GetParB("UseShapeCovar")){
if (covar) delete covar;
covar = StatUtils::GetInvert(fShapeCovar);
if (fDecomp) delete fDecomp;
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
// Setup fMCHist from data
fMCHist = (TH1D*)fDataHist->Clone();
fMCHist->SetNameTitle((fSettings.GetName() + "_MC").c_str(),
(fSettings.GetFullTitles()).c_str());
fMCHist->Reset();
// Setup fMCFine
fMCFine = new TH1D("mcfine", "mcfine", fDataHist->GetNbinsX() * 8,
fMCHist->GetBinLowEdge(1),
fMCHist->GetBinLowEdge(fDataHist->GetNbinsX() + 1));
fMCFine->SetNameTitle((fSettings.GetName() + "_MC_FINE").c_str(),
(fSettings.GetFullTitles()).c_str());
fMCFine->Reset();
// Setup MC Stat
fMCStat = (TH1D*)fMCHist->Clone();
fMCStat->Reset();
// Search drawopts for possible types to include by default
std::string drawopts = FitPar::Config().GetParS("drawopts");
if (drawopts.find("MODES") != std::string::npos) {
fMCHist_Modes = new TrueModeStack( (fSettings.GetName() + "_MODES").c_str(),
("True Channels"), fMCHist);
SetAutoProcessTH1(fMCHist_Modes, kCMD_Reset, kCMD_Norm, kCMD_Write);
}
// Setup bin masks using sample name
if (fIsMask) {
std::string curname = fName;
std::string origname = fSettings.GetS("originalname");
// Check rename.mask
std::string maskloc = FitPar::Config().GetParDIR(curname + ".mask");
// Check origname.mask
if (maskloc.empty()) maskloc = FitPar::Config().GetParDIR(origname + ".mask");
// Check database
if (maskloc.empty()) {
maskloc = FitPar::GetDataBase() + "/masks/" + origname + ".mask";
}
// Setup Bin Mask
SetBinMask(maskloc);
}
if (fScaleFactor < 0) {
ERR(FTL) << "I found a negative fScaleFactor in " << __FILE__ << ":" << __LINE__ << std::endl;
ERR(FTL) << "fScaleFactor = " << fScaleFactor << std::endl;
ERR(FTL) << "EXITING" << std::endl;
throw;
}
// Create and fill Weighted Histogram
if (!fMCWeighted) {
fMCWeighted = (TH1D*)fMCHist->Clone();
fMCWeighted->SetNameTitle((fName + "_MCWGHTS").c_str(),
(fName + "_MCWGHTS" + fPlotTitles).c_str());
fMCWeighted->GetYaxis()->SetTitle("Weighted Events");
}
}
//********************************************************************
void Measurement1D::SetFitOptions(std::string opt) {
//********************************************************************
// Do nothing if default given
if (opt == "DEFAULT") return;
// CHECK Conflicting Fit Options
std::vector<std::string> fit_option_allow =
GeneralUtils::ParseToStr(fAllowedTypes, "/");
for (UInt_t i = 0; i < fit_option_allow.size(); i++) {
std::vector<std::string> fit_option_section =
GeneralUtils::ParseToStr(fit_option_allow.at(i), ",");
bool found_option = false;
for (UInt_t j = 0; j < fit_option_section.size(); j++) {
std::string av_opt = fit_option_section.at(j);
if (!found_option and opt.find(av_opt) != std::string::npos) {
found_option = true;
} else if (found_option and opt.find(av_opt) != std::string::npos) {
ERR(FTL) << "ERROR: Conflicting fit options provided: "
<< opt << std::endl
<< "Conflicting group = " << fit_option_section.at(i) << std::endl
<< "You should only supply one of these options in card file." << std::endl;
throw;
}
}
}
// Check all options are allowed
std::vector<std::string> fit_options_input =
GeneralUtils::ParseToStr(opt, "/");
for (UInt_t i = 0; i < fit_options_input.size(); i++) {
if (fAllowedTypes.find(fit_options_input.at(i)) == std::string::npos) {
ERR(FTL) << "ERROR: Fit Option '" << fit_options_input.at(i)
<< "' Provided is not allowed for this measurement."
<< std::endl;
ERR(FTL) << "Fit Options should be provided as a '/' seperated list "
"(e.g. FREE/DIAG/NORM)"
<< std::endl;
ERR(FTL) << "Available options for " << fName << " are '" << fAllowedTypes
<< "'" << std::endl;
throw;
}
}
// Set TYPE
fFitType = opt;
// FIX,SHAPE,FREE
if (opt.find("FIX") != std::string::npos) {
fIsFree = fIsShape = false;
fIsFix = true;
} else if (opt.find("SHAPE") != std::string::npos) {
fIsFree = fIsFix = false;
fIsShape = true;
} else if (opt.find("FREE") != std::string::npos) {
fIsFix = fIsShape = false;
fIsFree = true;
}
// DIAG,FULL (or default to full)
if (opt.find("DIAG") != std::string::npos) {
fIsDiag = true;
fIsFull = false;
} else if (opt.find("FULL") != std::string::npos) {
fIsDiag = false;
fIsFull = true;
}
// CHI2/LL (OTHERS?)
if (opt.find("LOG") != std::string::npos) {
fIsChi2 = false;
ERR(FTL) << "No other LIKELIHOODS properly supported!" << std::endl;
ERR(FTL) << "Try to use a chi2!" << std::endl;
throw;
} else {
fIsChi2 = true;
}
// EXTRAS
if (opt.find("RAW") != std::string::npos) fIsRawEvents = true;
if (opt.find("NOWIDTH") != std::string::npos) fIsNoWidth = true;
if (opt.find("DIF") != std::string::npos) fIsDifXSec = true;
if (opt.find("ENU1D") != std::string::npos) fIsEnu1D = true;
if (opt.find("NORM") != std::string::npos) fAddNormPen = true;
if (opt.find("MASK") != std::string::npos) fIsMask = true;
return;
};
//********************************************************************
void Measurement1D::SetSmearingMatrix(std::string smearfile, int truedim,
int recodim) {
//********************************************************************
// The smearing matrix describes the migration from true bins (rows) to reco
// bins (columns)
// Counter over the true bins!
int row = 0;
std::string line;
std::ifstream smear(smearfile.c_str(), ifstream::in);
// Note that the smearing matrix may be rectangular.
fSmearMatrix = new TMatrixD(truedim, recodim);
if (smear.is_open())
LOG(SAM) << "Reading smearing matrix from file: " << smearfile << std::endl;
else
ERR(FTL) << "Smearing matrix provided is incorrect: " << smearfile
<< std::endl;
while (std::getline(smear >> std::ws, line, '\n')) {
int column = 0;
std::vector<double> entries = GeneralUtils::ParseToDbl(line, " ");
for (std::vector<double>::iterator iter = entries.begin();
iter != entries.end(); iter++) {
(*fSmearMatrix)(row, column) =
(*iter) / 100.; // Convert to fraction from
// percentage (this may not be
// general enough)
column++;
}
row++;
}
return;
}
//********************************************************************
void Measurement1D::ApplySmearingMatrix() {
//********************************************************************
if (!fSmearMatrix) {
ERR(WRN) << fName
<< ": attempted to apply smearing matrix, but none was set"
<< std::endl;
return;
}
TH1D* unsmeared = (TH1D*)fMCHist->Clone();
TH1D* smeared = (TH1D*)fMCHist->Clone();
smeared->Reset();
// Loop over reconstructed bins
// true = row; reco = column
for (int rbin = 0; rbin < fSmearMatrix->GetNcols(); ++rbin) {
// Sum up the constributions from all true bins
double rBinVal = 0;
// Loop over true bins
for (int tbin = 0; tbin < fSmearMatrix->GetNrows(); ++tbin) {
rBinVal +=
(*fSmearMatrix)(tbin, rbin) * unsmeared->GetBinContent(tbin + 1);
}
smeared->SetBinContent(rbin + 1, rBinVal);
}
fMCHist = (TH1D*)smeared->Clone();
return;
}
/*
Reconfigure LOOP
*/
//********************************************************************
void Measurement1D::ResetAll() {
//********************************************************************
fMCHist->Reset();
fMCFine->Reset();
fMCStat->Reset();
return;
};
//********************************************************************
void Measurement1D::FillHistograms() {
//********************************************************************
if (Signal) {
fMCHist->Fill(fXVar, Weight);
fMCFine->Fill(fXVar, Weight);
fMCStat->Fill(fXVar, 1.0);
if (fMCHist_Modes) fMCHist_Modes->Fill(Mode, fXVar, Weight);
}
return;
};
//********************************************************************
void Measurement1D::ScaleEvents() {
//********************************************************************
// Fill MCWeighted;
// for (int i = 0; i < fMCHist->GetNbinsX(); i++) {
// fMCWeighted->SetBinContent(i + 1, fMCHist->GetBinContent(i + 1));
// fMCWeighted->SetBinError(i + 1, fMCHist->GetBinError(i + 1));
// }
// Setup Stat ratios for MC and MC Fine
double* statratio = new double[fMCHist->GetNbinsX()];
for (int i = 0; i < fMCHist->GetNbinsX(); i++) {
if (fMCHist->GetBinContent(i + 1) != 0) {
statratio[i] = fMCHist->GetBinError(i + 1) / fMCHist->GetBinContent(i + 1);
} else {
statratio[i] = 0.0;
}
}
double* statratiofine = new double[fMCFine->GetNbinsX()];
for (int i = 0; i < fMCFine->GetNbinsX(); i++) {
if (fMCFine->GetBinContent(i + 1) != 0) {
statratiofine[i] = fMCFine->GetBinError(i + 1) / fMCFine->GetBinContent(i + 1);
} else {
statratiofine[i] = 0.0;
}
}
// Scaling for raw event rates
if (fIsRawEvents) {
double datamcratio = fDataHist->Integral() / fMCHist->Integral();
fMCHist->Scale(datamcratio);
fMCFine->Scale(datamcratio);
if (fMCHist_Modes) fMCHist_Modes->Scale(datamcratio);
// Scaling for XSec as function of Enu
} else if (fIsEnu1D) {
PlotUtils::FluxUnfoldedScaling(fMCHist, GetFluxHistogram(),
GetEventHistogram(), fScaleFactor,
fNEvents);
PlotUtils::FluxUnfoldedScaling(fMCFine, GetFluxHistogram(),
GetEventHistogram(), fScaleFactor,
fNEvents);
// if (fMCHist_Modes) {
// PlotUtils::FluxUnfoldedScaling(fMCHist_Modes, GetFluxHistogram(),
// GetEventHistogram(), fScaleFactor,
// fNEvents);
// }
} else if (fIsNoWidth) {
fMCHist->Scale(fScaleFactor);
fMCFine->Scale(fScaleFactor);
if (fMCHist_Modes) fMCHist_Modes->Scale(fScaleFactor);
// Any other differential scaling
} else {
fMCHist->Scale(fScaleFactor, "width");
fMCFine->Scale(fScaleFactor, "width");
if (fMCHist_Modes) fMCHist_Modes->Scale(fScaleFactor, "width");
}
// Proper error scaling - ROOT Freaks out with xsec weights sometimes
for (int i = 0; i < fMCStat->GetNbinsX(); i++) {
fMCHist->SetBinError(i + 1, fMCHist->GetBinContent(i + 1) * statratio[i]);
}
for (int i = 0; i < fMCFine->GetNbinsX(); i++) {
fMCFine->SetBinError(i + 1, fMCFine->GetBinContent(i + 1) * statratiofine[i]);
}
// Clean up
delete statratio;
delete statratiofine;
return;
};
//********************************************************************
void Measurement1D::ApplyNormScale(double norm) {
//********************************************************************
fCurrentNorm = norm;
fMCHist->Scale(1.0 / norm);
fMCFine->Scale(1.0 / norm);
return;
};
/*
Statistic Functions - Outsources to StatUtils
*/
//********************************************************************
int Measurement1D::GetNDOF() {
//********************************************************************
int ndof = fDataHist->GetNbinsX();
if (fMaskHist and fIsMask) ndof -= fMaskHist->Integral();
return ndof;
}
//********************************************************************
double Measurement1D::GetLikelihood() {
//********************************************************************
// If this is for a ratio, there is no data histogram to compare to!
if (fNoData || !fDataHist) return 0.;
// Apply Masking to MC if Required.
if (fIsMask and fMaskHist) {
PlotUtils::MaskBins(fMCHist, fMaskHist);
}
// Sort Shape Scaling
double scaleF = 0.0;
// TODO Include !fIsRawEvents
if (fIsShape) {
if (fMCHist->Integral(1, fMCHist->GetNbinsX(), "width")) {
scaleF = fDataHist->Integral(1, fDataHist->GetNbinsX(), "width") /
fMCHist->Integral(1, fMCHist->GetNbinsX(), "width");
fMCHist->Scale(scaleF);
fMCFine->Scale(scaleF);
}
}
// Likelihood Calculation
double stat = 0.;
if (fIsChi2) {
if (fIsRawEvents) {
stat = StatUtils::GetChi2FromEventRate(fDataHist, fMCHist, fMaskHist);
} else if (fIsDiag) {
stat = StatUtils::GetChi2FromDiag(fDataHist, fMCHist, fMaskHist);
} else if (!fIsDiag and !fIsRawEvents) {
stat = StatUtils::GetChi2FromCov(fDataHist, fMCHist, covar, fMaskHist);
}
}
// Sort Penalty Terms
if (fAddNormPen) {
double penalty =
(1. - fCurrentNorm) * (1. - fCurrentNorm) / (fNormError * fNormError);
stat += penalty;
}
// Return to normal scaling
if (fIsShape) { // and !FitPar::Config().GetParB("saveshapescaling")) {
fMCHist->Scale(1. / scaleF);
fMCFine->Scale(1. / scaleF);
}
fLikelihood = stat;
return stat;
}
/*
Fake Data Functions
*/
//********************************************************************
void Measurement1D::SetFakeDataValues(std::string fakeOption) {
//********************************************************************
// Setup original/datatrue
TH1D* tempdata = (TH1D*) fDataHist->Clone();
if (!fIsFakeData) {
fIsFakeData = true;
// Make a copy of the original data histogram.
if (!fDataOrig) fDataOrig = (TH1D*)fDataHist->Clone((fName + "_data_original").c_str());
} else {
ResetFakeData();
}
// Setup Inputs
fFakeDataInput = fakeOption;
LOG(SAM) << "Setting fake data from : " << fFakeDataInput << std::endl;
// From MC
if (fFakeDataInput.compare("MC") == 0) {
fDataHist = (TH1D*)fMCHist->Clone((fName + "_MC").c_str());
// Fake File
} else {
if (!fFakeDataFile) fFakeDataFile = new TFile(fFakeDataInput.c_str(), "READ");
fDataHist = (TH1D*)fFakeDataFile->Get((fName + "_MC").c_str());
}
// Setup Data Hist
fDataHist->SetNameTitle((fName + "_FAKE").c_str(),
(fName + fPlotTitles).c_str());
// Replace Data True
if (fDataTrue) delete fDataTrue;
fDataTrue = (TH1D*)fDataHist->Clone();
fDataTrue->SetNameTitle((fName + "_FAKE_TRUE").c_str(),
(fName + fPlotTitles).c_str());
// Make a new covariance for fake data hist.
int nbins = fDataHist->GetNbinsX();
double alpha_i = 0.0;
double alpha_j = 0.0;
for (int i = 0; i < nbins; i++) {
for (int j = 0; j < nbins; j++) {
alpha_i = fDataHist->GetBinContent(i + 1) / tempdata->GetBinContent(i + 1);
alpha_j = fDataHist->GetBinContent(j + 1) / tempdata->GetBinContent(j + 1);
(*fFullCovar)(i, j) = alpha_i * alpha_j * (*fFullCovar)(i, j);
}
}
// Setup Covariances
if (covar) delete covar;
covar = StatUtils::GetInvert(fFullCovar);
if (fDecomp) delete fDecomp;
fDecomp = StatUtils::GetInvert(fFullCovar);
delete tempdata;
return;
};
//********************************************************************
void Measurement1D::ResetFakeData() {
//********************************************************************
if (fIsFakeData) {
if (fDataHist) delete fDataHist;
fDataHist = (TH1D*)fDataTrue->Clone((fSettings.GetName() + "_FKDAT").c_str());
}
}
//********************************************************************
void Measurement1D::ResetData() {
//********************************************************************
if (fIsFakeData) {
if (fDataHist) delete fDataHist;
fDataHist = (TH1D*)fDataOrig->Clone((fSettings.GetName() + "_data").c_str());
}
fIsFakeData = false;
}
//********************************************************************
void Measurement1D::ThrowCovariance() {
//********************************************************************
// Take a fDecomposition and use it to throw the current dataset.
// Requires fDataTrue also be set incase used repeatedly.
if (!fDataTrue) fDataTrue = (TH1D*) fDataHist->Clone();
if (fDataHist) delete fDataHist;
fDataHist = StatUtils::ThrowHistogram(fDataTrue, fFullCovar);
return;
};
//********************************************************************
void Measurement1D::ThrowDataToy(){
//********************************************************************
if (!fDataTrue) fDataTrue = (TH1D*) fDataHist->Clone();
if (fMCHist) delete fMCHist;
fMCHist = StatUtils::ThrowHistogram(fDataTrue, fFullCovar);
}
/*
Access Functions
*/
//********************************************************************
TH1D* Measurement1D::GetMCHistogram() {
//********************************************************************
if (!fMCHist) return fMCHist;
std::ostringstream chi2;
chi2 << std::setprecision(5) << this->GetLikelihood();
int linecolor = kRed;
int linestyle = 1;
int linewidth = 1;
int fillcolor = 0;
int fillstyle = 1001;
// if (fSettings.Has("linecolor")) linecolor = fSettings.GetI("linecolor");
// if (fSettings.Has("linestyle")) linestyle = fSettings.GetI("linestyle");
// if (fSettings.Has("linewidth")) linewidth = fSettings.GetI("linewidth");
// if (fSettings.Has("fillcolor")) fillcolor = fSettings.GetI("fillcolor");
// if (fSettings.Has("fillstyle")) fillstyle = fSettings.GetI("fillstyle");
fMCHist->SetTitle(chi2.str().c_str());
fMCHist->SetLineColor(linecolor);
fMCHist->SetLineStyle(linestyle);
fMCHist->SetLineWidth(linewidth);
fMCHist->SetFillColor(fillcolor);
fMCHist->SetFillStyle(fillstyle);
return fMCHist;
};
//********************************************************************
TH1D* Measurement1D::GetDataHistogram() {
//********************************************************************
if (!fDataHist) return fDataHist;
int datacolor = kBlack;
int datastyle = 1;
int datawidth = 1;
// if (fSettings.Has("datacolor")) datacolor = fSettings.GetI("datacolor");
// if (fSettings.Has("datastyle")) datastyle = fSettings.GetI("datastyle");
// if (fSettings.Has("datawidth")) datawidth = fSettings.GetI("datawidth");
fDataHist->SetLineColor(datacolor);
fDataHist->SetLineWidth(datawidth);
fDataHist->SetMarkerStyle(datastyle);
return fDataHist;
};
/*
Write Functions
*/
// Save all the histograms at once
//********************************************************************
void Measurement1D::Write(std::string drawOpt) {
//********************************************************************
// Get Draw Options
drawOpt = FitPar::Config().GetParS("drawopts");
// Write Settigns
if (drawOpt.find("SETTINGS") != std::string::npos){
fSettings.Set("#chi^{2}",fLikelihood);
fSettings.Set("NDOF", this->GetNDOF() );
fSettings.Set("#chi^{2}/NDOF", fLikelihood / this->GetNDOF() );
fSettings.Write();
}
// Write Data/MC
GetDataList().at(0)->Write();
GetMCList().at(0)->Write();
// Write Fine Histogram
if (drawOpt.find("FINE") != std::string::npos)
GetFineList().at(0)->Write();
// Write Weighted Histogram
if (drawOpt.find("WEIGHTS") != std::string::npos && fMCWeighted)
fMCWeighted->Write();
// Save Flux/Evt if no event manager
if (!FitPar::Config().GetParB("EventManager")) {
if (drawOpt.find("FLUX") != std::string::npos && GetFluxHistogram())
GetFluxHistogram()->Write();
if (drawOpt.find("EVT") != std::string::npos && GetEventHistogram())
GetEventHistogram()->Write();
if (drawOpt.find("XSEC") != std::string::npos && GetEventHistogram())
GetXSecHistogram()->Write();
}
// Write Mask
if (fIsMask && (drawOpt.find("MASK") != std::string::npos)) {
fMaskHist->Write();
}
// Write Covariances
if (drawOpt.find("COV") != std::string::npos && fFullCovar) {
PlotUtils::GetFullCovarPlot(fFullCovar, fSettings.GetName());
}
if (drawOpt.find("INVCOV") != std::string::npos && covar) {
PlotUtils::GetInvCovarPlot(covar, fSettings.GetName());
}
if (drawOpt.find("DECOMP") != std::string::npos && fDecomp) {
PlotUtils::GetDecompCovarPlot(fDecomp, fSettings.GetName());
}
// // Likelihood residual plots
// if (drawOpt.find("RESIDUAL") != std::string::npos) {
// WriteResidualPlots();
// }
// Ratio and Shape Plots
if (drawOpt.find("RATIO") != std::string::npos) {
WriteRatioPlot();
}
if (drawOpt.find("SHAPE") != std::string::npos) {
WriteShapePlot();
if (drawOpt.find("RATIO") != std::string::npos)
WriteShapeRatioPlot();
}
// // RATIO
// if (drawOpt.find("CANVMC") != std::string::npos) {
// TCanvas* c1 = WriteMCCanvas(fDataHist, fMCHist);
// c1->Write();
// delete c1;
// }
// // PDG
// if (drawOpt.find("CANVPDG") != std::string::npos && fMCHist_Modes) {
// TCanvas* c2 = WritePDGCanvas(fDataHist, fMCHist, fMCHist_Modes);
// c2->Write();
// delete c2;
// }
// Write Extra Histograms
AutoWriteExtraTH1();
WriteExtraHistograms();
// Returning
LOG(SAM) << "Written Histograms: " << fName << std::endl;
return;
}
//********************************************************************
void Measurement1D::WriteRatioPlot() {
//********************************************************************
// Setup mc data ratios
TH1D* dataRatio = (TH1D*)fDataHist->Clone((fName + "_data_RATIO").c_str());
TH1D* mcRatio = (TH1D*)fMCHist->Clone((fName + "_MC_RATIO").c_str());
// Extra MC Data Ratios
for (int i = 0; i < mcRatio->GetNbinsX(); i++) {
dataRatio->SetBinContent(i + 1, fDataHist->GetBinContent(i + 1) / fMCHist->GetBinContent(i + 1));
dataRatio->SetBinError(i + 1, fDataHist->GetBinError(i + 1) / fMCHist->GetBinContent(i + 1));
mcRatio->SetBinContent(i + 1, fMCHist->GetBinContent(i + 1) / fMCHist->GetBinContent(i + 1));
mcRatio->SetBinError(i + 1, fMCHist->GetBinError(i + 1) / fMCHist->GetBinContent(i + 1));
}
// Write ratios
mcRatio->Write();
dataRatio->Write();
delete mcRatio;
delete dataRatio;
}
//********************************************************************
void Measurement1D::WriteShapePlot() {
//********************************************************************
TH1D* mcShape = (TH1D*)fMCHist->Clone((fName + "_MC_SHAPE").c_str());
TH1D* dataShape = (TH1D*)fDataHist->Clone((fName + "_data_SHAPE").c_str());
if (fShapeCovar) StatUtils::SetDataErrorFromCov(dataShape, fShapeCovar, 1E-38);
double shapeScale = 1.0;
if (fIsRawEvents) {
shapeScale = fDataHist->Integral() / fMCHist->Integral();
} else {
shapeScale = fDataHist->Integral("width") / fMCHist->Integral("width");
}
mcShape->Scale(shapeScale);
std::stringstream ss;
ss << shapeScale;
mcShape->SetTitle(ss.str().c_str());
mcShape->SetLineWidth(3);
mcShape->SetLineStyle(7);
mcShape->Write();
dataShape->Write();
delete mcShape;
}
//********************************************************************
void Measurement1D::WriteShapeRatioPlot() {
//********************************************************************
// Get a mcshape histogram
TH1D* mcShape = (TH1D*)fMCHist->Clone((fName + "_MC_SHAPE").c_str());
double shapeScale = 1.0;
if (fIsRawEvents) {
shapeScale = fDataHist->Integral() / fMCHist->Integral();
} else {
shapeScale = fDataHist->Integral("width") / fMCHist->Integral("width");
}
mcShape->Scale(shapeScale);
// Create shape ratio histograms
TH1D* mcShapeRatio = (TH1D*)mcShape->Clone((fName + "_MC_SHAPE_RATIO").c_str());
TH1D* dataShapeRatio = (TH1D*)fDataHist->Clone((fName + "_data_SHAPE_RATIO").c_str());
// Divide the histograms
mcShapeRatio->Divide(mcShape);
dataShapeRatio->Divide(mcShape);
// Colour the shape ratio plots
mcShapeRatio->SetLineWidth(3);
mcShapeRatio->SetLineStyle(7);
mcShapeRatio->Write();
dataShapeRatio->Write();
delete mcShapeRatio;
delete dataShapeRatio;
}
//// CRAP TO BE REMOVED
//********************************************************************
void Measurement1D::SetupMeasurement(std::string inputfile, std::string type,
FitWeight * rw, std::string fkdt) {
//********************************************************************
nuiskey samplekey = Config::CreateKey("sample");
samplekey.AddS("name", fName);
samplekey.AddS("type",type);
samplekey.AddS("input",inputfile);
fSettings = LoadSampleSettings(samplekey);
// Reset everything to NULL
// Init();
// Check if name contains Evt, indicating that it is a raw number of events
// measurements and should thus be treated as once
fIsRawEvents = false;
if ((fName.find("Evt") != std::string::npos) && fIsRawEvents == false) {
fIsRawEvents = true;
LOG(SAM) << "Found event rate measurement but fIsRawEvents == false!"
<< std::endl;
LOG(SAM) << "Overriding this and setting fIsRawEvents == true!"
<< std::endl;
}
fIsEnu1D = false;
if (fName.find("XSec_1DEnu") != std::string::npos) {
fIsEnu1D = true;
LOG(SAM) << "::" << fName << "::" << std::endl;
LOG(SAM) << "Found XSec Enu measurement, applying flux integrated scaling, "
"not flux averaged!"
<< std::endl;
}
if (fIsEnu1D && fIsRawEvents) {
LOG(SAM) << "Found 1D Enu XSec distribution AND fIsRawEvents, is this "
"really correct?!"
<< std::endl;
LOG(SAM) << "Check experiment constructor for " << fName
<< " and correct this!" << std::endl;
LOG(SAM) << "I live in " << __FILE__ << ":" << __LINE__ << std::endl;
exit(-1);
}
fRW = rw;
if (!fInput and !fIsJoint) SetupInputs(inputfile);
// Set Default Options
SetFitOptions(fDefaultTypes);
// Set Passed Options
SetFitOptions(type);
// Still adding support for flat flux inputs
// // Set Enu Flux Scaling
// if (isFlatFluxFolding) this->Input()->ApplyFluxFolding(
// this->defaultFluxHist );
// FinaliseMeasurement();
}
//********************************************************************
void Measurement1D::SetupDefaultHist() {
//********************************************************************
// Setup fMCHist
fMCHist = (TH1D*)fDataHist->Clone();
fMCHist->SetNameTitle((fName + "_MC").c_str(),
(fName + "_MC" + fPlotTitles).c_str());
// Setup fMCFine
Int_t nBins = fMCHist->GetNbinsX();
fMCFine = new TH1D(
(fName + "_MC_FINE").c_str(), (fName + "_MC_FINE" + fPlotTitles).c_str(),
nBins * 6, fMCHist->GetBinLowEdge(1), fMCHist->GetBinLowEdge(nBins + 1));
fMCStat = (TH1D*)fMCHist->Clone();
fMCStat->Reset();
fMCHist->Reset();
fMCFine->Reset();
// Setup the NEUT Mode Array
PlotUtils::CreateNeutModeArray((TH1D*)fMCHist, (TH1**)fMCHist_PDG);
PlotUtils::ResetNeutModeArray((TH1**)fMCHist_PDG);
// Setup bin masks using sample name
if (fIsMask) {
std::string maskloc = FitPar::Config().GetParDIR(fName + ".mask");
if (maskloc.empty()) {
maskloc = FitPar::GetDataBase() + "/masks/" + fName + ".mask";
}
SetBinMask(maskloc);
}
fMCHist_Modes = new TrueModeStack( (fName + "_MODES").c_str(), ("True Channels"), fMCHist);
SetAutoProcessTH1(fMCHist_Modes, kCMD_Reset, kCMD_Norm, kCMD_Write);
return;
}
//********************************************************************
void Measurement1D::SetDataValues(std::string dataFile) {
//********************************************************************
// Override this function if the input file isn't in a suitable format
LOG(SAM) << "Reading data from: " << dataFile.c_str() << std::endl;
fDataHist =
PlotUtils::GetTH1DFromFile(dataFile, (fName + "_data"), fPlotTitles);
fDataTrue = (TH1D*)fDataHist->Clone();
// Number of data points is number of bins
fNDataPointsX = fDataHist->GetXaxis()->GetNbins();
return;
};
//********************************************************************
void Measurement1D::SetDataFromDatabase(std::string inhistfile,
std::string histname) {
//********************************************************************
LOG(SAM) << "Filling histogram from " << inhistfile << "->" << histname
<< std::endl;
fDataHist = PlotUtils::GetTH1DFromRootFile(
(GeneralUtils::GetTopLevelDir() + "/data/" + inhistfile), histname);
fDataHist->SetNameTitle((fName + "_data").c_str(), (fName + "_data").c_str());
return;
};
//********************************************************************
void Measurement1D::SetDataFromFile(std::string inhistfile,
std::string histname) {
//********************************************************************
LOG(SAM) << "Filling histogram from " << inhistfile << "->" << histname
<< std::endl;
fDataHist = PlotUtils::GetTH1DFromRootFile((inhistfile), histname);
fDataHist->SetNameTitle((fName + "_data").c_str(), (fName + "_data").c_str());
return;
};
//********************************************************************
void Measurement1D::SetCovarMatrix(std::string covarFile) {
//********************************************************************
// Covariance function, only really used when reading in the MB Covariances.
TFile* tempFile = new TFile(covarFile.c_str(), "READ");
TH2D* covarPlot = new TH2D();
// TH2D* decmpPlot = new TH2D();
TH2D* covarInvPlot = new TH2D();
TH2D* fFullCovarPlot = new TH2D();
std::string covName = "";
std::string covOption = FitPar::Config().GetParS("thrown_covariance");
if (fIsShape || fIsFree) covName = "shp_";
if (fIsDiag)
covName += "diag";
else
covName += "full";
covarPlot = (TH2D*)tempFile->Get((covName + "cov").c_str());
covarInvPlot = (TH2D*)tempFile->Get((covName + "covinv").c_str());
if (!covOption.compare("SUB"))
fFullCovarPlot = (TH2D*)tempFile->Get((covName + "cov").c_str());
else if (!covOption.compare("FULL"))
fFullCovarPlot = (TH2D*)tempFile->Get("fullcov");
else
ERR(WRN) << "Incorrect thrown_covariance option in parameters."
<< std::endl;
int dim = int(fDataHist->GetNbinsX()); //-this->masked->Integral());
int covdim = int(fDataHist->GetNbinsX());
this->covar = new TMatrixDSym(dim);
fFullCovar = new TMatrixDSym(dim);
fDecomp = new TMatrixDSym(dim);
int row, column = 0;
row = 0;
column = 0;
for (Int_t i = 0; i < covdim; i++) {
// if (this->masked->GetBinContent(i+1) > 0) continue;
for (Int_t j = 0; j < covdim; j++) {
// if (this->masked->GetBinContent(j+1) > 0) continue;
(*this->covar)(row, column) = covarPlot->GetBinContent(i + 1, j + 1);
(*fFullCovar)(row, column) = fFullCovarPlot->GetBinContent(i + 1, j + 1);
column++;
}
column = 0;
row++;
}
// Set bin errors on data
if (!fIsDiag) {
StatUtils::SetDataErrorFromCov(fDataHist, fFullCovar);
}
// Get Deteriminant and inverse matrix
// fCovDet = this->covar->Determinant();
TDecompSVD LU = TDecompSVD(*this->covar);
this->covar = new TMatrixDSym(dim, LU.Invert().GetMatrixArray(), "");
return;
};
//********************************************************************
// Sets the covariance matrix from a provided file in a text format
// scale is a multiplicative pre-factor to apply in the case where the
// covariance is given in some unit (e.g. 1E-38)
void Measurement1D::SetCovarMatrixFromText(std::string covarFile, int dim,
double scale) {
//********************************************************************
// Make a counter to track the line number
int row = 0;
std::string line;
std::ifstream covarread(covarFile.c_str(), ifstream::in);
this->covar = new TMatrixDSym(dim);
fFullCovar = new TMatrixDSym(dim);
if (covarread.is_open())
LOG(SAM) << "Reading covariance matrix from file: " << covarFile
<< std::endl;
else
ERR(FTL) << "Covariance matrix provided is incorrect: " << covarFile
<< std::endl;
// Loop over the lines in the file
while (std::getline(covarread >> std::ws, line, '\n')) {
int column = 0;
// Loop over entries and insert them into matrix
std::vector<double> entries = GeneralUtils::ParseToDbl(line, " ");
if (entries.size() <= 1) {
ERR(WRN) << "SetCovarMatrixFromText -> Covariance matrix only has <= 1 "
"entries on this line: "
<< row << std::endl;
}
for (std::vector<double>::iterator iter = entries.begin();
iter != entries.end(); iter++) {
(*covar)(row, column) = *iter;
(*fFullCovar)(row, column) = *iter;
column++;
}
row++;
}
covarread.close();
// Scale the actualy covariance matrix by some multiplicative factor
(*fFullCovar) *= scale;
// Robust matrix inversion method
TDecompSVD LU = TDecompSVD(*this->covar);
// THIS IS ACTUALLY THE INVERSE COVARIANCE MATRIXA AAAAARGH
delete this->covar;
this->covar = new TMatrixDSym(dim, LU.Invert().GetMatrixArray(), "");
// Now need to multiply by the scaling factor
// If the covariance
(*this->covar) *= 1. / (scale);
return;
};
//********************************************************************
void Measurement1D::SetCovarMatrixFromCorrText(std::string corrFile, int dim) {
//********************************************************************
// Make a counter to track the line number
int row = 0;
std::string line;
std::ifstream corr(corrFile.c_str(), ifstream::in);
this->covar = new TMatrixDSym(dim);
this->fFullCovar = new TMatrixDSym(dim);
if (corr.is_open())
LOG(SAM) << "Reading and converting correlation matrix from file: "
<< corrFile << std::endl;
else {
ERR(FTL) << "Correlation matrix provided is incorrect: " << corrFile
<< std::endl;
exit(-1);
}
while (std::getline(corr >> std::ws, line, '\n')) {
int column = 0;
// Loop over entries and insert them into matrix
// Multiply by the errors to get the covariance, rather than the correlation
// matrix
std::vector<double> entries = GeneralUtils::ParseToDbl(line, " ");
for (std::vector<double>::iterator iter = entries.begin();
iter != entries.end(); iter++) {
double val = (*iter) * this->fDataHist->GetBinError(row + 1) * 1E38 *
this->fDataHist->GetBinError(column + 1) * 1E38;
if (val == 0) {
ERR(FTL) << "Found a zero value in the covariance matrix, assuming "
"this is an error!"
<< std::endl;
exit(-1);
}
(*this->covar)(row, column) = val;
(*this->fFullCovar)(row, column) = val;
column++;
}
row++;
}
// Robust matrix inversion method
TDecompSVD LU = TDecompSVD(*this->covar);
delete this->covar;
this->covar = new TMatrixDSym(dim, LU.Invert().GetMatrixArray(), "");
return;
};
//********************************************************************
// FullUnits refers to if we have "real" unscaled units in the covariance matrix, e.g. 1E-76.
// If this is the case we need to scale it so that the chi2 contribution is correct
// NUISANCE internally assumes the covariance matrix has units of 1E76
void Measurement1D::SetCovarFromDataFile(std::string covarFile,
std::string covName, bool FullUnits) {
//********************************************************************
LOG(SAM) << "Getting covariance from " << covarFile << "->" << covName
<< std::endl;
TFile* tempFile = new TFile(covarFile.c_str(), "READ");
TH2D* covPlot = (TH2D*)tempFile->Get(covName.c_str());
covPlot->SetDirectory(0);
// Scale the covariance matrix if it comes in normal units
if (FullUnits) {
covPlot->Scale(1.E76);
}
int dim = covPlot->GetNbinsX();
fFullCovar = new TMatrixDSym(dim);
for (int i = 0; i < dim; i++) {
for (int j = 0; j < dim; j++) {
(*fFullCovar)(i, j) = covPlot->GetBinContent(i + 1, j + 1);
}
}
this->covar = (TMatrixDSym*)fFullCovar->Clone();
fDecomp = (TMatrixDSym*)fFullCovar->Clone();
TDecompSVD LU = TDecompSVD(*this->covar);
this->covar = new TMatrixDSym(dim, LU.Invert().GetMatrixArray(), "");
TDecompChol LUChol = TDecompChol(*fDecomp);
LUChol.Decompose();
fDecomp = new TMatrixDSym(dim, LU.GetU().GetMatrixArray(), "");
return;
};
// //********************************************************************
// void Measurement1D::SetBinMask(std::string maskFile) {
// //********************************************************************
// // Create a mask histogram.
// int nbins = fDataHist->GetNbinsX();
// fMaskHist =
// new TH1I((fName + "_fMaskHist").c_str(),
// (fName + "_fMaskHist; Bin; Mask?").c_str(), nbins, 0, nbins);
// std::string line;
// std::ifstream mask(maskFile.c_str(), ifstream::in);
// if (mask.is_open())
// LOG(SAM) << "Reading bin mask from file: " << maskFile << std::endl;
// else
// LOG(FTL) << " Cannot find mask file." << std::endl;
// while (std::getline(mask >> std::ws, line, '\n')) {
// std::vector<int> entries = GeneralUtils::ParseToInt(line, " ");
// // Skip lines with poorly formatted lines
// if (entries.size() < 2) {
// LOG(WRN) << "Measurement1D::SetBinMask(), couldn't parse line: " << line
// << std::endl;
// continue;
// }
// // The first index should be the bin number, the second should be the mask
// // value.
// fMaskHist->SetBinContent(entries[0], entries[1]);
// }
// // Set masked data bins to zero
// PlotUtils::MaskBins(fDataHist, fMaskHist);
// return;
// }
// //********************************************************************
// void Measurement1D::GetBinContents(std::vector<double>& cont,
// std::vector<double>& err) {
// //********************************************************************
// // Return a vector of the main bin contents
// for (int i = 0; i < fMCHist->GetNbinsX(); i++) {
// cont.push_back(fMCHist->GetBinContent(i + 1));
// err.push_back(fMCHist->GetBinError(i + 1));
// }
// return;
// };
/*
XSec Functions
*/
// //********************************************************************
// void Measurement1D::SetFluxHistogram(std::string fluxFile, int minE, int
// maxE,
// double fluxNorm) {
// //********************************************************************
// // Note this expects the flux bins to be given in terms of MeV
// LOG(SAM) << "Reading flux from file: " << fluxFile << std::endl;
// TGraph f(fluxFile.c_str(), "%lg %lg");
// fFluxHist =
// new TH1D((fName + "_flux").c_str(), (fName + "; E_{#nu} (GeV)").c_str(),
// f.GetN() - 1, minE, maxE);
// Double_t* yVal = f.GetY();
// for (int i = 0; i < fFluxHist->GetNbinsX(); ++i)
// fFluxHist->SetBinContent(i + 1, yVal[i] * fluxNorm);
// };
// //********************************************************************
// double Measurement1D::TotalIntegratedFlux(std::string intOpt, double low,
// double high) {
// //********************************************************************
// if (fInput->GetType() == kGiBUU) {
// return 1.0;
// }
// // The default case of low = -9999.9 and high = -9999.9
// if (low == -9999.9) low = this->EnuMin;
// if (high == -9999.9) high = this->EnuMax;
// int minBin = fFluxHist->GetXaxis()->FindBin(low);
// int maxBin = fFluxHist->GetXaxis()->FindBin(high);
// // Get integral over custom range
// double integral = fFluxHist->Integral(minBin, maxBin + 1, intOpt.c_str());
// return integral;
// };
diff --git a/src/FitBase/Measurement1D.h b/src/FitBase/Measurement1D.h
index e373dfe..503e687 100644
--- a/src/FitBase/Measurement1D.h
+++ b/src/FitBase/Measurement1D.h
@@ -1,655 +1,656 @@
// Copyright 2016 L. Pickering, P towell, 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 <http://www.gnu.org/licenses/>.
*******************************************************************************/
#ifndef MEASUREMENT_1D_H_SEEN
#define MEASUREMENT_1D_H_SEEN
/*!
* \addtogroup FitBase
* @{
*/
#include <math.h>
#include <stdlib.h>
#include <deque>
#include <iomanip>
#include <iostream>
#include <numeric>
#include <sstream>
#include <string>
// ROOT includes
#include <TArrayF.h>
#include <TCanvas.h>
#include <TCut.h>
#include <TDecompChol.h>
#include <TDecompSVD.h>
#include <TGraph.h>
#include <TGraphErrors.h>
#include <TH1D.h>
#include <TH2D.h>
#include <TMatrixDSym.h>
#include <TROOT.h>
#include <TSystem.h>
// External data fit includes
#include "FitEvent.h"
#include "FitParameters.h"
#include "FitUtils.h"
#include "MeasurementBase.h"
#include "PlotUtils.h"
#include "StatUtils.h"
#include "SignalDef.h"
#include "MeasurementVariableBox.h"
#include "MeasurementVariableBox1D.h"
namespace NUISANCE {
namespace FitBase {
}
}
//********************************************************************
/// 1D Measurement base class. Histogram handling is done in this base layer.
class Measurement1D : public MeasurementBase {
//********************************************************************
public:
/*
Constructor/Deconstuctor
*/
Measurement1D(void);
virtual ~Measurement1D(void);
/*
Setup Functions
*/
/// \brief Setup all configs once initialised
///
/// Should be called after all configs have been setup inside fSettings container.
/// Handles the processing of inputs and setting up of types.
/// Replaces the old 'SetupMeasurement' function.
void FinaliseSampleSettings();
/// \brief Creates the 1D data distribution given the binning provided.
virtual void CreateDataHistogram(int dimx, double* binx);
/// \brief Read 1D data inputs from a text file.
///
/// Inputfile should have the format: \n
/// low_binedge_1 bin_content_1 bin_error_1 \n
/// low_binedge_2 bin_content_2 bin_error_2 \n
/// .... .... .... \n
/// high_bin_edge_N 0.0 0.0
virtual void SetDataFromTextFile(std::string datafile);
/// \brief Read 1D data inputs from a root file.
///
/// inhistfile specifies the path to the root file
/// histname specifies the name of the histogram.
///
/// If no histogram name is given the inhistfile value
/// is automatically parsed with ';' so that: \n
/// 'myhistfile.root;myhistname' \n
/// will also work.
virtual void SetDataFromRootFile(std::string inhistfile, std::string histname = "");
/// \brief Setup a default empty data histogram
///
/// Only used for flattree creators.
virtual void SetEmptyData();
/// \brief Set data bin errors to sqrt(entries)
///
/// \warning REQUIRES DATA HISTOGRAM TO BE SET FIRST
///
/// Sets the data errors as the sqrt of the bin contents
/// Should be use for counting experiments
virtual void SetPoissonErrors();
/// \brief Make diagonal covariance from data
///
/// \warning If no histogram passed, data must be setup first!
/// Setup the covariance inputs by taking the data histogram
/// errors and setting up a diagonal covariance matrix.
///
/// If no data is supplied, fDataHist is used if already set.
virtual void SetCovarFromDiagonal(TH1D* data = NULL);
/// \brief Read the data covariance from a text file.
///
/// Inputfile should have the format: \n
/// covariance_11 covariance_12 covariance_13 ... \n
/// covariance_21 covariance_22 covariance_23 ... \n
/// ... ... ... ... \n
///
/// If no dimensions are given, it is assumed from the number
/// entries in the first line of covfile.
virtual void SetCovarFromTextFile(std::string covfile, int dim = -1);
virtual void SetCovarFromMultipleTextFiles(std::string covfiles, int dim = -1);
/// \brief Read the data covariance from a ROOT file.
///
/// - covfile specifies the full path to the file
/// - histname specifies the name of the covariance object. Both TMatrixDSym and TH2D are supported.
///
/// If no histogram name is given the inhistfile value
/// is automatically parsed with ; so that: \n
/// mycovfile.root;myhistname \n
/// will also work.
virtual void SetCovarFromRootFile(std::string covfile, std::string histname="");
/// \brief Read the inverted data covariance from a text file.
///
/// Inputfile should have similar format to that shown
/// in SetCovarFromTextFile.
///
/// If no dimensions are given, it is assumed from the number
/// entries in the first line of covfile.
virtual void SetCovarInvertFromTextFile(std::string covfile, int dim = -1);
/// \brief Read the inverted data covariance from a ROOT file.
///
/// Inputfile should have similar format to that shown
/// in SetCovarFromRootFile.
///
/// If no histogram name is given the inhistfile value
/// is automatically parsed with ; so that: \n
/// mycovfile.root;myhistname \n
/// will also work.
virtual void SetCovarInvertFromRootFile(std::string covfile, std::string histname="");
/// \brief Read the data correlations from a text file.
///
/// \warning REQUIRES DATA HISTOGRAM TO BE SET FIRST
///
/// Inputfile should have similar format to that shown
/// in SetCovarFromTextFile.
///
/// If no dimensions are given, it is assumed from the number
/// entries in the first line of covfile.
virtual void SetCorrelationFromTextFile(std::string covfile, int dim = -1);
/// \brief Read the data correlations from multiple text files.
///
/// \warning REQUIRES DATA HISTOGRAM TO BE SET FIRST
///
/// Inputfile should have similar format to that shown
/// in SetCovarFromTextFile.
///
/// If no dimensions are given, it is assumed from the number
/// entries in the first line of the first corrfile.
virtual void SetCorrelationFromMultipleTextFiles(std::string corrfiles, int dim = -1);
/// \brief Read the data correlations from a ROOT file.
///
/// \warning REQUIRES DATA TO BE SET FIRST
///
/// Inputfile should have similar format to that shown
/// in SetCovarFromRootFile.
///
/// If no histogram name is given the inhistfile value
/// is automatically parsed with ; so that: \n
/// mycovfile.root;myhistname \n
/// will also work.
virtual void SetCorrelationFromRootFile(std::string covfile, std::string histname="");
/// \brief Read the cholesky decomposed covariance from a text file and turn it into a covariance
///
/// Inputfile should have similar format to that shown
/// in SetCovarFromTextFile.
///
/// If no dimensions are given, it is assumed from the number
/// entries in the first line of covfile.
virtual void SetCholDecompFromTextFile(std::string covfile, int dim = -1);
/// \brief Read the cholesky decomposed covariance from a ROOT file and turn it into a covariance
///
/// Inputfile should have similar format to that shown
/// in SetCovarFromRootFile.
///
/// If no histogram name is given the inhistfile value
/// is automatically parsed with ; so that: \n
/// mycovfile.root;myhistname \n
/// will also work.
virtual void SetCholDecompFromRootFile(std::string covfile, std::string histname="");
/// \brief Try to extract a shape-only matrix from the existing covariance
virtual void SetShapeCovar();
/// \brief Scale the data by some scale factor
virtual void ScaleData(double scale);
/// \brief Scale the data error bars by some scale factor
virtual void ScaleDataErrors(double scale);
/// \brief Scale the covariaince and its invert/decomp by some scale factor.
virtual void ScaleCovar(double scale);
/// \brief Setup a bin masking histogram and apply masking to data
///
/// \warning REQUIRES DATA HISTOGRAM TO BE SET FIRST
///
/// Reads in a list of bins in a text file to be masked. Format is: \n
/// bin_index_1 1 \n
/// bin_index_2 1 \n
/// bin_index_3 1 \n
///
/// If 0 is given then a bin entry will NOT be masked. So for example: \n\n
/// 1 1 \n
/// 2 1 \n
/// 3 0 \n
/// 4 1 \n\n
/// Will mask only the 1st, 2nd, and 4th bins.
///
/// Masking can be turned on by specifiying the MASK option when creating a sample.
/// When this is passed NUISANCE will look in the following locations for the mask file:
/// - FitPar::Config().GetParS(fName + ".mask")
/// - "data/masks/" + fName + ".mask";
virtual void SetBinMask(std::string maskfile);
/// \brief Set the current fit options from a string.
///
/// This is called twice for each sample, once to set the default
/// and once to set the current setting (if anything other than default given)
///
/// For this to work properly it requires the default and allowed types to be
/// set correctly. These should be specified as a string listing options.
///
/// To split up options so that NUISANCE can automatically detect ones that
/// are conflicting. Any options seperated with the '/' symbol are non conflicting
/// and can be given together, whereas any seperated with the ',' symbol cannot
/// be specified by the end user at the same time.
///
/// Default Type Examples:
/// - DIAG/FIX = Default option will be a diagonal covariance, with FIXED norm.
/// - MASK/SHAPE = Default option will be a masked hist, with SHAPE always on.
///
/// Allowed Type examples:
/// - 'FULL/DIAG/NORM/MASK' = Any of these options can be specified.
/// - 'FULL,FREE,SHAPE/MASK/NORM' = User can give either FULL, FREE, or SHAPE as on option.
/// MASK and NORM can also be included as options.
virtual void SetFitOptions(std::string opt);
/// \brief Final constructor setup
/// \warning Should be called right at the end of the constructor.
///
/// Contains a series of checks to ensure the data and inputs have been setup.
/// Also creates the MC histograms needed for fitting.
void FinaliseMeasurement();
/*
Smearing
*/
/// \brief Read in smearing matrix from file
///
/// Set the smearing matrix from a text file given the size of the matrix
virtual void SetSmearingMatrix(std::string smearfile, int truedim,
int recodim);
/// \brief Apply smearing to MC true to get MC reco
///
/// Apply smearing matrix to fMCHist using fSmearingMatrix
virtual void ApplySmearingMatrix(void);
/*
Reconfigure Functions
*/
/// \brief Create a Measurement1D box
///
/// Creates a new 1D variable box containing just fXVar.
///
/// This box is the bare minimum required by the JointFCN when
/// running fast reconfigures during a routine.
/// If for some reason a sample needs extra variables to be saved then
/// it should override this function creating its own MeasurementVariableBox
/// that contains the extra variables.
virtual MeasurementVariableBox* CreateBox() {return new MeasurementVariableBox1D();};
/// \brief Reset all MC histograms
///
/// Resets all standard histograms and those registered to auto
/// process to zero.
///
/// If extra histograms are not included in auto processing, then they must be reset
/// by overriding this function and doing it manually if required.
virtual void ResetAll(void);
/// \brief Fill MC Histograms from XVar
///
/// Fill standard histograms using fXVar, Weight read from the variable box.
///
/// WARNING : Any extra MC histograms need to be filled by overriding this function,
/// even if they have been set to auto process.
virtual void FillHistograms(void);
// \brief Convert event rates to final histogram
///
/// Apply standard scaling procedure to standard mc histograms to convert from
/// raw events to xsec prediction.
///
/// If any distributions have been set to auto process
/// that is done during this function call, and a differential xsec is assumed.
/// If that is not the case this function must be overriden.
virtual void ScaleEvents(void);
/// \brief Scale MC by a factor=1/norm
///
/// Apply a simple normalisation scaling if the option FREE or a norm_parameter
/// has been specified in the NUISANCE routine.
virtual void ApplyNormScale(double norm);
/*
Statistical Functions
*/
/// \brief Get Number of degrees of freedom
///
/// Returns the number bins inside the data histogram accounting for
/// any bin masking applied.
virtual int GetNDOF(void);
/// \brief Return Data/MC Likelihood at current state
///
/// Returns the likelihood of the data given the current MC prediction.
/// Diferent likelihoods definitions are used depending on the FitOptions.
virtual double GetLikelihood(void);
/*
Fake Data
*/
/// \brief Set the fake data values from either a file, or MC
///
/// - Setting from a file "path": \n
/// When reading from a file the full path must be given to a standard
/// nuisance output. The standard MC histogram should have a name that matches
/// this sample for it to be read in.
/// \n\n
/// - Setting from "MC": \n
/// If the MC option is given the current MC prediction is used as fake data.
virtual void SetFakeDataValues(std::string fakeOption);
/// \brief Reset fake data back to starting fake data
///
/// Reset the fake data back to original fake data (Reset back to before
/// ThrowCovariance was first called)
virtual void ResetFakeData(void);
/// \brief Reset fake data back to original data
///
/// Reset the data histogram back to the true original dataset for this sample
/// before any fake data was defined.
virtual void ResetData(void);
/// \brief Generate fake data by throwing the covariance.
///
/// Can be used on fake MC data or just the original dataset.
/// Call ResetFakeData or ResetData to return to values before the throw.
virtual void ThrowCovariance(void);
/// \brief Throw the data by its assigned errors and assign this to MC
///
/// Used when creating data toys by assign the MC to this thrown data
/// so that the likelihood is calculated between data and thrown data
virtual void ThrowDataToy(void);
/*
Access Functions
*/
/// \brief Returns nicely formatted MC Histogram
///
/// Format options can also be given in the samplesettings:
/// - linecolor
/// - linestyle
/// - linewidth
/// - fillcolor
/// - fillstyle
///
/// So to have a sample line colored differently in the xml cardfile put: \n
/// <sample name="MiniBooNE_CCQE_XSec_1DQ2_nu" input="NEUT:input.root"
/// linecolor="2" linestyle="7" linewidth="2" />
virtual TH1D* GetMCHistogram(void);
/// \brief Returns nicely formatted data Histogram
///
/// Format options can also be given in the samplesettings:
/// - datacolor
/// - datastyle
/// - datawidth
///
/// So to have a sample data colored differently in the xml cardfile put: \n
/// <sample name="MiniBooNE_CCQE_XSec_1DQ2_nu" input="NEUT:input.root"
/// datacolor="2" datastyle="7" datawidth="2" />
virtual TH1D* GetDataHistogram(void);
/// \brief Returns a list of all MC histograms.
///
/// Override this if you have extra histograms that need to be
/// accessed outside of the Measurement1D class.
inline virtual std::vector<TH1*> GetMCList(void) {
return std::vector<TH1*>(1, GetMCHistogram());
}
/// \brief Returns a list of all Data histograms.
///
/// Override this if you have extra histograms that need to be
/// accessed outside of the Measurement1D class.
inline virtual std::vector<TH1*> GetDataList(void) {
return std::vector<TH1*>(1, GetDataHistogram());
}
/// \brief Returns a list of all Mask histograms.
///
/// Override this if you have extra histograms that need to be
/// accessed outside of the Measurement1D class.
inline virtual std::vector<TH1*> GetMaskList(void) {
return std::vector<TH1*>(1, fMaskHist);
};
/// \brief Returns a list of all Fine histograms.
///
/// Override this if you have extra histograms that need to be
/// accessed outside of the Measurement1D class.
inline virtual std::vector<TH1*> GetFineList(void) {
return std::vector<TH1*>(1, fMCFine);
};
/*
Write Functions
*/
/// \brief Save the current state to the current TFile directory \n
///
/// Data/MC are both saved by default.
/// A range of other histograms can be saved by setting the
/// config option 'drawopts'.
///
/// Possible options: \n
/// - FINE = Write Fine Histogram \n
/// - WEIGHTS = Write Weighted MC Histogram (before scaling) \n
/// - FLUX = Write Flux Histogram from MC Input \n
/// - EVT = Write Event Histogram from MC Input \n
/// - XSEC = Write XSec Histogram from MC Input \n
/// - MASK = Write Mask Histogram \n
/// - COV = Write Covariance Histogram \n
/// - INVCOV = Write Inverted Covariance Histogram \n
/// - DECMOP = Write Decomp. Covariance Histogram \n
/// - RESIDUAL= Write Resudial Histograms \n
/// - RATIO = Write Data/MC Ratio Histograms \n
/// - SHAPE = Write MC Shape Histograms norm. to Data \n
/// - CANVMC = Build MC Canvas Showing Data, MC, Shape \n
/// - MODES = Write PDG Stack \n
/// - CANVPDG = Build MC Canvas Showing Data, PDGStack \n
///
/// So to save a range of these in parameters/config.xml set: \n
/// <config drawopts='FINE/COV/SHAPE/RATIO' />
virtual void Write(std::string drawOpt);
virtual void WriteRatioPlot();
virtual void WriteShapePlot();
virtual void WriteShapeRatioPlot();
//*
// OLD DEFUNCTIONS
//
/// OLD FUNCTION
virtual void SetupMeasurement(std::string input, std::string type,
FitWeight* rw, std::string fkdt);
/// OLD FUNCTION
virtual void SetupDefaultHist(void);
/// OLD FUNCTION
virtual void SetDataValues(std::string dataFile);
/// OLD FUNCTION
virtual void SetDataFromFile(std::string inhistfile, std::string histname);
/// OLD FUNCTION
virtual void SetDataFromDatabase(std::string inhistfile,
std::string histname);
/// OLD FUNCTION
virtual void SetCovarMatrix(std::string covarFile);
/// OLD FUNCTION
virtual void SetCovarMatrixFromText(std::string covarFile, int dim,
double scale = 1.0);
/// OLD FUNCTION
virtual void SetCovarMatrixFromCorrText(std::string covarFile, int dim);
/// OLD FUNCTION
virtual void SetCovarFromDataFile(std::string covarFile, std::string covName,
bool FullUnits = false);
/// OLD FUNCTION
// virtual THStack GetModeStack(void);
protected:
// Data
TH1D* fDataHist; ///< default data histogram
TH1D* fDataOrig; ///< histogram to store original data before throws.
TH1D* fDataTrue; ///< histogram to store true dataset
std::string fPlotTitles; ///< Plot title x and y for the histograms
// MC
TH1D* fMCHist; ///< default MC Histogram used in the chi2 fits
TH1D* fMCFine; ///< finely binned MC histogram
TH1D* fMCStat; ///< histogram with unweighted events to properly calculate
TH1D* fMCWeighted; ///< Weighted histogram before xsec scaling
TH1I* fMaskHist; ///< Mask histogram for neglecting specific bins
TMatrixD* fSmearMatrix; ///< Smearing matrix (note, this is not symmetric)
TrueModeStack* fMCHist_Modes; ///< Optional True Mode Stack
// Statistical
TMatrixDSym* covar; ///< Inverted Covariance
TMatrixDSym* fFullCovar; ///< Full Covariance
TMatrixDSym* fDecomp; ///< Decomposed Covariance
TMatrixDSym* fCorrel; ///< Correlation Matrix
TMatrixDSym* fShapeCovar; ///< Shape-only covariance
TMatrixDSym* fShapeDecomp; ///< Decomposed shape-only covariance
+ TMatrixDSym* fShapeInvert; ///< Inverted shape-only covariance
TMatrixDSym* fCovar; ///< New FullCovar
TMatrixDSym* fInvert; ///< New covar
double fNormError; ///< Sample norm error
double fLikelihood; ///< Likelihood value
// Fake Data
bool fIsFakeData; ///< Flag: is the current data fake from MC
std::string fFakeDataInput; ///< Input fake data file path
TFile* fFakeDataFile; ///< Input fake data file
// Fit specific flags
std::string fFitType; ///< Current fit type
std::string fAllowedTypes; ///< Fit Types Possible
std::string fDefaultTypes; ///< Starting Default Fit Types
bool fIsShape; ///< Flag : Perform Shape-only fit
bool fIsFree; ///< Flag : Perform normalisation free fit
bool fIsDiag; ///< Flag : only include uncorrelated diagonal errors
bool fIsMask; ///< Flag : Apply bin masking
bool fIsRawEvents; ///< Flag : Are bin contents just event rates
bool fIsEnu1D; ///< Flag : Perform Flux Unfolded Scaling
bool fIsChi2SVD; ///< Flag : Use alternative Chi2 SVD Method (Do not use)
bool fAddNormPen; ///< Flag : Add a normalisation penalty term to the chi2.
bool fIsFix; ///< Flag : keeping norm fixed
bool fIsFull; ///< Flag : using full covariaince
bool fIsDifXSec; ///< Flag : creating a dif xsec
bool fIsChi2; ///< Flag : using Chi2 over LL methods
bool fIsSmeared; ///< Flag : Apply smearing?
/// OLD STUFF TO REMOVE
TH1D* fMCHist_PDG[61]; ///< REMOVE OLD MC PDG Plot
// Arrays for data entries
Double_t* fXBins; ///< REMOVE xBin edges
Double_t* fDataValues; ///< REMOVE data bin contents
Double_t* fDataErrors; ///< REMOVE data bin errors
Int_t fNDataPointsX; ///< REMOVE number of data points
};
/*! @} */
#endif
diff --git a/src/Utils/StatUtils.cxx b/src/Utils/StatUtils.cxx
index 24a458a..a32ec94 100644
--- a/src/Utils/StatUtils.cxx
+++ b/src/Utils/StatUtils.cxx
@@ -1,1300 +1,1301 @@
// Copyright 2016 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 <http://www.gnu.org/licenses/>.
*******************************************************************************/
#include "TH1D.h"
#include "StatUtils.h"
//*******************************************************************
Double_t StatUtils::GetChi2FromDiag(TH1D* data, TH1D* mc, TH1I* mask) {
//*******************************************************************
Double_t Chi2 = 0.0;
TH1D* calc_data = (TH1D*)data->Clone();
TH1D* calc_mc = (TH1D*)mc->Clone();
// Add MC Error to data if required
if (FitPar::Config().GetParB("statutils.addmcerror")) {
for (int i = 0; i < calc_data->GetNbinsX(); i++) {
double dterr = calc_data->GetBinError(i + 1);
double mcerr = calc_mc->GetBinError(i + 1);
if (dterr > 0.0) {
calc_data->SetBinError(i + 1, sqrt(dterr * dterr + mcerr * mcerr));
}
}
}
// Apply masking if required
if (mask) {
calc_data = ApplyHistogramMasking(data, mask);
calc_mc = ApplyHistogramMasking(mc, mask);
}
// Iterate over bins in X
for (int i = 0; i < calc_data->GetNbinsX(); i++) {
// Ignore bins with zero data or zero bin error
if (calc_data->GetBinError(i + 1) <= 0.0 ||
calc_data->GetBinContent(i + 1) == 0.0) continue;
// Take mc data difference
double diff = calc_data->GetBinContent(i + 1) - calc_mc->GetBinContent(i + 1);
double err = calc_data->GetBinError(i + 1);
Chi2 += (diff * diff) / (err * err);
}
// cleanup
delete calc_data;
delete calc_mc;
return Chi2;
};
//*******************************************************************
Double_t StatUtils::GetChi2FromDiag(TH2D* data, TH2D* mc,
TH2I* map, TH2I* mask) {
//*******************************************************************
// Generate a simple map
if (!map) map = GenerateMap(data);
// Convert to 1D Histograms
TH1D* data_1D = MapToTH1D(data, map);
TH1D* mc_1D = MapToTH1D(mc, map);
TH1I* mask_1D = MapToMask(mask, map);
// Calculate 1D chi2 from 1D Plots
Double_t Chi2 = StatUtils:: GetChi2FromDiag(data_1D, mc_1D, mask_1D);
// CleanUp
delete data_1D;
delete mc_1D;
delete mask_1D;
return Chi2;
};
//*******************************************************************
Double_t StatUtils::GetChi2FromCov(TH1D* data, TH1D* mc,
TMatrixDSym* invcov, TH1I* mask,
double data_scale, double covar_scale) {
//*******************************************************************
Double_t Chi2 = 0.0;
TMatrixDSym* calc_cov = (TMatrixDSym*) invcov->Clone();
TH1D* calc_data = (TH1D*) data->Clone();
TH1D* calc_mc = (TH1D*) mc->Clone();
- calc_data->Scale(data_scale);
- calc_mc ->Scale(data_scale);
- (*calc_cov) *= covar_scale;
-
// If a mask if applied we need to apply it before the matrix is inverted
if (mask) {
calc_cov = ApplyInvertedMatrixMasking(invcov, mask);
calc_data = ApplyHistogramMasking(data, mask);
calc_mc = ApplyHistogramMasking(mc, mask);
}
// Add MC Error to data if required
if (FitPar::Config().GetParB("statutils.addmcerror")) {
// Make temp cov
TMatrixDSym* newcov = StatUtils::GetInvert(calc_cov);
// Add MC err to diag
for (int i = 0; i < calc_data->GetNbinsX(); i++) {
double mcerr = calc_mc->GetBinError(i + 1) * sqrt(covar_scale);
double oldval = (*newcov)(i, i);
+ std::cout << "Adding cov stat " << mcerr*mcerr << " to " << (*newcov)(i,i) << std::endl;
(*newcov)(i, i) = oldval + mcerr * mcerr;
}
// Reset the calc_cov to new invert
delete calc_cov;
calc_cov = GetInvert(newcov);
// Delete the tempcov
delete newcov;
}
+ calc_data->Scale(data_scale);
+ calc_mc ->Scale(data_scale);
+ (*calc_cov) *= covar_scale;
+
// iterate over bins in X (i,j)
for (int i = 0; i < calc_data->GetNbinsX(); i++) {
for (int j = 0; j < calc_data->GetNbinsX(); j++) {
if (calc_data->GetBinContent(i + 1) != 0 || calc_mc->GetBinContent(i + 1) != 0) {
LOG(DEB) << "i j = " << i << " " << j << std::endl;
LOG(DEB) << "Calc_data mc i = " << calc_data->GetBinContent(i + 1)
<< " " << calc_mc->GetBinContent(i + 1)
<< " Dif = "
<< ( calc_data->GetBinContent(i + 1) - calc_mc->GetBinContent(i + 1) )
<< std::endl;
LOG(DEB) << "Calc_data mc i = " << calc_data->GetBinContent(j + 1)
<< " " << calc_mc->GetBinContent(j + 1)
<< " Dif = "
<< ( calc_data->GetBinContent(j + 1) - calc_mc->GetBinContent(j + 1) )
<< std::endl;
LOG(DEB) << "Covar = " << (*calc_cov)(i, j) << std::endl;
LOG(DEB) << "Cont chi2 = " \
<< ( ( calc_data->GetBinContent(i + 1) - calc_mc->GetBinContent(i + 1) ) \
* (*calc_cov)(i, j) \
* ( calc_data->GetBinContent(j + 1) - calc_mc->GetBinContent(j + 1)))
<< " " << Chi2 << std::endl;
Chi2 += ( ( calc_data->GetBinContent(i + 1) - calc_mc->GetBinContent(i + 1) ) \
* (*calc_cov)(i, j) \
* ( calc_data->GetBinContent(j + 1) - calc_mc->GetBinContent(j + 1) ) );
} else {
LOG(DEB) << "Error on bin (i,j) = (" << i << "," << j << ")" << std::endl;
LOG(DEB) << "data->GetBinContent(i+1) = " << calc_data->GetBinContent(i + 1) << std::endl;
LOG(DEB) << "mc->GetBinContent(i+1) = " << calc_mc->GetBinContent(i + 1) << std::endl;
LOG(DEB) << "Adding zero to chi2 instead of dying horrifically " << std::endl;
Chi2 += 0.;
}
}
}
// Cleanup
delete calc_cov;
delete calc_data;
delete calc_mc;
return Chi2;
}
//*******************************************************************
Double_t StatUtils::GetChi2FromCov( TH2D* data, TH2D* mc,
TMatrixDSym* invcov, TH2I* map, TH2I* mask) {
//*******************************************************************
// Generate a simple map
if (!map) {
map = StatUtils::GenerateMap(data);
}
// Convert to 1D Histograms
TH1D* data_1D = MapToTH1D(data, map);
TH1D* mc_1D = MapToTH1D(mc, map);
TH1I* mask_1D = MapToMask(mask, map);
// Calculate 1D chi2 from 1D Plots
Double_t Chi2 = StatUtils::GetChi2FromCov(data_1D, mc_1D, invcov, mask_1D);
// CleanUp
delete data_1D;
delete mc_1D;
delete mask_1D;
return Chi2;
}
//*******************************************************************
Double_t StatUtils::GetChi2FromSVD( TH1D* data, TH1D* mc,
TMatrixDSym* cov, TH1I* mask) {
//*******************************************************************
Double_t Chi2 = 0.0;
TMatrixDSym* calc_cov = (TMatrixDSym*) cov->Clone();
TH1D* calc_data = (TH1D*) data->Clone();
TH1D* calc_mc = (TH1D*) mc->Clone();
// If a mask if applied we need to apply it before the matrix is inverted
if (mask) {
calc_cov = StatUtils::ApplyMatrixMasking(cov, mask);
calc_data = StatUtils::ApplyHistogramMasking(data, mask);
calc_mc = StatUtils::ApplyHistogramMasking(mc, mask);
}
// Decompose matrix
TDecompSVD LU = TDecompSVD((*calc_cov));
LU.Decompose();
TMatrixDSym* cov_U = new TMatrixDSym(calc_data->GetNbinsX(), LU .GetU().GetMatrixArray(), "");
TVectorD* cov_S = new TVectorD( LU.GetSig() );
// Apply basis rotation before adding up chi2
Double_t rotated_difference = 0.0;
for (int i = 0; i < calc_data->GetNbinsX(); i++) {
rotated_difference = 0.0;
// Rotate basis of Data - MC
for (int j = 0; j < calc_data->GetNbinsY(); j++)
rotated_difference += ( calc_data->GetBinContent(j + 1) - calc_mc ->GetBinContent(j + 1) ) * (*cov_U)(j, i) ;
// Divide by rotated error cov_S
Chi2 += rotated_difference * rotated_difference * 1E76 / (*cov_S)(i);
}
// Cleanup
delete calc_cov;
delete calc_data;
delete calc_mc;
delete cov_U;
delete cov_S;
return Chi2;
}
//*******************************************************************
Double_t StatUtils::GetChi2FromSVD( TH2D* data, TH2D* mc,
TMatrixDSym* cov, TH2I* map, TH2I* mask) {
//*******************************************************************
// Generate a simple map
if (!map)
map = StatUtils::GenerateMap(data);
// Convert to 1D Histograms
TH1D* data_1D = MapToTH1D(data, map);
TH1D* mc_1D = MapToTH1D(mc, map);
TH1I* mask_1D = MapToMask(mask, map);
// Calculate from 1D
Double_t Chi2 = StatUtils::GetChi2FromSVD(data_1D, mc_1D, cov, mask_1D);
// CleanUp
delete data_1D;
delete mc_1D;
delete mask_1D;
return Chi2;
}
//*******************************************************************
double StatUtils::GetChi2FromEventRate(TH1D* data, TH1D* mc, TH1I* mask) {
//*******************************************************************
// If just an event rate, for chi2 just use Poission Likelihood to calculate the chi2 component
double chi2 = 0.0;
TH1D* calc_data = (TH1D*)data->Clone();
TH1D* calc_mc = (TH1D*)mc->Clone();
// Apply masking if required
if (mask) {
calc_data = ApplyHistogramMasking(data, mask);
calc_mc = ApplyHistogramMasking(mc, mask);
}
// Iterate over bins in X
for (int i = 0; i < calc_data->GetNbinsX(); i++) {
double dt = calc_data->GetBinContent(i + 1);
double mc = calc_mc->GetBinContent(i + 1);
if (mc <= 0) continue;
if (dt <= 0) {
// Only add difference
chi2 += 2 * (mc - dt);
} else {
// Do the chi2 for Poisson distributions
chi2 += 2 * (mc - dt + (dt * log(dt / mc)));
}
/*
LOG(REC)<<"Evt Chi2 cont = "<<i<<" "
<<mc<<" "<<dt<<" "
<<2 * (mc - dt + (dt+0.) * log((dt+0.) / (mc+0.)))
<<" "<<Chi2<<std::endl;
*/
}
// cleanup
delete calc_data;
delete calc_mc;
return chi2;
}
//*******************************************************************
Double_t StatUtils::GetChi2FromEventRate(TH2D* data, TH2D* mc, TH2I* map, TH2I* mask) {
//*******************************************************************
// Generate a simple map
if (!map)
map = StatUtils::GenerateMap(data);
// Convert to 1D Histograms
TH1D* data_1D = MapToTH1D(data, map);
TH1D* mc_1D = MapToTH1D(mc, map);
TH1I* mask_1D = MapToMask(mask, map);
// Calculate from 1D
Double_t Chi2 = StatUtils::GetChi2FromEventRate(data_1D, mc_1D, mask_1D);
// CleanUp
delete data_1D;
delete mc_1D;
delete mask_1D;
return Chi2;
}
//*******************************************************************
Double_t StatUtils::GetLikelihoodFromDiag(TH1D* data, TH1D* mc, TH1I* mask) {
//*******************************************************************
// Currently just a placeholder!
(void) data;
(void) mc;
(void) mask;
return 0.0;
};
//*******************************************************************
Double_t StatUtils::GetLikelihoodFromDiag(TH2D* data, TH2D* mc, TH2I* map, TH2I* mask) {
//*******************************************************************
// Generate a simple map
if (!map)
map = StatUtils::GenerateMap(data);
// Convert to 1D Histograms
TH1D* data_1D = MapToTH1D(data, map);
TH1D* mc_1D = MapToTH1D(mc, map);
TH1I* mask_1D = MapToMask(mask, map);
// Calculate from 1D
Double_t MLE = StatUtils::GetLikelihoodFromDiag(data_1D, mc_1D, mask_1D);
// CleanUp
delete data_1D;
delete mc_1D;
delete mask_1D;
return MLE;
};
//*******************************************************************
Double_t StatUtils::GetLikelihoodFromCov( TH1D* data, TH1D* mc, TMatrixDSym* invcov, TH1I* mask) {
//*******************************************************************
// Currently just a placeholder !
(void) data;
(void) mc;
(void) invcov;
(void) mask;
return 0.0;
};
//*******************************************************************
Double_t StatUtils::GetLikelihoodFromCov( TH2D* data, TH2D* mc, TMatrixDSym* invcov, TH2I* map, TH2I* mask) {
//*******************************************************************
// Generate a simple map
if (!map)
map = StatUtils::GenerateMap(data);
// Convert to 1D Histograms
TH1D* data_1D = MapToTH1D(data, map);
TH1D* mc_1D = MapToTH1D(mc, map);
TH1I* mask_1D = MapToMask(mask, map);
// Calculate from 1D
Double_t MLE = StatUtils::GetLikelihoodFromCov(data_1D, mc_1D, invcov, mask_1D);
// CleanUp
delete data_1D;
delete mc_1D;
delete mask_1D;
return MLE;
};
//*******************************************************************
Double_t StatUtils::GetLikelihoodFromSVD( TH1D* data, TH1D* mc, TMatrixDSym* cov, TH1I* mask) {
//*******************************************************************
// Currently just a placeholder!
(void) data;
(void) mc;
(void) cov;
(void) mask;
return 0.0;
};
//*******************************************************************
Double_t StatUtils::GetLikelihoodFromSVD( TH2D* data, TH2D* mc, TMatrixDSym* cov, TH2I* map, TH2I* mask) {
//*******************************************************************
// Generate a simple map
if (!map)
map = StatUtils::GenerateMap(data);
// Convert to 1D Histograms
TH1D* data_1D = MapToTH1D(data, map);
TH1D* mc_1D = MapToTH1D(mc, map);
TH1I* mask_1D = MapToMask(mask, map);
// Calculate from 1D
Double_t MLE = StatUtils::GetLikelihoodFromSVD(data_1D, mc_1D, cov, mask_1D);
// CleanUp
delete data_1D;
delete mc_1D;
delete mask_1D;
return MLE;
};
//*******************************************************************
Double_t StatUtils::GetLikelihoodFromEventRate(TH1D* data, TH1D* mc, TH1I* mask) {
//*******************************************************************
// Currently just a placeholder!
(void) data;
(void) mc;
(void) mask;
return 0.0;
};
//*******************************************************************
Double_t StatUtils::GetLikelihoodFromEventRate(TH2D* data, TH2D* mc, TH2I* map, TH2I* mask) {
//*******************************************************************
// Generate a simple map
if (!map)
map = StatUtils::GenerateMap(data);
// Convert to 1D Histograms
TH1D* data_1D = MapToTH1D(data, map);
TH1D* mc_1D = MapToTH1D(mc, map);
TH1I* mask_1D = MapToMask(mask, map);
// Calculate from 1D
Double_t MLE = StatUtils::GetChi2FromEventRate(data_1D, mc_1D, mask_1D);
// CleanUp
delete data_1D;
delete mc_1D;
delete mask_1D;
return MLE;
};
//*******************************************************************
Int_t StatUtils::GetNDOF(TH1D* hist, TH1I* mask) {
//*******************************************************************
TH1D* calc_hist = (TH1D*)hist->Clone();
// If a mask is provided we need to apply it before getting NDOF
if (mask) {
calc_hist = StatUtils::ApplyHistogramMasking(hist, mask);
}
// NDOF is defined as total number of bins with non-zero errors
Int_t NDOF = 0;
for (int i = 0; i < calc_hist->GetNbinsX(); i++) {
if (calc_hist->GetBinError(i + 1) > 0.0) NDOF++;
}
delete calc_hist;
return NDOF;
};
//*******************************************************************
Int_t StatUtils::GetNDOF(TH2D* hist, TH2I* map, TH2I* mask) {
//*******************************************************************
Int_t NDOF = 0;
if (!map) map = StatUtils::GenerateMap(hist);
for (int i = 0; i < hist->GetNbinsX(); i++) {
for (int j = 0; j < hist->GetNbinsY(); j++) {
if (mask->GetBinContent(i + 1, j + 1)) continue;
if (map->GetBinContent(i + 1, j + 1) <= 0) continue;
NDOF++;
}
}
return NDOF;
};
//*******************************************************************
TH1D* StatUtils::ThrowHistogram(TH1D* hist, TMatrixDSym* cov, bool throwdiag, TH1I* mask) {
//*******************************************************************
TH1D* calc_hist = (TH1D*) hist->Clone( (std::string(hist->GetName()) + "_THROW" ).c_str() );
TMatrixDSym* calc_cov = (TMatrixDSym*) cov->Clone();
Double_t correl_val = 0.0;
// If a mask if applied we need to apply it before the matrix is decomposed
if (mask) {
calc_cov = ApplyMatrixMasking(cov, mask);
calc_hist = ApplyHistogramMasking(calc_hist, mask);
}
// If a covariance is provided we need a preset random vector and a decomp
std::vector<Double_t> rand_val;
TMatrixDSym* decomp_cov;
if (cov) {
for (int i = 0; i < hist->GetNbinsX(); i++) {
rand_val.push_back(gRandom->Gaus(0.0, 1.0));
}
// Decomp the matrix
decomp_cov = StatUtils::GetDecomp(calc_cov);
}
// iterate over bins
for (int i = 0; i < hist->GetNbinsX(); i++) {
// By Default the errors on the histogram are thrown uncorrelated to the other errors
// if (throwdiag) {
// calc_hist->SetBinContent(i + 1, (calc_hist->GetBinContent(i + 1) + \
// gRandom->Gaus(0.0, 1.0) * calc_hist->GetBinError(i + 1)) );
// }
// If a covariance is provided that is also thrown
if (cov) {
correl_val = 0.0;
for (int j = 0; j < hist->GetNbinsX(); j++) {
correl_val += rand_val[j] * (*decomp_cov)(j, i) ;
}
calc_hist->SetBinContent(i + 1, (calc_hist->GetBinContent(i + 1) + correl_val * 1E-38));
}
}
delete calc_cov;
delete decomp_cov;
// return this new thrown data
return calc_hist;
};
//*******************************************************************
TH2D* StatUtils::ThrowHistogram(TH2D* hist, TMatrixDSym* cov, TH2I* map, bool throwdiag, TH2I* mask) {
//*******************************************************************
// PLACEHOLDER!!!!!!!!!
// Currently no support for throwing 2D Histograms from a covariance
(void) hist;
(void) cov;
(void) map;
(void) throwdiag;
(void) mask;
// /todo
// Sort maps if required
// Throw the covariance for a 1D plot
// Unmap back to 2D Histogram
return hist;
}
//*******************************************************************
TH1D* StatUtils::ApplyHistogramMasking(TH1D* hist, TH1I* mask) {
//*******************************************************************
if (!mask) return ( (TH1D*)hist->Clone() );
// This masking is only sufficient for chi2 calculations, and will have dodgy bin edges.
// Get New Bin Count
Int_t NBins = 0;
for (int i = 0; i < hist->GetNbinsX(); i++) {
if (mask->GetBinContent(i + 1)) continue;
NBins++;
}
// Make new hist
std::string newmaskname = std::string(hist->GetName()) + "_MSKD";
TH1D* calc_hist = new TH1D( newmaskname.c_str(), newmaskname.c_str(), NBins, 0, NBins);
// fill new hist
int binindex = 0;
for (int i = 0; i < hist->GetNbinsX(); i++) {
if (mask->GetBinContent(i + 1)) {
LOG(REC) << "Applying mask to bin " << i + 1 << " " << hist->GetName() << std::endl;
continue;
}
calc_hist->SetBinContent(binindex + 1, hist->GetBinContent(i + 1));
calc_hist->SetBinError(binindex + 1, hist->GetBinError(i + 1));
binindex++;
}
return calc_hist;
};
//*******************************************************************
TH2D* StatUtils::ApplyHistogramMasking(TH2D* hist, TH2I* mask) {
//*******************************************************************
TH2D* newhist = (TH2D*) hist->Clone();
if (!mask) return newhist;
for (int i = 0; i < hist->GetNbinsX(); i++) {
for (int j = 0; j < hist->GetNbinsY(); j++) {
if (mask->GetBinContent(i + 1, j + 1) > 0) {
newhist->SetBinContent(i + 1, j + 1, 0.0);
newhist->SetBinContent(i + 1, j + 1, 0.0);
}
}
}
return newhist;
}
//*******************************************************************
TMatrixDSym* StatUtils::ApplyMatrixMasking(TMatrixDSym* mat, TH1I* mask) {
//*******************************************************************
if (!mask) return (TMatrixDSym*)(mat->Clone());
// Get New Bin Count
Int_t NBins = 0;
for (int i = 0; i < mask->GetNbinsX(); i++) {
if (mask->GetBinContent(i + 1)) continue;
NBins++;
}
// make new matrix
TMatrixDSym* calc_mat = new TMatrixDSym(NBins);
int col, row;
// Need to mask out bins in the current matrix
row = 0;
for (int i = 0; i < mask->GetNbinsX(); i++) {
col = 0;
// skip if masked
if (mask->GetBinContent(i + 1) > 0.5) continue;
for (int j = 0; j < mask->GetNbinsX(); j++) {
// skip if masked
if (mask->GetBinContent(j + 1) > 0.5) continue;
(*calc_mat)(row, col) = (*mat)(i, j);
col++;
}
row++;
}
return calc_mat;
};
//*******************************************************************
TMatrixDSym* StatUtils::ApplyMatrixMasking(TMatrixDSym* mat, TH2D* data, TH2I* mask, TH2I* map) {
//*******************************************************************
if (!map) map = StatUtils::GenerateMap(data);
TH1I* mask_1D = StatUtils::MapToMask(mask, map);
TMatrixDSym* newmat = StatUtils::ApplyMatrixMasking(mat, mask_1D);
delete mask_1D;
return newmat;
}
//*******************************************************************
TMatrixDSym* StatUtils::ApplyInvertedMatrixMasking(TMatrixDSym* mat, TH1I* mask) {
//*******************************************************************
TMatrixDSym* new_mat = GetInvert(mat);
TMatrixDSym* masked_mat = ApplyMatrixMasking(new_mat, mask);
TMatrixDSym* inverted_mat = GetInvert(masked_mat);
delete masked_mat;
delete new_mat;
return inverted_mat;
};
//*******************************************************************
TMatrixDSym* StatUtils::ApplyInvertedMatrixMasking(TMatrixDSym* mat, TH2D* data, TH2I* mask, TH2I* map) {
//*******************************************************************
if (!map) map = StatUtils::GenerateMap(data);
TH1I* mask_1D = StatUtils::MapToMask(mask, map);
TMatrixDSym* newmat = ApplyInvertedMatrixMasking(mat, mask_1D);
delete mask_1D;
return newmat;
}
//*******************************************************************
TMatrixDSym* StatUtils::GetInvert(TMatrixDSym* mat) {
//*******************************************************************
TMatrixDSym* new_mat = (TMatrixDSym*)mat->Clone();
// Check for diagonal
bool non_diagonal = false;
for (int i = 0; i < new_mat->GetNrows(); i++) {
for (int j = 0; j < new_mat->GetNrows(); j++) {
if (i == j) continue;
if ((*new_mat)(i, j) != 0.0) {
non_diagonal = true;
break;
}
}
}
// If diag, just flip the diag
if (!non_diagonal or new_mat->GetNrows() == 1) {
for (int i = 0; i < new_mat->GetNrows(); i++) {
if ((*new_mat)(i, i) != 0.0)
(*new_mat)(i, i) = 1.0 / (*new_mat)(i, i);
else
(*new_mat)(i, i) = 0.0;
}
return new_mat;
}
// Invert full matrix
TDecompSVD LU = TDecompSVD((*new_mat));
new_mat = new TMatrixDSym(new_mat->GetNrows(), LU.Invert().GetMatrixArray(), "");
return new_mat;
}
//*******************************************************************
TMatrixDSym* StatUtils::GetDecomp(TMatrixDSym* mat) {
//*******************************************************************
TMatrixDSym* new_mat = (TMatrixDSym*)mat->Clone();
int nrows = new_mat->GetNrows();
// Check for diagonal
bool diagonal = true;
for (int i = 0; i < nrows; i++) {
for (int j = 0; j < nrows; j++) {
if (i == j) continue;
if ((*new_mat)(i, j) != 0.0) {
diagonal = false;
break;
}
}
}
// If diag, just flip the diag
if (diagonal or nrows == 1) {
for (int i = 0; i < nrows; i++) {
if ((*new_mat)(i, i) > 0.0)
(*new_mat)(i, i) = sqrt((*new_mat)(i, i));
else
(*new_mat)(i, i) = 0.0;
}
return new_mat;
}
TDecompChol LU = TDecompChol(*new_mat);
LU.Decompose();
delete new_mat;
TMatrixDSym* dec_mat = new TMatrixDSym(nrows, LU.GetU().GetMatrixArray(), "");
return dec_mat;
}
//*******************************************************************
void StatUtils::ForceNormIntoCovar(TMatrixDSym* mat, TH1D* hist, double norm) {
//*******************************************************************
if (!mat) mat = MakeDiagonalCovarMatrix(hist);
int nbins = mat->GetNrows();
TMatrixDSym* new_mat = new TMatrixDSym(nbins);
for (int i = 0; i < nbins; i++) {
for (int j = 0; j < nbins; j++) {
double valx = hist->GetBinContent(i + 1) * 1E38;
double valy = hist->GetBinContent(j + 1) * 1E38;
(*new_mat)(i, j) = (*mat)(i, j) + norm * norm * valx * valy;
}
}
// Swap the two
delete mat;
mat = new_mat;
return;
};
//*******************************************************************
void StatUtils::ForceNormIntoCovar(TMatrixDSym* mat, TH2D* data, double norm, TH2I* map ) {
//*******************************************************************
if (!map) map = StatUtils::GenerateMap(data);
TH1D* data_1D = MapToTH1D(data, map);
StatUtils::ForceNormIntoCovar(mat, data_1D, norm);
delete data_1D;
return;
}
//*******************************************************************
TMatrixDSym* StatUtils::MakeDiagonalCovarMatrix(TH1D* data, double scaleF) {
//*******************************************************************
TMatrixDSym* newmat = new TMatrixDSym(data->GetNbinsX());
for (int i = 0; i < data->GetNbinsX(); i++) {
(*newmat)(i, i) = data->GetBinError(i + 1) * data->GetBinError(i + 1) * scaleF * scaleF;
}
return newmat;
}
//*******************************************************************
TMatrixDSym* StatUtils::MakeDiagonalCovarMatrix(TH2D* data, TH2I* map, double scaleF) {
//*******************************************************************
if (!map) map = StatUtils::GenerateMap(data);
TH1D* data_1D = MapToTH1D(data, map);
return StatUtils::MakeDiagonalCovarMatrix(data_1D, scaleF);
};
//*******************************************************************
void StatUtils::SetDataErrorFromCov(TH1D* data, TMatrixDSym* cov, double scale) {
//*******************************************************************
// Check
if (cov->GetNrows() != data->GetNbinsX()) {
ERR(WRN) << "Nrows in cov don't match nbins in data for SetDataErrorFromCov" << std::endl;
}
// Set bin errors form cov diag
for (int i = 0; i < data->GetNbinsX(); i++) {
data->SetBinError(i + 1, sqrt((*cov)(i, i)) * scale );
}
return;
}
//*******************************************************************
void StatUtils::SetDataErrorFromCov(TH2D* data, TMatrixDSym* cov, TH2I* map, double scale) {
//*******************************************************************
// Create map if required
if (!map) map = StatUtils::GenerateMap(data);
// Set Bin Errors from cov diag
int count = 0;
for (int i = 0; i < data->GetNbinsX(); i++) {
for (int j = 0; j < data->GetNbinsY(); j++) {
if (data->GetBinContent(i + 1, j + 1) == 0.0) continue;
count = map->GetBinContent(i + 1, j + 1) - 1;
data->SetBinError(i + 1, j + 1, sqrt((*cov)(count, count)) * scale );
}
}
return;
}
TMatrixDSym* StatUtils::ExtractShapeOnlyCovar(TMatrixDSym* full_covar, TH1* data_hist, double data_scale){
int nbins = full_covar->GetNrows();
TMatrixDSym* shape_covar = new TMatrixDSym(nbins);
// Check nobody is being silly
if (data_hist->GetNbinsX() != nbins){
ERR(WRN) << "Inconsistent matrix and data histogram passed to StatUtils::ExtractShapeOnlyCovar!" << std::endl;
ERR(WRN) << "data_hist has " << data_hist->GetNbinsX() << " matrix has " << nbins << std::endl;
int err_bins = data_hist->GetNbinsX();
if (nbins > err_bins) err_bins = nbins;
for (int i = 0; i < err_bins; ++i){
ERR(WRN) << "Matrix diag. = " << (*full_covar)(i, i) << " data = " << data_hist->GetBinContent(i+1) << std::endl;
}
return NULL;
}
double total_data = 0;
double total_covar = 0;
// Initial loop to calculate some constants
for (int i = 0; i < nbins; ++i) {
total_data += data_hist->GetBinContent(i+1)*data_scale;
for (int j = 0; j < nbins; ++j) {
total_covar += (*full_covar)(i,j);
}
}
if (total_data == 0 || total_covar == 0){
ERR(WRN) << "Stupid matrix or data histogram passed to StatUtils::ExtractShapeOnlyCovar! Ignoring..." << std::endl;
return NULL;
}
LOG(SAM) << "Norm error = " << sqrt(total_covar)/total_data << std::endl;
// Now loop over and calculate the shape-only matrix
for (int i = 0; i < nbins; ++i) {
double data_i = data_hist->GetBinContent(i+1)*data_scale;
for (int j = 0; j < nbins; ++j) {
double data_j = data_hist->GetBinContent(j+1)*data_scale;
double norm_term = data_i*data_j*total_covar/total_data/total_data;
double mix_sum1 = 0;
double mix_sum2 = 0;
for (int k = 0; k < nbins; ++k){
mix_sum1 += (*full_covar)(k,j);
mix_sum2 += (*full_covar)(i,k);
}
double mix_term1 = data_i*(mix_sum1/total_data - total_covar*data_j/total_data/total_data);
double mix_term2 = data_j*(mix_sum2/total_data - total_covar*data_i/total_data/total_data);
(*shape_covar)(i, j) = (*full_covar)(i, j) - mix_term1 - mix_term2 - norm_term;
}
}
return shape_covar;
}
//*******************************************************************
TH2I* StatUtils::GenerateMap(TH2D* hist) {
//*******************************************************************
std::string maptitle = std::string(hist->GetName()) + "_MAP";
TH2I* map = new TH2I( maptitle.c_str(), maptitle.c_str(),
hist->GetNbinsX(), 0, hist->GetNbinsX(),
hist->GetNbinsY(), 0, hist->GetNbinsY());
Int_t index = 1;
for (int i = 0; i < hist->GetNbinsX(); i++) {
for (int j = 0; j < hist->GetNbinsY(); j++) {
if (hist->GetBinContent(i + 1, j + 1) > 0 && hist->GetBinError(i + 1, j + 1) > 0) {
map->SetBinContent(i + 1, j + 1, index);
index++;
} else {
map->SetBinContent(i + 1, j + 1, 0);
}
}
}
return map;
}
//*******************************************************************
TH1D* StatUtils::MapToTH1D(TH2D* hist, TH2I* map) {
//*******************************************************************
if (!hist) return NULL;
// Get N bins for 1D plot
Int_t Nbins = map->GetMaximum();
std::string name1D = std::string(hist->GetName()) + "_1D";
// Make new 1D Hist
TH1D* newhist = new TH1D(name1D.c_str(), name1D.c_str(), Nbins, 0, Nbins);
// map bin contents
for (int i = 0; i < map->GetNbinsX(); i++) {
for (int j = 0; j < map->GetNbinsY(); j++) {
if (map->GetBinContent(i + 1, j + 1) == 0) continue;
newhist->SetBinContent(map->GetBinContent(i + 1, j + 1), hist->GetBinContent(i + 1, j + 1));
newhist->SetBinError(map->GetBinContent(i + 1, j + 1), hist->GetBinError(i + 1, j + 1));
}
}
// return
return newhist;
}
//*******************************************************************
TH1I* StatUtils::MapToMask(TH2I* hist, TH2I* map) {
//*******************************************************************
TH1I* newhist = NULL;
if (!hist) return newhist;
// Get N bins for 1D plot
Int_t Nbins = map->GetMaximum();
std::string name1D = std::string(hist->GetName()) + "_1D";
// Make new 1D Hist
newhist = new TH1I(name1D.c_str(), name1D.c_str(), Nbins, 0, Nbins);
// map bin contents
for (int i = 0; i < map->GetNbinsX(); i++) {
for (int j = 0; j < map->GetNbinsY(); j++) {
if (map->GetBinContent(i + 1, j + 1) == 0) continue;
newhist->SetBinContent(map->GetBinContent(i + 1, j + 1), hist->GetBinContent(i + 1, j + 1));
}
}
// return
return newhist;
}
TMatrixDSym* StatUtils::GetCovarFromCorrel(TMatrixDSym* correl, TH1D* data) {
int nbins = correl->GetNrows();
TMatrixDSym* covar = new TMatrixDSym(nbins);
for (int i = 0; i < nbins; i++) {
for (int j = 0; j < nbins; j++) {
(*covar)(i, j) = (*correl)(i, j) * data->GetBinError(i + 1) * data->GetBinError(j + 1);
}
}
return covar;
}
//*******************************************************************
TMatrixD* StatUtils::GetMatrixFromTextFile(std::string covfile, int dimx, int dimy) {
//*******************************************************************
// Determine dim
if (dimx == -1 and dimy == -1) {
std::string line;
std::ifstream covar(covfile.c_str(), std::ifstream::in);
int row = 0;
while (std::getline(covar >> std::ws, line, '\n')) {
int column = 0;
std::vector<double> entries = GeneralUtils::ParseToDbl(line, " ");
if (entries.size() <= 1) {
ERR(WRN) << "StatUtils::GetMatrixFromTextFile, matrix only has <= 1 "
"entries on this line: " << row << std::endl;
}
for (std::vector<double>::iterator iter = entries.begin();
iter != entries.end(); iter++) {
column++;
if (column > dimx) dimx = column;
}
row++;
if (row > dimy) dimy = row;
}
}
// Or assume symmetric
if (dimx != -1 and dimy == -1) {
dimy = dimx;
}
assert(dimy != -1 && " matrix dimy not set.");
// Make new matrix
TMatrixD* mat = new TMatrixD(dimx, dimy);
std::string line;
std::ifstream covar(covfile.c_str(), std::ifstream::in);
int row = 0;
while (std::getline(covar >> std::ws, line, '\n')) {
int column = 0;
std::vector<double> entries = GeneralUtils::ParseToDbl(line, " ");
if (entries.size() <= 1) {
ERR(WRN) << "StatUtils::GetMatrixFromTextFile, matrix only has <= 1 "
"entries on this line: " << row << std::endl;
}
for (std::vector<double>::iterator iter = entries.begin();
iter != entries.end(); iter++) {
// Check Rows
//assert(row > mat->GetNrows() && " covar rows doesn't match matrix rows.");
//assert(column > mat->GetNcols() && " covar cols doesn't match matrix cols.");
// Fill Matrix
(*mat)(row, column) = (*iter);
column++;
}
row++;
}
return mat;
}
//*******************************************************************
TMatrixD* StatUtils::GetMatrixFromRootFile(std::string covfile, std::string histname) {
//*******************************************************************
std::string inputfile = covfile + ";" + histname;
std::vector<std::string> splitfile = GeneralUtils::ParseToStr(inputfile, ";");
if (splitfile.size() < 2) {
ERR(FTL) << "No object name given!" << std::endl;
throw;
}
// Get file
TFile* tempfile = new TFile(splitfile[0].c_str(), "READ");
// Get Object
TObject* obj = tempfile->Get(splitfile[1].c_str());
if (!obj) {
ERR(FTL) << "Object " << splitfile[1] << " doesn't exist!" << std::endl;
throw;
}
// Try casting
TMatrixD* mat = dynamic_cast<TMatrixD*>(obj);
if (mat) {
TMatrixD* newmat = (TMatrixD*)mat->Clone();
delete mat;
tempfile->Close();
return newmat;
}
TMatrixDSym* matsym = dynamic_cast<TMatrixDSym*>(obj);
if (matsym) {
TMatrixD* newmat = new TMatrixD(matsym->GetNrows(), matsym->GetNrows());
for (int i = 0; i < matsym->GetNrows(); i++) {
for (int j = 0; j < matsym->GetNrows(); j++) {
(*newmat)(i, j) = (*matsym)(i, j);
}
}
delete matsym;
tempfile->Close();
return newmat;
}
TH2D* mathist = dynamic_cast<TH2D*>(obj);
if (mathist) {
TMatrixD* newmat = new TMatrixD(mathist->GetNbinsX(), mathist->GetNbinsX());
for (int i = 0; i < mathist->GetNbinsX(); i++) {
for (int j = 0; j < mathist->GetNbinsX(); j++) {
(*newmat)(i, j) = mathist->GetBinContent(i + 1, j + 1);
}
}
delete mathist;
tempfile->Close();
return newmat;
}
return NULL;
}
//*******************************************************************
TMatrixDSym* StatUtils::GetCovarFromTextFile(std::string covfile, int dim){
//*******************************************************************
// Delete TempMat
TMatrixD* tempmat = GetMatrixFromTextFile(covfile, dim, dim);
// Make a symmetric covariance
TMatrixDSym* newmat = new TMatrixDSym(tempmat->GetNrows());
for (int i = 0; i < tempmat->GetNrows(); i++){
for (int j = 0; j < tempmat->GetNrows(); j++){
(*newmat)(i,j) = (*tempmat)(i,j);
}
}
delete tempmat;
return newmat;
}
//*******************************************************************
TMatrixDSym* StatUtils::GetCovarFromRootFile(std::string covfile, std::string histname){
//*******************************************************************
TMatrixD* tempmat = GetMatrixFromRootFile(covfile, histname);
TMatrixDSym* newmat = new TMatrixDSym(tempmat->GetNrows());
for (int i = 0; i < tempmat->GetNrows(); i++){
for (int j = 0; j < tempmat->GetNrows(); j++){
(*newmat)(i,j) = (*tempmat)(i,j);
}
}
delete tempmat;
return newmat;
}

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