Page Menu
Home
HEPForge
Search
Configure Global Search
Log In
Files
F7879056
No One
Temporary
Actions
View File
Edit File
Delete File
View Transforms
Subscribe
Mute Notifications
Award Token
Flag For Later
Size
120 KB
Subscribers
None
View Options
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;
}
File Metadata
Details
Attached
Mime Type
text/x-diff
Expires
Tue, Nov 19, 7:25 PM (1 d, 7 h)
Storage Engine
blob
Storage Format
Raw Data
Storage Handle
3805818
Default Alt Text
(120 KB)
Attached To
rNUISANCEGIT nuisancegit
Event Timeline
Log In to Comment