diff --git a/src/FitBase/Measurement1D.cxx b/src/FitBase/Measurement1D.cxx
index d9e09cb..3bc668a 100644
--- a/src/FitBase/Measurement1D.cxx
+++ b/src/FitBase/Measurement1D.cxx
@@ -1,1940 +1,1966 @@
// 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 .
*******************************************************************************/
#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;
fResidualHist = NULL;
fChi2LessBinHist = 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;
+ fIsWriting = 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;
delete fResidualHist;
delete fChi2LessBinHist;
}
//********************************************************************
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
NUIS_LOG(SAM, "Finalising Sample Settings: " << fName);
if ((fSettings.GetS("originalname").find("Evt") != std::string::npos)) {
fIsRawEvents = true;
NUIS_LOG(SAM,
"Found event rate measurement but using poisson likelihoods.");
}
if (fSettings.GetS("originalname").find("XSec_1DEnu") != std::string::npos) {
fIsEnu1D = true;
NUIS_LOG(SAM, "::" << fName << "::");
NUIS_LOG(SAM,
"Found XSec Enu measurement, applying flux integrated scaling, "
<< "not flux averaged!");
}
if (fIsEnu1D && fIsRawEvents) {
NUIS_ERR(FTL, "Found 1D Enu XSec distribution AND fIsRawEvents, is this "
"really correct?!");
NUIS_ERR(FTL, "Check experiment constructor for " << fName
<< " and correct this!");
NUIS_ERR(FTL, "I live in " << __FILE__ << ":" << __LINE__);
throw;
}
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) {
NUIS_ERR(FTL, "Norm error for class " << fName << " is 0.0!");
NUIS_ERR(FTL, "If you want to use it please add fNormError=VAL");
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) {
//********************************************************************
NUIS_LOG(SAM, "Reading data from text file: " << datafile);
fDataHist = PlotUtils::GetTH1DFromFile(
datafile, fSettings.GetName() + "_data", fSettings.GetFullTitles());
}
//********************************************************************
void Measurement1D::SetDataFromRootFile(std::string datafile,
std::string histname) {
//********************************************************************
NUIS_LOG(SAM, "Reading data from root file: " << datafile << ";" << histname);
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) {
NUIS_ERR(FTL, "Need a data hist to setup possion errors! ");
NUIS_ERR(FTL, "Setup Data First!");
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) {
NUIS_LOG(SAM, "Setting diagonal covariance for: " << data->GetName());
fFullCovar = StatUtils::MakeDiagonalCovarMatrix(data);
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
} else {
NUIS_ABORT("No data input provided to set diagonal covar from!");
}
// if (!fIsDiag) {
// ERR(FTL) << "SetCovarMatrixFromDiag called for measurement "
// << "that is not set as diagonal." );
// throw;
// }
}
//********************************************************************
void Measurement1D::SetCovarFromTextFile(std::string covfile, int dim) {
//********************************************************************
if (dim == -1) {
dim = fDataHist->GetNbinsX();
}
NUIS_LOG(SAM, "Reading covariance from text file: " << covfile);
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 covList = GeneralUtils::ParseToStr(covfiles, ";");
fFullCovar = new TMatrixDSym(dim);
for (uint i = 0; i < covList.size(); ++i) {
NUIS_LOG(SAM, "Reading covariance from text file: " << covList[i]);
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) {
//********************************************************************
NUIS_LOG(SAM,
"Reading covariance from text file: " << covfile << ";" << histname);
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();
}
NUIS_LOG(SAM, "Reading inverted covariance from text file: " << covfile);
covar = StatUtils::GetCovarFromTextFile(covfile, dim);
fFullCovar = StatUtils::GetInvert(covar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
//********************************************************************
void Measurement1D::SetCovarInvertFromRootFile(std::string covfile,
std::string histname) {
//********************************************************************
NUIS_LOG(SAM, "Reading inverted covariance from text file: " << covfile << ";"
<< histname);
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();
NUIS_LOG(SAM, "Reading data correlations from text file: " << covfile << ";"
<< dim);
TMatrixDSym *correlation = StatUtils::GetCovarFromTextFile(covfile, dim);
if (!fDataHist) {
NUIS_ABORT("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");
}
// 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 corrList = GeneralUtils::ParseToStr(corrfiles, ";");
fFullCovar = new TMatrixDSym(dim);
for (uint i = 0; i < corrList.size(); ++i) {
NUIS_LOG(SAM, "Reading covariance from text file: " << corrList[i]);
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) {
//********************************************************************
NUIS_LOG(SAM, "Reading data correlations from text file: " << covfile << ";"
<< histname);
TMatrixDSym *correlation = StatUtils::GetCovarFromRootFile(covfile, histname);
if (!fDataHist) {
NUIS_ABORT("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");
}
// 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();
}
NUIS_LOG(SAM, "Reading cholesky from text file: " << covfile);
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) {
//********************************************************************
NUIS_LOG(SAM, "Reading cholesky decomp from root file: " << covfile << ";"
<< histname);
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;
NUIS_LOG(SAM, "Reading bin mask from file: " << maskfile);
// 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(), std::ifstream::in);
if (!mask.is_open()) {
NUIS_ABORT("Cannot find mask file.");
}
while (std::getline(mask >> std::ws, line, '\n')) {
std::vector entries = GeneralUtils::ParseToInt(line, " ");
// Skip lines with poorly formatted lines
if (entries.size() < 2) {
NUIS_LOG(WRN,
"Measurement1D::SetBinMask(), couldn't parse line: " << line);
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() {
//********************************************************************
NUIS_LOG(SAM, "Finalising Measurement: " << fName);
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) {
NUIS_ABORT("No data has been setup inside " << fName << " constructor!");
}
// Make sure covariances are setup
if (!fFullCovar) {
fIsDiag = true;
SetCovarFromDiagonal(fDataHist);
+ } else if (fIsDiag) { // Have covariance but also set Diag
+ NUIS_LOG(SAM, "Have full covariance for sample "
+ << GetName()
+ << " but only using diagonal elements for likelihood");
+ size_t nbins = fFullCovar->GetNcols();
+ for (int i = 0; i < nbins; ++i) {
+ for (int j = 0; j < nbins; ++j) {
+ if (i != j) {
+ (*fFullCovar)[i][j] = 0;
+ }
+ }
+ }
+ delete covar;
+ covar = NULL;
+ delete fDecomp;
+ fDecomp = NULL;
}
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 && 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) {
+ if (fSettings.Has("maskfile") && fSettings.Has("maskhist")) {
+ fMaskHist = dynamic_cast(PlotUtils::GetTH1FromRootFile(
+ fSettings.GetS("maskfile"), fSettings.GetS("maskhist")));
+ fIsMask = bool(fMaskHist);
+ NUIS_LOG(SAM, "Loaded mask histogram: " << fSettings.GetS("maskhist")
+ << " from "
+ << fSettings.GetS("maskfile"));
+ } else if (fIsMask) { // Setup bin masks using sample name
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) {
NUIS_ERR(FTL, "I found a negative fScaleFactor in " << __FILE__ << ":"
<< __LINE__);
NUIS_ERR(FTL, "fScaleFactor = " << fScaleFactor);
NUIS_ERR(FTL, "EXITING");
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 fit_option_allow =
GeneralUtils::ParseToStr(fAllowedTypes, "/");
for (UInt_t i = 0; i < fit_option_allow.size(); i++) {
std::vector 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) {
NUIS_ABORT(
"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.");
}
}
}
// Check all options are allowed
std::vector 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) {
NUIS_ERR(WRN, "ERROR: Fit Option '"
<< fit_options_input.at(i)
<< "' Provided is not allowed for this measurement.");
NUIS_ERR(WRN, "Fit Options should be provided as a '/' seperated list "
"(e.g. FREE/DIAG/NORM)");
NUIS_ABORT("Available options for " << fName << " are '" << fAllowedTypes
<< "'");
}
}
// 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;
NUIS_ERR(FTL, "No other LIKELIHOODS properly supported!");
NUIS_ERR(FTL, "Try to use a chi2!");
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(), std::ifstream::in);
// Note that the smearing matrix may be rectangular.
fSmearMatrix = new TMatrixD(truedim, recodim);
if (smear.is_open()) {
NUIS_LOG(SAM, "Reading smearing matrix from file: " << smearfile);
} else {
NUIS_ABORT("Smearing matrix provided is incorrect: " << smearfile);
}
while (std::getline(smear >> std::ws, line, '\n')) {
int column = 0;
std::vector entries = GeneralUtils::ParseToDbl(line, " ");
for (std::vector::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) {
NUIS_ERR(WRN,
fName << ": attempted to apply smearing matrix, but none was set");
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) {
NUIS_LOG(DEB, "Fill MCHist: " << fXVar << ", " << Weight);
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) {
// Loop over the modes
fMCHist_Modes->FluxUnfold(GetFluxHistogram(), GetEventHistogram(),
fScaleFactor, fNEvents);
// 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);
stat = StatUtils::GetChi2FromCov(fDataHist, fMCHist, covar, fMaskHist, 1,
- 1E76, fResidualHist);
- if (fChi2LessBinHist) {
- TH1I *binmask = new TH1I("mask", "", fDataHist->GetNbinsX(), 0,
- fDataHist->GetNbinsX());
- binmask->SetDirectory(NULL);
+ 1E76, fIsWriting ? fResidualHist : NULL);
+ if (fChi2LessBinHist && fIsWriting) {
for (int xi = 0; xi < fDataHist->GetNbinsX(); ++xi) {
- binmask->Reset();
+ TH1I *binmask = fMaskHist
+ ? static_cast(fMaskHist->Clone("mask"))
+ : new TH1I("mask", "", fDataHist->GetNbinsX(), 0,
+ fDataHist->GetNbinsX());
+ binmask->SetDirectory(NULL);
binmask->SetBinContent(xi + 1, 1);
fChi2LessBinHist->SetBinContent(
xi + 1,
StatUtils::GetChi2FromCov(fDataHist, fMCHist, covar, binmask));
+ delete binmask;
}
- delete binmask;
}
}
}
// 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;
NUIS_LOG(SAM, "Setting fake data from : " << fFakeDataInput);
// 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
if (drawOpt.find("DATA") != std::string::npos)
GetDataList().at(0)->Write();
if (drawOpt.find("MC") != std::string::npos) {
GetMCList().at(0)->Write();
if ((fEvtRateScaleFactor != 0xdeadbeef) && GetMCList().at(0)) {
TH1D *PredictedEvtRate = static_cast(GetMCList().at(0)->Clone());
PredictedEvtRate->Scale(fEvtRateScaleFactor);
PredictedEvtRate->GetYaxis()->SetTitle("Predicted event rate");
PredictedEvtRate->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())->Write();
}
if (drawOpt.find("INVCOV") != std::string::npos && covar) {
PlotUtils::GetInvCovarPlot(covar, fSettings.GetName())->Write();
}
if (drawOpt.find("DECOMP") != std::string::npos && fDecomp) {
PlotUtils::GetDecompCovarPlot(fDecomp, fSettings.GetName())->Write();
}
// // 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;
// }
if (fIsChi2 && !fIsDiag) {
fResidualHist = (TH1D *)fMCHist->Clone((fName + "_RESIDUAL").c_str());
fResidualHist->GetYaxis()->SetTitle("#Delta#chi^{2}");
fResidualHist->Reset();
fChi2LessBinHist =
(TH1D *)fMCHist->Clone((fName + "_Chi2NMinusOne").c_str());
fChi2LessBinHist->GetYaxis()->SetTitle("Total #chi^{2} without bin_{i}");
fChi2LessBinHist->Reset();
+ fIsWriting = true;
(void)GetLikelihood();
+ fIsWriting = false;
- fResidualHist->Write();
- fChi2LessBinHist->Write();
+ fResidualHist->Write((fName + "_RESIDUAL").c_str());
+ fChi2LessBinHist->Write((fName + "_Chi2NMinusOne").c_str());
}
// Write Extra Histograms
AutoWriteExtraTH1();
WriteExtraHistograms();
// Returning
NUIS_LOG(SAM, "Written Histograms: " << fName);
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());
// Don't check error
if (fShapeCovar)
StatUtils::SetDataErrorFromCov(dataShape, fShapeCovar, 1E-38, false);
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.Set("name", fName);
samplekey.Set("type", type);
samplekey.Set("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;
NUIS_LOG(SAM, "Found event rate measurement but fIsRawEvents == false!");
NUIS_LOG(SAM, "Overriding this and setting fIsRawEvents == true!");
}
fIsEnu1D = false;
if (fName.find("XSec_1DEnu") != std::string::npos) {
fIsEnu1D = true;
NUIS_LOG(SAM, "::" << fName << "::");
NUIS_LOG(SAM,
"Found XSec Enu measurement, applying flux integrated scaling, "
"not flux averaged!");
}
if (fIsEnu1D && fIsRawEvents) {
NUIS_ERR(FTL, "Found 1D Enu XSec distribution AND fIsRawEvents, is this "
"really correct?!");
NUIS_ERR(FTL, "Check experiment constructor for " << fName
<< " and correct this!");
NUIS_ERR(FTL, "I live in " << __FILE__ << ":" << __LINE__);
throw;
}
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
NUIS_LOG(SAM, "Reading data from: " << dataFile.c_str());
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) {
//********************************************************************
NUIS_LOG(SAM, "Filling histogram from " << inhistfile << "->" << histname);
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) {
//********************************************************************
NUIS_LOG(SAM, "Filling histogram from " << inhistfile << "->" << histname);
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 *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());
if (!covOption.compare("SUB"))
fFullCovarPlot = (TH2D *)tempFile->Get((covName + "cov").c_str());
else if (!covOption.compare("FULL"))
fFullCovarPlot = (TH2D *)tempFile->Get("fullcov");
else {
NUIS_ERR(WRN, "Incorrect thrown_covariance option in parameters.");
}
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(), std::ifstream::in);
this->covar = new TMatrixDSym(dim);
fFullCovar = new TMatrixDSym(dim);
if (covarread.is_open()) {
NUIS_LOG(SAM, "Reading covariance matrix from file: " << covarFile);
} else {
NUIS_ABORT("Covariance matrix provided is incorrect: " << covarFile);
}
// 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 entries = GeneralUtils::ParseToDbl(line, " ");
if (entries.size() <= 1) {
NUIS_ERR(WRN, "SetCovarMatrixFromText -> Covariance matrix only has <= 1 "
"entries on this line: "
<< row);
}
for (std::vector::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(), std::ifstream::in);
this->covar = new TMatrixDSym(dim);
this->fFullCovar = new TMatrixDSym(dim);
if (corr.is_open()) {
NUIS_LOG(SAM, "Reading and converting correlation matrix from file: "
<< corrFile);
} else {
NUIS_ABORT("Correlation matrix provided is incorrect: " << corrFile);
}
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 entries = GeneralUtils::ParseToDbl(line, " ");
for (std::vector::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) {
NUIS_ABORT("Found a zero value in the covariance matrix, assuming "
"this is an error!");
}
(*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) {
//********************************************************************
NUIS_LOG(SAM, "Getting covariance from " << covarFile << "->" << covName);
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(), std::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 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& cont,
// std::vector& 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 6c6b0c1..fada37f 100644
--- a/src/FitBase/Measurement1D.h
+++ b/src/FitBase/Measurement1D.h
@@ -1,659 +1,658 @@
// 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 .
*******************************************************************************/
#ifndef MEASUREMENT_1D_H_SEEN
#define MEASUREMENT_1D_H_SEEN
/*!
* \addtogroup FitBase
* @{
*/
#include
#include
#include
#include
#include
#include
#include
#include
// ROOT includes
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
// External data fit includes
#include "FitEvent.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
///
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
///
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 GetMCList(void) {
return std::vector(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 GetDataList(void) {
return std::vector(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 GetMaskList(void) {
return std::vector(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 GetFineList(void) {
return std::vector(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
///
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)
TH1D *fResidualHist;
TH1D *fChi2LessBinHist;
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?
-
-
+ bool fIsWriting;
/// 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/FitBase/Measurement2D.cxx b/src/FitBase/Measurement2D.cxx
index d91a124..1a3186b 100644
--- a/src/FitBase/Measurement2D.cxx
+++ b/src/FitBase/Measurement2D.cxx
@@ -1,2032 +1,2057 @@
// 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 .
*******************************************************************************/
#include "Measurement2D.h"
#include "TDecompChol.h"
//********************************************************************
Measurement2D::Measurement2D(void) {
//********************************************************************
covar = NULL;
fDecomp = NULL;
fFullCovar = NULL;
fMCHist = NULL;
fMCFine = NULL;
fDataHist = NULL;
fMCHist_X = NULL;
fMCHist_Y = NULL;
fDataHist_X = NULL;
fDataHist_Y = NULL;
fMaskHist = NULL;
fMapHist = NULL;
fDataOrig = NULL;
fDataTrue = NULL;
fMCWeighted = NULL;
fResidualHist = NULL;
fChi2LessBinHist = NULL;
fDefaultTypes = "FIX/FULL/CHI2";
fAllowedTypes =
"FIX,FREE,SHAPE/FULL,DIAG/CHI2/NORM/ENUCORR/Q2CORR/ENU1D/FITPROJX/"
"FITPROJY";
fIsFix = false;
fIsShape = false;
fIsFree = false;
fIsDiag = false;
fIsFull = false;
fAddNormPen = false;
fIsMask = false;
fIsChi2SVD = false;
fIsRawEvents = false;
fIsDifXSec = false;
fIsEnu = false;
// 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;
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";
fIsFix = false;
fIsShape = false;
fIsFree = false;
fIsDiag = false;
fIsFull = false;
fAddNormPen = false;
fIsMask = false;
fIsChi2SVD = false;
fIsRawEvents = false;
fIsDifXSec = false;
fIsEnu1D = false;
+ fIsWriting = false;
// Inputs
fInput = NULL;
fRW = NULL;
// Extra Histograms
fMCHist_Modes = NULL;
}
//********************************************************************
Measurement2D::~Measurement2D(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 (fCovar)
delete fCovar;
if (fInvert)
delete fInvert;
if (fDecomp)
delete fDecomp;
delete fResidualHist;
delete fChi2LessBinHist;
}
//********************************************************************
void Measurement2D::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
NUIS_LOG(SAM, "Finalising Sample Settings: " << fName);
if ((fSettings.GetS("originalname").find("Evt") != std::string::npos)) {
fIsRawEvents = true;
NUIS_LOG(SAM,
"Found event rate measurement but using poisson likelihoods.");
}
if (fSettings.GetS("originalname").find("Enu") != std::string::npos) {
fIsEnu1D = true;
NUIS_LOG(SAM, "::" << fName << "::");
NUIS_LOG(SAM,
"Found XSec Enu measurement, applying flux integrated scaling, "
<< "not flux averaged!");
}
if (fIsEnu1D && fIsRawEvents) {
NUIS_ERR(FTL, "Found 2D Enu XSec distribution AND fIsRawEvents, is this "
"really correct?!");
NUIS_ERR(FTL, "Check experiment constructor for " << fName
<< " and correct this!");
NUIS_ABORT("I live in " << __FILE__ << ":" << __LINE__);
}
if (!fRW)
fRW = FitBase::GetRW();
if (!fInput)
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) {
fNormError = fSettings.GetNormError();
if (fNormError <= 0.0) {
NUIS_ERR(FTL, "Norm error for class " << fName << " is 0.0!");
NUIS_ABORT("If you want to use it please add fNormError=VAL");
}
}
}
void Measurement2D::CreateDataHistogram(int dimx, double *binx, int dimy,
double *biny) {
if (fDataHist)
delete fDataHist;
NUIS_LOG(SAM, "Creating Data Histogram dim : " << dimx << " " << dimy);
fDataHist = new TH2D((fSettings.GetName() + "_data").c_str(),
(fSettings.GetFullTitles()).c_str(), dimx - 1, binx,
dimy - 1, biny);
}
void Measurement2D::SetDataFromTextFile(std::string data, std::string binx,
std::string biny) {
// Get the data hist
fDataHist = PlotUtils::GetTH2DFromTextFile(data, binx, biny);
// Set the name properly
fDataHist->SetName((fSettings.GetName() + "_data").c_str());
fDataHist->SetTitle(fSettings.GetFullTitles().c_str());
}
void Measurement2D::SetDataFromRootFile(std::string datfile,
std::string histname) {
NUIS_LOG(SAM, "Reading data from root file: " << datfile << ";" << histname);
fDataHist = PlotUtils::GetTH2DFromRootFile(datfile, histname);
fDataHist->SetNameTitle((fSettings.GetName() + "_data").c_str(),
(fSettings.GetFullTitles()).c_str());
}
void Measurement2D::SetDataValuesFromTextFile(std::string datfile, TH2D *hist) {
NUIS_LOG(SAM, "Setting data values from text file");
if (!hist)
hist = fDataHist;
// Read TH2D From textfile
TH2D *valhist = (TH2D *)hist->Clone();
valhist->Reset();
PlotUtils::Set2DHistFromText(datfile, valhist, 1.0, true);
NUIS_LOG(SAM, " -> Filling values from read hist.");
for (int i = 0; i < valhist->GetNbinsX(); i++) {
for (int j = 0; j < valhist->GetNbinsY(); j++) {
hist->SetBinContent(i + 1, j + 1, valhist->GetBinContent(i + 1, j + 1));
}
}
NUIS_LOG(SAM, " --> Done");
}
void Measurement2D::SetDataErrorsFromTextFile(std::string datfile, TH2D *hist) {
NUIS_LOG(SAM, "Setting data errors from text file");
if (!hist)
hist = fDataHist;
// Read TH2D From textfile
TH2D *valhist = (TH2D *)hist->Clone();
valhist->Reset();
PlotUtils::Set2DHistFromText(datfile, valhist, 1.0);
// Fill Errors
NUIS_LOG(SAM, " -> Filling errors from read hist.");
for (int i = 0; i < valhist->GetNbinsX(); i++) {
for (int j = 0; j < valhist->GetNbinsY(); j++) {
hist->SetBinError(i + 1, j + 1, valhist->GetBinContent(i + 1, j + 1));
}
}
NUIS_LOG(SAM, " --> Done");
}
void Measurement2D::SetMapValuesFromText(std::string dataFile) {
TH2D *hist = fDataHist;
std::vector edgex;
std::vector edgey;
for (int i = 0; i <= hist->GetNbinsX(); i++)
edgex.push_back(hist->GetXaxis()->GetBinLowEdge(i + 1));
for (int i = 0; i <= hist->GetNbinsY(); i++)
edgey.push_back(hist->GetYaxis()->GetBinLowEdge(i + 1));
fMapHist = new TH2I((fName + "_map").c_str(), (fName + fPlotTitles).c_str(),
edgex.size() - 1, &edgex[0], edgey.size() - 1, &edgey[0]);
NUIS_LOG(SAM, "Reading map from: " << dataFile);
PlotUtils::Set2DHistFromText(dataFile, fMapHist, 1.0);
}
//********************************************************************
void Measurement2D::SetPoissonErrors() {
//********************************************************************
if (!fDataHist) {
NUIS_ERR(FTL, "Need a data hist to setup possion errors! ");
NUIS_ABORT("Setup Data First!");
}
for (int i = 0; i < fDataHist->GetNbinsX() + 1; i++) {
fDataHist->SetBinError(i + 1, sqrt(fDataHist->GetBinContent(i + 1)));
}
}
//********************************************************************
void Measurement2D::SetCovarFromDiagonal(TH2D *data) {
//********************************************************************
if (!data and fDataHist) {
data = fDataHist;
}
if (data) {
NUIS_LOG(SAM, "Setting diagonal covariance for: " << data->GetName());
fFullCovar = StatUtils::MakeDiagonalCovarMatrix(data);
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
} else {
NUIS_ABORT("No data input provided to set diagonal covar from!");
}
// if (!fIsDiag) {
// ERR(FTL) << "SetCovarMatrixFromDiag called for measurement "
// << "that is not set as diagonal." << std::endl;
// throw;
// }
}
//********************************************************************
void Measurement2D::SetCovarFromTextFile(std::string covfile, int dim) {
//********************************************************************
if (dim == -1) {
dim = this->GetNDOF();
}
NUIS_LOG(SAM, "Reading covariance from text file: " << covfile << " " << dim);
fFullCovar = StatUtils::GetCovarFromTextFile(covfile, dim);
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
//********************************************************************
void Measurement2D::SetCovarFromRootFile(std::string covfile,
std::string histname) {
//********************************************************************
NUIS_LOG(SAM,
"Reading covariance from text file: " << covfile << ";" << histname);
fFullCovar = StatUtils::GetCovarFromRootFile(covfile, histname);
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
//********************************************************************
void Measurement2D::SetCovarInvertFromTextFile(std::string covfile, int dim) {
//********************************************************************
if (dim == -1) {
dim = this->GetNDOF();
}
NUIS_LOG(SAM, "Reading inverted covariance from text file: " << covfile);
covar = StatUtils::GetCovarFromTextFile(covfile, dim);
fFullCovar = StatUtils::GetInvert(covar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
//********************************************************************
void Measurement2D::SetCovarInvertFromRootFile(std::string covfile,
std::string histname) {
//********************************************************************
NUIS_LOG(SAM, "Reading inverted covariance from text file: " << covfile << ";"
<< histname);
covar = StatUtils::GetCovarFromRootFile(covfile, histname);
fFullCovar = StatUtils::GetInvert(covar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
//********************************************************************
void Measurement2D::SetCorrelationFromTextFile(std::string covfile, int dim) {
//********************************************************************
if (dim == -1)
dim = this->GetNDOF();
NUIS_LOG(SAM, "Reading data correlations from text file: " << covfile << ";"
<< dim);
TMatrixDSym *correlation = StatUtils::GetCovarFromTextFile(covfile, dim);
if (!fDataHist) {
NUIS_ABORT("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");
}
// 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 Measurement2D::SetCorrelationFromRootFile(std::string covfile,
std::string histname) {
//********************************************************************
NUIS_LOG(SAM, "Reading data correlations from text file: " << covfile << ";"
<< histname);
TMatrixDSym *correlation = StatUtils::GetCovarFromRootFile(covfile, histname);
if (!fDataHist) {
NUIS_ABORT("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");
}
// 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 Measurement2D::SetCholDecompFromTextFile(std::string covfile, int dim) {
//********************************************************************
if (dim == -1) {
dim = this->GetNDOF();
}
NUIS_LOG(SAM, "Reading cholesky from text file: " << covfile << " " << dim);
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 Measurement2D::SetCholDecompFromRootFile(std::string covfile,
std::string histname) {
//********************************************************************
NUIS_LOG(SAM, "Reading cholesky decomp from root file: " << covfile << ";"
<< histname);
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 Measurement2D::ScaleData(double scale) {
//********************************************************************
fDataHist->Scale(scale);
}
//********************************************************************
void Measurement2D::ScaleDataErrors(double scale) {
//********************************************************************
for (int i = 0; i < fDataHist->GetNbinsX(); i++) {
for (int j = 0; j < fDataHist->GetNbinsY(); j++) {
fDataHist->SetBinError(i + 1, j + 1,
fDataHist->GetBinError(i + 1, j + 1) * scale);
}
}
}
//********************************************************************
void Measurement2D::ScaleCovar(double scale) {
//********************************************************************
(*fFullCovar) *= scale;
(*covar) *= 1.0 / scale;
(*fDecomp) *= sqrt(scale);
}
//********************************************************************
void Measurement2D::SetBinMask(std::string maskfile) {
//********************************************************************
if (!fIsMask)
return;
NUIS_LOG(SAM, "Reading bin mask from file: " << maskfile);
// Create a mask histogram with dim of data
int nbinsx = fDataHist->GetNbinsX();
int nbinxy = fDataHist->GetNbinsY();
fMaskHist = new TH2I((fSettings.GetName() + "_BINMASK").c_str(),
(fSettings.GetName() + "_BINMASK; Bin; Mask?").c_str(),
nbinsx, 0, nbinsx, nbinxy, 0, nbinxy);
std::string line;
std::ifstream mask(maskfile.c_str(), std::ifstream::in);
if (!mask.is_open()) {
NUIS_LOG(FTL, " Cannot find mask file.");
throw;
}
while (std::getline(mask >> std::ws, line, '\n')) {
std::vector entries = GeneralUtils::ParseToInt(line, " ");
// Skip lines with poorly formatted lines
if (entries.size() < 2) {
NUIS_LOG(WRN,
"Measurement2D::SetBinMask(), couldn't parse line: " << line);
continue;
}
// The first index should be the bin number, the second should be the mask
// value.
int val = 0;
if (entries[2] > 0)
val = 1;
fMaskHist->SetBinContent(entries[0], entries[1], val);
}
// Apply masking by setting masked data bins to zero
PlotUtils::MaskBins(fDataHist, fMaskHist);
return;
}
//********************************************************************
void Measurement2D::FinaliseMeasurement() {
//********************************************************************
NUIS_LOG(SAM, "Finalising Measurement: " << fName);
if (fSettings.GetB("onlymc")) {
if (fDataHist)
delete fDataHist;
fDataHist = new TH2D("empty_data", "empty_data", 1, 0.0, 1.0, 1, 0.0, 1.0);
}
// Make sure data is setup
if (!fDataHist) {
NUIS_ABORT("No data has been setup inside " << fName << " constructor!");
}
// Make sure covariances are setup
if (!fFullCovar) {
fIsDiag = true;
SetCovarFromDiagonal(fDataHist);
+ } else if (fIsDiag) { // Have covariance but also set Diag
+ NUIS_LOG(SAM, "Have full covariance for sample "
+ << GetName()
+ << " but only using diagonal elements for likelihood");
+ size_t nbins = fFullCovar->GetNcols();
+ for (int i = 0; i < nbins; ++i) {
+ for (int j = 0; j < nbins; ++j) {
+ if (i != j) {
+ (*fFullCovar)[i][j] = 0;
+ }
+ }
+ }
+ delete covar;
+ covar = NULL;
+ delete fDecomp;
+ fDecomp = NULL;
}
if (!covar) {
covar = StatUtils::GetInvert(fFullCovar);
}
if (!fDecomp) {
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
// Setup fMCHist from data
fMCHist = (TH2D *)fDataHist->Clone();
fMCHist->SetNameTitle((fSettings.GetName() + "_MC").c_str(),
(fSettings.GetFullTitles()).c_str());
fMCHist->Reset();
// Setup fMCFine
fMCFine = new TH2D(
"mcfine", "mcfine", fDataHist->GetNbinsX() * 6,
fMCHist->GetXaxis()->GetBinLowEdge(1),
fMCHist->GetXaxis()->GetBinLowEdge(fDataHist->GetNbinsX() + 1),
fDataHist->GetNbinsY() * 6, fMCHist->GetYaxis()->GetBinLowEdge(1),
fMCHist->GetYaxis()->GetBinLowEdge(fDataHist->GetNbinsY() + 1));
fMCFine->SetNameTitle((fSettings.GetName() + "_MC_FINE").c_str(),
(fSettings.GetFullTitles()).c_str());
fMCFine->Reset();
// Setup MC Stat
fMCStat = (TH2D *)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);
}
if (fSettings.Has("maskfile") && fSettings.Has("maskhist")) {
fMaskHist = PlotUtils::GetTH2FromRootFile(fSettings.GetS("maskfile"),
fSettings.GetS("maskhist"));
- fIsMask = true;
- }
-
- // Setup bin masks using sample name
- if (fIsMask && !fMaskHist) {
+ fIsMask = bool(fMaskHist);
+ NUIS_LOG(SAM, "Loaded mask histogram: " << fSettings.GetS("maskhist")
+ << " from "
+ << fSettings.GetS("maskfile"));
+ } else if (fIsMask) { // Setup bin masks using sample name
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) {
NUIS_ERR(FTL, "I found a negative fScaleFactor in " << __FILE__ << ":"
<< __LINE__);
NUIS_ERR(FTL, "fScaleFactor = " << fScaleFactor);
NUIS_ABORT("EXITING");
}
// Create and fill Weighted Histogram
if (!fMCWeighted) {
fMCWeighted = (TH2D *)fMCHist->Clone();
fMCWeighted->SetNameTitle((fName + "_MCWGHTS").c_str(),
(fName + "_MCWGHTS" + fPlotTitles).c_str());
fMCWeighted->GetYaxis()->SetTitle("Weighted Events");
}
if (!fMapHist)
fMapHist = StatUtils::GenerateMap(fDataHist);
}
//********************************************************************
void Measurement2D::SetFitOptions(std::string opt) {
//********************************************************************
// Do nothing if default given
if (opt == "DEFAULT")
return;
// CHECK Conflicting Fit Options
std::vector fit_option_allow =
GeneralUtils::ParseToStr(fAllowedTypes, "/");
for (UInt_t i = 0; i < fit_option_allow.size(); i++) {
std::vector 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) {
NUIS_ABORT(
"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.");
}
}
}
// Check all options are allowed
std::vector 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) {
NUIS_ERR(FTL, "ERROR: Fit Option '"
<< fit_options_input.at(i)
<< "' Provided is not allowed for this measurement.");
NUIS_ERR(FTL, "Fit Options should be provided as a '/' seperated list "
"(e.g. FREE/DIAG/NORM)");
NUIS_ABORT("Available options for " << fName << " are '" << fAllowedTypes
<< "'");
}
}
// 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;
NUIS_ERR(FTL, "No other LIKELIHOODS properly supported!");
NUIS_ABORT("Try to use a chi2!");
} else {
fIsChi2 = true;
}
// EXTRAS
if (opt.find("RAW") != std::string::npos)
fIsRawEvents = 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;
// 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;
else
fIsChi2 = true;
// EXTRAS
if (opt.find("RAW") != std::string::npos)
fIsRawEvents = true;
if (opt.find("DIF") != std::string::npos)
fIsDifXSec = true;
if (opt.find("ENU1D") != std::string::npos)
fIsEnu = true;
if (opt.find("NORM") != std::string::npos)
fAddNormPen = true;
if (opt.find("MASK") != std::string::npos)
fIsMask = true;
fIsProjFitX = (opt.find("FITPROJX") != std::string::npos);
fIsProjFitY = (opt.find("FITPROJY") != std::string::npos);
return;
};
/*
Reconfigure LOOP
*/
//********************************************************************
void Measurement2D::ResetAll() {
//********************************************************************
fMCHist->Reset();
fMCFine->Reset();
fMCStat->Reset();
return;
};
//********************************************************************
void Measurement2D::FillHistograms() {
//********************************************************************
if (Signal) {
fMCHist->Fill(fXVar, fYVar, Weight);
fMCFine->Fill(fXVar, fYVar, Weight);
fMCStat->Fill(fXVar, fYVar, 1.0);
if (fMCHist_Modes)
fMCHist_Modes->Fill(Mode, fXVar, fYVar, Weight);
}
return;
};
//********************************************************************
void Measurement2D::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);
PlotUtils::FluxUnfoldedScaling(fMCFine, GetFluxHistogram(),
GetEventHistogram(), fScaleFactor);
// if (fMCHist_Modes) {
// PlotUtils::FluxUnfoldedScaling(fMCHist_Modes, GetFluxHistogram(),
// GetEventHistogram(), fScaleFactor,
// fNEvents);
// }
// 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 Measurement2D::ApplyNormScale(double norm) {
//********************************************************************
fCurrentNorm = norm;
fMCHist->Scale(1.0 / norm);
fMCFine->Scale(1.0 / norm);
return;
};
/*
Statistic Functions - Outsources to StatUtils
*/
//********************************************************************
int Measurement2D::GetNDOF() {
//********************************************************************
// Just incase it has gone...
if (!fDataHist)
return -1;
int nDOF = 0;
// If datahist has no errors make sure we don't include those bins as they are
// not data points
for (int xBin = 0; xBin < fDataHist->GetNbinsX(); ++xBin) {
for (int yBin = 0; yBin < fDataHist->GetNbinsY(); ++yBin) {
if (fDataHist->GetBinError(xBin + 1, yBin + 1) != 0)
++nDOF;
}
}
// Account for possible bin masking
int nMasked = 0;
if (fMaskHist and fIsMask)
if (fMaskHist->Integral() > 0)
for (int xBin = 0; xBin < fMaskHist->GetNbinsX() + 1; ++xBin)
for (int yBin = 0; yBin < fMaskHist->GetNbinsY() + 1; ++yBin)
if (fMaskHist->GetBinContent(xBin, yBin) > 0.5)
++nMasked;
// Take away those masked DOF
if (fIsMask) {
nDOF -= nMasked;
}
return nDOF;
}
//********************************************************************
double Measurement2D::GetLikelihood() {
//********************************************************************
// If this is for a ratio, there is no data histogram to compare to!
if (fNoData || !fDataHist)
return 0.;
// Fix weird masking bug
if (!fIsMask) {
if (fMaskHist) {
fMaskHist = NULL;
}
} else {
if (fMaskHist) {
PlotUtils::MaskBins(fMCHist, fMaskHist);
}
}
// if (fIsProjFitX or fIsProjFitY) return GetProjectedChi2();
// Scale up the results to match each other (Not using width might be
// inconsistent with Meas1D)
double scaleF = fDataHist->Integral() / fMCHist->Integral();
if (fIsShape) {
fMCHist->Scale(scaleF);
fMCFine->Scale(scaleF);
// PlotUtils::ScaleNeutModeArray((TH1**)fMCHist_PDG, scaleF);
}
- if (!fMapHist)
+ if (!fMapHist) {
fMapHist = StatUtils::GenerateMap(fDataHist);
+ }
// Get the chi2 from either covar or diagonals
double chi2 = 0.0;
if (fIsChi2) {
if (fIsDiag) {
chi2 =
StatUtils::GetChi2FromDiag(fDataHist, fMCHist, fMapHist, fMaskHist);
} else {
chi2 = StatUtils::GetChi2FromCov(fDataHist, fMCHist, covar, fMapHist,
- fMaskHist, fResidualHist);
- if (fChi2LessBinHist) {
- TH2I *binmask = new TH2I("mask", "", fDataHist->GetNbinsX(), 0,
- fDataHist->GetNbinsX(), fDataHist->GetNbinsY(),
- 0, fDataHist->GetNbinsY());
- binmask->SetDirectory(NULL);
+ fMaskHist,
+ fIsWriting ? fResidualHist : NULL);
+ if (fChi2LessBinHist && fIsWriting) {
+ NUIS_LOG(SAM, "Building n-1 chi2 contribution plot for " << GetName());
for (int xi = 0; xi < fDataHist->GetNbinsX(); ++xi) {
for (int yi = 0; yi < fDataHist->GetNbinsY(); ++yi) {
- binmask->Reset();
+ TH2I *binmask =
+ fMaskHist
+ ? static_cast(fMaskHist->Clone("mask"))
+ : new TH2I("mask", "", fDataHist->GetNbinsX(), 0,
+ fDataHist->GetNbinsX(), fDataHist->GetNbinsY(),
+ 0, fDataHist->GetNbinsY());
+ binmask->SetDirectory(NULL);
+
binmask->SetBinContent(xi + 1, yi + 1, 1);
fChi2LessBinHist->SetBinContent(
xi + 1, yi + 1,
StatUtils::GetChi2FromCov(fDataHist, fMCHist, covar, fMapHist,
binmask));
+ delete binmask;
}
}
- delete binmask;
}
}
}
// Add a normal penalty term
if (fAddNormPen) {
chi2 +=
(1 - (fCurrentNorm)) * (1 - (fCurrentNorm)) / (fNormError * fNormError);
NUIS_LOG(REC, "Norm penalty = " << (1 - (fCurrentNorm)) *
(1 - (fCurrentNorm)) /
(fNormError * fNormError));
}
// Adjust the shape back to where it was.
if (fIsShape and !FitPar::Config().GetParB("saveshapescaling")) {
fMCHist->Scale(1. / scaleF);
fMCFine->Scale(1. / scaleF);
}
fLikelihood = chi2;
return chi2;
}
/*
Fake Data Functions
*/
//********************************************************************
void Measurement2D::SetFakeDataValues(std::string fakeOption) {
//********************************************************************
// Setup original/datatrue
TH2D *tempdata = (TH2D *)fDataHist->Clone();
if (!fIsFakeData) {
fIsFakeData = true;
// Make a copy of the original data histogram.
if (!fDataOrig)
fDataOrig = (TH2D *)fDataHist->Clone((fName + "_data_original").c_str());
} else {
ResetFakeData();
}
// Setup Inputs
fFakeDataInput = fakeOption;
NUIS_LOG(SAM, "Setting fake data from : " << fFakeDataInput);
// From MC
if (fFakeDataInput.compare("MC") == 0) {
fDataHist = (TH2D *)fMCHist->Clone((fName + "_MC").c_str());
// Fake File
} else {
if (!fFakeDataFile)
fFakeDataFile = new TFile(fFakeDataInput.c_str(), "READ");
fDataHist = (TH2D *)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 = (TH2D *)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() * fDataHist->GetNbinsY();
double alpha_i = 0.0;
double alpha_j = 0.0;
for (int i = 0; i < nbins; i++) {
for (int j = 0; j < nbins; j++) {
if (tempdata->GetBinContent(i + 1) && tempdata->GetBinContent(j + 1)) {
alpha_i =
fDataHist->GetBinContent(i + 1) / tempdata->GetBinContent(i + 1);
alpha_j =
fDataHist->GetBinContent(j + 1) / tempdata->GetBinContent(j + 1);
} else {
alpha_i = 0.0;
alpha_j = 0.0;
}
(*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 Measurement2D::ResetFakeData() {
//********************************************************************
if (fIsFakeData) {
if (fDataHist)
delete fDataHist;
fDataHist =
(TH2D *)fDataTrue->Clone((fSettings.GetName() + "_FKDAT").c_str());
}
}
//********************************************************************
void Measurement2D::ResetData() {
//********************************************************************
if (fIsFakeData) {
if (fDataHist)
delete fDataHist;
fDataHist =
(TH2D *)fDataOrig->Clone((fSettings.GetName() + "_data").c_str());
}
fIsFakeData = false;
}
//********************************************************************
void Measurement2D::ThrowCovariance() {
//********************************************************************
// Take a fDecomposition and use it to throw the current dataset.
// Requires fDataTrue also be set incase used repeatedly.
if (fDataHist)
delete fDataHist;
fDataHist = StatUtils::ThrowHistogram(fDataTrue, fFullCovar);
return;
};
//********************************************************************
void Measurement2D::ThrowDataToy() {
//********************************************************************
if (!fDataTrue)
fDataTrue = (TH2D *)fDataHist->Clone();
if (fMCHist)
delete fMCHist;
fMCHist = StatUtils::ThrowHistogram(fDataTrue, fFullCovar);
}
/*
Access Functions
*/
//********************************************************************
TH2D *Measurement2D::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;
};
//********************************************************************
TH2D *Measurement2D::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 Measurement2D::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();
}
if (fIsChi2 && !fIsDiag) {
fResidualHist = (TH2D *)fMCHist->Clone((fName + "_RESIDUAL").c_str());
fResidualHist->GetYaxis()->SetTitle("#Delta#chi^{2}");
fResidualHist->Reset();
fChi2LessBinHist =
(TH2D *)fMCHist->Clone((fName + "_Chi2NMinusOne").c_str());
fChi2LessBinHist->GetYaxis()->SetTitle("Total #chi^{2} without bin_{i}");
fChi2LessBinHist->Reset();
+ fIsWriting = true;
(void)GetLikelihood();
+ fIsWriting = false;
- fResidualHist->Write();
- fChi2LessBinHist->Write();
+ fResidualHist->Write((fName + "_RESIDUAL").c_str());
+ fChi2LessBinHist->Write((fName + "_Chi2NMinusOne").c_str());
}
// // Likelihood residual plots
// if (drawOpt.find("RESIDUAL") != std::string::npos) {
// WriteResidualPlots();
//}
// // 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;
// }
/// 2D VERSION
// If null pointer return
if (!fMCHist and !fDataHist) {
NUIS_LOG(SAM, fName << "Incomplete histogram set!");
return;
}
// Config::Get().out->cd();
// Get Draw Options
drawOpt = FitPar::Config().GetParS("drawopts");
bool drawData = (drawOpt.find("DATA") != std::string::npos);
bool drawNormal = (drawOpt.find("MC") != std::string::npos);
bool drawEvents = (drawOpt.find("EVT") != std::string::npos);
bool drawFine = (drawOpt.find("FINE") != std::string::npos);
bool drawRatio = (drawOpt.find("RATIO") != std::string::npos);
// bool drawModes = (drawOpt.find("MODES") != std::string::npos);
bool drawShape = (drawOpt.find("SHAPE") != std::string::npos);
bool residual = (drawOpt.find("RESIDUAL") != std::string::npos);
bool drawMatrix = (drawOpt.find("MATRIX") != std::string::npos);
bool drawFlux = (drawOpt.find("FLUX") != std::string::npos);
bool drawMask = (drawOpt.find("MASK") != std::string::npos);
bool drawMap = (drawOpt.find("MAP") != std::string::npos);
bool drawProj = (drawOpt.find("PROJ") != std::string::npos);
// bool drawCanvPDG = (drawOpt.find("CANVPDG") != std::string::npos);
bool drawCov = (drawOpt.find("COV") != std::string::npos);
bool drawSliceMC = (drawOpt.find("CANVSLICEMC") != std::string::npos);
bool drawWeighted =
(drawOpt.find("WEIGHTS") != std::string::npos && fMCWeighted);
if (FitPar::Config().GetParB("EventManager")) {
drawFlux = false;
drawEvents = false;
}
// Save standard plots
if (drawData) {
GetDataList().at(0)->Write();
// Generate a simple map
if (!fMapHist)
fMapHist = StatUtils::GenerateMap(fDataHist);
// Convert to 1D Lists
TH1D *data_1D = StatUtils::MapToTH1D(fDataHist, fMapHist);
data_1D->Write();
delete data_1D;
}
if (drawNormal) {
GetMCList().at(0)->Write();
if (!fMapHist)
fMapHist = StatUtils::GenerateMap(fDataHist);
TH1D *mc_1D = StatUtils::MapToTH1D(fMCHist, fMapHist);
mc_1D->SetLineColor(kRed);
mc_1D->Write();
delete mc_1D;
}
// Write Weighted Histogram
if (drawWeighted)
fMCWeighted->Write();
if (drawCov) {
TH2D(*fFullCovar).Write((fName + "_COV").c_str());
}
if (drawOpt.find("INVCOV") != std::string::npos) {
TH2D(*covar).Write((fName + "_INVCOV").c_str());
}
// Save only mc and data if splines
if (fEventType == 4 or fEventType == 3) {
return;
}
// Draw Extra plots
if (drawFine)
this->GetFineList().at(0)->Write();
if (drawFlux and GetFluxHistogram()) {
GetFluxHistogram()->Write();
}
if (drawEvents and GetEventHistogram()) {
GetEventHistogram()->Write();
}
if (fIsMask and drawMask) {
fMaskHist->Write((fName + "_MSK").c_str()); //< save mask
TH1I *mask_1D = StatUtils::MapToMask(fMaskHist, fMapHist);
if (mask_1D) {
mask_1D->Write();
TMatrixDSym *calc_cov =
StatUtils::ApplyInvertedMatrixMasking(covar, mask_1D);
TH1D *data_1D = StatUtils::MapToTH1D(fDataHist, fMapHist);
TH1D *mc_1D = StatUtils::MapToTH1D(fMCHist, fMapHist);
TH1D *calc_data = StatUtils::ApplyHistogramMasking(data_1D, mask_1D);
TH1D *calc_mc = StatUtils::ApplyHistogramMasking(mc_1D, mask_1D);
TH2D *bin_cov = new TH2D(*calc_cov);
bin_cov->Write();
calc_data->Write();
calc_mc->Write();
delete mask_1D;
delete calc_cov;
delete calc_data;
delete calc_mc;
delete bin_cov;
delete data_1D;
delete mc_1D;
}
}
if (drawMap)
fMapHist->Write((fName + "_MAP").c_str()); //< save map
// // Save neut stack
// if (drawModes) {
// THStack combo_fMCHist_PDG = PlotUtils::GetNeutModeStack(
// (fName + "_MC_PDG").c_str(),
// (TH1**)fMCHist_PDG, 0);
// combo_fMCHist_PDG.Write();
// }
// Save Matrix plots
if (drawMatrix and fFullCovar and covar and fDecomp) {
TH2D cov = TH2D((*fFullCovar));
cov.SetNameTitle((fName + "_cov").c_str(),
(fName + "_cov;Bins; Bins;").c_str());
cov.Write();
TH2D covinv = TH2D((*this->covar));
covinv.SetNameTitle((fName + "_covinv").c_str(),
(fName + "_cov;Bins; Bins;").c_str());
covinv.Write();
TH2D covdec = TH2D((*fDecomp));
covdec.SetNameTitle((fName + "_covdec").c_str(),
(fName + "_cov;Bins; Bins;").c_str());
covdec.Write();
}
// Save ratio plots if required
if (drawRatio) {
// Needed for error bars
for (int i = 0; i < fMCHist->GetNbinsX() * fMCHist->GetNbinsY(); i++)
fMCHist->SetBinError(i + 1, 0.0);
fDataHist->GetSumw2();
fMCHist->GetSumw2();
// Create Ratio Histograms
TH2D *dataRatio = (TH2D *)fDataHist->Clone((fName + "_data_RATIO").c_str());
TH2D *mcRatio = (TH2D *)fMCHist->Clone((fName + "_MC_RATIO").c_str());
mcRatio->Divide(fMCHist);
dataRatio->Divide(fMCHist);
// Cancel bin errors on MC
for (int i = 0; i < mcRatio->GetNbinsX() * mcRatio->GetNbinsY(); i++) {
mcRatio->SetBinError(i + 1, fMCHist->GetBinError(i + 1) /
fMCHist->GetBinContent(i + 1));
}
mcRatio->SetMinimum(0);
mcRatio->SetMaximum(2);
dataRatio->SetMinimum(0);
dataRatio->SetMaximum(2);
mcRatio->Write();
dataRatio->Write();
delete mcRatio;
delete dataRatio;
}
// Save Shape Plots if required
if (drawShape) {
// Create Shape Histogram
TH2D *mcShape = (TH2D *)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);
mcShape->SetLineWidth(3);
mcShape->SetLineStyle(7); // dashes
mcShape->Write();
// Save shape ratios
if (drawRatio) {
// Needed for error bars
mcShape->GetSumw2();
// Create shape ratio histograms
TH2D *mcShapeRatio =
(TH2D *)mcShape->Clone((fName + "_MC_SHAPE_RATIO").c_str());
TH2D *dataShapeRatio =
(TH2D *)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); // dashes
mcShapeRatio->Write();
dataShapeRatio->Write();
delete mcShapeRatio;
delete dataShapeRatio;
}
delete mcShape;
}
// Save residual calculations of what contributed to the chi2 values.
if (residual) {
}
if (fIsProjFitX || fIsProjFitY || drawProj) {
// If not already made, make the projections
if (!fMCHist_X) {
PlotUtils::MatchEmptyBins(fDataHist, fMCHist);
fMCHist_X = PlotUtils::GetProjectionX(fMCHist, fMaskHist);
fMCHist_Y = PlotUtils::GetProjectionY(fMCHist, fMaskHist);
fDataHist_X = PlotUtils::GetProjectionX(fDataHist, fMaskHist);
fDataHist_Y = PlotUtils::GetProjectionY(fDataHist, fMaskHist);
// This is not the correct way of doing it
// double chi2X = StatUtils::GetChi2FromDiag(fDataHist_X, fMCHist_X);
// double chi2Y = StatUtils::GetChi2FromDiag(fDataHist_Y, fMCHist_Y);
// fMCHist_X->SetTitle(Form("%f", chi2X));
// fMCHist_Y->SetTitle(Form("%f", chi2Y));
}
// Save the histograms
fDataHist_X->Write();
fMCHist_X->Write();
fDataHist_Y->Write();
fMCHist_Y->Write();
}
if (drawSliceMC) {
TCanvas *c1 = new TCanvas((fName + "_MC_CANV_Y").c_str(),
(fName + "_MC_CANV_Y").c_str(), 1024, 1024);
c1->Divide(2, int(fDataHist->GetNbinsY() / 3. + 1));
TH2D *mcShape = (TH2D *)fMCHist->Clone((fName + "_MC_SHAPE").c_str());
double shapeScale =
fDataHist->Integral("width") / fMCHist->Integral("width");
mcShape->Scale(shapeScale);
mcShape->SetLineStyle(7);
c1->cd(1);
TLegend *leg = new TLegend(0.0, 0.0, 1.0, 1.0);
leg->AddEntry(fDataHist, (fName + " Data").c_str(), "lep");
leg->AddEntry(fMCHist, (fName + " MC").c_str(), "l");
leg->AddEntry(mcShape, (fName + " Shape").c_str(), "l");
leg->SetLineColor(0);
leg->SetLineStyle(0);
leg->SetFillColor(0);
leg->SetLineStyle(0);
leg->Draw("SAME");
// Make Y slices
for (int i = 1; i < fDataHist->GetNbinsY() + 1; i++) {
c1->cd(i + 1);
TH1D *fDataHist_SliceY = PlotUtils::GetSliceY(fDataHist, i);
fDataHist_SliceY->Draw("E1");
TH1D *fMCHist_SliceY = PlotUtils::GetSliceY(fMCHist, i);
fMCHist_SliceY->Draw("SAME");
TH1D *mcShape_SliceY = PlotUtils::GetSliceY(mcShape, i);
mcShape_SliceY->Draw("SAME");
mcShape_SliceY->SetLineStyle(mcShape->GetLineStyle());
}
c1->Write();
delete c1;
delete leg;
TCanvas *c2 = new TCanvas((fName + "_MC_CANV_X").c_str(),
(fName + "_MC_CANV_X").c_str(), 1024, 1024);
c2->Divide(2, int(fDataHist->GetNbinsX() / 3. + 1));
mcShape = (TH2D *)fMCHist->Clone((fName + "_MC_SHAPE").c_str());
shapeScale = fDataHist->Integral("width") / fMCHist->Integral("width");
mcShape->Scale(shapeScale);
mcShape->SetLineStyle(7);
c2->cd(1);
TLegend *leg2 = new TLegend(0.0, 0.0, 1.0, 1.0);
leg2->AddEntry(fDataHist, (fName + " Data").c_str(), "lep");
leg2->AddEntry(fMCHist, (fName + " MC").c_str(), "l");
leg2->AddEntry(mcShape, (fName + " Shape").c_str(), "l");
leg2->SetLineColor(0);
leg2->SetLineStyle(0);
leg2->SetFillColor(0);
leg2->SetLineStyle(0);
leg2->Draw("SAME");
// Make Y slices
for (int i = 1; i < fDataHist->GetNbinsX() + 1; i++) {
c2->cd(i + 1);
TH1D *fDataHist_SliceX = PlotUtils::GetSliceX(fDataHist, i);
fDataHist_SliceX->Draw("E1");
TH1D *fMCHist_SliceX = PlotUtils::GetSliceX(fMCHist, i);
fMCHist_SliceX->Draw("SAME");
TH1D *mcShape_SliceX = PlotUtils::GetSliceX(mcShape, i);
mcShape_SliceX->Draw("SAME");
mcShape_SliceX->SetLineStyle(mcShape->GetLineStyle());
}
c2->Write();
delete c2;
delete leg2;
}
// Write Extra Histograms
AutoWriteExtraTH1();
WriteExtraHistograms();
// Returning
NUIS_LOG(SAM, "Written Histograms: " << fName);
return;
}
/*
Setup Functions
*/
//********************************************************************
void Measurement2D::SetupMeasurement(std::string inputfile, std::string type,
FitWeight *rw, std::string fkdt) {
//********************************************************************
// 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;
NUIS_LOG(SAM, "Found event rate measurement but fIsRawEvents == false!");
NUIS_LOG(SAM, "Overriding this and setting fIsRawEvents == true!");
}
fIsEnu = false;
if ((fName.find("XSec") != std::string::npos) &&
(fName.find("Enu") != std::string::npos)) {
fIsEnu = true;
NUIS_LOG(SAM, "::" << fName << "::");
NUIS_LOG(SAM,
"Found XSec Enu measurement, applying flux integrated scaling, "
"not flux averaged!");
if (FitPar::Config().GetParB("EventManager")) {
NUIS_ERR(FTL, "Enu Measurements do not yet work with the Event Manager!");
NUIS_ERR(FTL, "If you want decent flux unfolded results please run in "
"series mode (-q EventManager=0)");
sleep(2);
throw;
}
}
if (fIsEnu && fIsRawEvents) {
NUIS_ERR(FTL, "Found 1D Enu XSec distribution AND fIsRawEvents, is this "
"really correct?!");
NUIS_ERR(FTL, "Check experiment constructor for " << fName
<< " and correct this!");
NUIS_ABORT("I live in " << __FILE__ << ":" << __LINE__);
}
// Reset everything to NULL
fRW = rw;
// Setting up 2D Inputs
this->SetupInputs(inputfile);
// Set Default Options
SetFitOptions(fDefaultTypes);
// Set Passed Options
SetFitOptions(type);
}
//********************************************************************
void Measurement2D::SetupDefaultHist() {
//********************************************************************
// Setup fMCHist
fMCHist = (TH2D *)fDataHist->Clone();
fMCHist->SetNameTitle((fName + "_MC").c_str(),
(fName + "_MC" + fPlotTitles).c_str());
// Setup fMCFine
Int_t nBinsX = fMCHist->GetNbinsX();
Int_t nBinsY = fMCHist->GetNbinsY();
fMCFine = new TH2D((fName + "_MC_FINE").c_str(),
(fName + "_MC_FINE" + fPlotTitles).c_str(), nBinsX * 3,
fMCHist->GetXaxis()->GetBinLowEdge(1),
fMCHist->GetXaxis()->GetBinLowEdge(nBinsX + 1), nBinsY * 3,
fMCHist->GetYaxis()->GetBinLowEdge(1),
fMCHist->GetYaxis()->GetBinLowEdge(nBinsY + 1));
// Setup MC Stat
fMCStat = (TH2D *)fMCHist->Clone();
fMCStat->Reset();
// Setup the NEUT Mode Array
// PlotUtils::CreateNeutModeArray(fMCHist, (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);
}
return;
}
//********************************************************************
void Measurement2D::SetDataValues(std::string dataFile, std::string TH2Dname) {
//********************************************************************
if (dataFile.find(".root") == std::string::npos) {
NUIS_ERR(FTL, "Error! " << dataFile << " is not a .root file");
NUIS_ERR(FTL, "Currently only .root file reading is supported (MiniBooNE "
"CC1pi+ 2D), but implementing .txt should be dirt easy");
NUIS_ABORT("See me at " << __FILE__ << ":" << __LINE__);
} else {
TFile *inFile = new TFile(dataFile.c_str(), "READ");
fDataHist = (TH2D *)(inFile->Get(TH2Dname.c_str())->Clone());
fDataHist->SetDirectory(0);
fDataHist->SetNameTitle((fName + "_data").c_str(),
(fName + "_MC" + fPlotTitles).c_str());
delete inFile;
}
return;
}
//********************************************************************
void Measurement2D::SetDataValues(std::string dataFile, double dataNorm,
std::string errorFile, double errorNorm) {
//********************************************************************
// Make a counter to track the line number
int yBin = 0;
std::string line;
std::ifstream data(dataFile.c_str(), std::ifstream::in);
fDataHist = new TH2D((fName + "_data").c_str(), (fName + fPlotTitles).c_str(),
fNDataPointsX - 1, fXBins, fNDataPointsY - 1, fYBins);
if (data.is_open()) {
NUIS_LOG(SAM, "Reading data from: " << dataFile.c_str());
}
while (std::getline(data >> std::ws, line, '\n')) {
int xBin = 0;
// Loop over entries and insert them into the histogram
std::vector entries = GeneralUtils::ParseToDbl(line, " ");
for (std::vector::iterator iter = entries.begin();
iter != entries.end(); iter++) {
fDataHist->SetBinContent(xBin + 1, yBin + 1, (*iter) * dataNorm);
xBin++;
}
yBin++;
}
yBin = 0;
std::ifstream error(errorFile.c_str(), std::ifstream::in);
if (error.is_open()) {
NUIS_LOG(SAM, "Reading errors from: " << errorFile.c_str());
}
while (std::getline(error >> std::ws, line, '\n')) {
int xBin = 0;
// Loop over entries and insert them into the histogram
std::vector entries = GeneralUtils::ParseToDbl(line, " ");
for (std::vector::iterator iter = entries.begin();
iter != entries.end(); iter++) {
fDataHist->SetBinError(xBin + 1, yBin + 1, (*iter) * errorNorm);
xBin++;
}
yBin++;
}
return;
};
//********************************************************************
void Measurement2D::SetDataValuesFromText(std::string dataFile,
double dataNorm) {
//********************************************************************
fDataHist = new TH2D((fName + "_data").c_str(), (fName + fPlotTitles).c_str(),
fNDataPointsX - 1, fXBins, fNDataPointsY - 1, fYBins);
NUIS_LOG(SAM, "Reading data from: " << dataFile);
PlotUtils::Set2DHistFromText(dataFile, fDataHist, dataNorm, true);
return;
};
//********************************************************************
void Measurement2D::SetCovarMatrix(std::string covarFile) {
//********************************************************************
// Used to read a covariance matrix from a root file
TFile *tempFile = new TFile(covarFile.c_str(), "READ");
// Make plots that we want
TH2D *covarPlot = new TH2D();
TH2D *fFullCovarPlot = new TH2D();
// Get covariance options for fake data studies
std::string covName = "";
std::string covOption = FitPar::Config().GetParS("throw_covariance");
// Which matrix to get?
if (fIsShape || fIsFree)
covName = "shp_";
if (fIsDiag)
covName += "diag";
else
covName += "full";
covarPlot = (TH2D *)tempFile->Get((covName + "cov").c_str());
// Throw either the sub matrix or the full matrix
if (!covOption.compare("SUB"))
fFullCovarPlot = (TH2D *)tempFile->Get((covName + "cov").c_str());
else if (!covOption.compare("FULL"))
fFullCovarPlot = (TH2D *)tempFile->Get("fullcov");
else {
NUIS_ERR(WRN, " Incorrect thrown_covariance option in parameters.");
}
// Bin masking?
int dim = int(fDataHist->GetNbinsX()); //-this->masked->Integral());
int covdim = int(fDataHist->GetNbinsX());
// Make new covars
this->covar = new TMatrixDSym(dim);
fFullCovar = new TMatrixDSym(dim);
fDecomp = new TMatrixDSym(dim);
// Full covariance values
int row, column = 0;
row = 0;
column = 0;
for (Int_t i = 0; i < covdim; i++) {
// masking can be dodgy
// 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) {
for (Int_t i = 0; i < fDataHist->GetNbinsX(); i++) {
fDataHist->SetBinError(
i + 1, sqrt((covarPlot->GetBinContent(i + 1, i + 1))) * 1E-38);
}
}
TDecompSVD LU = TDecompSVD(*this->covar);
this->covar = new TMatrixDSym(dim, LU.Invert().GetMatrixArray(), "");
tempFile->Close();
delete tempFile;
return;
};
//********************************************************************
void Measurement2D::SetCovarMatrixFromText(std::string covarFile, int dim) {
//********************************************************************
// Make a counter to track the line number
int row = 0;
std::string line;
std::ifstream covar(covarFile.c_str(), std::ifstream::in);
this->covar = new TMatrixDSym(dim);
fFullCovar = new TMatrixDSym(dim);
if (covar.is_open()) {
NUIS_LOG(SAM, "Reading covariance matrix from file: " << covarFile);
}
while (std::getline(covar >> 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 entries = GeneralUtils::ParseToDbl(line, " ");
for (std::vector::iterator iter = entries.begin();
iter != entries.end(); iter++) {
double val = (*iter) * fDataHist->GetBinError(row + 1) * 1E38 *
fDataHist->GetBinError(column + 1) * 1E38;
(*this->covar)(row, column) = val;
(*fFullCovar)(row, column) = val;
column++;
}
row++;
}
// Robust matrix inversion method
TDecompSVD LU = TDecompSVD(*this->covar);
this->covar = new TMatrixDSym(dim, LU.Invert().GetMatrixArray(), "");
return;
};
//********************************************************************
void Measurement2D::SetCovarMatrixFromChol(std::string covarFile, int dim) {
//********************************************************************
// Make a counter to track the line number
int row = 0;
std::string line;
std::ifstream covarread(covarFile.c_str(), std::ifstream::in);
TMatrixD *newcov = new TMatrixD(dim, dim);
if (covarread.is_open()) {
NUIS_LOG(SAM, "Reading covariance matrix from file: " << covarFile);
}
while (std::getline(covarread >> 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 entries = GeneralUtils::ParseToDbl(line, " ");
for (std::vector::iterator iter = entries.begin();
iter != entries.end(); iter++) {
(*newcov)(row, column) = *iter;
column++;
}
row++;
}
covarread.close();
// Form full covariance
TMatrixD *trans = (TMatrixD *)(newcov)->Clone();
trans->T();
(*trans) *= (*newcov);
fFullCovar = new TMatrixDSym(dim, trans->GetMatrixArray(), "");
delete newcov;
delete trans;
// Robust matrix inversion method
TDecompChol LU = TDecompChol(*this->fFullCovar);
this->covar = new TMatrixDSym(dim, LU.Invert().GetMatrixArray(), "");
return;
};
// //********************************************************************
// void Measurement2D::SetMapValuesFromText(std::string dataFile) {
// //********************************************************************
// fMapHist = new TH2I((fName + "_map").c_str(), (fName +
// fPlotTitles).c_str(),
// fNDataPointsX - 1, fXBins, fNDataPointsY - 1, fYBins);
// LOG(SAM) << "Reading map from: " << dataFile << std::endl;
// PlotUtils::Set2DHistFromText(dataFile, fMapHist, 1.0);
// return;
// };
diff --git a/src/FitBase/Measurement2D.h b/src/FitBase/Measurement2D.h
index 00d32f8..e540454 100644
--- a/src/FitBase/Measurement2D.h
+++ b/src/FitBase/Measurement2D.h
@@ -1,644 +1,644 @@
// 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 .
*******************************************************************************/
#ifndef MEASUREMENT_2D_HXX_SEEN
#define MEASUREMENT_2D_HXX_SEEN
/*!
* \addtogroup FitBase
* @{
*/
#include
#include
#include
#include
#include
#include
#include
#include
#include
// ROOT includes
#include
#include
#include
#include
#include
#include
#include
#include
#include
// External data fit includes
#include "FitEvent.h"
#include "FitUtils.h"
#include "MeasurementBase.h"
#include "PlotUtils.h"
#include "SignalDef.h"
#include "StatUtils.h"
#include "MeasurementVariableBox2D.h"
//********************************************************************
//! 2D Measurement base class. Histogram handling is done in this base layer.
class Measurement2D : public MeasurementBase {
//********************************************************************
public:
/*
Constructor/Deconstuctor
*/
//! Default Constructor
Measurement2D();
//! Default Destructor
virtual ~Measurement2D();
/*
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 2D data distribution given the binning provided.
virtual void CreateDataHistogram(int dimx, double* binx, int dimy, double* biny);
/// \brief Set Data Histogram from a list of contents in a text file
///
/// Assumes the format: \n
/// x_low_1 y_low_1 cont_11 err_11 \n
/// x_low_1 y_low_2 cont_12 err_12 \n
/// x_low_2 y_low_1 cont_21 err_21 \n
/// x_low_2 y_low_2 cont_22 err_22 \n
/// x_low_2 y_low_3 cont_23 err_23 \n
/// x_low_3 y_low_2 cont_32 err_32 \n
virtual void SetDataFromTextFile(std::string data, std::string binx, std::string biny);
/// \brief Set Data Histogram from a TH2D in a file
///
/// - datfile = Full path to data file
/// - histname = Name of histogram
///
/// If histname not given it assumes that datfile
/// is in the format: \n
/// 'file.root;histname'
virtual void SetDataFromRootFile(std::string datfile, std::string histname="");
/// \brief Set data values from a 2D array in text file
///
/// \warning requires DATA HISTOGRAM TO BE SET FIRST
///
/// Assumes form: \n
/// cont_11 cont_12 ... cont_1N \n
/// cont_21 cont_22 ... cont_2N \n
/// ... ... ... ... \n
/// cont_N1 cont_N2 ... cont_NN \n
virtual void SetDataValuesFromTextFile(std::string datfile, TH2D* hist = NULL);
/// \brief Set data errors from a 2D array in text file
///
/// \warning requires DATA HISTOGRAM TO BE SET FIRST
///
/// Assumes form: \n
/// errs_11 errs_12 ... errs_1N \n
/// errs_21 errs_22 ... errs_2N \n
/// ... ... ... ... \n
/// errs_N1 errs_N2 ... errs_NN \n
virtual void SetDataErrorsFromTextFile(std::string datfile, TH2D* hist = NULL);
/// \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(TH2D* 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);
/// \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 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 Read the map values from a text file
///
/// \warning Requires DATA hist to be set beforehand.
/// Format should be a 2D array of mappings.
/// -1 indicates empty bins. \n
/// e.g.: \n
/// 1 2 3 4 5 \n
/// -1 6 7 8 9 \n
/// -1 -1 10 11 -1 \n
virtual void SetMapValuesFromText(std::string dataFile);
/// \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_x_1 bin_index_y_1 1 \n
/// bin_index_x_2 bin_index_y_2 1 \n
/// bin_index_x_3 bin_index_y_3 1 \n
///
/// If 0 is given then a bin entry will NOT be masked. So for example: \n\n
/// 1 1 1 \n
/// 2 0 1 \n
/// 3 4 0 \n
/// 4 0 1 \n\n
/// Will mask only the (1,1), (2,0), and (4,0) 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();
/*
Reconfigure
*/
/// \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 MeasurementVariableBox2D();};
/// \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, YVar
///
/// Fill standard histograms using fXVar, fYVar, 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();
/// \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
///
virtual TH2D* 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
///
virtual TH2D* 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 GetMCList(void) {
return std::vector(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 GetDataList(void) {
return std::vector(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 GetMaskList(void) {
return std::vector(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 GetFineList(void) {
return std::vector(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
///
virtual void Write(std::string drawOpt);
//////// OLD FUNCTIONS ////////////
//! Intial setup of common measurement variables. Parse input files, types,
//! etc.
virtual void SetupMeasurement(std::string input, std::string type,
FitWeight* rw, std::string fkdt);
//! Setup the default mc Hist given a data histogram
virtual void SetupDefaultHist();
//! Set the data values and errors from two files
virtual void SetDataValues(std::string dataFile, double dataNorm,
std::string errorFile, double errorNorm);
virtual void SetDataValues(std::string dataFile, std::string TH2Dname);
//! Set the data values only from a text file
virtual void SetDataValuesFromText(std::string dataFile, double norm);
//! Read a covariance matrix from a file (Default name "covar" in file)
virtual void SetCovarMatrix(std::string covarFile);
//! Set the covariance matrix from a text file
virtual void SetCovarMatrixFromText(std::string covarFile, int dim);
//! Set the covariance matrix from a text file containing the cholesky
//! fDecomposition
virtual void SetCovarMatrixFromChol(std::string covarFile, int dim);
protected:
// The data histograms
TH2D* fDataHist; //!< default data histogram (use in chi2 calculations)
TH2D* fDataOrig; //!< histogram to store original data before throws.
TH2D* fDataTrue; //!< histogram to store true dataset
TH1D* fDataHist_X; //!< Projections onto X of the fDataHist
TH1D* fDataHist_Y; //!< Projections onto Y of the fDataHist
// The MC histograms
TH2D* fMCHist; //!< MC Histogram (used in chi2 calculations)
TH2D* fMCFine; //!< Finely binned MC Histogram
TH2D* fMCHist_PDG[61]; //!< MC Histograms for each interaction mode
TH1D* fMCHist_X; //!< Projections onto X of the fMCHist
TH1D* fMCHist_Y; //!< Projections onto Y of the fMCHist
TH2D* fMCWeighted; //!< Raw Event Weights
TH2D* fMCStat;
TH2I* fMaskHist; //!< mask histogram for the data
TH2I* fMapHist; //!< map histogram used to convert 2D to 1D distributions
TH2D *fResidualHist;
TH2D *fChi2LessBinHist;
bool fIsFakeData; //!< is current data actually fake
std::string fakeDataFile; //!< MC fake data input file
std::string fPlotTitles; //!< X and Y plot titles.
std::string fFitType;
std::string fDefaultTypes; //!< Default Fit Options
std::string fAllowedTypes; //!< Any allowed Fit Options
TMatrixDSym* covar; //!< inverted covariance matrix
TMatrixDSym* fFullCovar; //!< covariance matrix
TMatrixDSym* fDecomp; //!< fDecomposed covariance matrix
TMatrixDSym* fCorrel; //!< correlation matrix
double fCovDet; //!< covariance deteriminant
double fNormError; //!< Normalisation on the error on the data
double fLikelihood; //!< Likelihood value
Double_t* fXBins; //!< X Bin Edges
Double_t* fYBins; //!< Y Bin Edges
Int_t fNDataPointsX; //!< Number of X data points
Int_t fNDataPointsY; //!< NUmber of Y data points
// Fit specific flags
bool fIsShape; //!< Flag: Perform shape-only fit
bool fIsFree; //!< Flag: Perform normalisation free fit
bool fIsDiag; //!< Flag: Only use diagonal bin errors in stats
bool fIsMask; //!< Flag: Apply bin masking
bool fIsRawEvents; //!< Flag: Only event rates in histograms
bool fIsEnu; //!< Needs Enu Unfolding
bool fIsChi2SVD; //!< Flag: Chi2 SVD Method (DO NOT USE)
bool fAddNormPen; //!< Flag: Add normalisation penalty to fi
bool fIsProjFitX; //!< Flag: Use 1D projections onto X and Y to calculate the
//!Chi2 Method. If flagged X will be used to set the rate.
bool fIsProjFitY; //!< Flag: Use 1D projections onto X and Y to calculate the
//!Chi2 Method. If flagged Y will be used to set the rate.
bool fIsFix; //!< Flag: Fixed Histogram Norm
bool fIsFull; //!< Flag; Use Full Covar
bool fIsDifXSec; //!< Flag: Differential XSec
bool fIsEnu1D; //!< Flag: Flux Unfolded XSec
bool fIsChi2; //!< Flag; Use Chi2 over LL
-
+ bool fIsWriting;
TrueModeStack* fMCHist_Modes; ///< Optional True Mode Stack
TMatrixDSym* fCovar; ///< New FullCovar
TMatrixDSym* fInvert; ///< New covar
// Fake Data
std::string fFakeDataInput; ///< Input fake data file path
TFile* fFakeDataFile; ///< Input fake data file
// Arrays for data entries
Double_t* fDataValues; ///< REMOVE data bin contents
Double_t* fDataErrors; ///< REMOVE data bin errors
};
/*! @} */
#endif
diff --git a/src/InputHandler/InputHandler.cxx b/src/InputHandler/InputHandler.cxx
index 9101762..304b445 100644
--- a/src/InputHandler/InputHandler.cxx
+++ b/src/InputHandler/InputHandler.cxx
@@ -1,313 +1,311 @@
// 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 .
*******************************************************************************/
#include "InputHandler.h"
#include "InputUtils.h"
InputHandlerBase::InputHandlerBase() {
fName = "";
fFluxHist = NULL;
fEventHist = NULL;
fNEvents = 0;
fNUISANCEEvent = NULL;
fBaseEvent = NULL;
kRemoveUndefParticles = FitPar::Config().GetParB("RemoveUndefParticles");
kRemoveFSIParticles = FitPar::Config().GetParB("RemoveFSIParticles");
kRemoveNuclearParticles = FitPar::Config().GetParB("RemoveNuclearParticles");
fMaxEvents = FitPar::Config().GetParI("MAXEVENTS");
fTTreePerformance = NULL;
fSkip = 0;
if (FitPar::Config().HasConfig("NSKIPEVENTS")) {
fSkip = FitPar::Config().GetParI("NSKIPEVENTS");
- std::cout << "Skipping " << fSkip << " events when reading input trees."
- << std::endl;
}
};
InputHandlerBase::~InputHandlerBase() {
if (fFluxHist)
delete fFluxHist;
if (fEventHist)
delete fEventHist;
// if (fXSecHist) delete fXSecHist;
// if (fNUISANCEEvent) delete fNUISANCEEvent;
jointfluxinputs.clear();
jointeventinputs.clear();
jointindexlow.clear();
jointindexhigh.clear();
jointindexallowed.clear();
jointindexscale.clear();
// if (fTTreePerformance) {
// fTTreePerformance->SaveAs(("ttreeperfstats_" + fName +
// ".root").c_str());
// }
}
void InputHandlerBase::Print(){};
TH1D *InputHandlerBase::GetXSecHistogram(void) {
fXSecHist = (TH1D *)fFluxHist->Clone();
fXSecHist->Divide(fEventHist);
return fXSecHist;
};
double InputHandlerBase::PredictedEventRate(double low, double high,
std::string intOpt) {
Int_t minBin = fEventHist->GetXaxis()->FindFixBin(low);
Int_t maxBin = fEventHist->GetXaxis()->FindFixBin(high);
if ((fEventHist->IsBinOverflow(minBin) && (low != -9999.9))) {
minBin = 1;
}
if ((fEventHist->IsBinOverflow(maxBin) && (high != -9999.9))) {
maxBin = fEventHist->GetXaxis()->GetNbins() + 1;
}
// If we are within a single bin
if (minBin == maxBin) {
// Get the contained fraction of the single bin's width
return ((high - low) / fEventHist->GetXaxis()->GetBinWidth(minBin)) *
fEventHist->Integral(minBin, minBin, intOpt.c_str());
}
double lowBinUpEdge = fEventHist->GetXaxis()->GetBinUpEdge(minBin);
double highBinLowEdge = fEventHist->GetXaxis()->GetBinLowEdge(maxBin);
double lowBinfracIntegral =
((lowBinUpEdge - low) / fEventHist->GetXaxis()->GetBinWidth(minBin)) *
fEventHist->Integral(minBin, minBin, intOpt.c_str());
double highBinfracIntegral =
((high - highBinLowEdge) / fEventHist->GetXaxis()->GetBinWidth(maxBin)) *
fEventHist->Integral(maxBin, maxBin, intOpt.c_str());
// If they are neighbouring bins
if ((minBin + 1) == maxBin) {
std::cout << "Get lowfrac + highfrac" << std::endl;
// Get the contained fraction of the two bin's width
return lowBinfracIntegral + highBinfracIntegral;
}
double ContainedIntegral =
fEventHist->Integral(minBin + 1, maxBin - 1, intOpt.c_str());
// If there are filled bins between them
return lowBinfracIntegral + highBinfracIntegral + ContainedIntegral;
};
double InputHandlerBase::TotalIntegratedFlux(double low, double high,
std::string intOpt) {
Int_t minBin = fFluxHist->GetXaxis()->FindFixBin(low);
Int_t maxBin = fFluxHist->GetXaxis()->FindFixBin(high);
if ((fFluxHist->IsBinOverflow(minBin) && (low != -9999.9))) {
minBin = 1;
}
if ((fFluxHist->IsBinOverflow(maxBin) && (high != -9999.9))) {
maxBin = fFluxHist->GetXaxis()->GetNbins();
high = fFluxHist->GetXaxis()->GetBinLowEdge(maxBin + 1);
}
// If we are within a single bin
if (minBin == maxBin) {
// Get the contained fraction of the single bin's width
return ((high - low) / fFluxHist->GetXaxis()->GetBinWidth(minBin)) *
fFluxHist->Integral(minBin, minBin, intOpt.c_str());
}
double lowBinUpEdge = fFluxHist->GetXaxis()->GetBinUpEdge(minBin);
double highBinLowEdge = fFluxHist->GetXaxis()->GetBinLowEdge(maxBin);
double lowBinfracIntegral =
((lowBinUpEdge - low) / fFluxHist->GetXaxis()->GetBinWidth(minBin)) *
fFluxHist->Integral(minBin, minBin, intOpt.c_str());
double highBinfracIntegral =
((high - highBinLowEdge) / fFluxHist->GetXaxis()->GetBinWidth(maxBin)) *
fFluxHist->Integral(maxBin, maxBin, intOpt.c_str());
// If they are neighbouring bins
if ((minBin + 1) == maxBin) {
std::cout << "Get lowfrac + highfrac" << std::endl;
// Get the contained fraction of the two bin's width
return lowBinfracIntegral + highBinfracIntegral;
}
double ContainedIntegral =
fFluxHist->Integral(minBin + 1, maxBin - 1, intOpt.c_str());
// If there are filled bins between them
return lowBinfracIntegral + highBinfracIntegral + ContainedIntegral;
}
std::vector InputHandlerBase::GetFluxList(void) {
return std::vector(1, fFluxHist);
};
std::vector InputHandlerBase::GetEventList(void) {
return std::vector(1, fEventHist);
};
std::vector InputHandlerBase::GetXSecList(void) {
return std::vector(1, GetXSecHistogram());
};
FitEvent *InputHandlerBase::FirstNuisanceEvent() {
fCurrentIndex = 0;
return GetNuisanceEvent(fCurrentIndex);
};
FitEvent *InputHandlerBase::NextNuisanceEvent() {
fCurrentIndex++;
if ((fMaxEvents != -1) && (fCurrentIndex > fMaxEvents)) {
return NULL;
}
return GetNuisanceEvent(fCurrentIndex);
};
BaseFitEvt *InputHandlerBase::FirstBaseEvent() {
fCurrentIndex = 0;
return GetBaseEvent(fCurrentIndex);
};
BaseFitEvt *InputHandlerBase::NextBaseEvent() {
fCurrentIndex++;
if (jointinput and fMaxEvents != -1) {
while (fCurrentIndex < jointindexlow[jointindexswitch] ||
fCurrentIndex >= jointindexhigh[jointindexswitch]) {
jointindexswitch++;
// Loop Around
if (jointindexswitch == jointindexlow.size()) {
jointindexswitch = 0;
}
}
if (fCurrentIndex >
jointindexlow[jointindexswitch] + jointindexallowed[jointindexswitch]) {
fCurrentIndex = jointindexlow[jointindexswitch];
}
}
return GetBaseEvent(fCurrentIndex);
};
void InputHandlerBase::RegisterJointInput(std::string input, int n, TH1D *f,
TH1D *e) {
if (jointfluxinputs.size() == 0) {
jointindexswitch = 0;
fNEvents = 0;
}
// Push into individual input vectors
jointfluxinputs.push_back((TH1D *)f->Clone());
jointeventinputs.push_back((TH1D *)e->Clone());
jointindexlow.push_back(fNEvents);
jointindexhigh.push_back(fNEvents + n);
fNEvents += n;
// Add to the total flux/event hist
if (!fFluxHist)
fFluxHist = (TH1D *)f->Clone();
else
fFluxHist->Add(f);
if (!fEventHist)
fEventHist = (TH1D *)e->Clone();
else
fEventHist->Add(e);
}
void InputHandlerBase::SetupJointInputs() {
if (jointeventinputs.size() <= 1) {
jointinput = false;
} else if (jointeventinputs.size() > 1) {
jointinput = true;
jointindexswitch = 0;
}
fMaxEvents = FitPar::Config().GetParI("MAXEVENTS");
if (fMaxEvents != -1 and jointeventinputs.size() > 1) {
NUIS_ABORT("Can only handle joint inputs when config MAXEVENTS = -1!");
}
if (jointeventinputs.size() > 1) {
NUIS_ERR(
WRN,
"GiBUU sample contains multiple inputs. This will only work for "
"samples that expect multi-species inputs. If this sample does, you "
"can ignore this warning.");
}
for (size_t i = 0; i < jointeventinputs.size(); i++) {
double scale = double(fNEvents) / fEventHist->Integral("width");
scale *= jointeventinputs.at(i)->Integral("width");
scale /= double(jointindexhigh[i] - jointindexlow[i]);
jointindexscale.push_back(scale);
}
fEventHist->SetNameTitle((fName + "_EVT").c_str(), (fName + "_EVT").c_str());
fFluxHist->SetNameTitle((fName + "_FLUX").c_str(), (fName + "_FLUX").c_str());
// Setup Max Events
if (fMaxEvents > 1 && fMaxEvents < fNEvents) {
if (LOG_LEVEL(SAM)) {
std::cout << "\t\t|-> Read Max Entries : " << fMaxEvents << std::endl;
}
fNEvents = fMaxEvents;
}
// Print out Status
if (LOG_LEVEL(SAM)) {
std::cout << "\t\t|-> Total Entries : " << fNEvents << std::endl
<< "\t\t|-> Event Integral : "
<< fEventHist->Integral("width") * 1.E-38 << " events/nucleon"
<< std::endl
<< "\t\t|-> Flux Integral : " << fFluxHist->Integral("width")
<< " /cm2" << std::endl
<< "\t\t|-> Event/Flux : "
<< fEventHist->Integral("width") * 1.E-38 /
fFluxHist->Integral("width")
<< " cm2/nucleon" << std::endl;
}
}
BaseFitEvt *InputHandlerBase::GetBaseEvent(const UInt_t entry) {
// Do some light processing: don't calculate the kinematics
return static_cast(GetNuisanceEvent(entry, true));
}
double InputHandlerBase::GetInputWeight(int entry) {
if (!jointinput)
return 1.0;
// Find Switch Scale
while (entry < jointindexlow[jointindexswitch] ||
entry >= jointindexhigh[jointindexswitch]) {
jointindexswitch++;
// Loop Around
if (jointindexswitch >= jointindexlow.size()) {
jointindexswitch = 0;
}
}
return jointindexscale[jointindexswitch];
};
diff --git a/src/MINERvA/MINERvA_CC0pi_XSec_2D_nu.cxx b/src/MINERvA/MINERvA_CC0pi_XSec_2D_nu.cxx
index 705b97e..519685d 100644
--- a/src/MINERvA/MINERvA_CC0pi_XSec_2D_nu.cxx
+++ b/src/MINERvA/MINERvA_CC0pi_XSec_2D_nu.cxx
@@ -1,263 +1,288 @@
// 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 .
*******************************************************************************/
/*
Authors: Adrian Orea (v1 2017)
Clarence Wret (v2 2018)
*/
#include "MINERvA_CC0pi_XSec_2D_nu.h"
#include "MINERvA_SignalDef.h"
//********************************************************************
void MINERvA_CC0pi_XSec_2D_nu::SetupDataSettings() {
//********************************************************************
// Define what files to use from the dist
std::string datafile = "";
std::string corrfile = "";
std::string titles = "";
std::string distdescript = "";
std::string histname = "";
datafile = "MINERvA/CC0pi_2D/cov_ptpl_2D_qelike.root";
corrfile = "MINERvA/CC0pi_2D/cov_ptpl_2D_qelike.root";
titles = "MINERvA CC0#pi #nu_{#mu} p_{t} p_{z};p_{z} (GeV);p_{t} "
- "(GeV);d^{2}#sigma/dP_{t}dP_{z} (cm^{2}/GeV^{2}/nucleon)";
+ "(GeV);d^{2}#sigma/dP_{t}dP_{z} (cm^{2}/GeV^{2}/nucleon)";
distdescript = "MINERvA_CC0pi_XSec_2Dptpz_nu sample";
histname = "pt_pl_cross_section";
fSettings.SetTitle(GeneralUtils::ParseToStr(titles, ";")[0]);
fSettings.SetXTitle(GeneralUtils::ParseToStr(titles, ";")[1]);
fSettings.SetYTitle(GeneralUtils::ParseToStr(titles, ";")[2]);
fSettings.SetZTitle(GeneralUtils::ParseToStr(titles, ";")[3]);
// Sample overview ---------------------------------------------------
std::string descrip = distdescript + "\n"
- "Target: CH \n"
- "Flux: MINERvA Low Energy FHC numu \n"
- "Signal: CC-0pi \n";
+ "Target: CH \n"
+ "Flux: MINERvA Low Energy FHC numu \n"
+ "Signal: CC-0pi \n";
fSettings.SetDescription(descrip);
// The input ROOT file
fSettings.SetDataInput(FitPar::GetDataBase() + datafile);
fSettings.SetCovarInput(FitPar::GetDataBase() + corrfile);
// Set the data file
SetDataValues(fSettings.GetDataInput(), histname);
}
//********************************************************************
MINERvA_CC0pi_XSec_2D_nu::MINERvA_CC0pi_XSec_2D_nu(nuiskey samplekey) {
//********************************************************************
// Setup common settings
fSettings = LoadSampleSettings(samplekey);
fSettings.SetAllowedTypes("FIX,FREE,SHAPE/FULL,DIAG/MASK", "FIX/FULL");
fSettings.SetEnuRange(0.0, 100.0);
fSettings.DefineAllowedTargets("C,H");
fSettings.DefineAllowedSpecies("numu");
SetupDataSettings();
FinaliseSampleSettings();
fScaleFactor =
- (GetEventHistogram()->Integral("width") * 1E-38 / (fNEvents + 0.)) /
- this->TotalIntegratedFlux();
+ (GetEventHistogram()->Integral("width") * 1E-38 / (fNEvents + 0.)) /
+ this->TotalIntegratedFlux();
TMatrixDSym *tempmat = StatUtils::GetCovarFromRootFile(
fSettings.GetCovarInput(), "TotalCovariance");
fFullCovar = tempmat;
// Decomposition is stable for entries that aren't E-xxx
double ScalingFactor = 1E38 * 1E38;
(*fFullCovar) *= ScalingFactor;
// Just check that the data error and covariance are the same
for (int i = 0; i < fFullCovar->GetNrows(); ++i) {
for (int j = 0; j < fFullCovar->GetNcols(); ++j) {
// Get the global bin
int xbin1, ybin1, zbin1;
fDataHist->GetBinXYZ(i, xbin1, ybin1, zbin1);
double xlo1 = fDataHist->GetXaxis()->GetBinLowEdge(xbin1);
double xhi1 = fDataHist->GetXaxis()->GetBinLowEdge(xbin1 + 1);
double ylo1 = fDataHist->GetYaxis()->GetBinLowEdge(ybin1);
double yhi1 = fDataHist->GetYaxis()->GetBinLowEdge(ybin1 + 1);
if (xlo1 < fDataHist->GetXaxis()->GetBinLowEdge(1) ||
ylo1 < fDataHist->GetYaxis()->GetBinLowEdge(1) ||
xhi1 > fDataHist->GetXaxis()->GetBinLowEdge(
- fDataHist->GetXaxis()->GetNbins() + 1) ||
+ fDataHist->GetXaxis()->GetNbins() + 1) ||
yhi1 > fDataHist->GetYaxis()->GetBinLowEdge(
- fDataHist->GetYaxis()->GetNbins() + 1))
+ fDataHist->GetYaxis()->GetNbins() + 1))
continue;
double data_error = fDataHist->GetBinError(xbin1, ybin1);
double cov_error = sqrt((*fFullCovar)(i, i) / ScalingFactor);
if (fabs(data_error - cov_error) > 1E-5) {
std::cerr << "Error on data is different to that of covariance"
- << std::endl;
+ << std::endl;
NUIS_ERR(FTL, "Data error: " << data_error);
NUIS_ERR(FTL, "Cov error: " << cov_error);
NUIS_ERR(FTL, "Data/Cov: " << data_error / cov_error);
NUIS_ERR(FTL, "Data-Cov: " << data_error - cov_error);
NUIS_ERR(FTL, "For x: " << xlo1 << "-" << xhi1);
NUIS_ABORT("For y: " << ylo1 << "-" << yhi1);
}
}
}
// Now can make the inverted covariance
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
// Now scale back
(*fFullCovar) *= 1.0 / ScalingFactor;
- (*covar) *= ScalingFactor;
(*fDecomp) *= ScalingFactor;
+ //Don't scale this back as GetLikelihood expects it to look like this
+ // (*covar) *= ScalingFactor;
+
+ fMapHist = new TH2I("MINERvA_CC0pi_XSec_2D_nu_maphist", "",
+ fDataHist->GetNbinsX(), 0, fDataHist->GetNbinsX(),
+ fDataHist->GetNbinsY(), 0, fDataHist->GetNbinsY());
+
+ int nbinsx = fDataHist->GetNbinsX();
+ int nbinsy = fDataHist->GetNbinsY();
+ Int_t Nbins = nbinsx * nbinsy;
+
+ // Loop over the covariance matrix bins
+ for (int i = 0; i < Nbins; ++i) {
+ int xbin = (i % nbinsx) + 1;
+ int ybin = (i / nbinsx) + 1;
+
+ fMapHist->SetBinContent(xbin, ybin, i+1);
+ }
// Final setup ---------------------------------------------------
FinaliseMeasurement();
};
//********************************************************************
void MINERvA_CC0pi_XSec_2D_nu::FillEventVariables(FitEvent *event) {
//********************************************************************
// Checking to see if there is a Muon
if (event->NumFSParticle(13) == 0)
return;
// Get the muon kinematics
TLorentzVector Pmu = event->GetHMFSParticle(13)->fP;
TLorentzVector Pnu = event->GetNeutrinoIn()->fP;
Double_t px = Pmu.X() / 1000;
Double_t py = Pmu.Y() / 1000;
Double_t pt = sqrt(px * px + py * py);
// y-axis is transverse momentum for both measurements
fYVar = pt;
// Don't want to assume the event generators all have neutrino coming along
// z pz is muon momentum projected onto the neutrino direction
Double_t pz = Pmu.Vect().Dot(Pnu.Vect() * (1.0 / Pnu.Vect().Mag())) / 1000.;
// Set Hist Variables
fXVar = pz;
};
//********************************************************************
bool MINERvA_CC0pi_XSec_2D_nu::isSignal(FitEvent *event) {
//********************************************************************
return SignalDef::isCC0pi_MINERvAPTPZ(event, 14, EnuMin, EnuMax);
};
-//********************************************************************
-// Custom likelihood calculator because binning of covariance matrix
-double MINERvA_CC0pi_XSec_2D_nu::GetLikelihood() {
- //********************************************************************
-
- // The calculated chi2
- double chi2 = 0.0;
-
- // Support shape comparisons
- double scaleF = fDataHist->Integral() / fMCHist->Integral();
- if (fIsShape) {
- fMCHist->Scale(scaleF);
- fMCFine->Scale(scaleF);
- }
-
- // Even though this chi2 calculation looks ugly it is _EXACTLY_ what MINERvA
- // used for their measurement Can be prettified in due time but for now keep
-
- int nbinsx = fMCHist->GetNbinsX();
- int nbinsy = fMCHist->GetNbinsY();
- Int_t Nbins = nbinsx * nbinsy;
-
- // Loop over the covariance matrix bins
- for (int i = 0; i < Nbins; ++i) {
- int xbin = (i % nbinsx) + 1;
- int ybin = (i / nbinsx) + 1;
- double datax = fDataHist->GetBinContent(xbin, ybin);
- double mcx = fMCHist->GetBinContent(xbin, ybin);
- double chi2_bin = 0;
- for (int j = 0; j < Nbins; ++j) {
- int xbin2 = (j % nbinsx) + 1;
- int ybin2 = (j / nbinsx) + 1;
-
- double datay = fDataHist->GetBinContent(xbin2, ybin2);
- double mcy = fMCHist->GetBinContent(xbin2, ybin2);
-
- double chi2_xy = (datax - mcx) * (*covar)(i, j) * (datay - mcy);
- chi2_bin += chi2_xy;
- }
- if (fResidualHist) {
- fResidualHist->SetBinContent(xbin, ybin, chi2_bin);
- }
- chi2 += chi2_bin;
- }
-
- if (fChi2LessBinHist) {
- for (int igbin = 0; igbin < Nbins; ++igbin) {
- int igxbin = (igbin % nbinsx) + 1;
- int igybin = (igbin / nbinsx) + 1;
- double tchi2 = 0;
- for (int i = 0; i < Nbins; ++i) {
- int xbin = (i % nbinsx) + 1;
- int ybin = (i / nbinsx) + 1;
- if ((xbin == igxbin) && (ybin == igybin)) {
- continue;
- }
- double datax = fDataHist->GetBinContent(xbin, ybin);
- double mcx = fMCHist->GetBinContent(xbin, ybin);
- double chi2_bin = 0;
- for (int j = 0; j < Nbins; ++j) {
- int xbin2 = (j % nbinsx) + 1;
- int ybin2 = (j / nbinsx) + 1;
- if ((xbin2 == igxbin) && (ybin2 == igybin)) {
- continue;
- }
- double datay = fDataHist->GetBinContent(xbin2, ybin2);
- double mcy = fMCHist->GetBinContent(xbin2, ybin2);
-
- double chi2_xy = (datax - mcx) * (*covar)(i, j) * (datay - mcy);
- chi2_bin += chi2_xy;
- }
- tchi2 += chi2_bin;
- }
-
- fChi2LessBinHist->SetBinContent(igxbin, igybin, tchi2);
- }
- }
-
- // Normalisation penalty term if included
- if (fAddNormPen) {
- chi2 +=
- (1 - (fCurrentNorm)) * (1 - (fCurrentNorm)) / (fNormError * fNormError);
- NUIS_LOG(REC, "Norm penalty = " << (1 - (fCurrentNorm)) *
- (1 - (fCurrentNorm)) /
- (fNormError * fNormError));
- }
-
- // Adjust the shape back to where it was.
- if (fIsShape and !FitPar::Config().GetParB("saveshapescaling")) {
- fMCHist->Scale(1. / scaleF);
- fMCFine->Scale(1. / scaleF);
- }
-
- fLikelihood = chi2;
-
- return chi2;
-};
+// //********************************************************************
+// // Custom likelihood calculator because binning of covariance matrix
+// double MINERvA_CC0pi_XSec_2D_nu::GetLikelihood() {
+// //********************************************************************
+
+// return Measurement2D::GetLikelihood();
+
+// // The calculated chi2
+// double chi2 = 0.0;
+
+// // Support shape comparisons
+// double scaleF = fDataHist->Integral() / fMCHist->Integral();
+// if (fIsShape) {
+// fMCHist->Scale(scaleF);
+// fMCFine->Scale(scaleF);
+// }
+
+// int nbinsx = fMCHist->GetNbinsX();
+// int nbinsy = fMCHist->GetNbinsY();
+// Int_t Nbins = nbinsx * nbinsy;
+
+// // Even though this chi2 calculation looks ugly it is _EXACTLY_ what MINERvA
+// // used for their measurement Can be prettified in due time but for now keep
+
+// // Loop over the covariance matrix bins
+// for (int i = 0; i < Nbins; ++i) {
+// int xbin = (i % nbinsx) + 1;
+// int ybin = (i / nbinsx) + 1;
+// double datax = fDataHist->GetBinContent(xbin, ybin);
+// double mcx = fMCHist->GetBinContent(xbin, ybin);
+
+// // std::cout << "CMap( " << xbin << ", " << ybin << ") = " << i
+// // << ", mc = " << mcx << ", mapped("
+// // << fMapHist->GetBinContent(xbin, ybin)
+// // << ") = " << Mapped_MC->GetBinContent(i) << std::endl;
+
+// double chi2_bin = 0;
+// for (int j = 0; j < Nbins; ++j) {
+// int xbin2 = (j % nbinsx) + 1;
+// int ybin2 = (j / nbinsx) + 1;
+
+// double datay = fDataHist->GetBinContent(xbin2, ybin2);
+// double mcy = fMCHist->GetBinContent(xbin2, ybin2);
+
+// double chi2_xy = (datax - mcx) * (*covar)(i, j) * (datay - mcy);
+// chi2_bin += chi2_xy;
+// }
+// if (fResidualHist) {
+// fResidualHist->SetBinContent(xbin, ybin, chi2_bin);
+// }
+// chi2 += chi2_bin;
+// }
+
+// if (fChi2LessBinHist) {
+// for (int igbin = 0; igbin < Nbins; ++igbin) {
+// int igxbin = (igbin % nbinsx) + 1;
+// int igybin = (igbin / nbinsx) + 1;
+// double tchi2 = 0;
+// for (int i = 0; i < Nbins; ++i) {
+// int xbin = (i % nbinsx) + 1;
+// int ybin = (i / nbinsx) + 1;
+// if ((xbin == igxbin) && (ybin == igybin)) {
+// continue;
+// }
+// double datax = fDataHist->GetBinContent(xbin, ybin);
+// double mcx = fMCHist->GetBinContent(xbin, ybin);
+// double chi2_bin = 0;
+// for (int j = 0; j < Nbins; ++j) {
+// int xbin2 = (j % nbinsx) + 1;
+// int ybin2 = (j / nbinsx) + 1;
+// if ((xbin2 == igxbin) && (ybin2 == igybin)) {
+// continue;
+// }
+// double datay = fDataHist->GetBinContent(xbin2, ybin2);
+// double mcy = fMCHist->GetBinContent(xbin2, ybin2);
+
+// double chi2_xy = (datax - mcx) * (*covar)(i, j) * (datay - mcy);
+// chi2_bin += chi2_xy;
+// }
+// tchi2 += chi2_bin;
+// }
+
+// fChi2LessBinHist->SetBinContent(igxbin, igybin, tchi2);
+// }
+// }
+
+// // Normalisation penalty term if included
+// if (fAddNormPen) {
+// chi2 +=
+// (1 - (fCurrentNorm)) * (1 - (fCurrentNorm)) / (fNormError * fNormError);
+// NUIS_LOG(REC, "Norm penalty = " << (1 - (fCurrentNorm)) *
+// (1 - (fCurrentNorm)) /
+// (fNormError * fNormError));
+// }
+
+// // Adjust the shape back to where it was.
+// if (fIsShape and !FitPar::Config().GetParB("saveshapescaling")) {
+// fMCHist->Scale(1. / scaleF);
+// fMCFine->Scale(1. / scaleF);
+// }
+
+// fLikelihood = chi2;
+
+// return chi2;
+// }
diff --git a/src/MINERvA/MINERvA_CC0pi_XSec_2D_nu.h b/src/MINERvA/MINERvA_CC0pi_XSec_2D_nu.h
index cead984..2219083 100644
--- a/src/MINERvA/MINERvA_CC0pi_XSec_2D_nu.h
+++ b/src/MINERvA/MINERvA_CC0pi_XSec_2D_nu.h
@@ -1,50 +1,50 @@
// 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 .
*******************************************************************************/
#ifndef MINERVA_CC0PI_XSEC_2D_NU_H_SEEN
#define MINERVA_CC0PI_XSEC_2D_NU_H_SEEN
#include "Measurement2D.h"
-//********************************************************************
+//********************************************************************
class MINERvA_CC0pi_XSec_2D_nu : public Measurement2D {
-//********************************************************************
+//********************************************************************
public:
// Constructor
MINERvA_CC0pi_XSec_2D_nu(nuiskey samplekey);
// Destructor
virtual ~MINERvA_CC0pi_XSec_2D_nu() {};
// Required functions
bool isSignal(FitEvent *nvect);
void FillEventVariables(FitEvent *event);
-
+
protected:
// Converted covariance matrix to provide global binning method in GetLikelihood
- double GetLikelihood();
+ // double GetLikelihood();
// Set up settings based on distribution
void SetupDataSettings();
};
-
+
#endif
diff --git a/src/MINERvA/MINERvA_CC0pi_XSec_3DptpzTp_nu.cxx b/src/MINERvA/MINERvA_CC0pi_XSec_3DptpzTp_nu.cxx
index 43e4228..042a490 100644
--- a/src/MINERvA/MINERvA_CC0pi_XSec_3DptpzTp_nu.cxx
+++ b/src/MINERvA/MINERvA_CC0pi_XSec_3DptpzTp_nu.cxx
@@ -1,255 +1,255 @@
// 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 .
*******************************************************************************/
/*
Authors: Clarence Wret (v2 2018)
*/
#include "MINERvA_CC0pi_XSec_3DptpzTp_nu.h"
#include "MINERvA_SignalDef.h"
//********************************************************************
void MINERvA_CC0pi_XSec_3DptpzTp_nu::SetupDataSettings() {
//********************************************************************
// Define what files to use from the dist
std::string datafile = "";
std::string corrfile = "";
std::string titles = "";
std::string distdescript = "";
std::string histname = "";
datafile = "MINERvA/CC0pi_3D/cov_ptpl_3DptpzTp_qelike.root";
corrfile = "MINERvA/CC0pi_3D/cov_ptpl_3DptpzTp_qelike.root";
titles = "MINERvA CC0#pi #nu_{#mu} p_{t} p_{z};p_{z} (GeV);p_{t} "
"(GeV);d^{2}#sigma/dP_{t}dP_{z} (cm^{2}/GeV^{2}/nucleon)";
distdescript = "MINERvA_CC0pi_XSec_3DptpzTp_nu sample";
histname = "pt_pl_cross_section";
fSettings.SetTitle(GeneralUtils::ParseToStr(titles, ";")[0]);
fSettings.SetXTitle(GeneralUtils::ParseToStr(titles, ";")[1]);
fSettings.SetYTitle(GeneralUtils::ParseToStr(titles, ";")[2]);
fSettings.SetZTitle(GeneralUtils::ParseToStr(titles, ";")[3]);
// Sample overview ---------------------------------------------------
std::string descrip = distdescript + "\n"
"Target: CH \n"
"Flux: MINERvA Low Energy FHC numu \n"
"Signal: CC-0pi \n";
fSettings.SetDescription(descrip);
// The input ROOT file
//fSettings.SetDataInput(FitPar::GetDataBase() + datafile);
//fSettings.SetCovarInput(FitPar::GetDataBase() + corrfile);
nptbins = 7;
ptbins = {0, 0.15, 0.25, 0.4, 0.7, 1, 2.5}; // GeV
npzbins = 4;
pzbins = {1.5, 3.5, 8, 20}; // GeV
ntpbins = 15;
sumTpbins = {0, 40, 80, 120, 160, 200, 240, 280, 320, 360, 400, 600, 800, 1000, 10000}; // MeV
// Data is actually an array of 2D measurements
// Have the 2D in pt pz and then Tp to be the left over axis
//fDataHist = new TH2D("minerva_test", "minerva_test", nptbins, ptbins, npzbins, pzbins);
for (int i = 0; i < ntpbins; ++i) {
fDataHist_Slices.push_back(new TH2D(Form("minerva_data_test_%i", i), Form("minerva_data_test_%i", i), nptbins, ptbins, npzbins, pzbins);
fMCHist_Slices.push_back(new TH2D(Form("minerva_mc_test_%i", i), Form("minerva_mc_test_%i", i), nptbins, ptbins, npzbins, pzbins);
}
//fDataHist->SetName((fSettings.GetName() + "_data").c_str());
//fDataHist->SetTitle(fSettings.GetFullTitles().c_str());
}
//********************************************************************
MINERvA_CC0pi_XSec_3DptpzTp_nu::MINERvA_CC0pi_XSec_3DptpzTp_nu(nuiskey samplekey) {
//********************************************************************
// Setup common settings
fSettings = LoadSampleSettings(samplekey);
fSettings.SetAllowedTypes("FIX,FREE,SHAPE/FULL,DIAG/MASK", "FIX/FULL");
fSettings.SetEnuRange(0.0, 100.0);
fSettings.DefineAllowedTargets("C,H");
fSettings.DefineAllowedSpecies("numu");
SetupDataSettings();
FinaliseSampleSettings();
fScaleFactor =
(GetEventHistogram()->Integral("width") * 1E-38 / (fNEvents + 0.)) /
this->TotalIntegratedFlux();
// Final setup ---------------------------------------------------
FinaliseMeasurement();
};
//********************************************************************
void MINERvA_CC0pi_XSec_3DptpzTp_nu::FillEventVariables(FitEvent *event) {
//********************************************************************
// Checking to see if there is a Muon
if (event->NumFSParticle(13) == 0) return;
// Get the muon kinematics
TLorentzVector Pmu = event->GetHMFSParticle(13)->fP;
TLorentzVector Pnu = event->GetNeutrinoIn()->fP;
Double_t px = Pmu.X() / 1000;
Double_t py = Pmu.Y() / 1000;
Double_t pt = sqrt(px * px + py * py);
// y-axis is transverse momentum for both measurements
fYVar = pt;
// Don't want to assume the event generators all have neutrino coming along
// z pz is muon momentum projected onto the neutrino direction
Double_t pz = Pmu.Vect().Dot(Pnu.Vect() * (1.0 / Pnu.Vect().Mag())) / 1000.;
// Set Hist Variables
fXVar = pz;
// Sum up kinetic energy of protons
double sum = 0.0;
- for (std::vector::iterator it = event->GetAllFSProton().begin();
+ for (std::vector::iterator it = event->GetAllFSProton().begin();
it != event->GetAllFSProton().end(); ++it) {
sum += (*it)->KE();
}
fZVar = sum;
};
void MINERvA_CC0pi_XSec_3DptpzTp_nu::FillHistograms() {
Measurement2D::FillHistograms();
if (Signal) {
FillMCSlice(fXVar, fYVar, fZVar, Weight);
}
}
void MINERvA_CC0pi_XSec_3DptpzTp_nu::FillMCSlice(double x, double y, double z, double w) {
// Find the bin
for (int i = 0; i < ntpbins; ++i) {
if (z > sumTpbins[i] && z < sumTpbins[i+1]) fMCHist_Slices[i]->Fill(y, x, w);
}
}
//********************************************************************
bool MINERvA_CC0pi_XSec_3DptpzTp_nu::isSignal(FitEvent *event) {
//********************************************************************
return SignalDef::isCC0pi_MINERvAPTPZ(event, 14, EnuMin, EnuMax);
// From Dan
// if not (2212 or 2112 or 22 and E<10) BAD BAD BAD
};
//********************************************************************
// Custom likelihood calculator because binning of covariance matrix
double MINERvA_CC0pi_XSec_3DptpzTp_nu::GetLikelihood() {
//********************************************************************
// The calculated chi2
double chi2 = 0.0;
// Support shape comparisons
double scaleF = fDataHist->Integral() / fMCHist->Integral();
if (fIsShape) {
fMCHist->Scale(scaleF);
fMCFine->Scale(scaleF);
}
// Even though this chi2 calculation looks ugly it is _EXACTLY_ what MINERvA
// used for their measurement Can be prettified in due time but for now keep
int nbinsx = fMCHist->GetNbinsX();
int nbinsy = fMCHist->GetNbinsY();
Int_t Nbins = nbinsx * nbinsy;
// Loop over the covariance matrix bins
for (int i = 0; i < Nbins; ++i) {
int xbin = (i % nbinsx) + 1;
int ybin = (i / nbinsx) + 1;
double datax = fDataHist->GetBinContent(xbin, ybin);
double mcx = fMCHist->GetBinContent(xbin, ybin);
double chi2_bin = 0;
for (int j = 0; j < Nbins; ++j) {
int xbin2 = (j % nbinsx) + 1;
int ybin2 = (j / nbinsx) + 1;
double datay = fDataHist->GetBinContent(xbin2, ybin2);
double mcy = fMCHist->GetBinContent(xbin2, ybin2);
double chi2_xy = (datax - mcx) * (*covar)(i, j) * (datay - mcy);
chi2_bin += chi2_xy;
}
if (fResidualHist) {
fResidualHist->SetBinContent(xbin, ybin, chi2_bin);
}
chi2 += chi2_bin;
}
if (fChi2LessBinHist) {
for (int igbin = 0; igbin < Nbins; ++igbin) {
int igxbin = (igbin % nbinsx) + 1;
int igybin = (igbin / nbinsx) + 1;
double tchi2 = 0;
for (int i = 0; i < Nbins; ++i) {
int xbin = (i % nbinsx) + 1;
int ybin = (i / nbinsx) + 1;
if ((xbin == igxbin) && (ybin == igybin)) {
continue;
}
double datax = fDataHist->GetBinContent(xbin, ybin);
double mcx = fMCHist->GetBinContent(xbin, ybin);
double chi2_bin = 0;
for (int j = 0; j < Nbins; ++j) {
int xbin2 = (j % nbinsx) + 1;
int ybin2 = (j / nbinsx) + 1;
if ((xbin2 == igxbin) && (ybin2 == igybin)) {
continue;
}
double datay = fDataHist->GetBinContent(xbin2, ybin2);
double mcy = fMCHist->GetBinContent(xbin2, ybin2);
double chi2_xy = (datax - mcx) * (*covar)(i, j) * (datay - mcy);
chi2_bin += chi2_xy;
}
tchi2 += chi2_bin;
}
fChi2LessBinHist->SetBinContent(igxbin, igybin, tchi2);
}
}
// Normalisation penalty term if included
if (fAddNormPen) {
chi2 +=
(1 - (fCurrentNorm)) * (1 - (fCurrentNorm)) / (fNormError * fNormError);
NUIS_LOG(REC, "Norm penalty = " << (1 - (fCurrentNorm)) *
(1 - (fCurrentNorm)) /
(fNormError * fNormError));
}
// Adjust the shape back to where it was.
if (fIsShape and !FitPar::Config().GetParB("saveshapescaling")) {
fMCHist->Scale(1. / scaleF);
fMCFine->Scale(1. / scaleF);
}
fLikelihood = chi2;
return chi2;
};
diff --git a/src/Reweight/ModeNormEngine.h b/src/Reweight/ModeNormEngine.h
index 124b189..e28b455 100644
--- a/src/Reweight/ModeNormEngine.h
+++ b/src/Reweight/ModeNormEngine.h
@@ -1,86 +1,88 @@
#ifndef ModeNormEngine_H
#define ModeNormEngine_H
#include "FitLogger.h"
#include "GeneratorUtils.h"
#include "WeightEngineBase.h"
class ModeNormEngine : public WeightEngineBase {
public:
ModeNormEngine(std::string name = "ModeNormEngine") : fName(name){};
~ModeNormEngine(){};
void IncludeDial(std::string name, double startval) {
int rwenum = Reweight::ConvDial(name, kMODENORM);
int mode = Reweight::RemoveDialType(rwenum);
if (fDialEnumIndex.count(mode)) {
NUIS_ABORT("Mode dial: " << mode
- << " already included. Cannot include twice.");
+ << " already included. Cannot include twice.");
}
fDialEnumIndex[mode] = fDialValues.size();
fDialValues.push_back(startval);
- NUIS_LOG(FIT, "Added mode dial for mode: " << mode);
+ NUIS_LOG(FIT, "Added mode dial for mode: " << DialToMode(mode));
}
void SetDialValue(int rwenum, double val) {
int mode = Reweight::RemoveDialType(rwenum);
if (!fDialEnumIndex.count(mode)) {
NUIS_ABORT("Mode dial: " << mode
- << " has not been included. Cannot set value.");
+ << " has not been included. Cannot set value.");
}
- NUIS_LOG(DEB, "[INFO]: ModeNormEngine ObsMode: " << mode << " weight " << val
- << ", rwenum = " << rwenum);
+ NUIS_LOG(DEB, "[INFO]: ModeNormEngine ObsMode: "
+ << mode << " weight " << val << ", rwenum = " << rwenum);
fDialValues[fDialEnumIndex[mode]] = val;
}
void SetDialValue(std::string name, double val) {
SetDialValue(Reweight::ConvDial(name, kMODENORM), val);
}
void Reconfigure(bool silent = false) { (void)silent; }
- static int ModeToDial(int mode) { return 60 + mode; }
+ static int ModeToDial(int mode) { return 100 + mode; }
+ static int DialToMode(int dial) { return dial - 100; }
double CalcWeight(BaseFitEvt *evt) {
int mode = ModeToDial(abs(evt->Mode));
if (!fDialEnumIndex.count(mode)) {
return 1;
}
NUIS_LOG(DEB, "[INFO]: Ev mode "
- << evt->Mode << ", ObsMode: " << mode
- << ", weight = " << fDialValues[fDialEnumIndex[mode]]);
+ << evt->Mode << ", ObsMode: " << mode
+ << ", weight = " << fDialValues[fDialEnumIndex[mode]]);
return fDialValues[fDialEnumIndex[mode]];
};
bool NeedsEventReWeight() { return false; };
double GetDialValue(std::string name) {
int rwenum = Reweight::ConvDial(name, kMODENORM);
int mode = Reweight::RemoveDialType(rwenum);
if (fDialEnumIndex.count(mode)) {
return fDialValues[fDialEnumIndex[mode]];
} else {
return 0xdeadbeef;
}
}
static int SystEnumFromString(std::string const &name) {
std::vector splits = GeneralUtils::ParseToStr(name, "_");
if (splits.size() != 2) {
- NUIS_ABORT("Attempting to parse dial name: \""
- << name
- << "\" as a mode norm dial but failed. Expect e.g. \"mode_2\".");
+ NUIS_ABORT(
+ "Attempting to parse dial name: \""
+ << name
+ << "\" as a mode norm dial but failed. Expect e.g. \"mode_2\".");
}
int mode_num = GeneralUtils::StrToInt(splits[1]);
if (!mode_num) {
NUIS_ABORT("Attempting to parse dial name: \""
- << name << "\" as a mode norm dial but failed.");
+ << name << "\" as a mode norm dial but failed.");
}
return ModeToDial(mode_num);
}
std::map fDialEnumIndex;
std::vector fDialValues;
std::string fName;
};
#endif
diff --git a/src/Statistical/StatUtils.cxx b/src/Statistical/StatUtils.cxx
index a12a28a..87a41fa 100644
--- a/src/Statistical/StatUtils.cxx
+++ b/src/Statistical/StatUtils.cxx
@@ -1,1451 +1,1437 @@
// 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 .
*******************************************************************************/
#include "StatUtils.h"
#include "GeneralUtils.h"
#include "NuisConfig.h"
#include "TH1D.h"
//*******************************************************************
Double_t StatUtils::GetChi2FromDiag(TH1D *data, TH1D *mc, TH1I *mask) {
//*******************************************************************
Double_t Chi2 = 0.0;
TH1D *calc_data = (TH1D *)data->Clone("calc_data");
calc_data->SetDirectory(NULL);
TH1D *calc_mc = (TH1D *)mc->Clone("calc_mc");
calc_mc->SetDirectory(NULL);
// Add MC Error to data if required
if (FitPar::Config().GetParB("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_data->SetDirectory(NULL);
calc_mc = ApplyHistogramMasking(mc, mask);
calc_mc->SetDirectory(NULL);
}
// 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, TH1D *outchi2perbin) {
//*******************************************************************
Double_t Chi2 = 0.0;
TMatrixDSym *calc_cov = (TMatrixDSym *)invcov->Clone("local_invcov");
TH1D *calc_data = (TH1D *)data->Clone("local_data");
TH1D *calc_mc = (TH1D *)mc->Clone("local_mc");
calc_data->SetDirectory(NULL);
calc_mc->SetDirectory(NULL);
// 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);
NUIS_LOG(FIT, "Adding cov stat " << mcerr * mcerr << " to "
<< (*newcov)(i, i));
(*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)
NUIS_LOG(DEB, "START Chi2 Calculation=================");
for (int i = 0; i < calc_data->GetNbinsX(); i++) {
double ibin_contrib = 0;
NUIS_LOG(DEB, "[CHI2] i = "
<< i << " ["
<< calc_data->GetXaxis()->GetBinLowEdge(i + 1) << " -- "
<< calc_data->GetXaxis()->GetBinUpEdge(i + 1) << "].");
for (int j = 0; j < calc_data->GetNbinsX(); j++) {
NUIS_LOG(DEB, "[CHI2]\t j = "
<< i << " ["
<< calc_data->GetXaxis()->GetBinLowEdge(j + 1) << " -- "
<< calc_data->GetXaxis()->GetBinUpEdge(j + 1) << "].");
if ((calc_data->GetBinContent(i + 1) != 0 ||
calc_mc->GetBinContent(i + 1) != 0) &&
((*calc_cov)(i, j) != 0)) {
NUIS_LOG(DEB, "[CHI2]\t\t Chi2 contribution (i,j) = (" << i << "," << j
<< ")");
NUIS_LOG(DEB, "[CHI2]\t\t Data - MC(i) = "
<< calc_data->GetBinContent(i + 1) << " - "
<< calc_mc->GetBinContent(i + 1) << " = "
<< (calc_data->GetBinContent(i + 1) -
calc_mc->GetBinContent(i + 1)));
NUIS_LOG(DEB, "[CHI2]\t\t Data - MC(j) = "
<< calc_data->GetBinContent(j + 1) << " - "
<< calc_mc->GetBinContent(j + 1) << " = "
<< (calc_data->GetBinContent(j + 1) -
calc_mc->GetBinContent(j + 1)));
NUIS_LOG(DEB, "[CHI2]\t\t Covar = " << (*calc_cov)(i, j));
NUIS_LOG(DEB, "[CHI2]\t\t 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);
double bin_cont =
((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 += bin_cont;
ibin_contrib += bin_cont;
} else {
NUIS_LOG(DEB, "Skipping chi2 contribution (i,j) = ("
<< i << "," << j
<< "), Data = " << calc_data->GetBinContent(i + 1)
<< ", MC = " << calc_mc->GetBinContent(i + 1)
<< ", Cov = " << (*calc_cov)(i, j));
Chi2 += 0.;
}
}
if (outchi2perbin) {
outchi2perbin->SetBinContent(i + 1, ibin_contrib);
}
}
// 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, TH2D *outchi2perbin) {
//*******************************************************************
// 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);
- TH1D *outchi2perbin_1D = NULL;
- TH1D *outchi2perbin_map_1D = NULL;
-
- if (outchi2perbin) {
- outchi2perbin_1D = MapToTH1D(outchi2perbin, map);
- for (Int_t xbi_it = 0; xbi_it < outchi2perbin->GetXaxis()->GetNbins();
- ++xbi_it) {
- for (Int_t ybi_it = 0; ybi_it < outchi2perbin->GetYaxis()->GetNbins();
- ++ybi_it) {
- int gbin = outchi2perbin->GetBin(xbi_it + 1, ybi_it + 1);
- // std::cout << " gbin " << gbin << " corresponds to "
- // << " x: " << (xbi_it + 1) << ", y: " << (ybi_it + 1)
- // << std::endl;
- outchi2perbin->SetBinContent(xbi_it + 1, ybi_it + 1, gbin);
- }
- }
- outchi2perbin_map_1D = MapToTH1D(outchi2perbin, map);
- }
+ TH1D *outchi2perbin_1D = outchi2perbin ? MapToTH1D(outchi2perbin, map) : NULL;
// Calculate 1D chi2 from 1D Plots
Double_t Chi2 = StatUtils::GetChi2FromCov(data_1D, mc_1D, invcov, mask_1D, 1,
1E76, outchi2perbin_1D);
- if (outchi2perbin) {
- for (int xbi_it = 0; xbi_it < outchi2perbin_1D->GetXaxis()->GetNbins();
- ++xbi_it) {
- // std::cout << " adding chi2 "
- // << outchi2perbin_1D->GetBinContent(xbi_it + 1)
- // << " from 1d bin " << (xbi_it + 1) << " to gbin "
- // << outchi2perbin_map_1D->GetBinContent(xbi_it + 1) <<
- // std::endl;
- outchi2perbin->SetBinContent(
- outchi2perbin_map_1D->GetBinContent(xbi_it + 1),
- outchi2perbin_1D->GetBinContent(xbi_it + 1));
- }
+ if (outchi2perbin && outchi2perbin_1D) {
+ MapFromTH1D(outchi2perbin, outchi2perbin_1D, map);
}
// CleanUp
delete data_1D;
delete mc_1D;
delete mask_1D;
delete outchi2perbin_1D;
- delete outchi2perbin_map_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 = "<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 rand_val;
TMatrixDSym *decomp_cov = NULL;
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)) {
NUIS_LOG(DEB, "Applying mask to bin " << i + 1 << " " << hist->GetName());
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 *DataHist, TMatrixDSym *cov,
double scale, bool ErrorCheck) {
//*******************************************************************
// Check
if (ErrorCheck) {
if (cov->GetNrows() != DataHist->GetNbinsX()) {
NUIS_ERR(
FTL,
"Nrows in cov don't match nbins in DataHist for SetDataErrorFromCov");
NUIS_ERR(FTL, "Nrows = " << cov->GetNrows());
NUIS_ABORT("Nbins = " << DataHist->GetNbinsX());
}
}
// Set bin errors form cov diag
// Check if the errors are set
bool ErrorsSet = false;
for (int i = 0; i < DataHist->GetNbinsX(); i++) {
if (ErrorsSet == true)
break;
if (DataHist->GetBinError(i + 1) != 0 && DataHist->GetBinContent(i + 1) > 0)
ErrorsSet = true;
}
// Now loop over
if (ErrorsSet && ErrorCheck) {
for (int i = 0; i < DataHist->GetNbinsX(); i++) {
double DataHisterr = DataHist->GetBinError(i + 1);
double coverr = sqrt((*cov)(i, i)) * scale;
// Check that the errors are within 1% of eachother
if (fabs(DataHisterr - coverr) / DataHisterr > 0.01) {
NUIS_ERR(WRN, "Data error does not match covariance error for bin "
<< i + 1 << " ("
<< DataHist->GetXaxis()->GetBinLowEdge(i + 1) << "-"
<< DataHist->GetXaxis()->GetBinLowEdge(i + 2) << ")");
NUIS_ERR(WRN, "Data error: " << DataHisterr);
NUIS_ERR(WRN, "Cov error: " << coverr);
}
}
// Else blindly trust the covariance
} else {
for (int i = 0; i < DataHist->GetNbinsX(); i++) {
DataHist->SetBinError(i + 1, sqrt((*cov)(i, i)) * scale);
}
}
return;
}
//*******************************************************************
void StatUtils::SetDataErrorFromCov(TH2D *data, TMatrixDSym *cov, TH2I *map,
double scale, bool ErrorCheck) {
//*******************************************************************
// Check
if (ErrorCheck) {
if (cov->GetNrows() != data->GetNbinsX() * data->GetNbinsY()) {
NUIS_ERR(FTL, "Nrows in cov don't match nbins in data for "
"SetDataNUIS_ERRorFromCov");
NUIS_ERR(FTL, "Nrows = " << cov->GetNrows());
NUIS_ABORT("Nbins = " << data->GetNbinsX());
}
}
// Set bin errors form cov diag
// Check if the errors are set
bool ErrorsSet = false;
for (int i = 0; i < data->GetNbinsX(); i++) {
for (int j = 0; j < data->GetNbinsX(); j++) {
if (ErrorsSet == true)
break;
if (data->GetBinError(i + 1, j + 1) != 0)
ErrorsSet = true;
}
}
// 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;
// If we have errors on our histogram the map is good
count = map->GetBinContent(i + 1, j + 1) - 1;
double dataerr = data->GetBinError(i + 1, j + 1);
double coverr = sqrt((*cov)(count, count)) * scale;
// Check that the errors are within 1% of eachother
if (ErrorsSet && ErrorCheck) {
if (fabs(dataerr - coverr) / dataerr > 0.01) {
NUIS_ERR(WRN, "Data error does not match covariance error for bin "
<< i + 1 << " ("
<< data->GetXaxis()->GetBinLowEdge(i + 1) << "-"
<< data->GetXaxis()->GetBinLowEdge(i + 2) << ")");
NUIS_ERR(WRN, "Data error: " << dataerr);
NUIS_ERR(WRN, "Cov error: " << coverr);
}
} else {
data->SetBinError(i + 1, j + 1, sqrt((*cov)(count, count)) * scale);
}
}
}
// Delete the map now that we don't need it
// Woops, it's needed elsewhere there! (Grrrrr)
// map->Delete();
}
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) {
NUIS_ERR(WRN, "Inconsistent matrix and data histogram passed to "
"StatUtils::ExtractShapeOnlyCovar!");
NUIS_ERR(WRN, "data_hist has " << data_hist->GetNbinsX() << " matrix has "
<< nbins);
int err_bins = data_hist->GetNbinsX();
if (nbins > err_bins)
err_bins = nbins;
for (int i = 0; i < err_bins; ++i) {
NUIS_ERR(WRN, "Matrix diag. = " << (*full_covar)(i, i) << " data = "
<< data_hist->GetBinContent(i + 1));
}
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) {
NUIS_ERR(WRN, "Stupid matrix or data histogram passed to "
"StatUtils::ExtractShapeOnlyCovar! Ignoring...");
return NULL;
}
NUIS_LOG(SAM, "Norm error = " << sqrt(total_covar) / total_data);
// 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) {
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;
}
+void StatUtils::MapFromTH1D(TH2 *fillhist, TH1 *fromhist, TH2I *map) {
+ fillhist->Clear();
+
+ 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;
+ int gb = map->GetBinContent(i + 1, j + 1);
+ fillhist->SetBinContent(i + 1, j + 1, fromhist->GetBinContent(gb));
+ fillhist->SetBinError(i + 1, j + 1, fromhist->GetBinError(gb));
+ }
+ }
+}
+
//*******************************************************************
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 entries = GeneralUtils::ParseToDbl(line, " ");
if (entries.size() <= 1) {
NUIS_ERR(WRN, "StatUtils::GetMatrixFromTextFile, matrix only has <= 1 "
"entries on this line: "
<< row);
}
for (std::vector::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 entries = GeneralUtils::ParseToDbl(line, " ");
if (entries.size() <= 1) {
NUIS_ERR(WRN, "StatUtils::GetMatrixFromTextFile, matrix only has <= 1 "
"entries on this line: "
<< row);
}
for (std::vector::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 splitfile = GeneralUtils::ParseToStr(inputfile, ";");
if (splitfile.size() < 2) {
NUIS_ABORT("No object name given!");
}
// Get file
TFile *tempfile = new TFile(splitfile[0].c_str(), "READ");
// Get Object
TObject *obj = tempfile->Get(splitfile[1].c_str());
if (!obj) {
NUIS_ABORT("Object " << splitfile[1] << " doesn't exist!");
}
// Try casting
TMatrixD *mat = dynamic_cast(obj);
if (mat) {
TMatrixD *newmat = (TMatrixD *)mat->Clone();
delete mat;
tempfile->Close();
return newmat;
}
TMatrixDSym *matsym = dynamic_cast(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(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;
}
diff --git a/src/Statistical/StatUtils.h b/src/Statistical/StatUtils.h
index d2f82c9..0e9e37b 100644
--- a/src/Statistical/StatUtils.h
+++ b/src/Statistical/StatUtils.h
@@ -1,255 +1,258 @@
// 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 .
*******************************************************************************/
#ifndef STATUTILS_H
#define STATUTILS_H
// C Includes
#include
#include
#include
#include
#include
#include
#include
#include
#include "assert.h"
// Root Includes
#include "TH1D.h"
#include "TH2I.h"
#include "TH2D.h"
#include "TFile.h"
#include "TMatrixDSym.h"
#include "TDecompSVD.h"
#include "TMath.h"
#include "TRandom3.h"
#include "TDecompChol.h"
#include "TGraphErrors.h"
// Fit Includes
#include "FitLogger.h"
/*!
* \addtogroup Utils
* @{
*/
//! Functions for handling statistics calculations
namespace StatUtils{
/*
Chi2 Functions
*/
//! Get Chi2 using diagonal bin errors from the histogram. Masking applied before calculation if mask provided.
Double_t GetChi2FromDiag(TH1D* data, TH1D* mc, TH1I* mask=NULL);
//! Get Chi2 using diagonal bin errors from the histogram.
//! Plots converted to 1D histograms before using 1D calculation.
Double_t GetChi2FromDiag(TH2D* data, TH2D* mc, TH2I* map=NULL, TH2I* mask=NULL);
//! Get Chi2 using an inverted covariance for the data
Double_t GetChi2FromCov( TH1D* data, TH1D* mc, TMatrixDSym* invcov, TH1I* mask=NULL, double data_scale=1, double covar_scale=1E76, TH1D *outchi2perbin=NULL);
//! Get Chi2 using an inverted covariance for the data
//! Plots converted to 1D histograms before using 1D calculation.
Double_t GetChi2FromCov( TH2D* data, TH2D* mc, TMatrixDSym* invcov, TH2I* map=NULL, TH2I* mask=NULL, TH2D *outchi2perbin=NULL);
//! Get Chi2 using an SVD method on the covariance before calculation.
//! Method suggested by Rex at MiniBooNE. Shown that it doesn't actually work.
Double_t GetChi2FromSVD( TH1D* data, TH1D* mc, TMatrixDSym* cov, TH1I* mask=NULL);
//! Get Chi2 using an SVD method on the covariance before calculation.
//! Method suggested by Rex at MiniBooNE. Shown that it doesn't actually work.
//! Plots converted to 1D histograms before using 1D calculation.
Double_t GetChi2FromSVD( TH2D* data, TH2D* mc, TMatrixDSym* cov, TH2I* map=NULL, TH2I* mask=NULL);
//! Get Chi2 using only the raw event rates given in each bin using a -2LL method.
Double_t GetChi2FromEventRate(TH1D* data, TH1D* mc, TH1I* mask=NULL);
//! Get Chi2 using only the raw event rates given in each bin using a -2LL method.
//! Plots converted to 1D histograms before using 1D calculation.
Double_t GetChi2FromEventRate(TH2D* data, TH2D* mc, TH2I* map=NULL, TH2I* mask=NULL);
// Likelihood Functions
//! Placeholder for 1D binned likelihood method
Double_t GetLikelihoodFromDiag(TH1D* data, TH1D* mc, TH1I* mask=NULL);
//! Placeholder for 2D binned likelihood method
Double_t GetLikelihoodFromDiag(TH2D* data, TH2D* mc, TH2I* map=NULL, TH2I* mask=NULL);
//! Placeholder for 1D binned likelihood method
Double_t GetLikelihoodFromCov( TH1D* data, TH1D* mc, TMatrixDSym* invcov, TH1I* mask=NULL);
//! Placeholder for 2D binned likelihood method
Double_t GetLikelihoodFromCov( TH2D* data, TH2D* mc, TMatrixDSym* invcov, TH2I* map=NULL, TH2I* mask=NULL);
//! Placeholder for 1D binned likelihood method
Double_t GetLikelihoodFromSVD( TH1D* data, TH1D* mc, TMatrixDSym* cov, TH1I* mask=NULL);
//! Placeholder for 2D binned likelihood method
Double_t GetLikelihoodFromSVD( TH2D* data, TH2D* mc, TMatrixDSym* cov, TH2I* map=NULL, TH2I* mask=NULL);
//! Placeholder for 1D binned likelihood method
Double_t GetLikelihoodFromEventRate(TH1D* data, TH1D* mc, TH1I* mask=NULL);
//! Placeholder for 2D binned likelihood method
Double_t GetLikelihoodFromEventRate(TH2D* data, TH2D* mc, TH2I* map=NULL, TH2I* mask=NULL);
/*
NDOF Functions
*/
//! Return 1D Histogram NDOF considering masking and empty bins
Int_t GetNDOF(TH1D* hist, TH1I* mask=NULL);
//! Return 2D Histogram NDOF considering masking and empty bins
Int_t GetNDOF(TH2D* hist, TH2I* map=NULL, TH2I* mask=NULL);
/*
Fake Data Functions
*/
//! Given a full covariance for a 1D data set throw the decomposition to generate fake data.
//! throwdiag determines whether diagonal statistical errors are thrown.
//! If no covariance is provided only statistical errors are thrown.
TH1D* ThrowHistogram(TH1D* hist, TMatrixDSym* cov, bool throwdiag=true, TH1I* mask=NULL);
//! Given a full covariance for a 2D data set throw the decomposition to generate fake data.
//! Plots are converted to 1D histograms and the 1D ThrowHistogram is used, before being converted back to 2D histograms.
TH2D* ThrowHistogram(TH2D* hist, TMatrixDSym* cov, TH2I* map=NULL, bool throwdiag=true, TH2I* mask=NULL);
/*
Masking Functions
*/
//! Given a mask histogram, mask out any bins in hist with non zero entries in mask.
TH1D* ApplyHistogramMasking(TH1D* hist, TH1I* mask);
//! Given a mask histogram, mask out any bins in hist with non zero entries in mask.
TH2D* ApplyHistogramMasking(TH2D* hist, TH2I* mask);
//! Given a mask histogram apply the masking procedure to each of the rows/columns in a covariance, before recalculating its inverse.
TMatrixDSym* ApplyInvertedMatrixMasking(TMatrixDSym* mat, TH1I* mask);
//! Given a mask histogram apply the masking procedure to each of the rows/columns in a covariance, before recalculating its inverse.
//! Converts to 1D data before using the 1D ApplyInvertedMatrixMasking function and converting back to 2D.
TMatrixDSym* ApplyInvertedMatrixMasking(TMatrixDSym* mat, TH2D* data, TH2I* mask, TH2I* map=NULL);
//! Given a mask histogram apply the masking procedure to each of the rows/columns in a covariance
TMatrixDSym* ApplyMatrixMasking(TMatrixDSym* mat, TH1I* mask);
//! Given a mask histogram apply the masking procedure to each of the rows/columns in a covariance
//! Converts to 1D data before using the 1D ApplyInvertedMatrixMasking function and converting back to 2D.
TMatrixDSym* ApplyMatrixMasking(TMatrixDSym* mat, TH2D* data, TH2I* mask, TH2I* map=NULL);
/*
Covariance Handling Functions
*/
//! Return inverted matrix of TMatrixDSym
TMatrixDSym* GetInvert(TMatrixDSym* mat);
//! Return Cholesky Decomposed matrix of TMatrixDSym
TMatrixDSym* GetDecomp(TMatrixDSym* mat);
//! Return full covariances
TMatrixDSym* GetCovarFromCorrel(TMatrixDSym* correl, TH1D* data);
//! Given a normalisation factor for a dataset add in a new normalisation term to the covariance.
void ForceNormIntoCovar(TMatrixDSym*& mat, TH1D* data, double norm);
//! Given a normalisation factor for a dataset add in a new normalisation term to the covariance.
//! Convertes 2D to 1D, before using 1D ForceNormIntoCovar
void ForceNormIntoCovar(TMatrixDSym* mat, TH2D* data, double norm, TH2I* map=NULL);
//! Given a dataset generate an uncorrelated covariance matrix using the bin errors.
TMatrixDSym* MakeDiagonalCovarMatrix(TH1D* data, double scaleF=1E38);
//! Given a dataset generate an uncorrelated covariance matrix using the bin errors.
TMatrixDSym* MakeDiagonalCovarMatrix(TH2D* data, TH2I* map=NULL, double scaleF=1E38);
//! Given a covariance set the errors in each bin on the data from the covariance diagonals.
void SetDataErrorFromCov(TH1D* data, TMatrixDSym* cov, double scale=1.0, bool ErrorCheck = true);
//! Given a covariance set the errors in each bin on the data from the covariance diagonals.
void SetDataErrorFromCov(TH2D* data, TMatrixDSym* cov, TH2I* map=NULL, double scale=1.0, bool ErrorCheck = true);
//! Given a covariance, extracts the shape-only matrix using the method from the MiniBooNE TN
TMatrixDSym* ExtractShapeOnlyCovar(TMatrixDSym* full_covar, TH1* data_hist, double data_scale=1E38);
/*
Mapping Functions
*/
//! If no map is provided for the 2D histogram, generate one by counting up through the bins along x and y.
TH2I* GenerateMap(TH2D* hist);
//! Apply a map to a 2D histogram converting it into a 1D histogram.
TH1D* MapToTH1D(TH2D* hist, TH2I* map);
+ //! Apply a map to fill a TH2D from a TH1D;
+ void MapFromTH1D(TH2* fillhist, TH1* fromhist, TH2I* map);
+
//! Apply a map to a 2D mask convering it into a 1D mask.
TH1I* MapToMask(TH2I* hist, TH2I* map);
/// \brief Read TMatrixD from a text file
///
/// - covfile = full path to text file
/// - dimx = x dimensions of matrix
/// - dimy = y dimensions of matrix
///
/// Format of textfile should be: \n
/// cov_11 cov_12 ... cov_1N \n
/// cov_21 cov_22 ... cov_2N \n
/// ... ... ... ... \n
/// cov_N1 ... ... cov_NN \n
///
/// If no dimensions are given, dimx and dimy are determined from rows/columns
/// inside textfile.
///
/// If only dimx is given a symmetric matrix is assumed.
TMatrixD* GetMatrixFromTextFile(std::string covfile, int dimx=-1, int dimy=-1);
/// \brief Read TMatrixD from a ROOT file
///
/// - covfile = full path to root file (+';histogram')
/// - histname = histogram name
///
/// If no histogram name is given function assumes it has been appended
/// covfile path as: \n
/// 'covfile.root;histname'
///
/// histname can point to a TMatrixD object, a TMatrixDSym object, or
/// a TH2D object.
TMatrixD* GetMatrixFromRootFile(std::string covfile, std::string histname="");
/// \brief Calls GetMatrixFromTextFile and turns it into a TMatrixDSym
TMatrixDSym* GetCovarFromTextFile(std::string covfile, int dim);
/// \brief Calls GetMatrixFromRootFile and turns it into a TMatrixDSym
TMatrixDSym* GetCovarFromRootFile(std::string covfile, std::string histname);
};
/*! @} */
#endif
diff --git a/src/Utils/PlotUtils.cxx b/src/Utils/PlotUtils.cxx
index 89dc4f2..612c493 100644
--- a/src/Utils/PlotUtils.cxx
+++ b/src/Utils/PlotUtils.cxx
@@ -1,1197 +1,1206 @@
// 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 .
*******************************************************************************/
#include "PlotUtils.h"
#include "FitEvent.h"
#include "StatUtils.h"
// MOVE TO MB UTILS!
// This function is intended to be modified to enforce a consistent masking for
// all models.
TH2D *PlotUtils::SetMaskHist(std::string type, TH2D *data) {
TH2D *fMaskHist = (TH2D *)data->Clone("fMaskHist");
for (int xBin = 0; xBin < fMaskHist->GetNbinsX(); ++xBin) {
for (int yBin = 0; yBin < fMaskHist->GetNbinsY(); ++yBin) {
if (data->GetBinContent(xBin + 1, yBin + 1) == 0)
fMaskHist->SetBinContent(xBin + 1, yBin + 1, 0);
else
fMaskHist->SetBinContent(xBin + 1, yBin + 1, 0.5);
if (!type.compare("MB_numu_2D")) {
if (yBin == 19 && xBin < 8)
fMaskHist->SetBinContent(xBin + 1, yBin + 1, 1.0);
} else {
if (yBin == 19 && xBin < 11)
fMaskHist->SetBinContent(xBin + 1, yBin + 1, 1.0);
}
if (yBin == 18 && xBin < 3)
fMaskHist->SetBinContent(xBin + 1, yBin + 1, 1.0);
if (yBin == 17 && xBin < 2)
fMaskHist->SetBinContent(xBin + 1, yBin + 1, 1.0);
if (yBin == 16 && xBin < 1)
fMaskHist->SetBinContent(xBin + 1, yBin + 1, 1.0);
}
}
return fMaskHist;
};
// MOVE TO GENERAL UTILS?
bool PlotUtils::CheckObjectWithName(TFile *inFile, std::string substring) {
TIter nextkey(inFile->GetListOfKeys());
TKey *key;
while ((key = (TKey *)nextkey())) {
std::string test(key->GetName());
if (test.find(substring) != std::string::npos)
return true;
}
return false;
};
// MOVE TO GENERAL UTILS?
std::string PlotUtils::GetObjectWithName(TFile *inFile, std::string substring) {
TIter nextkey(inFile->GetListOfKeys());
TKey *key;
std::string output = "";
while ((key = (TKey *)nextkey())) {
std::string test(key->GetName());
if (test.find(substring) != std::string::npos)
output = test;
}
return output;
};
void PlotUtils::CreateNeutModeArray(TH1 *hist, TH1 *neutarray[]) {
for (int i = 0; i < 60; i++) {
neutarray[i] = (TH1 *)hist->Clone(Form("%s_NMODE_%i", hist->GetName(), i));
}
return;
};
void PlotUtils::DeleteNeutModeArray(TH1 *neutarray[]) {
for (int i = 0; i < 60; i++) {
delete neutarray[i];
}
return;
};
void PlotUtils::FillNeutModeArray(TH1D *hist[], int mode, double xval,
double weight) {
if (abs(mode) > 60)
return;
hist[abs(mode)]->Fill(xval, weight);
return;
};
void PlotUtils::FillNeutModeArray(TH2D *hist[], int mode, double xval,
double yval, double weight) {
if (abs(mode) > 60)
return;
hist[abs(mode)]->Fill(xval, yval, weight);
return;
};
THStack PlotUtils::GetNeutModeStack(std::string title, TH1 *ModeStack[],
int option) {
(void)option;
THStack allmodes = THStack(title.c_str(), title.c_str());
for (int i = 0; i < 60; i++) {
allmodes.Add(ModeStack[i]);
}
// Credit to Clarence for copying all this out.
// CC
ModeStack[1]->SetTitle("CCQE");
ModeStack[1]->SetFillColor(kBlue);
// ModeStack[1]->SetFillStyle(3444);
ModeStack[1]->SetLineColor(kBlue);
ModeStack[2]->SetTitle("2p/2h Nieves");
ModeStack[2]->SetFillColor(kRed);
// ModeStack[2]->SetFillStyle(3344);
ModeStack[2]->SetLineColor(kRed);
// ModeStack[11]->SetTitle("#it{#nu + p #rightarrow l^{-} + p + #pi^{+}}");
ModeStack[11]->SetTitle("CC1#pi^{+} on p");
ModeStack[11]->SetFillColor(kGreen);
// ModeStack[11]->SetFillStyle(3004);
ModeStack[11]->SetLineColor(kGreen);
// ModeStack[12]->SetTitle("#it{#nu + n #rightarrow l^{-} + p + #pi^{0}}");
ModeStack[12]->SetTitle("CC1#pi^{0} on n");
ModeStack[12]->SetFillColor(kGreen + 3);
// ModeStack[12]->SetFillStyle(3005);
ModeStack[12]->SetLineColor(kGreen);
// ModeStack[13]->SetTitle("#it{#nu + n #rightarrow l^{-} + n + #pi^{+}}");
ModeStack[13]->SetTitle("CC1#pi^{+} on n");
ModeStack[13]->SetFillColor(kGreen - 2);
// ModeStack[13]->SetFillStyle(3004);
ModeStack[13]->SetLineColor(kGreen);
ModeStack[16]->SetTitle("CC coherent");
ModeStack[16]->SetFillColor(kBlue);
// ModeStack[16]->SetFillStyle(3644);
ModeStack[16]->SetLineColor(kBlue);
// ModeStack[17]->SetTitle("#it{#nu + n #rightarrow l^{-} + p + #gamma}");
ModeStack[17]->SetTitle("CC1#gamma");
ModeStack[17]->SetFillColor(kMagenta);
// ModeStack[17]->SetFillStyle(3001);
ModeStack[17]->SetLineColor(kMagenta);
ModeStack[21]->SetTitle("Multi #pi (1.3 < W < 2.0)");
ModeStack[21]->SetFillColor(kYellow);
// ModeStack[21]->SetFillStyle(3005);
ModeStack[21]->SetLineColor(kYellow);
// ModeStack[22]->SetTitle("#it{#nu + n #rightarrow l^{-} + p + #eta^{0}}");
ModeStack[22]->SetTitle("CC1#eta^{0} on n");
ModeStack[22]->SetFillColor(kYellow - 2);
// ModeStack[22]->SetFillStyle(3013);
ModeStack[22]->SetLineColor(kYellow - 2);
// ModeStack[23]->SetTitle("#it{#nu + n #rightarrow l^{-} + #Lambda +
// K^{+}}");
ModeStack[23]->SetTitle("CC1#Labda1K^{+}");
ModeStack[23]->SetFillColor(kYellow - 6);
// ModeStack[23]->SetFillStyle(3013);
ModeStack[23]->SetLineColor(kYellow - 6);
ModeStack[26]->SetTitle("DIS (W > 2.0)");
ModeStack[26]->SetFillColor(kRed);
// ModeStack[26]->SetFillStyle(3006);
ModeStack[26]->SetLineColor(kRed);
// NC
// ModeStack[31]->SetTitle("#it{#nu + n #rightarrow #nu + n + #pi^{0}}");
ModeStack[31]->SetTitle("NC1#pi^{0} on n");
ModeStack[31]->SetFillColor(kBlue);
// ModeStack[31]->SetFillStyle(3004);
ModeStack[31]->SetLineColor(kBlue);
// ModeStack[32]->SetTitle("#it{#nu + p #rightarrow #nu + p + #pi^{0}}");
ModeStack[32]->SetTitle("NC1#pi^{0} on p");
ModeStack[32]->SetFillColor(kBlue + 3);
// ModeStack[32]->SetFillStyle(3004);
ModeStack[32]->SetLineColor(kBlue + 3);
// ModeStack[33]->SetTitle("#it{#nu + n #rightarrow #nu + p + #pi^{-}}");
ModeStack[33]->SetTitle("NC1#pi^{-} on n");
ModeStack[33]->SetFillColor(kBlue - 2);
// ModeStack[33]->SetFillStyle(3005);
ModeStack[33]->SetLineColor(kBlue - 2);
// ModeStack[34]->SetTitle("#it{#nu + p #rightarrow #nu + n + #pi^{+}}");
ModeStack[34]->SetTitle("NC1#pi^{+} on p");
ModeStack[34]->SetFillColor(kBlue - 8);
// ModeStack[34]->SetFillStyle(3005);
ModeStack[34]->SetLineColor(kBlue - 8);
ModeStack[36]->SetTitle("NC Coherent");
ModeStack[36]->SetFillColor(kBlue + 8);
// ModeStack[36]->SetFillStyle(3644);
ModeStack[36]->SetLineColor(kBlue + 8);
// ModeStack[38]->SetTitle("#it{#nu + n #rightarrow #nu + n + #gamma}");
ModeStack[38]->SetTitle("NC1#gamma on n");
ModeStack[38]->SetFillColor(kMagenta);
// ModeStack[38]->SetFillStyle(3001);
ModeStack[38]->SetLineColor(kMagenta);
// ModeStack[39]->SetTitle("#it{#nu + p #rightarrow #nu + p + #gamma}");
ModeStack[39]->SetTitle("NC1#gamma on p");
ModeStack[39]->SetFillColor(kMagenta - 10);
// ModeStack[39]->SetFillStyle(3001);
ModeStack[39]->SetLineColor(kMagenta - 10);
ModeStack[41]->SetTitle("Multi #pi (1.3 < W < 2.0)");
ModeStack[41]->SetFillColor(kBlue - 10);
// ModeStack[41]->SetFillStyle(3005);
ModeStack[41]->SetLineColor(kBlue - 10);
// ModeStack[42]->SetTitle("#it{#nu + n #rightarrow #nu + n + #eta^{0}}");
ModeStack[42]->SetTitle("NC1#eta^{0} on n");
ModeStack[42]->SetFillColor(kYellow - 2);
// ModeStack[42]->SetFillStyle(3013);
ModeStack[42]->SetLineColor(kYellow - 2);
// ModeStack[43]->SetTitle("#it{#nu + p #rightarrow #nu + p + #eta^{0}}");
ModeStack[43]->SetTitle("NC1#eta^{0} on p");
ModeStack[43]->SetFillColor(kYellow - 4);
// ModeStack[43]->SetFillStyle(3013);
ModeStack[43]->SetLineColor(kYellow - 4);
// ModeStack[44]->SetTitle("#it{#nu + n #rightarrow #nu + #Lambda + K^{0}}");
ModeStack[44]->SetTitle("NC1#Lambda1K^{0} on n");
ModeStack[44]->SetFillColor(kYellow - 6);
// ModeStack[44]->SetFillStyle(3014);
ModeStack[44]->SetLineColor(kYellow - 6);
// ModeStack[45]->SetTitle("#it{#nu + p #rightarrow #nu + #Lambda + K^{+}}");
ModeStack[45]->SetTitle("NC1#Lambda1K^{+}");
ModeStack[45]->SetFillColor(kYellow - 10);
// ModeStack[45]->SetFillStyle(3014);
ModeStack[45]->SetLineColor(kYellow - 10);
ModeStack[46]->SetTitle("DIS (W > 2.0)");
ModeStack[46]->SetFillColor(kRed);
// ModeStack[46]->SetFillStyle(3006);
ModeStack[46]->SetLineColor(kRed);
// ModeStack[51]->SetTitle("#it{#nu + p #rightarrow #nu + p}");
ModeStack[51]->SetTitle("NC on p");
ModeStack[51]->SetFillColor(kBlack);
// ModeStack[51]->SetFillStyle(3444);
ModeStack[51]->SetLineColor(kBlack);
// ModeStack[52]->SetTitle("#it{#nu + n #rightarrow #nu + n}");
ModeStack[52]->SetTitle("NC on n");
ModeStack[52]->SetFillColor(kGray);
// ModeStack[52]->SetFillStyle(3444);
ModeStack[52]->SetLineColor(kGray);
return allmodes;
};
TLegend PlotUtils::GenerateStackLegend(THStack stack, int xlow, int ylow,
int xhigh, int yhigh) {
TLegend leg = TLegend(xlow, ylow, xhigh, yhigh);
TObjArray *histarray = stack.GetStack();
int nhist = histarray->GetEntries();
for (int i = 0; i < nhist; i++) {
TH1 *hist = (TH1 *)(histarray->At(i));
leg.AddEntry((hist), ((TH1 *)histarray->At(i))->GetTitle(), "fl");
}
leg.SetName(Form("%s_LEG", stack.GetName()));
return leg;
};
void PlotUtils::ScaleNeutModeArray(TH1 *hist[], double factor,
std::string option) {
for (int i = 0; i < 60; i++) {
if (hist[i])
hist[i]->Scale(factor, option.c_str());
}
return;
};
void PlotUtils::ResetNeutModeArray(TH1 *hist[]) {
for (int i = 0; i < 60; i++) {
if (hist[i])
hist[i]->Reset();
}
return;
};
//********************************************************************
// This assumes the Enu axis is the x axis, as is the case for MiniBooNE 2D
// distributions
void PlotUtils::FluxUnfoldedScaling(TH2D *fMCHist, TH1D *fhist, TH1D *ehist,
double scalefactor) {
//********************************************************************
// Make clones to avoid changing stuff
TH1D *eventhist = (TH1D *)ehist->Clone();
TH1D *fFluxHist = (TH1D *)fhist->Clone();
// Undo width integral in SF
fMCHist->Scale(scalefactor /
eventhist->Integral(1, eventhist->GetNbinsX() + 1, "width"));
// Standardise The Flux
eventhist->Scale(1.0 / fFluxHist->Integral());
fFluxHist->Scale(1.0 / fFluxHist->Integral());
// Do interpolation for 2D plots?
// fFluxHist = PlotUtils::InterpolateFineHistogram(fFluxHist,100,"width");
// eventhist = PlotUtils::InterpolateFineHistogram(eventhist,100,"width");
// eventhist->Scale(1.0/fFluxHist->Integral());
// fFluxHist->Scale(1.0/fFluxHist->Integral());
// Scale fMCHist by eventhist integral
fMCHist->Scale(eventhist->Integral(1, eventhist->GetNbinsX() + 1));
// Find which axis is the Enu axis
bool EnuOnXaxis = false;
std::string xaxis = fMCHist->GetXaxis()->GetTitle();
if (xaxis.find("E") != std::string::npos &&
xaxis.find("nu") != std::string::npos)
EnuOnXaxis = true;
std::string yaxis = fMCHist->GetYaxis()->GetTitle();
if (yaxis.find("E") != std::string::npos &&
xaxis.find("nu") != std::string::npos) {
// First check that xaxis didn't also find Enu
if (EnuOnXaxis) {
NUIS_ERR(FTL, fMCHist->GetTitle() << " error:");
NUIS_ERR(FTL, "Found Enu in xaxis title: " << xaxis);
NUIS_ERR(FTL, "AND");
NUIS_ERR(FTL, "Found Enu in yaxis title: " << yaxis);
NUIS_ABORT("Enu on x and Enu on y flux unfolded scaling isn't "
- "implemented, please modify "
- << __FILE__ << ":" << __LINE__);
+ "implemented, please modify "
+ << __FILE__ << ":" << __LINE__);
}
EnuOnXaxis = false;
}
// Now Get a flux PDF assuming X axis is Enu
TH1D *pdfflux = NULL;
// If xaxis is Enu
if (EnuOnXaxis)
pdfflux = (TH1D *)fMCHist->ProjectionX()->Clone();
// If yaxis is Enu
else
pdfflux = (TH1D *)fMCHist->ProjectionY()->Clone();
// pdfflux->Write( (std::string(fMCHist->GetName()) + "_PROJX").c_str());
pdfflux->Reset();
// Awful MiniBooNE Check for the time being
// Needed because the flux is in GeV whereas the measurement is in MeV
bool ismb =
std::string(fMCHist->GetName()).find("MiniBooNE") != std::string::npos;
for (int i = 0; i < pdfflux->GetNbinsX(); i++) {
double Ml = pdfflux->GetXaxis()->GetBinLowEdge(i + 1);
double Mh = pdfflux->GetXaxis()->GetBinLowEdge(i + 2);
// double Mc = pdfflux->GetXaxis()->GetBinCenter(i+1);
// double Mw = pdfflux->GetBinWidth(i+1);
double fluxint = 0.0;
// Scaling to match flux for MB
if (ismb) {
Ml /= 1.E3;
Mh /= 1.E3;
// Mc /= 1.E3;
// Mw /= 1.E3;
}
for (int j = 0; j < fFluxHist->GetNbinsX(); j++) {
// double Fc = fFluxHist->GetXaxis()->GetBinCenter(j+1);
double Fl = fFluxHist->GetXaxis()->GetBinLowEdge(j + 1);
double Fh = fFluxHist->GetXaxis()->GetBinLowEdge(j + 2);
double Fe = fFluxHist->GetBinContent(j + 1);
double Fw = fFluxHist->GetXaxis()->GetBinWidth(j + 1);
if (Fl >= Ml and Fh <= Mh) {
fluxint += Fe;
} else if (Fl < Ml and Fl < Mh and Fh > Ml and Fh < Mh) {
fluxint += Fe * (Fh - Ml) / Fw;
} else if (Fh > Mh and Fl < Mh and Fh > Ml and Fl > Ml) {
fluxint += Fe * (Mh - Fl) / Fw;
} else if (Ml >= Fl and Mh <= Fh) {
fluxint += Fe * (Mh - Ml) / Fw;
} else {
continue;
}
}
pdfflux->SetBinContent(i + 1, fluxint);
}
// Then finally divide by the bin-width in
for (int i = 0; i < fMCHist->GetNbinsX(); i++) {
for (int j = 0; j < fMCHist->GetNbinsY(); j++) {
if (pdfflux->GetBinContent(i + 1) == 0.0)
continue;
// Different scaling depending on if Enu is on x or y axis
double scaling = 1.0;
// If Enu is on the x-axis, we want the ith entry of the flux
// And to divide by the bin width of the jth bin
if (EnuOnXaxis) {
double binWidth = fMCHist->GetYaxis()->GetBinLowEdge(j + 2) -
fMCHist->GetYaxis()->GetBinLowEdge(j + 1);
scaling = pdfflux->GetBinContent(i + 1) * binWidth;
} else {
double binWidth = fMCHist->GetXaxis()->GetBinLowEdge(i + 2) -
fMCHist->GetXaxis()->GetBinLowEdge(i + 1);
scaling = pdfflux->GetBinContent(j + 1) * binWidth;
}
// fMCHist->SetBinContent(i + 1, j + 1,
// fMCHist->GetBinContent(i + 1, j + 1) /
// pdfflux->GetBinContent(i + 1) / binWidth);
// fMCHist->SetBinError(i + 1, j + 1, fMCHist->GetBinError(i + 1, j + 1) /
// pdfflux->GetBinContent(i + 1) /
// binWidth);
fMCHist->SetBinContent(i + 1, j + 1,
fMCHist->GetBinContent(i + 1, j + 1) / scaling);
fMCHist->SetBinError(i + 1, j + 1,
fMCHist->GetBinError(i + 1, j + 1) / scaling);
}
}
delete eventhist;
delete fFluxHist;
};
TH1D *PlotUtils::InterpolateFineHistogram(TH1D *hist, int res,
std::string opt) {
int nbins = hist->GetNbinsX();
double elow = hist->GetXaxis()->GetBinLowEdge(1);
double ehigh = hist->GetXaxis()->GetBinLowEdge(nbins + 1);
bool width = true; // opt.find("width") != std::string::npos;
TH1D *fine = new TH1D("fine", "fine", nbins * res, elow, ehigh);
TGraph *temp = new TGraph();
for (int i = 0; i < nbins; i++) {
double E = hist->GetXaxis()->GetBinCenter(i + 1);
double C = hist->GetBinContent(i + 1);
double W = hist->GetXaxis()->GetBinWidth(i + 1);
if (!width)
W = 1.0;
if (W != 0.0)
temp->SetPoint(temp->GetN(), E, C / W);
}
for (int i = 0; i < fine->GetNbinsX(); i++) {
double E = fine->GetXaxis()->GetBinCenter(i + 1);
double W = fine->GetBinWidth(i + 1);
if (!width)
W = 1.0;
fine->SetBinContent(i + 1, temp->Eval(E, 0, "S") * W);
}
fine->Scale(hist->Integral(1, hist->GetNbinsX() + 1) /
fine->Integral(1, fine->GetNbinsX() + 1));
// std::cout << "Interpolation Difference = "
//<< fine->Integral(1, fine->GetNbinsX() + 1) << "/"
//<< hist->Integral(1, hist->GetNbinsX() + 1) << std::endl;
return fine;
}
//********************************************************************
// This interpolates the flux by a TGraph instead of requiring the flux and MC
// flux to have the same binning
void PlotUtils::FluxUnfoldedScaling(TH1D *mcHist, TH1D *fhist, TH1D *ehist,
double scalefactor, int nevents) {
//********************************************************************
TH1D *eventhist = (TH1D *)ehist->Clone();
TH1D *fFluxHist = (TH1D *)fhist->Clone();
if (FitPar::Config().GetParB("save_flux_debug")) {
std::string name = std::string(mcHist->GetName());
mcHist->Write((name + "_UNF_MC").c_str());
fFluxHist->Write((name + "_UNF_FLUX").c_str());
eventhist->Write((name + "_UNF_EVT").c_str());
TH1D *scalehist = new TH1D("scalehist", "scalehist", 1, 0.0, 1.0);
scalehist->SetBinContent(1, scalefactor);
scalehist->SetBinContent(2, nevents);
scalehist->Write((name + "_UNF_SCALE").c_str());
}
// Undo width integral in SF
mcHist->Scale(scalefactor /
eventhist->Integral(1, eventhist->GetNbinsX() + 1, "width"));
// Standardise The Flux
eventhist->Scale(1.0 / fFluxHist->Integral());
fFluxHist->Scale(1.0 / fFluxHist->Integral());
// Scale mcHist by eventhist integral
mcHist->Scale(eventhist->Integral(1, eventhist->GetNbinsX() + 1));
// Now Get a flux PDF
TH1D *pdfflux = (TH1D *)mcHist->Clone();
pdfflux->Reset();
for (int i = 0; i < mcHist->GetNbinsX(); i++) {
double Ml = mcHist->GetXaxis()->GetBinLowEdge(i + 1);
double Mh = mcHist->GetXaxis()->GetBinLowEdge(i + 2);
// double Mc = mcHist->GetXaxis()->GetBinCenter(i+1);
// double Me = mcHist->GetBinContent(i+1);
// double Mw = mcHist->GetBinWidth(i+1);
double fluxint = 0.0;
for (int j = 0; j < fFluxHist->GetNbinsX(); j++) {
// double Fc = fFluxHist->GetXaxis()->GetBinCenter(j+1);
double Fl = fFluxHist->GetXaxis()->GetBinLowEdge(j + 1);
double Fh = fFluxHist->GetXaxis()->GetBinLowEdge(j + 2);
double Fe = fFluxHist->GetBinContent(j + 1);
double Fw = fFluxHist->GetXaxis()->GetBinWidth(j + 1);
if (Fl >= Ml and Fh <= Mh) {
fluxint += Fe;
} else if (Fl < Ml and Fl < Mh and Fh > Ml and Fh < Mh) {
fluxint += Fe * (Fh - Ml) / Fw;
} else if (Fh > Mh and Fl < Mh and Fh > Ml and Fl > Ml) {
fluxint += Fe * (Mh - Fl) / Fw;
} else if (Ml >= Fl and Mh <= Fh) {
fluxint += Fe * (Mh - Ml) / Fw;
} else {
continue;
}
}
pdfflux->SetBinContent(i + 1, fluxint);
}
// Scale MC hist by pdfflux
for (int i = 0; i < mcHist->GetNbinsX(); i++) {
if (pdfflux->GetBinContent(i + 1) == 0.0)
continue;
mcHist->SetBinContent(i + 1, mcHist->GetBinContent(i + 1) /
pdfflux->GetBinContent(i + 1));
mcHist->SetBinError(i + 1, mcHist->GetBinError(i + 1) /
pdfflux->GetBinContent(i + 1));
}
delete eventhist;
delete fFluxHist;
};
// MOVE TO GENERAL UTILS
//********************************************************************
void PlotUtils::Set2DHistFromText(std::string dataFile, TH2 *hist, double norm,
bool skipbins) {
//********************************************************************
std::string line;
std::ifstream data(dataFile.c_str(), std::ifstream::in);
int yBin = 0;
while (std::getline(data >> std::ws, line, '\n')) {
std::vector entries = GeneralUtils::ParseToDbl(line, " ");
// Loop over entries and insert them into the histogram
for (uint xBin = 0; xBin < entries.size(); xBin++) {
if (!skipbins || entries[xBin] != -1.0)
hist->SetBinContent(xBin + 1, yBin + 1, entries[xBin] * norm);
}
yBin++;
}
return;
}
// MOVE TO GENERAL UTILS
TH1D *PlotUtils::GetTH1DFromFile(std::string dataFile, std::string title,
std::string fPlotTitles,
std::string alt_name) {
TH1D *tempPlot;
// If format is a root file
if (dataFile.find(".root") != std::string::npos) {
TFile *temp_infile = new TFile(dataFile.c_str(), "READ");
tempPlot = (TH1D *)temp_infile->Get(title.c_str());
tempPlot->SetDirectory(0);
temp_infile->Close();
delete temp_infile;
// Else its a space seperated txt file
} else {
// Make a TGraph Errors
TGraphErrors *gr = new TGraphErrors(dataFile.c_str(), "%lg %lg %lg");
if (gr->IsZombie()) {
- NUIS_ABORT(dataFile
- << " is a zombie and could not be read. Are you sure it exists?"
- << std::endl);
+ NUIS_ABORT(
+ dataFile
+ << " is a zombie and could not be read. Are you sure it exists?"
+ << std::endl);
}
double *bins = gr->GetX();
double *values = gr->GetY();
double *errors = gr->GetEY();
int npoints = gr->GetN();
// Fill the histogram from it
tempPlot = new TH1D(title.c_str(), title.c_str(), npoints - 1, bins);
for (int i = 0; i < npoints; ++i) {
tempPlot->SetBinContent(i + 1, values[i]);
// If only two columns are present in the input file, use the sqrt(values)
// as the error equivalent to assuming that the error is statistical. Also
// check that we're looking at an event rate rather than a cross section
if (!errors[i] && values[i] > 1E-30) {
tempPlot->SetBinError(i + 1, sqrt(values[i]));
} else {
tempPlot->SetBinError(i + 1, errors[i]);
}
}
delete gr;
}
// Allow alternate naming for root files
if (!alt_name.empty()) {
tempPlot->SetNameTitle(alt_name.c_str(), alt_name.c_str());
}
// Allow alternate axis titles
if (!fPlotTitles.empty()) {
tempPlot->SetNameTitle(
tempPlot->GetName(),
(std::string(tempPlot->GetTitle()) + fPlotTitles).c_str());
}
return tempPlot;
};
TH1D *PlotUtils::GetRatioPlot(TH1D *hist1, TH1D *hist2) {
// make copy of first hist
TH1D *new_hist = (TH1D *)hist1->Clone();
// Do bins and errors ourselves as scales can go awkward
for (int i = 0; i < new_hist->GetNbinsX(); i++) {
if (hist2->GetBinContent(i + 1) == 0.0) {
new_hist->SetBinContent(i + 1, 0.0);
}
new_hist->SetBinContent(i + 1, hist1->GetBinContent(i + 1) /
hist2->GetBinContent(i + 1));
new_hist->SetBinError(i + 1, hist1->GetBinError(i + 1) /
hist2->GetBinContent(i + 1));
}
return new_hist;
};
TH1D *PlotUtils::GetRenormalisedPlot(TH1D *hist1, TH1D *hist2) {
// make copy of first hist
TH1D *new_hist = (TH1D *)hist1->Clone();
if (hist1->Integral("width") == 0 or hist2->Integral("width") == 0) {
new_hist->Reset();
return new_hist;
}
Double_t scaleF = hist2->Integral("width") / hist1->Integral("width");
new_hist->Scale(scaleF);
return new_hist;
};
TH1D *PlotUtils::GetShapePlot(TH1D *hist1) {
// make copy of first hist
TH1D *new_hist = (TH1D *)hist1->Clone();
if (hist1->Integral("width") == 0) {
new_hist->Reset();
return new_hist;
}
Double_t scaleF1 = 1.0 / hist1->Integral("width");
new_hist->Scale(scaleF1);
return new_hist;
};
TH1D *PlotUtils::GetShapeRatio(TH1D *hist1, TH1D *hist2) {
TH1D *new_hist1 = GetShapePlot(hist1);
TH1D *new_hist2 = GetShapePlot(hist2);
// Do bins and errors ourselves as scales can go awkward
for (int i = 0; i < new_hist1->GetNbinsX(); i++) {
if (hist2->GetBinContent(i + 1) == 0) {
new_hist1->SetBinContent(i + 1, 0.0);
}
new_hist1->SetBinContent(i + 1, new_hist1->GetBinContent(i + 1) /
new_hist2->GetBinContent(i + 1));
new_hist1->SetBinError(i + 1, new_hist1->GetBinError(i + 1) /
new_hist2->GetBinContent(i + 1));
}
delete new_hist2;
return new_hist1;
};
TH2D *PlotUtils::GetCovarPlot(TMatrixDSym *cov, std::string name,
std::string title) {
TH2D *CovarPlot;
if (cov)
CovarPlot = new TH2D((*cov));
else
CovarPlot = new TH2D(name.c_str(), title.c_str(), 1, 0, 1, 1, 0, 1);
CovarPlot->SetName(name.c_str());
CovarPlot->SetTitle(title.c_str());
return CovarPlot;
}
TH2D *PlotUtils::GetFullCovarPlot(TMatrixDSym *cov, std::string name) {
return PlotUtils::GetCovarPlot(
cov, name + "_COV", name + "_COV;Bins;Bins;Covariance (#times10^{-76})");
}
TH2D *PlotUtils::GetInvCovarPlot(TMatrixDSym *cov, std::string name) {
return PlotUtils::GetCovarPlot(
cov, name + "_INVCOV",
name + "_INVCOV;Bins;Bins;Inv. Covariance (#times10^{-76})");
}
TH2D *PlotUtils::GetDecompCovarPlot(TMatrixDSym *cov, std::string name) {
return PlotUtils::GetCovarPlot(
cov, name + "_DECCOV",
name + "_DECCOV;Bins;Bins;Decomp Covariance (#times10^{-76})");
}
TH1D *PlotUtils::GetTH1DFromRootFile(std::string file, std::string name) {
if (name.empty()) {
std::vector tempfile = GeneralUtils::ParseToStr(file, ";");
file = tempfile[0];
name = tempfile[1];
}
TFile *rootHistFile = new TFile(file.c_str(), "READ");
TH1D *tempHist = (TH1D *)rootHistFile->Get(name.c_str())->Clone();
if (tempHist == NULL) {
NUIS_ABORT("Could not find distribution " << name << " in file " << file);
}
tempHist->SetDirectory(0);
rootHistFile->Close();
return tempHist;
}
TH2D *PlotUtils::GetTH2DFromRootFile(std::string file, std::string name) {
if (name.empty()) {
std::vector tempfile = GeneralUtils::ParseToStr(file, ";");
file = tempfile[0];
name = tempfile[1];
}
TFile *rootHistFile = new TFile(file.c_str(), "READ");
TH2D *tempHist = (TH2D *)rootHistFile->Get(name.c_str())->Clone();
tempHist->SetDirectory(0);
rootHistFile->Close();
delete rootHistFile;
return tempHist;
}
TH1 *PlotUtils::GetTH1FromRootFile(std::string file, std::string name) {
if (name.empty()) {
std::vector tempfile = GeneralUtils::ParseToStr(file, ";");
file = tempfile[0];
name = tempfile[1];
}
TFile *rootHistFile = new TFile(file.c_str(), "READ");
if (!rootHistFile || rootHistFile->IsZombie()) {
NUIS_ABORT("Couldn't open root file: \"" << file << "\".");
}
TH1 *tempHist = dynamic_cast(rootHistFile->Get(name.c_str())->Clone());
if (!tempHist) {
- NUIS_ABORT("Couldn't retrieve: \"" << name << "\" from root file: \"" << file
- << "\".");
+ NUIS_ABORT("Couldn't retrieve: \"" << name << "\" from root file: \""
+ << file << "\".");
}
tempHist->SetDirectory(0);
rootHistFile->Close();
delete rootHistFile;
return tempHist;
}
TGraph *PlotUtils::GetTGraphFromRootFile(std::string file, std::string name) {
if (name.empty()) {
std::vector tempfile = GeneralUtils::ParseToStr(file, ";");
file = tempfile[0];
name = tempfile[1];
}
TDirectory *olddir = gDirectory;
TFile *rootHistFile = new TFile(file.c_str(), "READ");
if (!rootHistFile || rootHistFile->IsZombie()) {
NUIS_ABORT("Couldn't open root file: \"" << file << "\".");
}
TDirectory *newdir = gDirectory;
TGraph *temp =
dynamic_cast(rootHistFile->Get(name.c_str())->Clone());
if (!temp) {
- NUIS_ABORT("Couldn't retrieve: \"" << name << "\" from root file: \"" << file
- << "\".");
+ NUIS_ABORT("Couldn't retrieve: \"" << name << "\" from root file: \""
+ << file << "\".");
}
newdir->Remove(temp);
olddir->Append(temp);
rootHistFile->Close();
olddir->cd();
return temp;
}
/// Returns a vector of named TH1*s found in a single input file.
///
/// Expects a descriptor like: file.root[hist1|hist2|...]
std::vector
PlotUtils::GetTH1sFromRootFile(std::string const &descriptor) {
std::vector descriptors =
GeneralUtils::ParseToStr(descriptor, ",");
std::vector hists;
for (size_t d_it = 0; d_it < descriptors.size(); ++d_it) {
std::string &d = descriptors[d_it];
std::vector fname = GeneralUtils::ParseToStr(d, "[");
if (!fname.size() || !fname[0].length()) {
NUIS_ABORT("Couldn't find input file when attempting to parse : \""
- << d << "\". Expected input.root[hist1|hist2|...].");
+ << d << "\". Expected input.root[hist1|hist2|...].");
}
if (fname[1][fname[1].length() - 1] == ']') {
fname[1] = fname[1].substr(0, fname[1].length() - 1);
}
std::vector histnames =
GeneralUtils::ParseToStr(fname[1], "|");
if (!histnames.size()) {
- NUIS_ABORT("Couldn't find any histogram name specifiers when attempting to "
- "parse "
- ": \""
- << fname[1] << "\". Expected hist1|hist2|...");
+ NUIS_ABORT(
+ "Couldn't find any histogram name specifiers when attempting to "
+ "parse "
+ ": \""
+ << fname[1] << "\". Expected hist1|hist2|...");
}
TFile *rootHistFile = new TFile(fname[0].c_str(), "READ");
if (!rootHistFile || rootHistFile->IsZombie()) {
NUIS_ABORT("Couldn't open root file: \"" << fname[0] << "\".");
}
for (size_t i = 0; i < histnames.size(); ++i) {
TH1 *tempHist =
dynamic_cast(rootHistFile->Get(histnames[i].c_str())->Clone());
if (!tempHist) {
NUIS_ABORT("Couldn't retrieve: \""
- << histnames[i] << "\" from root file: \"" << fname[0] << "\".");
+ << histnames[i] << "\" from root file: \"" << fname[0]
+ << "\".");
}
tempHist->SetDirectory(0);
hists.push_back(tempHist);
}
rootHistFile->Close();
}
return hists;
}
// Create an array from an input file
std::vector PlotUtils::GetArrayFromTextFile(std::string DataFile) {
std::string line;
std::ifstream data(DataFile.c_str(), std::ifstream::in);
// Get first line
std::getline(data >> std::ws, line, '\n');
// Convert from a string into a vector of double
std::vector entries = GeneralUtils::ParseToDbl(line, " ");
return entries;
}
// Get a 2D array from a text file
-std::vector >
+std::vector>
PlotUtils::Get2DArrayFromTextFile(std::string DataFile) {
std::string line;
- std::vector > DataArray;
+ std::vector> DataArray;
std::ifstream data(DataFile.c_str(), std::ifstream::in);
while (std::getline(data >> std::ws, line, '\n')) {
std::vector entries = GeneralUtils::ParseToDbl(line, " ");
DataArray.push_back(entries);
}
return DataArray;
}
TH2D *PlotUtils::GetTH2DFromTextFile(std::string data, std::string binx,
std::string biny) {
// First read in the binning
// Array of x binning
std::vector xbins = GetArrayFromTextFile(binx);
// Array of y binning
std::vector ybins = GetArrayFromTextFile(biny);
// Read in the data
- std::vector > Data = Get2DArrayFromTextFile(data);
+ std::vector> Data = Get2DArrayFromTextFile(data);
// And finally fill the data
TH2D *DataPlot = new TH2D("TempHist", "TempHist", xbins.size() - 1, &xbins[0],
ybins.size() - 1, &ybins[0]);
int nBinsX = 0;
int nBinsY = 0;
- for (std::vector >::iterator it = Data.begin();
+ for (std::vector>::iterator it = Data.begin();
it != Data.end(); ++it) {
nBinsX++;
// Get the inner vector
std::vector temp = *it;
// Save the previous number[of bins to make sure it's uniform binning
int oldBinsY = nBinsY;
// Reset the counter
nBinsY = 0;
for (std::vector::iterator jt = temp.begin(); jt != temp.end();
++jt) {
nBinsY++;
DataPlot->SetBinContent(nBinsX, nBinsY, *jt);
DataPlot->SetBinError(nBinsX, nBinsY, 0.0);
}
if (oldBinsY > 0 && oldBinsY != nBinsY) {
NUIS_ERR(FTL, "Found non-uniform y-binning in " << data);
NUIS_ERR(FTL, "Previous slice: " << oldBinsY);
NUIS_ERR(FTL, "Current slice: " << nBinsY);
NUIS_ABORT("Non-uniform binning is not supported in "
- "PlotUtils::GetTH2DFromTextFile");
+ "PlotUtils::GetTH2DFromTextFile");
}
}
// Check x bins
if (size_t(nBinsX + 1) != xbins.size()) {
- NUIS_ERR(FTL, "Number of x bins in data histogram does not match the binning "
- "histogram!");
NUIS_ERR(FTL,
- "Are they the wrong way around (i.e. xbinning should be ybinning)?");
+ "Number of x bins in data histogram does not match the binning "
+ "histogram!");
+ NUIS_ERR(
+ FTL,
+ "Are they the wrong way around (i.e. xbinning should be ybinning)?");
NUIS_ERR(FTL, "Data: " << nBinsX);
NUIS_ABORT("From " << binx << " binning: " << xbins.size());
}
// Check y bins
if (size_t(nBinsY + 1) != ybins.size()) {
- NUIS_ERR(FTL, "Number of y bins in data histogram does not match the binning "
- "histogram!");
NUIS_ERR(FTL,
- "Are they the wrong way around (i.e. xbinning should be ybinning)?");
+ "Number of y bins in data histogram does not match the binning "
+ "histogram!");
+ NUIS_ERR(
+ FTL,
+ "Are they the wrong way around (i.e. xbinning should be ybinning)?");
NUIS_ERR(FTL, "Data: " << nBinsY);
NUIS_ABORT("From " << biny << " binning: " << ybins.size());
}
return DataPlot;
}
TH1D *PlotUtils::GetSliceY(TH2D *Hist, int SliceNo) {
TH1D *Slice = Hist->ProjectionX(Form("%s_SLICEY%i", Hist->GetName(), SliceNo),
SliceNo, SliceNo, "e");
Slice->SetTitle(Form("%s, %.2f-%.2f", Hist->GetYaxis()->GetTitle(),
Hist->GetYaxis()->GetBinLowEdge(SliceNo),
Hist->GetYaxis()->GetBinLowEdge(SliceNo + 1)));
Slice->GetYaxis()->SetTitle(Hist->GetZaxis()->GetTitle());
return Slice;
}
TH1D *PlotUtils::GetSliceX(TH2D *Hist, int SliceNo) {
TH1D *Slice = Hist->ProjectionY(Form("%s_SLICEX%i", Hist->GetName(), SliceNo),
SliceNo, SliceNo, "e");
Slice->SetTitle(Form("%s, %.2f-%.2f", Hist->GetXaxis()->GetTitle(),
Hist->GetXaxis()->GetBinLowEdge(SliceNo),
Hist->GetXaxis()->GetBinLowEdge(SliceNo + 1)));
Slice->GetYaxis()->SetTitle(Hist->GetZaxis()->GetTitle());
return Slice;
}
void PlotUtils::AddNeutModeArray(TH1D *hist1[], TH1D *hist2[], double scaling) {
for (int i = 0; i < 60; i++) {
if (!hist2[i])
continue;
if (!hist1[i])
continue;
hist1[i]->Add(hist2[i], scaling);
}
return;
}
void PlotUtils::ScaleToData(TH1D *data, TH1D *mc, TH1I *mask) {
double scaleF = GetDataMCRatio(data, mc, mask);
mc->Scale(scaleF);
return;
}
void PlotUtils::MaskBins(TH1D *hist, TH1I *mask) {
for (int i = 0; i < hist->GetNbinsX(); i++) {
if (mask->GetBinContent(i + 1) <= 0.5)
continue;
hist->SetBinContent(i + 1, 0.0);
hist->SetBinError(i + 1, 0.0);
- NUIS_LOG(REC, "MaskBins: Set " << hist->GetName() << " Bin " << i + 1
- << " to 0.0 +- 0.0");
+ NUIS_LOG(DEB, "MaskBins: Set " << hist->GetName() << " Bin " << i + 1
+ << " to 0.0 +- 0.0");
}
return;
}
void PlotUtils::MaskBins(TH2D *hist, TH2I *mask) {
for (int i = 0; i < hist->GetNbinsX(); i++) {
for (int j = 0; j < hist->GetNbinsY(); j++) {
if (mask->GetBinContent(i + 1, j + 1) <= 0.5)
continue;
hist->SetBinContent(i + 1, j + 1, 0.0);
hist->SetBinError(i + 1, j + 1, 0.0);
- NUIS_LOG(REC, "MaskBins: Set " << hist->GetName() << " Bin " << i + 1 << " "
- << j + 1 << " to 0.0 +- 0.0");
+ NUIS_LOG(DEB, "MaskBins: Set " << hist->GetName() << " Bin " << i + 1
+ << " " << j + 1 << " to 0.0 +- 0.0");
}
}
return;
}
double PlotUtils::GetDataMCRatio(TH1D *data, TH1D *mc, TH1I *mask) {
double rat = 1.0;
TH1D *newmc = (TH1D *)mc->Clone();
TH1D *newdt = (TH1D *)data->Clone();
if (mask) {
MaskBins(newmc, mask);
MaskBins(newdt, mask);
}
rat = newdt->Integral() / newmc->Integral();
return rat;
}
TH2D *PlotUtils::GetCorrelationPlot(TH2D *cov, std::string name) {
TH2D *cor = (TH2D *)cov->Clone();
cor->Reset();
for (int i = 0; i < cov->GetNbinsX(); i++) {
for (int j = 0; j < cov->GetNbinsY(); j++) {
if (cov->GetBinContent(i + 1, i + 1) != 0.0 and
cov->GetBinContent(j + 1, j + 1) != 0.0)
cor->SetBinContent(i + 1, j + 1,
cov->GetBinContent(i + 1, j + 1) /
(sqrt(cov->GetBinContent(i + 1, i + 1) *
cov->GetBinContent(j + 1, j + 1))));
}
}
if (!name.empty()) {
cor->SetNameTitle(name.c_str(), (name + ";;correlation").c_str());
}
cor->SetMinimum(-1);
cor->SetMaximum(1);
return cor;
}
TH2D *PlotUtils::GetDecompPlot(TH2D *cov, std::string name) {
TMatrixDSym *covarmat = new TMatrixDSym(cov->GetNbinsX());
for (int i = 0; i < cov->GetNbinsX(); i++)
for (int j = 0; j < cov->GetNbinsY(); j++)
(*covarmat)(i, j) = cov->GetBinContent(i + 1, j + 1);
TMatrixDSym *decompmat = StatUtils::GetDecomp(covarmat);
TH2D *dec = (TH2D *)cov->Clone();
for (int i = 0; i < cov->GetNbinsX(); i++)
for (int j = 0; j < cov->GetNbinsY(); j++)
dec->SetBinContent(i + 1, j + 1, (*decompmat)(i, j));
delete covarmat;
delete decompmat;
dec->SetNameTitle(name.c_str(), (name + ";;;decomposition").c_str());
return dec;
}
TH2D *PlotUtils::MergeIntoTH2D(TH1D *xhist, TH1D *yhist, std::string zname) {
std::vector xedges, yedges;
for (int i = 0; i < xhist->GetNbinsX() + 2; i++) {
xedges.push_back(xhist->GetXaxis()->GetBinLowEdge(i + 1));
}
for (int i = 0; i < yhist->GetNbinsX() + 2; i++) {
yedges.push_back(yhist->GetXaxis()->GetBinLowEdge(i + 1));
}
int nbinsx = xhist->GetNbinsX();
int nbinsy = yhist->GetNbinsX();
std::string name =
std::string(xhist->GetName()) + "_vs_" + std::string(yhist->GetName());
std::string titles = ";" + std::string(xhist->GetXaxis()->GetTitle()) + ";" +
std::string(yhist->GetXaxis()->GetTitle()) + ";" + zname;
TH2D *newplot = new TH2D(name.c_str(), (name + titles).c_str(), nbinsx,
&xedges[0], nbinsy, &yedges[0]);
return newplot;
}
//***************************************************
void PlotUtils::MatchEmptyBins(TH1D *data, TH1D *mc) {
//**************************************************
for (int i = 0; i < data->GetNbinsX(); i++) {
if (data->GetBinContent(i + 1) == 0.0 or data->GetBinError(i + 1) == 0.0)
mc->SetBinContent(i + 1, 0.0);
}
return;
}
//***************************************************
void PlotUtils::MatchEmptyBins(TH2D *data, TH2D *mc) {
//**************************************************
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 or
data->GetBinError(i + 1, j + 1) == 0.0)
mc->SetBinContent(i + 1, j + 1, 0.0);
}
}
return;
}
//***************************************************
TH1D *PlotUtils::GetProjectionX(TH2D *hist, TH2I *mask) {
//***************************************************
TH2D *maskedhist = StatUtils::ApplyHistogramMasking(hist, mask);
// This includes the underflow/overflow
- TH1D* hist_X = maskedhist->ProjectionX("_px", 1, maskedhist->GetXaxis()->GetNbins());
+ TH1D *hist_X =
+ maskedhist->ProjectionX("_px", 1, maskedhist->GetXaxis()->GetNbins());
hist_X->SetTitle(Form("%s x no under/overflow", hist_X->GetTitle()));
delete maskedhist;
return hist_X;
}
//***************************************************
TH1D *PlotUtils::GetProjectionY(TH2D *hist, TH2I *mask) {
//***************************************************
TH2D *maskedhist = StatUtils::ApplyHistogramMasking(hist, mask);
// This includes the underflow/overflow
- TH1D* hist_Y = maskedhist->ProjectionY("_py", 1, maskedhist->GetYaxis()->GetNbins());
+ TH1D *hist_Y =
+ maskedhist->ProjectionY("_py", 1, maskedhist->GetYaxis()->GetNbins());
hist_Y->SetTitle(Form("%s y no under/overflow", hist_Y->GetTitle()));
delete maskedhist;
return hist_Y;
}