diff --git a/src/Statistical/StatUtils.cxx b/src/Statistical/StatUtils.cxx
index b2745f3..ecaf736 100644
--- a/src/Statistical/StatUtils.cxx
+++ b/src/Statistical/StatUtils.cxx
@@ -1,1328 +1,1329 @@
// Copyright 2016 L. Pickering, P Stowell, R. Terri, C. Wilkinson, C. Wret
/*******************************************************************************
* This file is part of NUISANCE.
*
* NUISANCE is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* NUISANCE is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with NUISANCE. If not, see <http://www.gnu.org/licenses/>.
*******************************************************************************/
#include "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();
TH1D* calc_mc = (TH1D*)mc->Clone();
// 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_mc = ApplyHistogramMasking(mc, mask);
}
// Iterate over bins in X
for (int i = 0; i < calc_data->GetNbinsX(); i++) {
// Ignore bins with zero data or zero bin error
if (calc_data->GetBinError(i + 1) <= 0.0 ||
calc_data->GetBinContent(i + 1) == 0.0)
continue;
// Take mc data difference
double diff =
calc_data->GetBinContent(i + 1) - calc_mc->GetBinContent(i + 1);
double err = calc_data->GetBinError(i + 1);
Chi2 += (diff * diff) / (err * err);
}
// cleanup
delete calc_data;
delete calc_mc;
return Chi2;
};
//*******************************************************************
Double_t StatUtils::GetChi2FromDiag(TH2D* data, TH2D* mc, TH2I* map,
TH2I* mask) {
//*******************************************************************
// Generate a simple map
if (!map) map = GenerateMap(data);
// Convert to 1D Histograms
TH1D* data_1D = MapToTH1D(data, map);
TH1D* mc_1D = MapToTH1D(mc, map);
TH1I* mask_1D = MapToMask(mask, map);
// Calculate 1D chi2 from 1D Plots
Double_t Chi2 = StatUtils::GetChi2FromDiag(data_1D, mc_1D, mask_1D);
// CleanUp
delete data_1D;
delete mc_1D;
delete mask_1D;
return Chi2;
};
//*******************************************************************
Double_t StatUtils::GetChi2FromCov(TH1D* data, TH1D* mc, TMatrixDSym* invcov,
TH1I* mask, double data_scale,
double covar_scale) {
//*******************************************************************
Double_t Chi2 = 0.0;
TMatrixDSym* calc_cov = (TMatrixDSym*)invcov->Clone();
TH1D* calc_data = (TH1D*)data->Clone();
TH1D* calc_mc = (TH1D*)mc->Clone();
// If a mask if applied we need to apply it before the matrix is inverted
if (mask) {
calc_cov = ApplyInvertedMatrixMasking(invcov, mask);
calc_data = ApplyHistogramMasking(data, mask);
calc_mc = ApplyHistogramMasking(mc, mask);
}
// Add MC Error to data if required
if (FitPar::Config().GetParB("statutils.addmcerror")) {
// Make temp cov
TMatrixDSym* newcov = StatUtils::GetInvert(calc_cov);
// Add MC err to diag
for (int i = 0; i < calc_data->GetNbinsX(); i++) {
double mcerr = calc_mc->GetBinError(i + 1) * sqrt(covar_scale);
double oldval = (*newcov)(i, i);
- std::cout << "Adding cov stat " << mcerr * mcerr << " to "
+ LOG(FIT) << "Adding cov stat " << mcerr * mcerr << " to "
<< (*newcov)(i, i) << std::endl;
(*newcov)(i, i) = oldval + mcerr * mcerr;
}
// Reset the calc_cov to new invert
delete calc_cov;
calc_cov = GetInvert(newcov);
// Delete the tempcov
delete newcov;
}
calc_data->Scale(data_scale);
calc_mc->Scale(data_scale);
(*calc_cov) *= covar_scale;
// iterate over bins in X (i,j)
QLOG(DEB, "START Chi2 Calculation=================");
for (int i = 0; i < calc_data->GetNbinsX(); i++) {
QLOG(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++) {
QLOG(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)) {
QLOG(DEB, "[CHI2]\t\t Chi2 contribution (i,j) = (" << i << "," << j
<< ")");
QLOG(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)));
QLOG(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)));
QLOG(DEB, "[CHI2]\t\t Covar = " << (*calc_cov)(i, j));
QLOG(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);
Chi2 +=
((calc_data->GetBinContent(i + 1) - calc_mc->GetBinContent(i + 1)) *
(*calc_cov)(i, j) *
(calc_data->GetBinContent(j + 1) - calc_mc->GetBinContent(j + 1)));
} else {
QLOG(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.;
}
}
}
// Cleanup
delete calc_cov;
delete calc_data;
delete calc_mc;
return Chi2;
}
//*******************************************************************
Double_t StatUtils::GetChi2FromCov(TH2D* data, TH2D* mc, TMatrixDSym* invcov,
TH2I* map, TH2I* mask) {
//*******************************************************************
// Generate a simple map
if (!map) {
map = StatUtils::GenerateMap(data);
}
// Convert to 1D Histograms
TH1D* data_1D = MapToTH1D(data, map);
TH1D* mc_1D = MapToTH1D(mc, map);
TH1I* mask_1D = MapToMask(mask, map);
// Calculate 1D chi2 from 1D Plots
Double_t Chi2 = StatUtils::GetChi2FromCov(data_1D, mc_1D, invcov, mask_1D);
// CleanUp
delete data_1D;
delete mc_1D;
delete mask_1D;
return Chi2;
}
//*******************************************************************
Double_t StatUtils::GetChi2FromSVD(TH1D* data, TH1D* mc, TMatrixDSym* cov,
TH1I* mask) {
//*******************************************************************
Double_t Chi2 = 0.0;
TMatrixDSym* calc_cov = (TMatrixDSym*)cov->Clone();
TH1D* calc_data = (TH1D*)data->Clone();
TH1D* calc_mc = (TH1D*)mc->Clone();
// If a mask if applied we need to apply it before the matrix is inverted
if (mask) {
calc_cov = StatUtils::ApplyMatrixMasking(cov, mask);
calc_data = StatUtils::ApplyHistogramMasking(data, mask);
calc_mc = StatUtils::ApplyHistogramMasking(mc, mask);
}
// Decompose matrix
TDecompSVD LU = TDecompSVD((*calc_cov));
LU.Decompose();
TMatrixDSym* cov_U =
new TMatrixDSym(calc_data->GetNbinsX(), LU.GetU().GetMatrixArray(), "");
TVectorD* cov_S = new TVectorD(LU.GetSig());
// Apply basis rotation before adding up chi2
Double_t rotated_difference = 0.0;
for (int i = 0; i < calc_data->GetNbinsX(); i++) {
rotated_difference = 0.0;
// Rotate basis of Data - MC
for (int j = 0; j < calc_data->GetNbinsY(); j++)
rotated_difference +=
(calc_data->GetBinContent(j + 1) - calc_mc->GetBinContent(j + 1)) *
(*cov_U)(j, i);
// Divide by rotated error cov_S
Chi2 += rotated_difference * rotated_difference * 1E76 / (*cov_S)(i);
}
// Cleanup
delete calc_cov;
delete calc_data;
delete calc_mc;
delete cov_U;
delete cov_S;
return Chi2;
}
//*******************************************************************
Double_t StatUtils::GetChi2FromSVD(TH2D* data, TH2D* mc, TMatrixDSym* cov,
TH2I* map, TH2I* mask) {
//*******************************************************************
// Generate a simple map
if (!map) map = StatUtils::GenerateMap(data);
// Convert to 1D Histograms
TH1D* data_1D = MapToTH1D(data, map);
TH1D* mc_1D = MapToTH1D(mc, map);
TH1I* mask_1D = MapToMask(mask, map);
// Calculate from 1D
Double_t Chi2 = StatUtils::GetChi2FromSVD(data_1D, mc_1D, cov, mask_1D);
// CleanUp
delete data_1D;
delete mc_1D;
delete mask_1D;
return Chi2;
}
//*******************************************************************
double StatUtils::GetChi2FromEventRate(TH1D* data, TH1D* mc, TH1I* mask) {
//*******************************************************************
// If just an event rate, for chi2 just use Poission Likelihood to calculate
// the chi2 component
double chi2 = 0.0;
TH1D* calc_data = (TH1D*)data->Clone();
TH1D* calc_mc = (TH1D*)mc->Clone();
// Apply masking if required
if (mask) {
calc_data = ApplyHistogramMasking(data, mask);
calc_mc = ApplyHistogramMasking(mc, mask);
}
// Iterate over bins in X
for (int i = 0; i < calc_data->GetNbinsX(); i++) {
double dt = calc_data->GetBinContent(i + 1);
double mc = calc_mc->GetBinContent(i + 1);
if (mc <= 0) continue;
if (dt <= 0) {
// Only add difference
chi2 += 2 * (mc - dt);
} else {
// Do the chi2 for Poisson distributions
chi2 += 2 * (mc - dt + (dt * log(dt / mc)));
}
/*
LOG(REC)<<"Evt Chi2 cont = "<<i<<" "
<<mc<<" "<<dt<<" "
<<2 * (mc - dt + (dt+0.) * log((dt+0.) / (mc+0.)))
<<" "<<Chi2<<std::endl;
*/
}
// cleanup
delete calc_data;
delete calc_mc;
return chi2;
}
//*******************************************************************
Double_t StatUtils::GetChi2FromEventRate(TH2D* data, TH2D* mc, TH2I* map,
TH2I* mask) {
//*******************************************************************
// Generate a simple map
if (!map) map = StatUtils::GenerateMap(data);
// Convert to 1D Histograms
TH1D* data_1D = MapToTH1D(data, map);
TH1D* mc_1D = MapToTH1D(mc, map);
TH1I* mask_1D = MapToMask(mask, map);
// Calculate from 1D
Double_t Chi2 = StatUtils::GetChi2FromEventRate(data_1D, mc_1D, mask_1D);
// CleanUp
delete data_1D;
delete mc_1D;
delete mask_1D;
return Chi2;
}
//*******************************************************************
Double_t StatUtils::GetLikelihoodFromDiag(TH1D* data, TH1D* mc, TH1I* mask) {
//*******************************************************************
// Currently just a placeholder!
(void)data;
(void)mc;
(void)mask;
return 0.0;
};
//*******************************************************************
Double_t StatUtils::GetLikelihoodFromDiag(TH2D* data, TH2D* mc, TH2I* map,
TH2I* mask) {
//*******************************************************************
// Generate a simple map
if (!map) map = StatUtils::GenerateMap(data);
// Convert to 1D Histograms
TH1D* data_1D = MapToTH1D(data, map);
TH1D* mc_1D = MapToTH1D(mc, map);
TH1I* mask_1D = MapToMask(mask, map);
// Calculate from 1D
Double_t MLE = StatUtils::GetLikelihoodFromDiag(data_1D, mc_1D, mask_1D);
// CleanUp
delete data_1D;
delete mc_1D;
delete mask_1D;
return MLE;
};
//*******************************************************************
Double_t StatUtils::GetLikelihoodFromCov(TH1D* data, TH1D* mc,
TMatrixDSym* invcov, TH1I* mask) {
//*******************************************************************
// Currently just a placeholder !
(void)data;
(void)mc;
(void)invcov;
(void)mask;
return 0.0;
};
//*******************************************************************
Double_t StatUtils::GetLikelihoodFromCov(TH2D* data, TH2D* mc,
TMatrixDSym* invcov, TH2I* map,
TH2I* mask) {
//*******************************************************************
// Generate a simple map
if (!map) map = StatUtils::GenerateMap(data);
// Convert to 1D Histograms
TH1D* data_1D = MapToTH1D(data, map);
TH1D* mc_1D = MapToTH1D(mc, map);
TH1I* mask_1D = MapToMask(mask, map);
// Calculate from 1D
Double_t MLE =
StatUtils::GetLikelihoodFromCov(data_1D, mc_1D, invcov, mask_1D);
// CleanUp
delete data_1D;
delete mc_1D;
delete mask_1D;
return MLE;
};
//*******************************************************************
Double_t StatUtils::GetLikelihoodFromSVD(TH1D* data, TH1D* mc, TMatrixDSym* cov,
TH1I* mask) {
//*******************************************************************
// Currently just a placeholder!
(void)data;
(void)mc;
(void)cov;
(void)mask;
return 0.0;
};
//*******************************************************************
Double_t StatUtils::GetLikelihoodFromSVD(TH2D* data, TH2D* mc, TMatrixDSym* cov,
TH2I* map, TH2I* mask) {
//*******************************************************************
// Generate a simple map
if (!map) map = StatUtils::GenerateMap(data);
// Convert to 1D Histograms
TH1D* data_1D = MapToTH1D(data, map);
TH1D* mc_1D = MapToTH1D(mc, map);
TH1I* mask_1D = MapToMask(mask, map);
// Calculate from 1D
Double_t MLE = StatUtils::GetLikelihoodFromSVD(data_1D, mc_1D, cov, mask_1D);
// CleanUp
delete data_1D;
delete mc_1D;
delete mask_1D;
return MLE;
};
//*******************************************************************
Double_t StatUtils::GetLikelihoodFromEventRate(TH1D* data, TH1D* mc,
TH1I* mask) {
//*******************************************************************
// Currently just a placeholder!
(void)data;
(void)mc;
(void)mask;
return 0.0;
};
//*******************************************************************
Double_t StatUtils::GetLikelihoodFromEventRate(TH2D* data, TH2D* mc, TH2I* map,
TH2I* mask) {
//*******************************************************************
// Generate a simple map
if (!map) map = StatUtils::GenerateMap(data);
// Convert to 1D Histograms
TH1D* data_1D = MapToTH1D(data, map);
TH1D* mc_1D = MapToTH1D(mc, map);
TH1I* mask_1D = MapToMask(mask, map);
// Calculate from 1D
Double_t MLE = StatUtils::GetChi2FromEventRate(data_1D, mc_1D, mask_1D);
// CleanUp
delete data_1D;
delete mc_1D;
delete mask_1D;
return MLE;
};
//*******************************************************************
Int_t StatUtils::GetNDOF(TH1D* hist, TH1I* mask) {
//*******************************************************************
TH1D* calc_hist = (TH1D*)hist->Clone();
// If a mask is provided we need to apply it before getting NDOF
if (mask) {
calc_hist = StatUtils::ApplyHistogramMasking(hist, mask);
}
// NDOF is defined as total number of bins with non-zero errors
Int_t NDOF = 0;
for (int i = 0; i < calc_hist->GetNbinsX(); i++) {
if (calc_hist->GetBinError(i + 1) > 0.0) NDOF++;
}
delete calc_hist;
return NDOF;
};
//*******************************************************************
Int_t StatUtils::GetNDOF(TH2D* hist, TH2I* map, TH2I* mask) {
//*******************************************************************
Int_t NDOF = 0;
if (!map) map = StatUtils::GenerateMap(hist);
for (int i = 0; i < hist->GetNbinsX(); i++) {
for (int j = 0; j < hist->GetNbinsY(); j++) {
if (mask->GetBinContent(i + 1, j + 1)) continue;
if (map->GetBinContent(i + 1, j + 1) <= 0) continue;
NDOF++;
}
}
return NDOF;
};
//*******************************************************************
TH1D* StatUtils::ThrowHistogram(TH1D* hist, TMatrixDSym* cov, bool throwdiag,
TH1I* mask) {
//*******************************************************************
TH1D* calc_hist =
(TH1D*)hist->Clone((std::string(hist->GetName()) + "_THROW").c_str());
TMatrixDSym* calc_cov = (TMatrixDSym*)cov->Clone();
Double_t correl_val = 0.0;
// If a mask if applied we need to apply it before the matrix is decomposed
if (mask) {
calc_cov = ApplyMatrixMasking(cov, mask);
calc_hist = ApplyHistogramMasking(calc_hist, mask);
}
// If a covariance is provided we need a preset random vector and a decomp
std::vector<Double_t> rand_val;
TMatrixDSym* decomp_cov;
if (cov) {
for (int i = 0; i < hist->GetNbinsX(); i++) {
rand_val.push_back(gRandom->Gaus(0.0, 1.0));
}
// Decomp the matrix
decomp_cov = StatUtils::GetDecomp(calc_cov);
}
// iterate over bins
for (int i = 0; i < hist->GetNbinsX(); i++) {
// By Default the errors on the histogram are thrown uncorrelated to the other errors
/*
if (throwdiag) {
calc_hist->SetBinContent(i + 1, (calc_hist->GetBinContent(i + 1) + \
gRandom->Gaus(0.0, 1.0) * calc_hist->GetBinError(i + 1)) );
}
*/
// If a covariance is provided that is also thrown
if (cov) {
correl_val = 0.0;
for (int j = 0; j < hist->GetNbinsX(); j++) {
correl_val += rand_val[j] * (*decomp_cov)(j, i);
}
calc_hist->SetBinContent(
i + 1, (calc_hist->GetBinContent(i + 1) + correl_val * 1E-38));
}
}
delete calc_cov;
delete decomp_cov;
// return this new thrown data
return calc_hist;
};
//*******************************************************************
TH2D* StatUtils::ThrowHistogram(TH2D* hist, TMatrixDSym* cov, TH2I* map,
bool throwdiag, TH2I* mask) {
//*******************************************************************
// PLACEHOLDER!!!!!!!!!
// Currently no support for throwing 2D Histograms from a covariance
(void)hist;
(void)cov;
(void)map;
(void)throwdiag;
(void)mask;
// /todo
// Sort maps if required
// Throw the covariance for a 1D plot
// Unmap back to 2D Histogram
return hist;
}
//*******************************************************************
TH1D* StatUtils::ApplyHistogramMasking(TH1D* hist, TH1I* mask) {
//*******************************************************************
if (!mask) return ((TH1D*)hist->Clone());
// This masking is only sufficient for chi2 calculations, and will have dodgy
// bin edges.
// Get New Bin Count
Int_t NBins = 0;
for (int i = 0; i < hist->GetNbinsX(); i++) {
if (mask->GetBinContent(i + 1)) continue;
NBins++;
}
// Make new hist
std::string newmaskname = std::string(hist->GetName()) + "_MSKD";
TH1D* calc_hist =
new TH1D(newmaskname.c_str(), newmaskname.c_str(), NBins, 0, NBins);
// fill new hist
int binindex = 0;
for (int i = 0; i < hist->GetNbinsX(); i++) {
if (mask->GetBinContent(i + 1)) {
LOG(REC) << "Applying mask to bin " << i + 1 << " " << hist->GetName()
<< std::endl;
continue;
}
calc_hist->SetBinContent(binindex + 1, hist->GetBinContent(i + 1));
calc_hist->SetBinError(binindex + 1, hist->GetBinError(i + 1));
binindex++;
}
return calc_hist;
};
//*******************************************************************
TH2D* StatUtils::ApplyHistogramMasking(TH2D* hist, TH2I* mask) {
//*******************************************************************
TH2D* newhist = (TH2D*)hist->Clone();
if (!mask) return newhist;
for (int i = 0; i < hist->GetNbinsX(); i++) {
for (int j = 0; j < hist->GetNbinsY(); j++) {
if (mask->GetBinContent(i + 1, j + 1) > 0) {
newhist->SetBinContent(i + 1, j + 1, 0.0);
newhist->SetBinContent(i + 1, j + 1, 0.0);
}
}
}
return newhist;
}
//*******************************************************************
TMatrixDSym* StatUtils::ApplyMatrixMasking(TMatrixDSym* mat, TH1I* mask) {
//*******************************************************************
if (!mask) return (TMatrixDSym*)(mat->Clone());
// Get New Bin Count
Int_t NBins = 0;
for (int i = 0; i < mask->GetNbinsX(); i++) {
if (mask->GetBinContent(i + 1)) continue;
NBins++;
}
// make new matrix
TMatrixDSym* calc_mat = new TMatrixDSym(NBins);
int col, row;
// Need to mask out bins in the current matrix
row = 0;
for (int i = 0; i < mask->GetNbinsX(); i++) {
col = 0;
// skip if masked
if (mask->GetBinContent(i + 1) > 0.5) continue;
for (int j = 0; j < mask->GetNbinsX(); j++) {
// skip if masked
if (mask->GetBinContent(j + 1) > 0.5) continue;
(*calc_mat)(row, col) = (*mat)(i, j);
col++;
}
row++;
}
return calc_mat;
};
//*******************************************************************
TMatrixDSym* StatUtils::ApplyMatrixMasking(TMatrixDSym* mat, TH2D* data,
TH2I* mask, TH2I* map) {
//*******************************************************************
if (!map) map = StatUtils::GenerateMap(data);
TH1I* mask_1D = StatUtils::MapToMask(mask, map);
TMatrixDSym* newmat = StatUtils::ApplyMatrixMasking(mat, mask_1D);
delete mask_1D;
return newmat;
}
//*******************************************************************
TMatrixDSym* StatUtils::ApplyInvertedMatrixMasking(TMatrixDSym* mat,
TH1I* mask) {
//*******************************************************************
TMatrixDSym* new_mat = GetInvert(mat);
TMatrixDSym* masked_mat = ApplyMatrixMasking(new_mat, mask);
TMatrixDSym* inverted_mat = GetInvert(masked_mat);
delete masked_mat;
delete new_mat;
return inverted_mat;
};
//*******************************************************************
TMatrixDSym* StatUtils::ApplyInvertedMatrixMasking(TMatrixDSym* mat, TH2D* data,
TH2I* mask, TH2I* map) {
//*******************************************************************
if (!map) map = StatUtils::GenerateMap(data);
TH1I* mask_1D = StatUtils::MapToMask(mask, map);
TMatrixDSym* newmat = ApplyInvertedMatrixMasking(mat, mask_1D);
delete mask_1D;
return newmat;
}
//*******************************************************************
TMatrixDSym* StatUtils::GetInvert(TMatrixDSym* mat) {
//*******************************************************************
TMatrixDSym* new_mat = (TMatrixDSym*)mat->Clone();
// Check for diagonal
bool non_diagonal = false;
for (int i = 0; i < new_mat->GetNrows(); i++) {
for (int j = 0; j < new_mat->GetNrows(); j++) {
if (i == j) continue;
if ((*new_mat)(i, j) != 0.0) {
non_diagonal = true;
break;
}
}
}
// If diag, just flip the diag
if (!non_diagonal or new_mat->GetNrows() == 1) {
for (int i = 0; i < new_mat->GetNrows(); i++) {
if ((*new_mat)(i, i) != 0.0)
(*new_mat)(i, i) = 1.0 / (*new_mat)(i, i);
else
(*new_mat)(i, i) = 0.0;
}
return new_mat;
}
// Invert full matrix
TDecompSVD LU = TDecompSVD((*new_mat));
new_mat =
new TMatrixDSym(new_mat->GetNrows(), LU.Invert().GetMatrixArray(), "");
return new_mat;
}
//*******************************************************************
TMatrixDSym* StatUtils::GetDecomp(TMatrixDSym* mat) {
//*******************************************************************
TMatrixDSym* new_mat = (TMatrixDSym*)mat->Clone();
int nrows = new_mat->GetNrows();
// Check for diagonal
bool diagonal = true;
for (int i = 0; i < nrows; i++) {
for (int j = 0; j < nrows; j++) {
if (i == j) continue;
if ((*new_mat)(i, j) != 0.0) {
diagonal = false;
break;
}
}
}
// If diag, just flip the diag
if (diagonal or nrows == 1) {
for (int i = 0; i < nrows; i++) {
if ((*new_mat)(i, i) > 0.0)
(*new_mat)(i, i) = sqrt((*new_mat)(i, i));
else
(*new_mat)(i, i) = 0.0;
}
return new_mat;
}
TDecompChol LU = TDecompChol(*new_mat);
LU.Decompose();
delete new_mat;
TMatrixDSym* dec_mat = new TMatrixDSym(nrows, LU.GetU().GetMatrixArray(), "");
return dec_mat;
}
//*******************************************************************
void StatUtils::ForceNormIntoCovar(TMatrixDSym* mat, TH1D* hist, double norm) {
//*******************************************************************
if (!mat) mat = MakeDiagonalCovarMatrix(hist);
int nbins = mat->GetNrows();
TMatrixDSym* new_mat = new TMatrixDSym(nbins);
for (int i = 0; i < nbins; i++) {
for (int j = 0; j < nbins; j++) {
double valx = hist->GetBinContent(i + 1) * 1E38;
double valy = hist->GetBinContent(j + 1) * 1E38;
(*new_mat)(i, j) = (*mat)(i, j) + norm * norm * valx * valy;
}
}
// Swap the two
delete mat;
mat = new_mat;
return;
};
//*******************************************************************
void StatUtils::ForceNormIntoCovar(TMatrixDSym* mat, TH2D* data, double norm,
TH2I* map) {
//*******************************************************************
if (!map) map = StatUtils::GenerateMap(data);
TH1D* data_1D = MapToTH1D(data, map);
StatUtils::ForceNormIntoCovar(mat, data_1D, norm);
delete data_1D;
return;
}
//*******************************************************************
TMatrixDSym* StatUtils::MakeDiagonalCovarMatrix(TH1D* data, double scaleF) {
//*******************************************************************
TMatrixDSym* newmat = new TMatrixDSym(data->GetNbinsX());
for (int i = 0; i < data->GetNbinsX(); i++) {
(*newmat)(i, i) =
data->GetBinError(i + 1) * data->GetBinError(i + 1) * scaleF * scaleF;
}
return newmat;
}
//*******************************************************************
TMatrixDSym* StatUtils::MakeDiagonalCovarMatrix(TH2D* data, TH2I* map,
double scaleF) {
//*******************************************************************
if (!map) map = StatUtils::GenerateMap(data);
TH1D* data_1D = MapToTH1D(data, map);
return StatUtils::MakeDiagonalCovarMatrix(data_1D, scaleF);
};
//*******************************************************************
void StatUtils::SetDataErrorFromCov(TH1D* data, TMatrixDSym* cov,
double scale) {
//*******************************************************************
// Check
if (cov->GetNrows() != data->GetNbinsX()) {
ERR(FTL) << "Nrows in cov don't match nbins in data for SetDataErrorFromCov" << std::endl;
ERR(FTL) << "Nrows = " << cov->GetNrows() << std::endl;
ERR(FTL) << "Nbins = " << data->GetNbinsX() << std::endl;
//throw;
}
// Set bin errors form cov diag
// Check if the errors are set
bool ErrorsSet = false;
for (int i = 0; i < data->GetNbinsX(); i++) {
if (ErrorsSet == true) break;
if (data->GetBinError(i+1) != 0) ErrorsSet = true;
}
// Now loop over
if (ErrorsSet) {
for (int i = 0; i < data->GetNbinsX(); i++) {
double dataerr = data->GetBinError(i + 1);
double coverr = sqrt((*cov)(i, i))*scale;
- if (fabs(dataerr-coverr) > 1E-44) {
+ // Check that the errors are within 1% of eachother
+ if (fabs(dataerr-coverr)/dataerr > 0.01) {
ERR(FTL) << "Data error does not match covariance error for bin " << i+1 << " (" << data->GetXaxis()->GetBinLowEdge(i+1) << "-" << data->GetXaxis()->GetBinLowEdge(i+2) << ")" << std::endl;
ERR(FTL) << "Data error: " << dataerr << std::endl;
ERR(FTL) << "Cov error: " << coverr << std::endl;
}
}
// Else blindly trust the covariance
} else {
for (int i = 0; i < data->GetNbinsX(); i++) {
data->SetBinError(i+1, sqrt((*cov)(i,i))*scale);
}
}
return;
}
//*******************************************************************
void StatUtils::SetDataErrorFromCov(TH2D* data, TMatrixDSym* cov, TH2I* map,
double scale) {
//*******************************************************************
// Create map if required
if (!map) map = StatUtils::GenerateMap(data);
// Set Bin Errors from cov diag
int count = 0;
for (int i = 0; i < data->GetNbinsX(); i++) {
for (int j = 0; j < data->GetNbinsY(); j++) {
if (data->GetBinContent(i + 1, j + 1) == 0.0) continue;
count = map->GetBinContent(i + 1, j + 1) - 1;
data->SetBinError(i + 1, j + 1, sqrt((*cov)(count, count)) * scale);
}
}
return;
}
TMatrixDSym* StatUtils::ExtractShapeOnlyCovar(TMatrixDSym* full_covar,
TH1* data_hist,
double data_scale) {
int nbins = full_covar->GetNrows();
TMatrixDSym* shape_covar = new TMatrixDSym(nbins);
// Check nobody is being silly
if (data_hist->GetNbinsX() != nbins) {
ERR(WRN) << "Inconsistent matrix and data histogram passed to "
"StatUtils::ExtractShapeOnlyCovar!"
<< std::endl;
ERR(WRN) << "data_hist has " << data_hist->GetNbinsX() << " matrix has "
<< nbins << std::endl;
int err_bins = data_hist->GetNbinsX();
if (nbins > err_bins) err_bins = nbins;
for (int i = 0; i < err_bins; ++i) {
ERR(WRN) << "Matrix diag. = " << (*full_covar)(i, i)
<< " data = " << data_hist->GetBinContent(i + 1) << std::endl;
}
return NULL;
}
double total_data = 0;
double total_covar = 0;
// Initial loop to calculate some constants
for (int i = 0; i < nbins; ++i) {
total_data += data_hist->GetBinContent(i + 1) * data_scale;
for (int j = 0; j < nbins; ++j) {
total_covar += (*full_covar)(i, j);
}
}
if (total_data == 0 || total_covar == 0) {
ERR(WRN) << "Stupid matrix or data histogram passed to "
"StatUtils::ExtractShapeOnlyCovar! Ignoring..."
<< std::endl;
return NULL;
}
LOG(SAM) << "Norm error = " << sqrt(total_covar) / total_data << std::endl;
// Now loop over and calculate the shape-only matrix
for (int i = 0; i < nbins; ++i) {
double data_i = data_hist->GetBinContent(i + 1) * data_scale;
for (int j = 0; j < nbins; ++j) {
double data_j = data_hist->GetBinContent(j + 1) * data_scale;
double norm_term =
data_i * data_j * total_covar / total_data / total_data;
double mix_sum1 = 0;
double mix_sum2 = 0;
for (int k = 0; k < nbins; ++k) {
mix_sum1 += (*full_covar)(k, j);
mix_sum2 += (*full_covar)(i, k);
}
double mix_term1 =
data_i * (mix_sum1 / total_data -
total_covar * data_j / total_data / total_data);
double mix_term2 =
data_j * (mix_sum2 / total_data -
total_covar * data_i / total_data / total_data);
(*shape_covar)(i, j) =
(*full_covar)(i, j) - mix_term1 - mix_term2 - norm_term;
}
}
return shape_covar;
}
//*******************************************************************
TH2I* StatUtils::GenerateMap(TH2D* hist) {
//*******************************************************************
std::string maptitle = std::string(hist->GetName()) + "_MAP";
TH2I* map =
new TH2I(maptitle.c_str(), maptitle.c_str(), hist->GetNbinsX(), 0,
hist->GetNbinsX(), hist->GetNbinsY(), 0, hist->GetNbinsY());
Int_t index = 1;
for (int i = 0; i < hist->GetNbinsX(); i++) {
for (int j = 0; j < hist->GetNbinsY(); j++) {
if (hist->GetBinContent(i + 1, j + 1) > 0 &&
hist->GetBinError(i + 1, j + 1) > 0) {
map->SetBinContent(i + 1, j + 1, index);
index++;
} else {
map->SetBinContent(i + 1, j + 1, 0);
}
}
}
return map;
}
//*******************************************************************
TH1D* StatUtils::MapToTH1D(TH2D* hist, TH2I* map) {
//*******************************************************************
if (!hist) return NULL;
// Get N bins for 1D plot
Int_t Nbins = map->GetMaximum();
std::string name1D = std::string(hist->GetName()) + "_1D";
// Make new 1D Hist
TH1D* newhist = new TH1D(name1D.c_str(), name1D.c_str(), Nbins, 0, Nbins);
// map bin contents
for (int i = 0; i < map->GetNbinsX(); i++) {
for (int j = 0; j < map->GetNbinsY(); j++) {
if (map->GetBinContent(i + 1, j + 1) == 0) continue;
newhist->SetBinContent(map->GetBinContent(i + 1, j + 1),
hist->GetBinContent(i + 1, j + 1));
newhist->SetBinError(map->GetBinContent(i + 1, j + 1),
hist->GetBinError(i + 1, j + 1));
}
}
// return
return newhist;
}
//*******************************************************************
TH1I* StatUtils::MapToMask(TH2I* hist, TH2I* map) {
//*******************************************************************
TH1I* newhist = NULL;
if (!hist) return newhist;
// Get N bins for 1D plot
Int_t Nbins = map->GetMaximum();
std::string name1D = std::string(hist->GetName()) + "_1D";
// Make new 1D Hist
newhist = new TH1I(name1D.c_str(), name1D.c_str(), Nbins, 0, Nbins);
// map bin contents
for (int i = 0; i < map->GetNbinsX(); i++) {
for (int j = 0; j < map->GetNbinsY(); j++) {
if (map->GetBinContent(i + 1, j + 1) == 0) continue;
newhist->SetBinContent(map->GetBinContent(i + 1, j + 1),
hist->GetBinContent(i + 1, j + 1));
}
}
// return
return newhist;
}
TMatrixDSym* StatUtils::GetCovarFromCorrel(TMatrixDSym* correl, TH1D* data) {
int nbins = correl->GetNrows();
TMatrixDSym* covar = new TMatrixDSym(nbins);
for (int i = 0; i < nbins; i++) {
for (int j = 0; j < nbins; j++) {
(*covar)(i, j) =
(*correl)(i, j) * data->GetBinError(i + 1) * data->GetBinError(j + 1);
}
}
return covar;
}
//*******************************************************************
TMatrixD* StatUtils::GetMatrixFromTextFile(std::string covfile, int dimx,
int dimy) {
//*******************************************************************
// Determine dim
if (dimx == -1 and dimy == -1) {
std::string line;
std::ifstream covar(covfile.c_str(), std::ifstream::in);
int row = 0;
while (std::getline(covar >> std::ws, line, '\n')) {
int column = 0;
std::vector<double> entries = GeneralUtils::ParseToDbl(line, " ");
if (entries.size() <= 1) {
ERR(WRN) << "StatUtils::GetMatrixFromTextFile, matrix only has <= 1 "
"entries on this line: "
<< row << std::endl;
}
for (std::vector<double>::iterator iter = entries.begin();
iter != entries.end(); iter++) {
column++;
if (column > dimx) dimx = column;
}
row++;
if (row > dimy) dimy = row;
}
}
// Or assume symmetric
if (dimx != -1 and dimy == -1) {
dimy = dimx;
}
assert(dimy != -1 && " matrix dimy not set.");
// Make new matrix
TMatrixD* mat = new TMatrixD(dimx, dimy);
std::string line;
std::ifstream covar(covfile.c_str(), std::ifstream::in);
int row = 0;
while (std::getline(covar >> std::ws, line, '\n')) {
int column = 0;
std::vector<double> entries = GeneralUtils::ParseToDbl(line, " ");
if (entries.size() <= 1) {
ERR(WRN) << "StatUtils::GetMatrixFromTextFile, matrix only has <= 1 "
"entries on this line: "
<< row << std::endl;
}
for (std::vector<double>::iterator iter = entries.begin();
iter != entries.end(); iter++) {
// Check Rows
// assert(row > mat->GetNrows() && " covar rows doesn't match matrix
// rows.");
// assert(column > mat->GetNcols() && " covar cols doesn't match matrix
// cols.");
// Fill Matrix
(*mat)(row, column) = (*iter);
column++;
}
row++;
}
return mat;
}
//*******************************************************************
TMatrixD* StatUtils::GetMatrixFromRootFile(std::string covfile,
std::string histname) {
//*******************************************************************
std::string inputfile = covfile + ";" + histname;
std::vector<std::string> splitfile = GeneralUtils::ParseToStr(inputfile, ";");
if (splitfile.size() < 2) {
ERR(FTL) << "No object name given!" << std::endl;
throw;
}
// Get file
TFile* tempfile = new TFile(splitfile[0].c_str(), "READ");
// Get Object
TObject* obj = tempfile->Get(splitfile[1].c_str());
if (!obj) {
ERR(FTL) << "Object " << splitfile[1] << " doesn't exist!" << std::endl;
throw;
}
// Try casting
TMatrixD* mat = dynamic_cast<TMatrixD*>(obj);
if (mat) {
TMatrixD* newmat = (TMatrixD*)mat->Clone();
delete mat;
tempfile->Close();
return newmat;
}
TMatrixDSym* matsym = dynamic_cast<TMatrixDSym*>(obj);
if (matsym) {
TMatrixD* newmat = new TMatrixD(matsym->GetNrows(), matsym->GetNrows());
for (int i = 0; i < matsym->GetNrows(); i++) {
for (int j = 0; j < matsym->GetNrows(); j++) {
(*newmat)(i, j) = (*matsym)(i, j);
}
}
delete matsym;
tempfile->Close();
return newmat;
}
TH2D* mathist = dynamic_cast<TH2D*>(obj);
if (mathist) {
TMatrixD* newmat = new TMatrixD(mathist->GetNbinsX(), mathist->GetNbinsX());
for (int i = 0; i < mathist->GetNbinsX(); i++) {
for (int j = 0; j < mathist->GetNbinsX(); j++) {
(*newmat)(i, j) = mathist->GetBinContent(i + 1, j + 1);
}
}
delete mathist;
tempfile->Close();
return newmat;
}
return NULL;
}
//*******************************************************************
TMatrixDSym* StatUtils::GetCovarFromTextFile(std::string covfile, int dim) {
//*******************************************************************
// Delete TempMat
TMatrixD* tempmat = GetMatrixFromTextFile(covfile, dim, dim);
// Make a symmetric covariance
TMatrixDSym* newmat = new TMatrixDSym(tempmat->GetNrows());
for (int i = 0; i < tempmat->GetNrows(); i++) {
for (int j = 0; j < tempmat->GetNrows(); j++) {
(*newmat)(i, j) = (*tempmat)(i, j);
}
}
delete tempmat;
return newmat;
}
//*******************************************************************
TMatrixDSym* StatUtils::GetCovarFromRootFile(std::string covfile,
std::string histname) {
//*******************************************************************
TMatrixD* tempmat = GetMatrixFromRootFile(covfile, histname);
TMatrixDSym* newmat = new TMatrixDSym(tempmat->GetNrows());
for (int i = 0; i < tempmat->GetNrows(); i++) {
for (int j = 0; j < tempmat->GetNrows(); j++) {
(*newmat)(i, j) = (*tempmat)(i, j);
}
}
delete tempmat;
return newmat;
}