diff --git a/parameters/config.xml b/parameters/config.xml
index 01df49d..17d4f2b 100644
--- a/parameters/config.xml
+++ b/parameters/config.xml
@@ -1,226 +1,229 @@
<nuisance>
<!-- # ###################################################### -->
<!-- # NUISANCE CONFIGURATION OPTIONS -->
<!-- # This file is read in by default at runtime -->
<!-- # If you want to override on a case by case bases use -q at runtime -->
<!-- # ###################################################### -->
<!-- # MAIN Configs -->
<!-- # ###################################################### -->
<!-- # Logger goes from -->
<!-- # 1 Quiet -->
<!-- # 2 Fitter -->
<!-- # 3 Samples -->
<!-- # 4 Reconfigure Loops -->
<!-- # 5 Every Event print out (SHOUT) -->
<!-- # -1 DEBUGGING -->
<config VERBOSITY='4'/>
<!-- # ERROR goes from -->
<!-- # 0 NONE -->
<!-- # 1 FATAL -->
<!-- # 2 WARN -->
<config ERROR='2'/>
<config TRACE='0'/>
<config cores='1' />
<config spline_test_throws='50' />
<config spline_cores='1' />
<config spline_chunks='20' />
<config spline_procchunk='-1' />
<config Electron_NThetaBins='4' />
<config Electron_NEnergyBins='4' />
<config Electron_ThetaWidth='1.0' />
<config Electron_EnergyWidth='0.10' />
<config RemoveFSIParticles='0' />
<config RemoveUndefParticles='0' />
<config RemoveNuclearParticles='0'/>
<config MINERvASaveExtraCCQE='0' />
<!-- # Input Configs -->
<!-- # ###################################################### -->
<!-- # Default Requirements file for the externalDataFitter Package -->
<!-- # MAX Events : -1 is no cut. Events will be scaled automatically to give good xsec predictions. -->
<config MAXEVENTS='-1'/>
<!-- Include empty stacks in the THStack -->
<config includeemptystackhists='0'/>
<!-- # Turn on/off event manager -->
<!-- # EventManager enables us to only loop number of events once for multiple projections of the same measurements -->
<!-- # e.g. MiniBooNE CC1pi+ Q2 and MiniBooNE CC1pi+ Tmu would ordinarily require 2 reconfigures, but with this enabled it requires only one -->
<config EventManager='1'/>
<!-- # Event Directories -->
<!-- # Can setup default directories and use @EVENT_DIR/path to link to it -->
<config EVENT_DIR='/data2/stowell/NIWG/'/>
<config NEUT_EVENT_DIR='/data2/stowell/NIWG/neut/fit_samples_neut5.3.3/'/>
<config GENIE_EVENT_DIR='/data2/stowell/NIWG/genie/fit_samples_R.2.10.0/'/>
<config NUWRO_EVENT_DIR='/data2/stowell/NIWG/nuwro/fit_samples/'/>
<config GIBUU_EVENT_DIR='/data/GIBUU/DIR/'/>
<config SaveNuWroExtra='0' />
<!-- # In PrepareGENIE the reconstructed splines can be saved into the file -->
<config save_genie_splines='1'/>
<!-- # In InputHandler the option to regenerate NuWro flux/xsec plots is available -->
<!-- # Going to move this to its own app soon -->
<!-- # DEVEL CONFIG OPTION, don't touch! -->
<config CacheSize='0'/>
<!-- # ReWeighting Configuration Options -->
<!-- # ###################################################### -->
<!-- # Convert Dials in output statements using dial conversion card -->
<config convert_dials='0'/>
<!-- # Vetos can be used to specify RW dials NOT to be loaded in -->
<!-- # Useful if one specific one has an issue -->
<config FitWeight_fNIWGRW_veto=''/>
<config FitWeight_fNuwroRW_veto=''/>
<config FitWeight_fNeutRW_veto=''/>
<config FitWeight_fGenieRW_veto=''/>
<!-- # Output Options -->
<!-- # ###################################################### -->
<!-- # Save Nominal prediction with all rw engines at default -->
<config savenominal='0'/>
<!-- # Save prefit with values at starting values -->
<config saveprefit='0'/>
<!-- # Here's the full list of drawing options -->
<!-- DATA/MC/EVT/FINE/RATIO/MODES/SHAPE/WGHT/WEIGHTS/FLUX/XSEC/MASK/COV/INCOV/DECOMP/CANVPDG/CANVMC/SETTINGS'/>
<!-- #config drawopts DATA/MC/EVT/FINE/RATIO/MODES/SHAPE/RESIDUAL/MATRIX/FLUX/MASK/MAP -->
<!-- #config drawopts DATA/MC -->
<config drawopts='DATA/MC/EVT/FINE/RATIO/MODES/SHAPE/WEIGHTS/FLUX/XSEC/MASK/COV/INCOV/DECOMP/CANVPDG/CANVMC/SETTINGS'/>
<config InterpolateSigmaQ0Histogram='1' />
<config InterpolateSigmaQ0HistogramRes='100' />
<config InterpolateSigmaQ0HistogramThrow='1' />
<config InterpolateSigmaQ0HistogramNTHROWS='100000' />
<!-- # Save the shape scaling applied with option SHAPE into the main MC hist -->
<config saveshapescaling='0'/>
<config CorrectGENIEMECNorm='1'/>
<!-- # Set style of 1D output histograms -->
<config linecolour='1'/>
<config linestyle='1'/>
<config linewidth='1'/>
<!-- # For GenericFlux -->
<config isLiteMode='0'/>
<!-- # Statistical Options -->
<!-- # ###################################################### -->
<!-- # Add MC Statistical error to likelihoods -->
<config addmcerror='0'/>
<!-- # NUISMIN Configurations -->
<!-- # ###################################################### -->
<config MAXCALLS='1000000'/>
<config MAXITERATIONS='1000000'/>
<config TOLERANCE='0.001'/>
<!-- # Number of events required in low stats routines -->
<config LOWSTATEVENTS='25000'/>
<!-- # Error band generator configs -->
<!-- # ###################################################### -->
<!-- # For -f ErrorBands creates error bands for given measurements -->
<!-- # How many throws do we want (The higher the better precision) -->
-<config error_throws='250'/>
+<config error_throws='500'/>
<!-- # Are we throwing uniform or according to Gaussian? -->
<!-- # Only use uniform if wanting to study the limits of a dial. -->
<config error_uniform='0'/>
<config WriteSeperateStacks='1'/>
<!-- # Other Individual Case Configs -->
<!-- # ###################################################### -->
<!-- # Covariance throw options for fake data studies with MiniBooNE data. -->
<config thrown_covariance='FULL'/>
<config throw_mc_stat='0.0'/>
<config throw_diag_syst='0'/>
<config throw_corr_syst='0'/>
<config throw_mc_stat='0'/>
<!-- # Apply a shift to the muon momentum before calculation of Q2 -->
<config muon_momentum_shift='0.0'/>
<config muon_momentum_throw='0'/>
<!-- # MINERvA Specific Configs -->
<config MINERvA_CCinc_XSec_2DEavq3_nu_hadron_cut='0'/>
<config MINERvA_CCinc_XSec_2DEavq3_nu_useq3true='0'/>
<config Modes_split_PN_NN='0'/>
-<config SignalReconfigures='0'/>
+
+<!-- Use only signal events when reconfiguring -->
+<config SignalReconfigures='false'/>
+<config FullEventOnSignalReconfigure="true"/>
<!-- # SciBooNE specific -->
<config SciBarDensity='1.04'/>
<config SciBarRecoDist='12.0'/>
<config PenetratingMuonEnergy='1.4'/>
<config FlatEfficiency='1.0'/>
<config NumRangeSteps='50'/>
<config UseProton='true'/>
<config UseZackEff='false'/>
<config MINERvADensity='1.04'/>
<config MINERvARecoDist='10.0'/>
<!-- Different way of reweighting in GENIE -->
<config GENIEWeightEngine_CCRESMode="kModeMaMv"/>
<!--
<config GENIEWeightEngine_CCRESMode="kModeMa"/>
-->
-<config GENIEWeightEngine_CCQEMode="kModeMa"/>
+<config GENIEWeightEngine_CCQEMode="kModeZExp"/> Using z-expansion
<!--
<config GENIEWeightEngine_CCQEMode="kModeMa"/> Normal MAQE Llewelyn-Smith
<config GENIEWeightEngine_CCQEMode="kModeNormAndMaShape"/> Taking shape into account (don't really use this...)
<config GENIEWeightEngine_CCQEMode="kModeZExp"/> Using z-expansion
-->
<!-- CCQE/2p2h/1pi Gaussian enhancement method -->
<!-- Apply tilt-shift weights or normal Gaussian parameters-->
<config Gaussian_Enhancement="Normal" />
<!--
<config Gaussian_Enhancement="Tilt-Shift" />
-->
<config NToyThrows='100000' />
<!-- Use NOvA Weights or not -->
<config NOvA_Weights="false" />
<!-- Tune name, for GENIE v3+ -->
<config GENIETune="G18_02a_00_000" />
<config GENIEEventGeneratorList="Default" />
<!--
<config GENIEXSecModelCCRES="genie::ReinSehgalRESPXSec" />
<config GENIEXSecModelCOH="genie::ReinSehgalCOHPiPXSec" />
<config GENIEXSecModelCCQE="genie::LwlynSmithQELCCPXSec" />
-->
<!--
<config GENIEXSecModelCCQE="genie::NievesQELCCPXSec" />
<config GENIEXSecModelCCRES="genie::BergerSehgalRESPXSec2014" />
<config GENIEXSecModelCOH="genie::BergerSehgalCOHPiPXSec2015" />
-->
<config UseShapeCovar="0" />
<config CC0piNBINS="156" />
-</nuisance>
+</nuisance>
diff --git a/src/FCN/JointFCN.cxx b/src/FCN/JointFCN.cxx
index 6e01efd..fcd5fac 100755
--- a/src/FCN/JointFCN.cxx
+++ b/src/FCN/JointFCN.cxx
@@ -1,1128 +1,1126 @@
#include "JointFCN.h"
#include "FitUtils.h"
#include <stdio.h>
//***************************************************
JointFCN::JointFCN(TFile *outfile) {
//***************************************************
fOutputDir = gDirectory;
if (outfile)
Config::Get().out = outfile;
std::vector<nuiskey> samplekeys = Config::QueryKeys("sample");
LoadSamples(samplekeys);
std::vector<nuiskey> covarkeys = Config::QueryKeys("covar");
LoadPulls(covarkeys);
fCurIter = 0;
fMCFilled = false;
fIterationTree = false;
fDialVals = NULL;
fNDials = 0;
fUsingEventManager = FitPar::Config().GetParB("EventManager");
fOutputDir->cd();
}
//***************************************************
JointFCN::JointFCN(std::vector<nuiskey> samplekeys, TFile *outfile) {
//***************************************************
fOutputDir = gDirectory;
if (outfile)
Config::Get().out = outfile;
LoadSamples(samplekeys);
fCurIter = 0;
fMCFilled = false;
fOutputDir->cd();
fIterationTree = false;
fDialVals = NULL;
fNDials = 0;
fUsingEventManager = FitPar::Config().GetParB("EventManager");
fOutputDir->cd();
}
//***************************************************
JointFCN::~JointFCN() {
//***************************************************
// Delete Samples
for (MeasListConstIter iter = fSamples.begin(); iter != fSamples.end();
iter++) {
MeasurementBase *exp = *iter;
delete exp;
}
for (PullListConstIter iter = fPulls.begin(); iter != fPulls.end(); iter++) {
ParamPull *pull = *iter;
delete pull;
}
// Sort Tree
if (fIterationTree)
DestroyIterationTree();
if (fDialVals)
delete fDialVals;
if (fSampleLikes)
delete fSampleLikes;
};
//***************************************************
void JointFCN::CreateIterationTree(std::string name, FitWeight *rw) {
//***************************************************
LOG(FIT) << " Creating new iteration container! " << std::endl;
DestroyIterationTree();
fIterationTreeName = name;
// Add sample likelihoods and ndof
for (MeasListConstIter iter = fSamples.begin(); iter != fSamples.end();
iter++) {
MeasurementBase *exp = *iter;
std::string name = exp->GetName();
std::string liketag = name + "_likelihood";
fNameValues.push_back(liketag);
fCurrentValues.push_back(0.0);
std::string ndoftag = name + "_ndof";
fNameValues.push_back(ndoftag);
fCurrentValues.push_back(0.0);
}
// Add Pull terms
for (PullListConstIter iter = fPulls.begin(); iter != fPulls.end(); iter++) {
ParamPull *pull = *iter;
std::string name = pull->GetName();
std::string liketag = name + "_likelihood";
fNameValues.push_back(liketag);
fCurrentValues.push_back(0.0);
std::string ndoftag = name + "_ndof";
fNameValues.push_back(ndoftag);
fCurrentValues.push_back(0.0);
}
// Add Likelihoods
fNameValues.push_back("total_likelihood");
fCurrentValues.push_back(0.0);
fNameValues.push_back("total_ndof");
fCurrentValues.push_back(0.0);
// Setup Containers
fSampleN = fSamples.size() + fPulls.size();
fSampleLikes = new double[fSampleN];
fSampleNDOF = new int[fSampleN];
// Add Dials
std::vector<std::string> dials = rw->GetDialNames();
for (size_t i = 0; i < dials.size(); i++) {
fNameValues.push_back(dials[i]);
fCurrentValues.push_back(0.0);
}
fNDials = dials.size();
fDialVals = new double[fNDials];
// Set IterationTree Flag
fIterationTree = true;
}
//***************************************************
void JointFCN::DestroyIterationTree() {
//***************************************************
fIterationCount.clear();
fCurrentValues.clear();
fNameValues.clear();
fIterationValues.clear();
}
//***************************************************
void JointFCN::WriteIterationTree() {
//***************************************************
LOG(FIT) << "Writing iteration tree" << std::endl;
// Make a new TTree
TTree *itree =
new TTree(fIterationTreeName.c_str(), fIterationTreeName.c_str());
double *vals = new double[fNameValues.size()];
int count = 0;
itree->Branch("iteration", &count, "Iteration/I");
for (size_t i = 0; i < fNameValues.size(); i++) {
itree->Branch(fNameValues[i].c_str(), &vals[i],
(fNameValues[i] + "/D").c_str());
}
// Fill Iterations
for (size_t i = 0; i < fIterationValues.size(); i++) {
std::vector<double> itervals = fIterationValues[i];
// Fill iteration state
count = fIterationCount[i];
for (size_t j = 0; j < itervals.size(); j++) {
vals[j] = itervals[j];
}
// Save to TTree
itree->Fill();
}
// Write to file
itree->Write();
}
//***************************************************
void JointFCN::FillIterationTree(FitWeight *rw) {
//***************************************************
// Loop over samples count
int count = 0;
for (int i = 0; i < fSampleN; i++) {
fCurrentValues[count++] = fSampleLikes[i];
fCurrentValues[count++] = double(fSampleNDOF[i]);
}
// Fill Totals
fCurrentValues[count++] = fLikelihood;
fCurrentValues[count++] = double(fNDOF);
// Loop Over Parameter Counts
rw->GetAllDials(fDialVals, fNDials);
for (int i = 0; i < fNDials; i++) {
fCurrentValues[count++] = double(fDialVals[i]);
}
// Push Back Into Container
fIterationCount.push_back(fCurIter);
fIterationValues.push_back(fCurrentValues);
}
//***************************************************
double JointFCN::DoEval(const double *x) {
//***************************************************
// WEIGHT ENGINE
fDialChanged = FitBase::GetRW()->HasRWDialChanged(x);
FitBase::GetRW()->UpdateWeightEngine(x);
if (fDialChanged) {
FitBase::GetRW()->Reconfigure();
FitBase::EvtManager().ResetWeightFlags();
}
if (LOG_LEVEL(REC)) {
FitBase::GetRW()->Print();
}
// SORT SAMPLES
ReconfigureSamples();
// GET TEST STAT
fLikelihood = GetLikelihood();
fNDOF = GetNDOF();
// PRINT PROGRESS
LOG(FIT) << "Current Stat (iter. " << this->fCurIter << ") = " << fLikelihood
<< std::endl;
// UPDATE TREE
- if (fIterationTree)
- FillIterationTree(FitBase::GetRW());
+ if (fIterationTree) FillIterationTree(FitBase::GetRW());
return fLikelihood;
}
//***************************************************
int JointFCN::GetNDOF() {
//***************************************************
int totaldof = 0;
int count = 0;
// Total number of Free bins in each MC prediction
for (MeasListConstIter iter = fSamples.begin(); iter != fSamples.end();
iter++) {
MeasurementBase *exp = *iter;
int dof = exp->GetNDOF();
// Save Seperate DOF
if (fIterationTree) {
fSampleNDOF[count] = dof;
}
// Add to total
totaldof += dof;
count++;
}
// Loop over pulls
for (PullListConstIter iter = fPulls.begin(); iter != fPulls.end(); iter++) {
ParamPull *pull = *iter;
double dof = pull->GetLikelihood();
// Save seperate DOF
if (fIterationTree) {
fSampleNDOF[count] = dof;
}
// Add to total
totaldof += dof;
count++;
}
// Set Data Variable
if (fIterationTree) {
fSampleNDOF[count] = totaldof;
}
return totaldof;
}
//***************************************************
double JointFCN::GetLikelihood() {
//***************************************************
LOG(MIN) << std::left << std::setw(43) << "Getting likelihoods..."
<< " : "
<< "-2logL" << std::endl;
// Loop and add up likelihoods in an uncorrelated way
double like = 0.0;
int count = 0;
for (MeasListConstIter iter = fSamples.begin(); iter != fSamples.end();
iter++) {
MeasurementBase *exp = *iter;
double newlike = exp->GetLikelihood();
int ndof = exp->GetNDOF();
// Save seperate likelihoods
if (fIterationTree) {
fSampleLikes[count] = newlike;
}
LOG(MIN) << "-> " << std::left << std::setw(40) << exp->GetName() << " : "
<< newlike << "/" << ndof << std::endl;
// Add Weight Scaling
// like *= FitBase::GetRW()->GetSampleLikelihoodWeight(exp->GetName());
// Add to total
like += newlike;
count++;
}
// Loop over pulls
for (PullListConstIter iter = fPulls.begin(); iter != fPulls.end(); iter++) {
ParamPull *pull = *iter;
double newlike = pull->GetLikelihood();
// Save seperate likelihoods
if (fIterationTree) {
fSampleLikes[count] = newlike;
}
// Add to total
like += newlike;
count++;
}
// Set Data Variable
fLikelihood = like;
if (fIterationTree) {
fSampleLikes[count] = fLikelihood;
}
return like;
};
void JointFCN::LoadSamples(std::vector<nuiskey> samplekeys) {
LOG(MIN) << "Loading Samples : " << samplekeys.size() << std::endl;
for (size_t i = 0; i < samplekeys.size(); i++) {
nuiskey key = samplekeys[i];
// Get Sample Options
std::string samplename = key.GetS("name");
std::string samplefile = key.GetS("input");
std::string sampletype = key.GetS("type");
std::string fakeData = "";
LOG(MIN) << "Loading Sample : " << samplename << std::endl;
fOutputDir->cd();
MeasurementBase *NewLoadedSample = SampleUtils::CreateSample(key);
if (!NewLoadedSample) {
ERR(FTL) << "Could not load sample provided: " << samplename << std::endl;
ERR(FTL) << "Check spelling with that in src/FCN/SampleList.cxx"
<< std::endl;
throw;
} else {
fSamples.push_back(NewLoadedSample);
}
}
}
//***************************************************
void JointFCN::LoadPulls(std::vector<nuiskey> pullkeys) {
//***************************************************
for (size_t i = 0; i < pullkeys.size(); i++) {
nuiskey key = pullkeys[i];
std::string pullname = key.GetS("name");
std::string pullfile = key.GetS("input");
std::string pulltype = key.GetS("type");
fOutputDir->cd();
fPulls.push_back(new ParamPull(pullname, pullfile, pulltype));
}
}
//***************************************************
void JointFCN::ReconfigureSamples(bool fullconfig) {
//***************************************************
int starttime = time(NULL);
LOG(REC) << "Starting Reconfigure iter. " << this->fCurIter << std::endl;
// std::cout << fUsingEventManager << " " << fullconfig << " " << fMCFilled <<
// std::endl;
// Event Manager Reconf
if (fUsingEventManager) {
- if (!fullconfig and fMCFilled)
+ if (!fullconfig && fMCFilled)
ReconfigureFastUsingManager();
else
ReconfigureUsingManager();
} else {
// Loop over all Measurement Classes
for (MeasListConstIter iter = fSamples.begin(); iter != fSamples.end();
iter++) {
MeasurementBase *exp = *iter;
// If RW Either do signal or full reconfigure.
if (fDialChanged or !fMCFilled or fullconfig) {
if (!fullconfig and fMCFilled)
exp->ReconfigureFast();
else
exp->Reconfigure();
// If RW Not needed just do normalisation
} else {
exp->Renormalise();
}
}
}
// Loop over pulls and update
for (PullListConstIter iter = fPulls.begin(); iter != fPulls.end(); iter++) {
ParamPull *pull = *iter;
pull->Reconfigure();
}
fMCFilled = true;
LOG(MIN) << "Finished Reconfigure iter. " << fCurIter << " in "
<< time(NULL) - starttime << "s" << std::endl;
fCurIter++;
}
//***************************************************
void JointFCN::ReconfigureSignal() {
//***************************************************
ReconfigureSamples(false);
}
//***************************************************
void JointFCN::ReconfigureAllEvents() {
//***************************************************
FitBase::GetRW()->Reconfigure();
FitBase::EvtManager().ResetWeightFlags();
ReconfigureSamples(true);
}
std::vector<InputHandlerBase *> JointFCN::GetInputList() {
std::vector<InputHandlerBase *> InputList;
fIsAllSplines = true;
MeasListConstIter iterSam = fSamples.begin();
for (; iterSam != fSamples.end(); iterSam++) {
MeasurementBase *exp = (*iterSam);
std::vector<MeasurementBase *> subsamples = exp->GetSubSamples();
for (size_t i = 0; i < subsamples.size(); i++) {
InputHandlerBase *inp = subsamples[i]->GetInput();
if (std::find(InputList.begin(), InputList.end(), inp) ==
InputList.end()) {
if (subsamples[i]->GetInput()->GetType() != kSPLINEPARAMETER)
fIsAllSplines = false;
InputList.push_back(subsamples[i]->GetInput());
}
}
}
return InputList;
}
std::vector<MeasurementBase *> JointFCN::GetSubSampleList() {
std::vector<MeasurementBase *> SampleList;
MeasListConstIter iterSam = fSamples.begin();
for (; iterSam != fSamples.end(); iterSam++) {
MeasurementBase *exp = (*iterSam);
std::vector<MeasurementBase *> subsamples = exp->GetSubSamples();
for (size_t i = 0; i < subsamples.size(); i++) {
SampleList.push_back(subsamples[i]);
}
}
return SampleList;
}
//***************************************************
void JointFCN::ReconfigureUsingManager() {
//***************************************************
// 'Slow' Event Manager Reconfigure
LOG(REC) << "Event Manager Reconfigure" << std::endl;
int timestart = time(NULL);
// Reset all samples
MeasListConstIter iterSam = fSamples.begin();
for (; iterSam != fSamples.end(); iterSam++) {
MeasurementBase *exp = (*iterSam);
exp->ResetAll();
}
// If we are siving signal, reset all containers.
bool savesignal = (FitPar::Config().GetParB("SignalReconfigures"));
if (savesignal) {
// Reset all of our event signal vectors
fSignalEventBoxes.clear();
fSignalEventFlags.clear();
fSampleSignalFlags.clear();
fSignalEventSplines.clear();
}
// Make sure we have a list of inputs
if (fInputList.empty()) {
fInputList = GetInputList();
fSubSampleList = GetSubSampleList();
}
// If all inputs are splines make sure the readers are told
// they need to be reconfigured.
std::vector<InputHandlerBase *>::iterator inp_iter = fInputList.begin();
if (fIsAllSplines) {
for (; inp_iter != fInputList.end(); inp_iter++) {
InputHandlerBase *curinput = (*inp_iter);
// Tell reader in each BaseEvent it needs a Reconfigure next weight calc.
BaseFitEvt *curevent = curinput->FirstBaseEvent();
if (curevent->fSplineRead) {
curevent->fSplineRead->SetNeedsReconfigure(true);
}
}
}
// MAIN INPUT LOOP ====================
int fillcount = 0;
int inputcount = 0;
inp_iter = fInputList.begin();
// Loop over each input in manager
for (; inp_iter != fInputList.end(); inp_iter++) {
InputHandlerBase *curinput = (*inp_iter);
// Get event information
FitEvent *curevent = curinput->FirstNuisanceEvent();
curinput->CreateCache();
int i = 0;
int nevents = curinput->GetNEvents();
int countwidth = nevents / 10;
// Start event loop iterating until we get a NULL pointer.
while (curevent) {
// Get Event Weight
// The reweighting weight
curevent->RWWeight = FitBase::GetRW()->CalcWeight(curevent);
// The Custom weight and reweight
curevent->Weight =
curevent->RWWeight * curevent->InputWeight * curevent->CustomWeight;
- // double rwweight = curevent->Weight;
- // std::cout << "RWWeight = " << curevent->RWWeight << " " <<
- // curevent->InputWeight << std::endl;
- // Logging
- // std::cout << CHECKLOG(1) << std::endl;
if (LOGGING(REC)) {
if (countwidth && (i % countwidth == 0)) {
QLOG(REC, curinput->GetName()
<< " : Processed " << i << " events. [M, W] = ["
<< curevent->Mode << ", " << curevent->Weight << "]");
}
}
// Setup flag for if signal found in at least one sample
bool foundsignal = false;
// Create a new signal bitset for this event
std::vector<bool> signalbitset(fSubSampleList.size());
// Create a new signal box vector for this event
std::vector<MeasurementVariableBox *> signalboxes;
// Start measurement iterator
size_t measitercount = 0;
std::vector<MeasurementBase *>::iterator meas_iter =
fSubSampleList.begin();
// Loop over all subsamples (sub in JointMeas)
for (; meas_iter != fSubSampleList.end(); meas_iter++) {
MeasurementBase *curmeas = (*meas_iter);
// Compare input pointers, to current input, skip if not.
// Pointer tells us if it matches without doing ID checks.
if (curinput != curmeas->GetInput()) {
if (savesignal) {
// Set bit to 0 as definitely not signal
signalbitset[measitercount] = 0;
}
// Count up what measurement we are on.
measitercount++;
// Skip sample as input not signal.
continue;
}
// Fill events for matching inputs.
MeasurementVariableBox *box = curmeas->FillVariableBox(curevent);
bool signal = curmeas->isSignal(curevent);
curmeas->SetSignal(signal);
curmeas->FillHistograms(curevent->Weight);
// If its Signal tally up fills
if (signal) {
fillcount++;
}
// If we are saving signal/splines fill the bitset
if (savesignal) {
signalbitset[measitercount] = signal;
}
// If signal save a clone of the event box for use later.
if (savesignal and signal) {
foundsignal = true;
signalboxes.push_back(box->CloneSignalBox());
}
// Keep track of Measurement we are on.
measitercount++;
}
// Once we've filled the measurements, if saving signal
// push back if any sample flagged this event as signal
if (savesignal) {
fSignalEventFlags.push_back(foundsignal);
}
// Save the vector of signal boxes for this event
- if (savesignal and foundsignal) {
+ if (savesignal && foundsignal) {
fSignalEventBoxes.push_back(signalboxes);
fSampleSignalFlags.push_back(signalbitset);
}
// If all inputs are splines we can save the spline coefficients
// for fast in memory reconfigures later.
- if (fIsAllSplines and savesignal and foundsignal) {
+ if (fIsAllSplines && savesignal && foundsignal) {
// Make temp vector to push back with
std::vector<float> coeff;
for (size_t l = 0; l < (UInt_t)curevent->fSplineRead->GetNPar(); l++) {
coeff.push_back(curevent->fSplineCoeff[l]);
}
// Push back to signal event splines. Kept in sync with
// fSignalEventBoxes size.
// int splinecount = fSignalEventSplines.size();
fSignalEventSplines.push_back(coeff);
// if (splinecount % 1000 == 0) {
// std::cout << "Pushed Back Coeff " << splinecount << " : ";
// for (size_t l = 0; l < fSignalEventSplines[splinecount].size(); l++)
// {
// std::cout << " " << fSignalEventSplines[splinecount][l];
// }
// std::cout << std::endl;
// }
}
// Clean up vectors once done with this event
signalboxes.clear();
signalbitset.clear();
// Iterate to the next event.
curevent = curinput->NextNuisanceEvent();
i++;
}
// curinput->RemoveCache();
// Keep track of what input we are on.
inputcount++;
}
// End of Event Loop ===============================
// Now event loop is finished loop over all Measurements
// Converting Binned events to XSec Distributions
iterSam = fSamples.begin();
for (; iterSam != fSamples.end(); iterSam++) {
MeasurementBase *exp = (*iterSam);
exp->ConvertEventRates();
}
// Print out statements on approximate memory usage for profiling.
LOG(REC) << "Filled " << fillcount << " signal events." << std::endl;
if (savesignal) {
int mem = ( // sizeof(fSignalEventBoxes) +
// fSignalEventBoxes.size() * sizeof(fSignalEventBoxes.at(0)) +
sizeof(MeasurementVariableBox1D) * fillcount) *
1E-6;
LOG(REC) << " -> Saved " << fillcount
<< " signal boxes for faster access. (~" << mem << " MB)"
<< std::endl;
if (fIsAllSplines and !fSignalEventSplines.empty()) {
int splmem = sizeof(float) * fSignalEventSplines.size() *
fSignalEventSplines[0].size() * 1E-6;
LOG(REC) << " -> Saved " << fillcount << " " << fSignalEventSplines.size()
<< " spline sets into memory. (~" << splmem << " MB)"
<< std::endl;
}
}
LOG(REC) << "Time taken ReconfigureUsingManager() : "
<< time(NULL) - timestart << std::endl;
// Check SignalReconfigures works for all samples
if (savesignal) {
double likefull = GetLikelihood();
ReconfigureFastUsingManager();
double likefast = GetLikelihood();
if (fabs(likefull - likefast) > 0.0001) {
ERROR(FTL, "Fast and Full Likelihoods DIFFER! : " << likefull << " : "
<< likefast);
ERROR(FTL, "This means some samples you are using are not setup to use "
"SignalReconfigures=1");
ERROR(FTL, "Please turn OFF signal reconfigures.");
throw;
} else {
LOG(FIT)
<< "Likelihoods for FULL and FAST match. Will use FAST next time."
<< std::endl;
}
}
-
- // End of reconfigure
- return;
};
//***************************************************
void JointFCN::ReconfigureFastUsingManager() {
//***************************************************
LOG(FIT) << " -> Doing FAST using manager" << std::endl;
// Get Start time for profilling
int timestart = time(NULL);
// Reset all samples
MeasListConstIter iterSam = fSamples.begin();
for (; iterSam != fSamples.end(); iterSam++) {
MeasurementBase *exp = (*iterSam);
exp->ResetAll();
}
// Check for saved variables if not do a full reconfigure.
if (fSignalEventFlags.empty()) {
ERR(WRN) << "Signal Flags Empty! Using normal manager." << std::endl;
ReconfigureUsingManager();
return;
}
bool fFillNuisanceEvent =
FitPar::Config().GetParB("FullEventOnSignalReconfigure");
// Setup fast vector iterators.
std::vector<bool>::iterator inpsig_iter = fSignalEventFlags.begin();
std::vector<std::vector<MeasurementVariableBox *> >::iterator box_iter =
fSignalEventBoxes.begin();
std::vector<std::vector<float> >::iterator spline_iter =
fSignalEventSplines.begin();
std::vector<std::vector<bool> >::iterator samsig_iter =
fSampleSignalFlags.begin();
int splinecount = 0;
// Setup stuff for logging
int fillcount = 0;
- int nevents = fSignalEventFlags.size();
+ // This is just the total number of events
+ //int nevents = fSignalEventFlags.size();
+ // This is the number of events that are signal
+ int nevents = fSignalEventBoxes.size();
int countwidth = nevents / 10;
// If All Splines tell splines they need a reconfigure.
std::vector<InputHandlerBase *>::iterator inp_iter = fInputList.begin();
if (fIsAllSplines) {
LOG(REC) << "All Spline Inputs so using fast spline loop." << std::endl;
for (; inp_iter != fInputList.end(); inp_iter++) {
InputHandlerBase *curinput = (*inp_iter);
// Tell each fSplineRead in BaseFitEvent to reconf next weight calc
BaseFitEvt *curevent = curinput->FirstBaseEvent();
if (curevent->fSplineRead)
curevent->fSplineRead->SetNeedsReconfigure(true);
}
}
// Loop over all possible spline inputs
double *coreeventweights = new double[fSignalEventBoxes.size()];
splinecount = 0;
inp_iter = fInputList.begin();
inpsig_iter = fSignalEventFlags.begin();
spline_iter = fSignalEventSplines.begin();
// Loop over all signal flags
// For each valid signal flag add one to splinecount
// Get Splines from that count and add to weight
// Add splinecount
int sigcount = 0;
- splinecount = 0;
// #pragma omp parallel for shared(splinecount,sigcount)
for (uint iinput = 0; iinput < fInputList.size(); iinput++) {
InputHandlerBase *curinput = fInputList[iinput];
BaseFitEvt *curevent = curinput->FirstBaseEvent();
+ // Loop over the events in each input
for (int i = 0; i < curinput->GetNEvents(); i++) {
double rwweight = 0.0;
+
+ // If the event is a signal event
if (fSignalEventFlags[sigcount]) {
// Get Event Info
if (!fIsAllSplines) {
if (fFillNuisanceEvent) {
curevent = curinput->GetNuisanceEvent(i);
} else {
curevent = curinput->GetBaseEvent(i);
}
} else {
curevent->fSplineCoeff = &fSignalEventSplines[splinecount][0];
}
curevent->RWWeight = FitBase::GetRW()->CalcWeight(curevent);
curevent->Weight =
curevent->RWWeight * curevent->InputWeight * curevent->CustomWeight;
rwweight = curevent->Weight;
coreeventweights[splinecount] = rwweight;
if (countwidth && ((splinecount % countwidth) == 0)) {
- LOG(REC) << "Processed " << splinecount
- << " event weights. W = " << rwweight << std::endl;
+ QLOG(REC, curinput->GetName()
+ << " : Processed " << i << " events. W = "
+ << curevent->Weight << std::endl);
}
// #pragma omp atomic
splinecount++;
}
// #pragma omp atomic
sigcount++;
}
}
+
LOG(SAM) << "Processed event weights." << std::endl;
// #pragma omp barrier
// Reset Iterators
inpsig_iter = fSignalEventFlags.begin();
spline_iter = fSignalEventSplines.begin();
box_iter = fSignalEventBoxes.begin();
samsig_iter = fSampleSignalFlags.begin();
int nsplineweights = splinecount;
splinecount = 0;
// Start of Fast Event Loop ============================
// Start input iterators
// Loop over number of inputs
for (int ispline = 0; ispline < nsplineweights; ispline++) {
double rwweight = coreeventweights[ispline];
// Get iterators for this event
std::vector<bool>::iterator subsamsig_iter = (*samsig_iter).begin();
std::vector<MeasurementVariableBox *>::iterator subbox_iter =
(*box_iter).begin();
// Loop over all sub measurements.
std::vector<MeasurementBase *>::iterator meas_iter = fSubSampleList.begin();
for (; meas_iter != fSubSampleList.end(); meas_iter++, subsamsig_iter++) {
MeasurementBase *curmeas = (*meas_iter);
// If event flagged as signal for this sample fill from the box.
if (*subsamsig_iter) {
curmeas->SetSignal(true);
curmeas->FillHistogramsFromBox((*subbox_iter), rwweight);
// Move onto next box if there is one.
subbox_iter++;
fillcount++;
}
}
if (ispline % countwidth == 0) {
LOG(REC) << "Filled " << ispline << " sample weights." << std::endl;
}
// Iterate over the main signal event containers.
samsig_iter++;
box_iter++;
spline_iter++;
splinecount++;
}
// End of Fast Event Loop ===================
LOG(SAM) << "Filled sample distributions." << std::endl;
// Now loop over all Measurements
// Convert Binned events
iterSam = fSamples.begin();
for (; iterSam != fSamples.end(); iterSam++) {
MeasurementBase *exp = (*iterSam);
exp->ConvertEventRates();
}
// Cleanup coreeventweights
delete coreeventweights;
// Print some reconfigure profiling.
LOG(REC) << "Filled " << fillcount << " signal events." << std::endl;
LOG(REC) << "Time taken ReconfigureFastUsingManager() : "
<< time(NULL) - timestart << std::endl;
}
//***************************************************
void JointFCN::Write() {
//***************************************************
// Save a likelihood/ndof plot
LOG(MIN) << "Writing likelihood plot.." << std::endl;
std::vector<double> likes;
std::vector<double> ndofs;
std::vector<std::string> names;
for (MeasListConstIter iter = fSamples.begin(); iter != fSamples.end();
iter++) {
MeasurementBase *exp = *iter;
double like = exp->GetLikelihood();
double ndof = exp->GetNDOF();
std::string name = exp->GetName();
likes.push_back(like);
ndofs.push_back(ndof);
names.push_back(name);
}
if (likes.size()) {
TH1D likehist = TH1D("likelihood_hist", "likelihood_hist", likes.size(),
0.0, double(likes.size()));
TH1D ndofhist =
TH1D("ndof_hist", "ndof_hist", ndofs.size(), 0.0, double(ndofs.size()));
TH1D divhist = TH1D("likedivndof_hist", "likedivndof_hist", likes.size(),
0.0, double(likes.size()));
for (int i = 0; i < likehist.GetNbinsX(); i++) {
likehist.SetBinContent(i + 1, likes[i]);
ndofhist.SetBinContent(i + 1, ndofs[i]);
if (ndofs[i] != 0.0) {
divhist.SetBinContent(i + 1, likes[i] / ndofs[i]);
}
likehist.GetXaxis()->SetBinLabel(i + 1, names[i].c_str());
ndofhist.GetXaxis()->SetBinLabel(i + 1, names[i].c_str());
divhist.GetXaxis()->SetBinLabel(i + 1, names[i].c_str());
}
likehist.Write();
ndofhist.Write();
divhist.Write();
}
// Loop over individual experiments and call Write
LOG(MIN) << "Writing each of the data classes..." << std::endl;
for (MeasListConstIter iter = fSamples.begin(); iter != fSamples.end();
iter++) {
MeasurementBase *exp = *iter;
exp->Write();
}
// Save Pull Terms
for (PullListConstIter iter = fPulls.begin(); iter != fPulls.end(); iter++) {
ParamPull *pull = *iter;
pull->Write();
}
if (FitPar::Config().GetParB("EventManager")) {
// Get list of inputs
std::map<int, InputHandlerBase *> fInputs =
FitBase::EvtManager().GetInputs();
std::map<int, InputHandlerBase *>::const_iterator iterInp;
for (iterInp = fInputs.begin(); iterInp != fInputs.end(); iterInp++) {
InputHandlerBase *input = (iterInp->second);
input->GetFluxHistogram()->Write();
input->GetXSecHistogram()->Write();
input->GetEventHistogram()->Write();
}
}
};
//***************************************************
void JointFCN::SetFakeData(std::string fakeinput) {
//***************************************************
LOG(MIN) << "Setting fake data from " << fakeinput << std::endl;
for (MeasListConstIter iter = fSamples.begin(); iter != fSamples.end();
iter++) {
MeasurementBase *exp = *iter;
exp->SetFakeDataValues(fakeinput);
}
return;
}
//***************************************************
void JointFCN::ThrowDataToy() {
//***************************************************
for (MeasListConstIter iter = fSamples.begin(); iter != fSamples.end();
iter++) {
MeasurementBase *exp = *iter;
exp->ThrowDataToy();
}
return;
}
//***************************************************
std::vector<std::string> JointFCN::GetAllNames() {
//***************************************************
// Vect of all likelihoods and total
std::vector<std::string> namevect;
// Loop over samples first
for (MeasListConstIter iter = fSamples.begin(); iter != fSamples.end();
iter++) {
MeasurementBase *exp = *iter;
// Get Likelihoods and push to vector
namevect.push_back(exp->GetName());
}
// Loop over pulls second
for (PullListConstIter iter = fPulls.begin(); iter != fPulls.end(); iter++) {
ParamPull *pull = *iter;
// Push back to vector
namevect.push_back(pull->GetName());
}
// Finally add the total
namevect.push_back("total");
return namevect;
}
//***************************************************
std::vector<double> JointFCN::GetAllLikelihoods() {
//***************************************************
// Vect of all likelihoods and total
std::vector<double> likevect;
double total_likelihood = 0.0;
LOG(MIN) << "Likelihoods : " << std::endl;
// Loop over samples first
for (MeasListConstIter iter = fSamples.begin(); iter != fSamples.end();
iter++) {
MeasurementBase *exp = *iter;
// Get Likelihoods and push to vector
double singlelike = exp->GetLikelihood();
likevect.push_back(singlelike);
total_likelihood += singlelike;
// Print Out
LOG(MIN) << "-> " << std::left << std::setw(40) << exp->GetName() << " : "
<< singlelike << std::endl;
}
// Loop over pulls second
for (PullListConstIter iter = fPulls.begin(); iter != fPulls.end(); iter++) {
ParamPull *pull = *iter;
// Push back to vector
double singlelike = pull->GetLikelihood();
likevect.push_back(singlelike);
total_likelihood += singlelike;
// Print Out
LOG(MIN) << "-> " << std::left << std::setw(40) << pull->GetName() << " : "
<< singlelike << std::endl;
}
// Finally add the total likelihood
likevect.push_back(total_likelihood);
return likevect;
}
//***************************************************
std::vector<int> JointFCN::GetAllNDOF() {
//***************************************************
// Vect of all ndof and total
std::vector<int> ndofvect;
int total_ndof = 0;
// Loop over samples first
for (MeasListConstIter iter = fSamples.begin(); iter != fSamples.end();
iter++) {
MeasurementBase *exp = *iter;
// Get Likelihoods and push to vector
int singlendof = exp->GetNDOF();
ndofvect.push_back(singlendof);
total_ndof += singlendof;
}
// Loop over pulls second
for (PullListConstIter iter = fPulls.begin(); iter != fPulls.end(); iter++) {
ParamPull *pull = *iter;
// Push back to vector
int singlendof = pull->GetNDOF();
ndofvect.push_back(singlendof);
total_ndof += singlendof;
}
// Finally add the total ndof
ndofvect.push_back(total_ndof);
return ndofvect;
}
diff --git a/src/FitBase/Measurement1D.cxx b/src/FitBase/Measurement1D.cxx
index 2e2a64b..e6737c7 100644
--- a/src/FitBase/Measurement1D.cxx
+++ b/src/FitBase/Measurement1D.cxx
@@ -1,1900 +1,1899 @@
// Copyright 2016 L. Pickering, P. Stowell, R. Terri, C. Wilkinson, C. Wret
/*******************************************************************************
* This ile is part of NUISANCE.
*
* NUISANCE is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* NUISANCE is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with NUISANCE. If not, see <http://www.gnu.org/licenses/>.
*******************************************************************************/
#include "Measurement1D.h"
//********************************************************************
Measurement1D::Measurement1D(void) {
//********************************************************************
// XSec Scalings
fScaleFactor = -1.0;
fCurrentNorm = 1.0;
// Histograms
fDataHist = NULL;
fDataTrue = NULL;
fMCHist = NULL;
fMCFine = NULL;
fMCWeighted = NULL;
fMaskHist = NULL;
// Covar
covar = NULL;
fFullCovar = NULL;
fShapeCovar = NULL;
fCovar = NULL;
fInvert = NULL;
fDecomp = NULL;
// Fake Data
fFakeDataInput = "";
fFakeDataFile = NULL;
// Options
fDefaultTypes = "FIX/FULL/CHI2";
fAllowedTypes =
"FIX,FREE,SHAPE/FULL,DIAG/CHI2/NORM/ENUCORR/Q2CORR/ENU1D/MASK/NOWIDTH";
fIsFix = false;
fIsShape = false;
fIsFree = false;
fIsDiag = false;
fIsFull = false;
fAddNormPen = false;
fIsMask = false;
fIsChi2SVD = false;
fIsRawEvents = false;
fIsNoWidth = false;
fIsDifXSec = false;
fIsEnu1D = false;
// Inputs
fInput = NULL;
fRW = NULL;
// Extra Histograms
fMCHist_Modes = NULL;
}
//********************************************************************
Measurement1D::~Measurement1D(void) {
//********************************************************************
if (fDataHist) delete fDataHist;
if (fDataTrue) delete fDataTrue;
if (fMCHist) delete fMCHist;
if (fMCFine) delete fMCFine;
if (fMCWeighted) delete fMCWeighted;
if (fMaskHist) delete fMaskHist;
if (covar) delete covar;
if (fFullCovar) delete fFullCovar;
if (fShapeCovar) delete fShapeCovar;
if (fCovar) delete fCovar;
if (fInvert) delete fInvert;
if (fDecomp) delete fDecomp;
}
//********************************************************************
void Measurement1D::FinaliseSampleSettings() {
//********************************************************************
MeasurementBase::FinaliseSampleSettings();
// Setup naming + renaming
fName = fSettings.GetName();
fSettings.SetS("originalname", fName);
if (fSettings.Has("rename")) {
fName = fSettings.GetS("rename");
fSettings.SetS("name", fName);
}
// Setup all other options
LOG(SAM) << "Finalising Sample Settings: " << fName << std::endl;
if ((fSettings.GetS("originalname").find("Evt") != std::string::npos)) {
fIsRawEvents = true;
LOG(SAM) << "Found event rate measurement but using poisson likelihoods."
<< std::endl;
}
if (fSettings.GetS("originalname").find("XSec_1DEnu") != std::string::npos) {
fIsEnu1D = true;
LOG(SAM) << "::" << fName << "::" << std::endl;
LOG(SAM) << "Found XSec Enu measurement, applying flux integrated scaling, "
<< "not flux averaged!" << std::endl;
}
if (fIsEnu1D && fIsRawEvents) {
LOG(SAM) << "Found 1D Enu XSec distribution AND fIsRawEvents, is this "
"really correct?!"
<< std::endl;
LOG(SAM) << "Check experiment constructor for " << fName
<< " and correct this!" << std::endl;
LOG(SAM) << "I live in " << __FILE__ << ":" << __LINE__ << std::endl;
exit(-1);
}
if (!fRW) fRW = FitBase::GetRW();
if (!fInput and !fIsJoint) SetupInputs(fSettings.GetS("input"));
// Setup options
SetFitOptions(fDefaultTypes); // defaults
SetFitOptions(fSettings.GetS("type")); // user specified
EnuMin = GeneralUtils::StrToDbl(fSettings.GetS("enu_min"));
EnuMax = GeneralUtils::StrToDbl(fSettings.GetS("enu_max"));
if (fAddNormPen) {
if (fNormError <= 0.0) {
ERR(WRN) << "Norm error for class " << fName << " is 0.0!" << std::endl;
ERR(WRN) << "If you want to use it please add fNormError=VAL" << std::endl;
throw;
}
}
}
//********************************************************************
void Measurement1D::CreateDataHistogram(int dimx, double* binx) {
//********************************************************************
if (fDataHist) delete fDataHist;
fDataHist = new TH1D( (fSettings.GetName() + "_data").c_str(), (fSettings.GetFullTitles()).c_str(),
dimx, binx) ;
}
//********************************************************************
void Measurement1D::SetDataFromTextFile(std::string datafile) {
//********************************************************************
LOG(SAM) << "Reading data from text file: " << datafile << std::endl;
fDataHist = PlotUtils::GetTH1DFromFile(datafile,
fSettings.GetName() + "_data",
fSettings.GetFullTitles());
}
//********************************************************************
void Measurement1D::SetDataFromRootFile(std::string datafile,
std::string histname) {
//********************************************************************
LOG(SAM) << "Reading data from root file: " << datafile << ";" << histname << std::endl;
fDataHist = PlotUtils::GetTH1DFromRootFile(datafile, histname);
fDataHist->SetNameTitle((fSettings.GetName() + "_data").c_str(),
(fSettings.GetFullTitles()).c_str());
return;
};
//********************************************************************
void Measurement1D::SetEmptyData(){
//********************************************************************
fDataHist = new TH1D("EMPTY_DATA","EMPTY_DATA",1,0.0,1.0);
}
//********************************************************************
void Measurement1D::SetPoissonErrors() {
//********************************************************************
if (!fDataHist) {
ERR(FTL) << "Need a data hist to setup possion errors! " << std::endl;
ERR(FTL) << "Setup Data First!" << std::endl;
throw;
}
for (int i = 0; i < fDataHist->GetNbinsX() + 1; i++) {
fDataHist->SetBinError(i + 1, sqrt(fDataHist->GetBinContent(i + 1)));
}
}
//********************************************************************
void Measurement1D::SetCovarFromDiagonal(TH1D* data) {
//********************************************************************
if (!data and fDataHist) {
data = fDataHist;
}
if (data) {
LOG(SAM) << "Setting diagonal covariance for: " << data->GetName() << std::endl;
fFullCovar = StatUtils::MakeDiagonalCovarMatrix(data);
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
} else {
ERR(FTL) << "No data input provided to set diagonal covar from!" << std::endl;
}
// if (!fIsDiag) {
// ERR(FTL) << "SetCovarMatrixFromDiag called for measurement "
// << "that is not set as diagonal." << std::endl;
// throw;
// }
}
//********************************************************************
void Measurement1D::SetCovarFromTextFile(std::string covfile, int dim) {
//********************************************************************
if (dim == -1) {
dim = fDataHist->GetNbinsX();
}
LOG(SAM) << "Reading covariance from text file: " << covfile << std::endl;
fFullCovar = StatUtils::GetCovarFromTextFile(covfile, dim);
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
//********************************************************************
void Measurement1D::SetCovarFromMultipleTextFiles(std::string covfiles, int dim) {
//********************************************************************
if (dim == -1) {
dim = fDataHist->GetNbinsX();
}
std::vector<std::string> covList = GeneralUtils::ParseToStr(covfiles, ";");
fFullCovar = new TMatrixDSym(dim);
for (uint i = 0; i < covList.size(); ++i){
LOG(SAM) << "Reading covariance from text file: " << covList[i] << std::endl;
TMatrixDSym* temp_cov = StatUtils::GetCovarFromTextFile(covList[i], dim);
(*fFullCovar) += (*temp_cov);
delete temp_cov;
}
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
//********************************************************************
void Measurement1D::SetCovarFromRootFile(std::string covfile, std::string histname) {
//********************************************************************
LOG(SAM) << "Reading covariance from text file: " << covfile << ";" << histname << std::endl;
fFullCovar = StatUtils::GetCovarFromRootFile(covfile, histname);
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
//********************************************************************
void Measurement1D::SetCovarInvertFromTextFile(std::string covfile, int dim) {
//********************************************************************
if (dim == -1) {
dim = fDataHist->GetNbinsX();
}
LOG(SAM) << "Reading inverted covariance from text file: " << covfile << std::endl;
covar = StatUtils::GetCovarFromTextFile(covfile, dim);
fFullCovar = StatUtils::GetInvert(covar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
//********************************************************************
void Measurement1D::SetCovarInvertFromRootFile(std::string covfile, std::string histname) {
//********************************************************************
LOG(SAM) << "Reading inverted covariance from text file: " << covfile << ";" << histname << std::endl;
covar = StatUtils::GetCovarFromRootFile(covfile, histname);
fFullCovar = StatUtils::GetInvert(covar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
//********************************************************************
void Measurement1D::SetCorrelationFromTextFile(std::string covfile, int dim) {
//********************************************************************
if (dim == -1) dim = fDataHist->GetNbinsX();
LOG(SAM) << "Reading data correlations from text file: " << covfile << ";" << dim << std::endl;
TMatrixDSym* correlation = StatUtils::GetCovarFromTextFile(covfile, dim);
if (!fDataHist) {
ERR(FTL) << "Trying to set correlations from text file but there is no data to build it from. \n"
<< "In constructor make sure data is set before SetCorrelationFromTextFile is called. \n" << std::endl;
throw;
}
// Fill covar from data errors and correlations
fFullCovar = new TMatrixDSym(dim);
for (int i = 0; i < fDataHist->GetNbinsX(); i++) {
for (int j = 0; j < fDataHist->GetNbinsX(); j++) {
(*fFullCovar)(i, j) = (*correlation)(i, j) * fDataHist->GetBinError(i + 1) * fDataHist->GetBinError(j + 1) * 1.E76;
}
}
// Fill other covars.
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
delete correlation;
}
//********************************************************************
void Measurement1D::SetCorrelationFromMultipleTextFiles(std::string corrfiles, int dim) {
//********************************************************************
if (dim == -1) {
dim = fDataHist->GetNbinsX();
}
std::vector<std::string> corrList = GeneralUtils::ParseToStr(corrfiles, ";");
fFullCovar = new TMatrixDSym(dim);
for (uint i = 0; i < corrList.size(); ++i){
LOG(SAM) << "Reading covariance from text file: " << corrList[i] << std::endl;
TMatrixDSym* temp_cov = StatUtils::GetCovarFromTextFile(corrList[i], dim);
for (int i = 0; i < fDataHist->GetNbinsX(); i++) {
for (int j = 0; j < fDataHist->GetNbinsX(); j++) {
(*temp_cov)(i, j) = (*temp_cov)(i, j) * fDataHist->GetBinError(i + 1) * fDataHist->GetBinError(j + 1) * 1.E76;
}
}
(*fFullCovar) += (*temp_cov);
delete temp_cov;
}
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
//********************************************************************
void Measurement1D::SetCorrelationFromRootFile(std::string covfile, std::string histname) {
//********************************************************************
LOG(SAM) << "Reading data correlations from text file: " << covfile << ";" << histname << std::endl;
TMatrixDSym* correlation = StatUtils::GetCovarFromRootFile(covfile, histname);
if (!fDataHist) {
ERR(FTL) << "Trying to set correlations from text file but there is no data to build it from. \n"
<< "In constructor make sure data is set before SetCorrelationFromTextFile is called. \n" << std::endl;
throw;
}
// Fill covar from data errors and correlations
fFullCovar = new TMatrixDSym(fDataHist->GetNbinsX());
for (int i = 0; i < fDataHist->GetNbinsX(); i++) {
for (int j = 0; j < fDataHist->GetNbinsX(); j++) {
(*fFullCovar)(i, j) = (*correlation)(i, j) * fDataHist->GetBinError(i + 1) * fDataHist->GetBinError(j + 1) * 1.E76;
}
}
// Fill other covars.
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
delete correlation;
}
//********************************************************************
void Measurement1D::SetCholDecompFromTextFile(std::string covfile, int dim) {
//********************************************************************
if (dim == -1) {
dim = fDataHist->GetNbinsX();
}
LOG(SAM) << "Reading cholesky from text file: " << covfile << std::endl;
TMatrixD* temp = StatUtils::GetMatrixFromTextFile(covfile, dim, dim);
TMatrixD* trans = (TMatrixD*)temp->Clone();
trans->T();
(*trans) *= (*temp);
fFullCovar = new TMatrixDSym(dim, trans->GetMatrixArray(), "");
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
delete temp;
delete trans;
}
//********************************************************************
void Measurement1D::SetCholDecompFromRootFile(std::string covfile, std::string histname) {
//********************************************************************
LOG(SAM) << "Reading cholesky decomp from root file: " << covfile << ";" << histname << std::endl;
TMatrixD* temp = StatUtils::GetMatrixFromRootFile(covfile, histname);
TMatrixD* trans = (TMatrixD*)temp->Clone();
trans->T();
(*trans) *= (*temp);
fFullCovar = new TMatrixDSym(temp->GetNrows(), trans->GetMatrixArray(), "");
covar = StatUtils::GetInvert(fFullCovar);
fDecomp = StatUtils::GetDecomp(fFullCovar);
delete temp;
delete trans;
}
void Measurement1D::SetShapeCovar(){
// Return if this is missing any pre-requisites
if (!fFullCovar) return;
if (!fDataHist) return;
// Also return if it's bloody stupid under the circumstances
if (fIsDiag) return;
fShapeCovar = StatUtils::ExtractShapeOnlyCovar(fFullCovar, fDataHist);
return;
}
//********************************************************************
void Measurement1D::ScaleData(double scale) {
//********************************************************************
fDataHist->Scale(scale);
}
//********************************************************************
void Measurement1D::ScaleDataErrors(double scale) {
//********************************************************************
for (int i = 0; i < fDataHist->GetNbinsX(); i++) {
fDataHist->SetBinError(i + 1, fDataHist->GetBinError(i + 1) * scale);
}
}
//********************************************************************
void Measurement1D::ScaleCovar(double scale) {
//********************************************************************
(*fFullCovar) *= scale;
(*covar) *= 1.0 / scale;
(*fDecomp) *= sqrt(scale);
}
//********************************************************************
void Measurement1D::SetBinMask(std::string maskfile) {
//********************************************************************
if (!fIsMask) return;
LOG(SAM) << "Reading bin mask from file: " << maskfile << std::endl;
// Create a mask histogram with dim of data
int nbins = fDataHist->GetNbinsX();
fMaskHist =
new TH1I((fSettings.GetName() + "_BINMASK").c_str(),
(fSettings.GetName() + "_BINMASK; Bin; Mask?").c_str(), nbins, 0, nbins);
std::string line;
std::ifstream mask(maskfile.c_str(), std::ifstream::in);
if (!mask.is_open()) {
LOG(FTL) << " Cannot find mask file." << std::endl;
throw;
}
while (std::getline(mask >> std::ws, line, '\n')) {
std::vector<int> entries = GeneralUtils::ParseToInt(line, " ");
// Skip lines with poorly formatted lines
if (entries.size() < 2) {
LOG(WRN) << "Measurement1D::SetBinMask(), couldn't parse line: " << line
<< std::endl;
continue;
}
// The first index should be the bin number, the second should be the mask
// value.
int val = 0;
if (entries[1] > 0) val = 1;
fMaskHist->SetBinContent(entries[0], val);
}
// Apply masking by setting masked data bins to zero
PlotUtils::MaskBins(fDataHist, fMaskHist);
return;
}
//********************************************************************
void Measurement1D::FinaliseMeasurement() {
//********************************************************************
LOG(SAM) << "Finalising Measurement: " << fName << std::endl;
if (fSettings.GetB("onlymc")){
if (fDataHist) delete fDataHist;
fDataHist = new TH1D("empty_data","empty_data",1,0.0,1.0);
}
// Make sure data is setup
if (!fDataHist) {
ERR(FTL) << "No data has been setup inside " << fName << " constructor!" << std::endl;
throw;
}
// Make sure covariances are setup
if (!fFullCovar) {
fIsDiag = true;
SetCovarFromDiagonal(fDataHist);
}
if (!covar) {
covar = StatUtils::GetInvert(fFullCovar);
}
if (!fDecomp) {
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
// Push the diagonals of fFullCovar onto the data histogram
// Comment this out until the covariance/data scaling is consistent!
StatUtils::SetDataErrorFromCov(fDataHist, fFullCovar, 1E-38);
// If shape only, set covar and fDecomp using the shape-only matrix (if set)
if (fIsShape && fShapeCovar and FitPar::Config().GetParB("UseShapeCovar")){
if (covar) delete covar;
covar = StatUtils::GetInvert(fShapeCovar);
if (fDecomp) delete fDecomp;
fDecomp = StatUtils::GetDecomp(fFullCovar);
}
// Setup fMCHist from data
fMCHist = (TH1D*)fDataHist->Clone();
fMCHist->SetNameTitle((fSettings.GetName() + "_MC").c_str(),
(fSettings.GetFullTitles()).c_str());
fMCHist->Reset();
// Setup fMCFine
fMCFine = new TH1D("mcfine", "mcfine", fDataHist->GetNbinsX() * 8,
fMCHist->GetBinLowEdge(1),
fMCHist->GetBinLowEdge(fDataHist->GetNbinsX() + 1));
fMCFine->SetNameTitle((fSettings.GetName() + "_MC_FINE").c_str(),
(fSettings.GetFullTitles()).c_str());
fMCFine->Reset();
// Setup MC Stat
fMCStat = (TH1D*)fMCHist->Clone();
fMCStat->Reset();
// Search drawopts for possible types to include by default
std::string drawopts = FitPar::Config().GetParS("drawopts");
if (drawopts.find("MODES") != std::string::npos) {
fMCHist_Modes = new TrueModeStack( (fSettings.GetName() + "_MODES").c_str(),
("True Channels"), fMCHist);
SetAutoProcessTH1(fMCHist_Modes, kCMD_Reset, kCMD_Norm, kCMD_Write);
}
// Setup bin masks using sample name
if (fIsMask) {
std::string curname = fName;
std::string origname = fSettings.GetS("originalname");
// Check rename.mask
std::string maskloc = FitPar::Config().GetParDIR(curname + ".mask");
// Check origname.mask
if (maskloc.empty()) maskloc = FitPar::Config().GetParDIR(origname + ".mask");
// Check database
if (maskloc.empty()) {
maskloc = FitPar::GetDataBase() + "/masks/" + origname + ".mask";
}
// Setup Bin Mask
SetBinMask(maskloc);
}
if (fScaleFactor < 0) {
ERR(FTL) << "I found a negative fScaleFactor in " << __FILE__ << ":" << __LINE__ << std::endl;
ERR(FTL) << "fScaleFactor = " << fScaleFactor << std::endl;
ERR(FTL) << "EXITING" << std::endl;
throw;
}
// Create and fill Weighted Histogram
if (!fMCWeighted) {
fMCWeighted = (TH1D*)fMCHist->Clone();
fMCWeighted->SetNameTitle((fName + "_MCWGHTS").c_str(),
(fName + "_MCWGHTS" + fPlotTitles).c_str());
fMCWeighted->GetYaxis()->SetTitle("Weighted Events");
}
}
//********************************************************************
void Measurement1D::SetFitOptions(std::string opt) {
//********************************************************************
// Do nothing if default given
if (opt == "DEFAULT") return;
// CHECK Conflicting Fit Options
std::vector<std::string> fit_option_allow =
GeneralUtils::ParseToStr(fAllowedTypes, "/");
for (UInt_t i = 0; i < fit_option_allow.size(); i++) {
std::vector<std::string> fit_option_section =
GeneralUtils::ParseToStr(fit_option_allow.at(i), ",");
bool found_option = false;
for (UInt_t j = 0; j < fit_option_section.size(); j++) {
std::string av_opt = fit_option_section.at(j);
if (!found_option and opt.find(av_opt) != std::string::npos) {
found_option = true;
} else if (found_option and opt.find(av_opt) != std::string::npos) {
ERR(FTL) << "ERROR: Conflicting fit options provided: "
<< opt << std::endl
<< "Conflicting group = " << fit_option_section.at(i) << std::endl
<< "You should only supply one of these options in card file." << std::endl;
throw;
}
}
}
// Check all options are allowed
std::vector<std::string> fit_options_input =
GeneralUtils::ParseToStr(opt, "/");
for (UInt_t i = 0; i < fit_options_input.size(); i++) {
if (fAllowedTypes.find(fit_options_input.at(i)) == std::string::npos) {
ERR(FTL) << "ERROR: Fit Option '" << fit_options_input.at(i)
<< "' Provided is not allowed for this measurement."
<< std::endl;
ERR(FTL) << "Fit Options should be provided as a '/' seperated list "
"(e.g. FREE/DIAG/NORM)"
<< std::endl;
ERR(FTL) << "Available options for " << fName << " are '" << fAllowedTypes
<< "'" << std::endl;
throw;
}
}
// Set TYPE
fFitType = opt;
// FIX,SHAPE,FREE
if (opt.find("FIX") != std::string::npos) {
fIsFree = fIsShape = false;
fIsFix = true;
} else if (opt.find("SHAPE") != std::string::npos) {
fIsFree = fIsFix = false;
fIsShape = true;
} else if (opt.find("FREE") != std::string::npos) {
fIsFix = fIsShape = false;
fIsFree = true;
}
// DIAG,FULL (or default to full)
if (opt.find("DIAG") != std::string::npos) {
fIsDiag = true;
fIsFull = false;
} else if (opt.find("FULL") != std::string::npos) {
fIsDiag = false;
fIsFull = true;
}
// CHI2/LL (OTHERS?)
if (opt.find("LOG") != std::string::npos) {
fIsChi2 = false;
ERR(FTL) << "No other LIKELIHOODS properly supported!" << std::endl;
ERR(FTL) << "Try to use a chi2!" << std::endl;
throw;
} else {
fIsChi2 = true;
}
// EXTRAS
if (opt.find("RAW") != std::string::npos) fIsRawEvents = true;
if (opt.find("NOWIDTH") != std::string::npos) fIsNoWidth = true;
if (opt.find("DIF") != std::string::npos) fIsDifXSec = true;
if (opt.find("ENU1D") != std::string::npos) fIsEnu1D = true;
if (opt.find("NORM") != std::string::npos) fAddNormPen = true;
if (opt.find("MASK") != std::string::npos) fIsMask = true;
return;
};
//********************************************************************
void Measurement1D::SetSmearingMatrix(std::string smearfile, int truedim,
int recodim) {
//********************************************************************
// The smearing matrix describes the migration from true bins (rows) to reco
// bins (columns)
// Counter over the true bins!
int row = 0;
std::string line;
std::ifstream smear(smearfile.c_str(), std::ifstream::in);
// Note that the smearing matrix may be rectangular.
fSmearMatrix = new TMatrixD(truedim, recodim);
if (smear.is_open())
LOG(SAM) << "Reading smearing matrix from file: " << smearfile << std::endl;
else
ERR(FTL) << "Smearing matrix provided is incorrect: " << smearfile
<< std::endl;
while (std::getline(smear >> std::ws, line, '\n')) {
int column = 0;
std::vector<double> entries = GeneralUtils::ParseToDbl(line, " ");
for (std::vector<double>::iterator iter = entries.begin();
iter != entries.end(); iter++) {
(*fSmearMatrix)(row, column) =
(*iter) / 100.; // Convert to fraction from
// percentage (this may not be
// general enough)
column++;
}
row++;
}
return;
}
//********************************************************************
void Measurement1D::ApplySmearingMatrix() {
//********************************************************************
if (!fSmearMatrix) {
ERR(WRN) << fName
<< ": attempted to apply smearing matrix, but none was set"
<< std::endl;
return;
}
TH1D* unsmeared = (TH1D*)fMCHist->Clone();
TH1D* smeared = (TH1D*)fMCHist->Clone();
smeared->Reset();
// Loop over reconstructed bins
// true = row; reco = column
for (int rbin = 0; rbin < fSmearMatrix->GetNcols(); ++rbin) {
// Sum up the constributions from all true bins
double rBinVal = 0;
// Loop over true bins
for (int tbin = 0; tbin < fSmearMatrix->GetNrows(); ++tbin) {
rBinVal +=
(*fSmearMatrix)(tbin, rbin) * unsmeared->GetBinContent(tbin + 1);
}
smeared->SetBinContent(rbin + 1, rBinVal);
}
fMCHist = (TH1D*)smeared->Clone();
return;
}
/*
Reconfigure LOOP
*/
//********************************************************************
void Measurement1D::ResetAll() {
//********************************************************************
fMCHist->Reset();
fMCFine->Reset();
fMCStat->Reset();
return;
};
//********************************************************************
void Measurement1D::FillHistograms() {
//********************************************************************
if (Signal) {
QLOG(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);
}
}
// Sort Penalty Terms
if (fAddNormPen) {
double penalty =
(1. - fCurrentNorm) * (1. - fCurrentNorm) / (fNormError * fNormError);
stat += penalty;
}
// Return to normal scaling
if (fIsShape) { // and !FitPar::Config().GetParB("saveshapescaling")) {
fMCHist->Scale(1. / scaleF);
fMCFine->Scale(1. / scaleF);
}
fLikelihood = stat;
return stat;
}
/*
Fake Data Functions
*/
//********************************************************************
void Measurement1D::SetFakeDataValues(std::string fakeOption) {
//********************************************************************
// Setup original/datatrue
TH1D* tempdata = (TH1D*) fDataHist->Clone();
if (!fIsFakeData) {
fIsFakeData = true;
// Make a copy of the original data histogram.
if (!fDataOrig) fDataOrig = (TH1D*)fDataHist->Clone((fName + "_data_original").c_str());
} else {
ResetFakeData();
}
// Setup Inputs
fFakeDataInput = fakeOption;
LOG(SAM) << "Setting fake data from : " << fFakeDataInput << std::endl;
// From MC
if (fFakeDataInput.compare("MC") == 0) {
fDataHist = (TH1D*)fMCHist->Clone((fName + "_MC").c_str());
// Fake File
} else {
if (!fFakeDataFile) fFakeDataFile = new TFile(fFakeDataInput.c_str(), "READ");
fDataHist = (TH1D*)fFakeDataFile->Get((fName + "_MC").c_str());
}
// Setup Data Hist
fDataHist->SetNameTitle((fName + "_FAKE").c_str(),
(fName + fPlotTitles).c_str());
// Replace Data True
if (fDataTrue) delete fDataTrue;
fDataTrue = (TH1D*)fDataHist->Clone();
fDataTrue->SetNameTitle((fName + "_FAKE_TRUE").c_str(),
(fName + fPlotTitles).c_str());
// Make a new covariance for fake data hist.
int nbins = fDataHist->GetNbinsX();
double alpha_i = 0.0;
double alpha_j = 0.0;
for (int i = 0; i < nbins; i++) {
for (int j = 0; j < nbins; j++) {
alpha_i = fDataHist->GetBinContent(i + 1) / tempdata->GetBinContent(i + 1);
alpha_j = fDataHist->GetBinContent(j + 1) / tempdata->GetBinContent(j + 1);
(*fFullCovar)(i, j) = alpha_i * alpha_j * (*fFullCovar)(i, j);
}
}
// Setup Covariances
if (covar) delete covar;
covar = StatUtils::GetInvert(fFullCovar);
if (fDecomp) delete fDecomp;
fDecomp = StatUtils::GetInvert(fFullCovar);
delete tempdata;
return;
};
//********************************************************************
void Measurement1D::ResetFakeData() {
//********************************************************************
if (fIsFakeData) {
if (fDataHist) delete fDataHist;
fDataHist = (TH1D*)fDataTrue->Clone((fSettings.GetName() + "_FKDAT").c_str());
}
}
//********************************************************************
void Measurement1D::ResetData() {
//********************************************************************
if (fIsFakeData) {
if (fDataHist) delete fDataHist;
fDataHist = (TH1D*)fDataOrig->Clone((fSettings.GetName() + "_data").c_str());
}
fIsFakeData = false;
}
//********************************************************************
void Measurement1D::ThrowCovariance() {
//********************************************************************
// Take a fDecomposition and use it to throw the current dataset.
// Requires fDataTrue also be set incase used repeatedly.
if (!fDataTrue) fDataTrue = (TH1D*) fDataHist->Clone();
if (fDataHist) delete fDataHist;
fDataHist = StatUtils::ThrowHistogram(fDataTrue, fFullCovar);
return;
};
//********************************************************************
void Measurement1D::ThrowDataToy(){
//********************************************************************
if (!fDataTrue) fDataTrue = (TH1D*) fDataHist->Clone();
if (fMCHist) delete fMCHist;
fMCHist = StatUtils::ThrowHistogram(fDataTrue, fFullCovar);
}
/*
Access Functions
*/
//********************************************************************
TH1D* Measurement1D::GetMCHistogram() {
//********************************************************************
if (!fMCHist) return fMCHist;
std::ostringstream chi2;
chi2 << std::setprecision(5) << this->GetLikelihood();
int linecolor = kRed;
int linestyle = 1;
int linewidth = 1;
int fillcolor = 0;
int fillstyle = 1001;
// if (fSettings.Has("linecolor")) linecolor = fSettings.GetI("linecolor");
// if (fSettings.Has("linestyle")) linestyle = fSettings.GetI("linestyle");
// if (fSettings.Has("linewidth")) linewidth = fSettings.GetI("linewidth");
// if (fSettings.Has("fillcolor")) fillcolor = fSettings.GetI("fillcolor");
// if (fSettings.Has("fillstyle")) fillstyle = fSettings.GetI("fillstyle");
fMCHist->SetTitle(chi2.str().c_str());
fMCHist->SetLineColor(linecolor);
fMCHist->SetLineStyle(linestyle);
fMCHist->SetLineWidth(linewidth);
fMCHist->SetFillColor(fillcolor);
fMCHist->SetFillStyle(fillstyle);
return fMCHist;
};
//********************************************************************
TH1D* Measurement1D::GetDataHistogram() {
//********************************************************************
if (!fDataHist) return fDataHist;
int datacolor = kBlack;
int datastyle = 1;
int datawidth = 1;
// if (fSettings.Has("datacolor")) datacolor = fSettings.GetI("datacolor");
// if (fSettings.Has("datastyle")) datastyle = fSettings.GetI("datastyle");
// if (fSettings.Has("datawidth")) datawidth = fSettings.GetI("datawidth");
fDataHist->SetLineColor(datacolor);
fDataHist->SetLineWidth(datawidth);
fDataHist->SetMarkerStyle(datastyle);
return fDataHist;
};
/*
Write Functions
*/
// Save all the histograms at once
//********************************************************************
void Measurement1D::Write(std::string drawOpt) {
//********************************************************************
// Get Draw Options
drawOpt = FitPar::Config().GetParS("drawopts");
// Write Settigns
if (drawOpt.find("SETTINGS") != std::string::npos){
fSettings.Set("#chi^{2}",fLikelihood);
fSettings.Set("NDOF", this->GetNDOF() );
fSettings.Set("#chi^{2}/NDOF", fLikelihood / this->GetNDOF() );
fSettings.Write();
}
// Write Data/MC
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<TH1D *>(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());
}
if (drawOpt.find("INVCOV") != std::string::npos && covar) {
PlotUtils::GetInvCovarPlot(covar, fSettings.GetName());
}
if (drawOpt.find("DECOMP") != std::string::npos && fDecomp) {
PlotUtils::GetDecompCovarPlot(fDecomp, fSettings.GetName());
}
// // Likelihood residual plots
// if (drawOpt.find("RESIDUAL") != std::string::npos) {
// WriteResidualPlots();
// }
// Ratio and Shape Plots
if (drawOpt.find("RATIO") != std::string::npos) {
WriteRatioPlot();
}
if (drawOpt.find("SHAPE") != std::string::npos) {
WriteShapePlot();
if (drawOpt.find("RATIO") != std::string::npos)
WriteShapeRatioPlot();
}
// // RATIO
// if (drawOpt.find("CANVMC") != std::string::npos) {
// TCanvas* c1 = WriteMCCanvas(fDataHist, fMCHist);
// c1->Write();
// delete c1;
// }
// // PDG
// if (drawOpt.find("CANVPDG") != std::string::npos && fMCHist_Modes) {
// TCanvas* c2 = WritePDGCanvas(fDataHist, fMCHist, fMCHist_Modes);
// c2->Write();
// delete c2;
// }
// Write Extra Histograms
AutoWriteExtraTH1();
WriteExtraHistograms();
// Returning
LOG(SAM) << "Written Histograms: " << fName << std::endl;
return;
}
//********************************************************************
void Measurement1D::WriteRatioPlot() {
//********************************************************************
// Setup mc data ratios
TH1D* dataRatio = (TH1D*)fDataHist->Clone((fName + "_data_RATIO").c_str());
TH1D* mcRatio = (TH1D*)fMCHist->Clone((fName + "_MC_RATIO").c_str());
// Extra MC Data Ratios
for (int i = 0; i < mcRatio->GetNbinsX(); i++) {
dataRatio->SetBinContent(i + 1, fDataHist->GetBinContent(i + 1) / fMCHist->GetBinContent(i + 1));
dataRatio->SetBinError(i + 1, fDataHist->GetBinError(i + 1) / fMCHist->GetBinContent(i + 1));
mcRatio->SetBinContent(i + 1, fMCHist->GetBinContent(i + 1) / fMCHist->GetBinContent(i + 1));
mcRatio->SetBinError(i + 1, fMCHist->GetBinError(i + 1) / fMCHist->GetBinContent(i + 1));
}
// Write ratios
mcRatio->Write();
dataRatio->Write();
delete mcRatio;
delete dataRatio;
}
//********************************************************************
void Measurement1D::WriteShapePlot() {
//********************************************************************
TH1D* mcShape = (TH1D*)fMCHist->Clone((fName + "_MC_SHAPE").c_str());
TH1D* dataShape = (TH1D*)fDataHist->Clone((fName + "_data_SHAPE").c_str());
// 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;
LOG(SAM) << "Found event rate measurement but fIsRawEvents == false!"
<< std::endl;
LOG(SAM) << "Overriding this and setting fIsRawEvents == true!"
<< std::endl;
}
fIsEnu1D = false;
if (fName.find("XSec_1DEnu") != std::string::npos) {
fIsEnu1D = true;
LOG(SAM) << "::" << fName << "::" << std::endl;
LOG(SAM) << "Found XSec Enu measurement, applying flux integrated scaling, "
"not flux averaged!"
<< std::endl;
}
if (fIsEnu1D && fIsRawEvents) {
LOG(SAM) << "Found 1D Enu XSec distribution AND fIsRawEvents, is this "
"really correct?!"
<< std::endl;
LOG(SAM) << "Check experiment constructor for " << fName
<< " and correct this!" << std::endl;
LOG(SAM) << "I live in " << __FILE__ << ":" << __LINE__ << std::endl;
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
LOG(SAM) << "Reading data from: " << dataFile.c_str() << std::endl;
fDataHist =
PlotUtils::GetTH1DFromFile(dataFile, (fName + "_data"), fPlotTitles);
fDataTrue = (TH1D*)fDataHist->Clone();
// Number of data points is number of bins
fNDataPointsX = fDataHist->GetXaxis()->GetNbins();
return;
};
//********************************************************************
void Measurement1D::SetDataFromDatabase(std::string inhistfile,
std::string histname) {
//********************************************************************
LOG(SAM) << "Filling histogram from " << inhistfile << "->" << histname
<< std::endl;
fDataHist = PlotUtils::GetTH1DFromRootFile(
(GeneralUtils::GetTopLevelDir() + "/data/" + inhistfile), histname);
fDataHist->SetNameTitle((fName + "_data").c_str(), (fName + "_data").c_str());
return;
};
//********************************************************************
void Measurement1D::SetDataFromFile(std::string inhistfile,
std::string histname) {
//********************************************************************
LOG(SAM) << "Filling histogram from " << inhistfile << "->" << histname
<< std::endl;
fDataHist = PlotUtils::GetTH1DFromRootFile((inhistfile), histname);
fDataHist->SetNameTitle((fName + "_data").c_str(), (fName + "_data").c_str());
return;
};
//********************************************************************
void Measurement1D::SetCovarMatrix(std::string covarFile) {
//********************************************************************
// Covariance function, only really used when reading in the MB Covariances.
TFile* tempFile = new TFile(covarFile.c_str(), "READ");
TH2D* covarPlot = new TH2D();
TH2D* 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
ERR(WRN) << "Incorrect thrown_covariance option in parameters."
<< std::endl;
int dim = int(fDataHist->GetNbinsX()); //-this->masked->Integral());
int covdim = int(fDataHist->GetNbinsX());
this->covar = new TMatrixDSym(dim);
fFullCovar = new TMatrixDSym(dim);
fDecomp = new TMatrixDSym(dim);
int row, column = 0;
row = 0;
column = 0;
for (Int_t i = 0; i < covdim; i++) {
// if (this->masked->GetBinContent(i+1) > 0) continue;
for (Int_t j = 0; j < covdim; j++) {
// if (this->masked->GetBinContent(j+1) > 0) continue;
(*this->covar)(row, column) = covarPlot->GetBinContent(i + 1, j + 1);
(*fFullCovar)(row, column) = fFullCovarPlot->GetBinContent(i + 1, j + 1);
column++;
}
column = 0;
row++;
}
// Set bin errors on data
if (!fIsDiag) {
StatUtils::SetDataErrorFromCov(fDataHist, fFullCovar);
}
// Get Deteriminant and inverse matrix
// fCovDet = this->covar->Determinant();
TDecompSVD LU = TDecompSVD(*this->covar);
this->covar = new TMatrixDSym(dim, LU.Invert().GetMatrixArray(), "");
return;
};
//********************************************************************
// Sets the covariance matrix from a provided file in a text format
// scale is a multiplicative pre-factor to apply in the case where the
// covariance is given in some unit (e.g. 1E-38)
void Measurement1D::SetCovarMatrixFromText(std::string covarFile, int dim,
double scale) {
//********************************************************************
// Make a counter to track the line number
int row = 0;
std::string line;
std::ifstream covarread(covarFile.c_str(), std::ifstream::in);
this->covar = new TMatrixDSym(dim);
fFullCovar = new TMatrixDSym(dim);
if (covarread.is_open())
LOG(SAM) << "Reading covariance matrix from file: " << covarFile
<< std::endl;
else
ERR(FTL) << "Covariance matrix provided is incorrect: " << covarFile
<< std::endl;
// Loop over the lines in the file
while (std::getline(covarread >> std::ws, line, '\n')) {
int column = 0;
// Loop over entries and insert them into matrix
std::vector<double> entries = GeneralUtils::ParseToDbl(line, " ");
if (entries.size() <= 1) {
ERR(WRN) << "SetCovarMatrixFromText -> Covariance matrix only has <= 1 "
"entries on this line: "
<< row << std::endl;
}
for (std::vector<double>::iterator iter = entries.begin();
iter != entries.end(); iter++) {
(*covar)(row, column) = *iter;
(*fFullCovar)(row, column) = *iter;
column++;
}
row++;
}
covarread.close();
// Scale the actualy covariance matrix by some multiplicative factor
(*fFullCovar) *= scale;
// Robust matrix inversion method
TDecompSVD LU = TDecompSVD(*this->covar);
// THIS IS ACTUALLY THE INVERSE COVARIANCE MATRIXA AAAAARGH
delete this->covar;
this->covar = new TMatrixDSym(dim, LU.Invert().GetMatrixArray(), "");
// Now need to multiply by the scaling factor
// If the covariance
(*this->covar) *= 1. / (scale);
return;
};
//********************************************************************
void Measurement1D::SetCovarMatrixFromCorrText(std::string corrFile, int dim) {
//********************************************************************
// Make a counter to track the line number
int row = 0;
std::string line;
std::ifstream corr(corrFile.c_str(), std::ifstream::in);
this->covar = new TMatrixDSym(dim);
this->fFullCovar = new TMatrixDSym(dim);
if (corr.is_open())
LOG(SAM) << "Reading and converting correlation matrix from file: "
<< corrFile << std::endl;
else {
ERR(FTL) << "Correlation matrix provided is incorrect: " << corrFile
<< std::endl;
exit(-1);
}
while (std::getline(corr >> std::ws, line, '\n')) {
int column = 0;
// Loop over entries and insert them into matrix
// Multiply by the errors to get the covariance, rather than the correlation
// matrix
std::vector<double> entries = GeneralUtils::ParseToDbl(line, " ");
for (std::vector<double>::iterator iter = entries.begin();
iter != entries.end(); iter++) {
double val = (*iter) * this->fDataHist->GetBinError(row + 1) * 1E38 *
this->fDataHist->GetBinError(column + 1) * 1E38;
if (val == 0) {
ERR(FTL) << "Found a zero value in the covariance matrix, assuming "
"this is an error!"
<< std::endl;
exit(-1);
}
(*this->covar)(row, column) = val;
(*this->fFullCovar)(row, column) = val;
column++;
}
row++;
}
// Robust matrix inversion method
TDecompSVD LU = TDecompSVD(*this->covar);
delete this->covar;
this->covar = new TMatrixDSym(dim, LU.Invert().GetMatrixArray(), "");
return;
};
//********************************************************************
// FullUnits refers to if we have "real" unscaled units in the covariance matrix, e.g. 1E-76.
// If this is the case we need to scale it so that the chi2 contribution is correct
// NUISANCE internally assumes the covariance matrix has units of 1E76
void Measurement1D::SetCovarFromDataFile(std::string covarFile,
std::string covName, bool FullUnits) {
//********************************************************************
LOG(SAM) << "Getting covariance from " << covarFile << "->" << covName
<< std::endl;
TFile* tempFile = new TFile(covarFile.c_str(), "READ");
TH2D* covPlot = (TH2D*)tempFile->Get(covName.c_str());
covPlot->SetDirectory(0);
// Scale the covariance matrix if it comes in normal units
if (FullUnits) {
covPlot->Scale(1.E76);
}
int dim = covPlot->GetNbinsX();
fFullCovar = new TMatrixDSym(dim);
for (int i = 0; i < dim; i++) {
for (int j = 0; j < dim; j++) {
(*fFullCovar)(i, j) = covPlot->GetBinContent(i + 1, j + 1);
}
}
this->covar = (TMatrixDSym*)fFullCovar->Clone();
fDecomp = (TMatrixDSym*)fFullCovar->Clone();
TDecompSVD LU = TDecompSVD(*this->covar);
this->covar = new TMatrixDSym(dim, LU.Invert().GetMatrixArray(), "");
TDecompChol LUChol = TDecompChol(*fDecomp);
LUChol.Decompose();
fDecomp = new TMatrixDSym(dim, LU.GetU().GetMatrixArray(), "");
return;
};
// //********************************************************************
// void Measurement1D::SetBinMask(std::string maskFile) {
// //********************************************************************
// // Create a mask histogram.
// int nbins = fDataHist->GetNbinsX();
// fMaskHist =
// new TH1I((fName + "_fMaskHist").c_str(),
// (fName + "_fMaskHist; Bin; Mask?").c_str(), nbins, 0, nbins);
// std::string line;
// std::ifstream mask(maskFile.c_str(), 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<int> entries = GeneralUtils::ParseToInt(line, " ");
// // Skip lines with poorly formatted lines
// if (entries.size() < 2) {
// LOG(WRN) << "Measurement1D::SetBinMask(), couldn't parse line: " << line
// << std::endl;
// continue;
// }
// // The first index should be the bin number, the second should be the mask
// // value.
// fMaskHist->SetBinContent(entries[0], entries[1]);
// }
// // Set masked data bins to zero
// PlotUtils::MaskBins(fDataHist, fMaskHist);
// return;
// }
// //********************************************************************
// void Measurement1D::GetBinContents(std::vector<double>& cont,
// std::vector<double>& err) {
// //********************************************************************
// // Return a vector of the main bin contents
// for (int i = 0; i < fMCHist->GetNbinsX(); i++) {
// cont.push_back(fMCHist->GetBinContent(i + 1));
// err.push_back(fMCHist->GetBinError(i + 1));
// }
// return;
// };
/*
XSec Functions
*/
// //********************************************************************
// void Measurement1D::SetFluxHistogram(std::string fluxFile, int minE, int
// maxE,
// double fluxNorm) {
// //********************************************************************
// // Note this expects the flux bins to be given in terms of MeV
// LOG(SAM) << "Reading flux from file: " << fluxFile << std::endl;
// TGraph f(fluxFile.c_str(), "%lg %lg");
// fFluxHist =
// new TH1D((fName + "_flux").c_str(), (fName + "; E_{#nu} (GeV)").c_str(),
// f.GetN() - 1, minE, maxE);
// Double_t* yVal = f.GetY();
// for (int i = 0; i < fFluxHist->GetNbinsX(); ++i)
// fFluxHist->SetBinContent(i + 1, yVal[i] * fluxNorm);
// };
// //********************************************************************
// double Measurement1D::TotalIntegratedFlux(std::string intOpt, double low,
// double high) {
// //********************************************************************
// if (fInput->GetType() == kGiBUU) {
// return 1.0;
// }
// // The default case of low = -9999.9 and high = -9999.9
// if (low == -9999.9) low = this->EnuMin;
// if (high == -9999.9) high = this->EnuMax;
// int minBin = fFluxHist->GetXaxis()->FindBin(low);
// int maxBin = fFluxHist->GetXaxis()->FindBin(high);
// // Get integral over custom range
// double integral = fFluxHist->Integral(minBin, maxBin + 1, intOpt.c_str());
// return integral;
// };
diff --git a/src/InputHandler/InputHandler.cxx b/src/InputHandler/InputHandler.cxx
index d606944..e7b3adb 100644
--- a/src/InputHandler/InputHandler.cxx
+++ b/src/InputHandler/InputHandler.cxx
@@ -1,298 +1,299 @@
// 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 "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;
};
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<TH1*> InputHandlerBase::GetFluxList(void) {
return std::vector<TH1*>(1, fFluxHist);
};
std::vector<TH1*> InputHandlerBase::GetEventList(void) {
return std::vector<TH1*>(1, fEventHist);
};
std::vector<TH1*> InputHandlerBase::GetXSecList(void) {
return std::vector<TH1*>(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) {
THROW("Can only handle joint inputs when config MAXEVENTS = -1!");
}
if (jointeventinputs.size() > 1) {
ERROR(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<BaseFitEvt*>(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/Routines/SystematicRoutines.cxx b/src/Routines/SystematicRoutines.cxx
index 04c98f6..24066b8 100755
--- a/src/Routines/SystematicRoutines.cxx
+++ b/src/Routines/SystematicRoutines.cxx
@@ -1,1437 +1,1418 @@
// 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 "SystematicRoutines.h"
void SystematicRoutines::Init(){
fInputFile = "";
fInputRootFile = NULL;
fOutputFile = "";
fOutputRootFile = NULL;
fCovar = fCovarFree = NULL;
fCorrel = fCorrelFree = NULL;
fDecomp = fDecompFree = NULL;
fStrategy = "ErrorBands";
fRoutines.clear();
fRoutines.push_back("ErrorBands");
fCardFile = "";
fFakeDataInput = "";
fSampleFCN = NULL;
fAllowedRoutines = ("ErrorBands,PlotLimits");
};
SystematicRoutines::~SystematicRoutines(){
};
SystematicRoutines::SystematicRoutines(int argc, char* argv[]){
// Initialise Defaults
Init();
nuisconfig configuration = Config::Get();
// Default containers
std::string cardfile = "";
std::string maxevents = "-1";
int errorcount = 0;
int verbocount = 0;
std::vector<std::string> xmlcmds;
std::vector<std::string> configargs;
fNThrows = 250;
fStartThrows = 0;
fThrowString = "";
// Make easier to handle arguments.
std::vector<std::string> args = GeneralUtils::LoadCharToVectStr(argc, argv);
ParserUtils::ParseArgument(args, "-c", fCardFile, true);
ParserUtils::ParseArgument(args, "-o", fOutputFile, false, false);
ParserUtils::ParseArgument(args, "-n", maxevents, false, false);
ParserUtils::ParseArgument(args, "-f", fStrategy, false, false);
ParserUtils::ParseArgument(args, "-d", fFakeDataInput, false, false);
ParserUtils::ParseArgument(args, "-s", fStartThrows, false, false);
ParserUtils::ParseArgument(args, "-t", fNThrows, false, false);
ParserUtils::ParseArgument(args, "-p", fThrowString, false, false);
ParserUtils::ParseArgument(args, "-i", xmlcmds);
ParserUtils::ParseArgument(args, "-q", configargs);
ParserUtils::ParseCounter(args, "e", errorcount);
ParserUtils::ParseCounter(args, "v", verbocount);
ParserUtils::CheckBadArguments(args);
// Add extra defaults if none given
if (fCardFile.empty() and xmlcmds.empty()) {
ERR(FTL) << "No input supplied!" << std::endl;
throw;
}
if (fOutputFile.empty() and !fCardFile.empty()) {
fOutputFile = fCardFile + ".root";
ERR(WRN) << "No output supplied so saving it to: " << fOutputFile << std::endl;
} else if (fOutputFile.empty()) {
ERR(FTL) << "No output file or cardfile supplied!" << std::endl;
throw;
}
// Configuration Setup =============================
// Check no comp key is available
if (Config::Get().GetNodes("nuiscomp").empty()) {
fCompKey = Config::Get().CreateNode("nuiscomp");
} else {
fCompKey = Config::Get().GetNodes("nuiscomp")[0];
}
if (!fCardFile.empty()) fCompKey.Set("cardfile", fCardFile);
if (!fOutputFile.empty()) fCompKey.Set("outputfile", fOutputFile);
if (!fStrategy.empty()) fCompKey.Set("strategy", fStrategy);
// Load XML Cardfile
configuration.LoadSettings( fCompKey.GetS("cardfile"), "");
// Add Config Args
for (size_t i = 0; i < configargs.size(); i++) {
configuration.OverrideConfig(configargs[i]);
}
if (maxevents.compare("-1")){
configuration.OverrideConfig("MAXEVENTS=" + maxevents);
}
// Finish configuration XML
configuration.FinaliseSettings(fCompKey.GetS("outputfile") + ".xml");
// Add Error Verbo Lines
verbocount += Config::GetParI("VERBOSITY");
errorcount += Config::GetParI("ERROR");
std::cout << "[ NUISANCE ]: Setting VERBOSITY=" << verbocount << std::endl;
std::cout << "[ NUISANCE ]: Setting ERROR=" << errorcount << std::endl;
SETVERBOSITY(verbocount);
// Proper Setup
if (fStrategy.find("ErrorBands") != std::string::npos ||
fStrategy.find("MergeErrors") != std::string::npos){
fOutputRootFile = new TFile(fCompKey.GetS("outputfile").c_str(), "RECREATE");
}
// fOutputRootFile = new TFile(fCompKey.GetS("outputfile").c_str(), "RECREATE");
SetupSystematicsFromXML();
SetupCovariance();
SetupRWEngine();
SetupFCN();
GetCovarFromFCN();
// Run();
return;
};
void SystematicRoutines::SetupSystematicsFromXML(){
LOG(FIT) << "Setting up nuismin" << std::endl;
// Setup Parameters ------------------------------------------
std::vector<nuiskey> parkeys = Config::QueryKeys("parameter");
if (!parkeys.empty()) {
LOG(FIT) << "Number of parameters : " << parkeys.size() << std::endl;
}
for (size_t i = 0; i < parkeys.size(); i++) {
nuiskey key = parkeys.at(i);
// Check for type,name,nom
if (!key.Has("type")) {
ERR(FTL) << "No type given for parameter " << i << std::endl;
throw;
} else if (!key.Has("name")) {
ERR(FTL) << "No name given for parameter " << i << std::endl;
throw;
} else if (!key.Has("nominal")) {
ERR(FTL) << "No nominal given for parameter " << i << std::endl;
throw;
}
// Get Inputs
std::string partype = key.GetS("type");
std::string parname = key.GetS("name");
double parnom = key.GetD("nominal");
double parlow = parnom - 1;
double parhigh = parnom + 1;
double parstep = 1;
// Override if state not given
if (!key.Has("state")){
key.SetS("state","FIX");
}
std::string parstate = key.GetS("state");
// Extra limits
if (key.Has("low")) {
parlow = key.GetD("low");
parhigh = key.GetD("high");
parstep = key.GetD("step");
LOG(FIT) << "Read " << partype << " : "
<< parname << " = "
<< parnom << " : "
<< parlow << " < p < " << parhigh
<< " : " << parstate << std::endl;
} else {
LOG(FIT) << "Read " << partype << " : "
<< parname << " = "
<< parnom << " : "
<< parstate << std::endl;
}
// Run Parameter Conversion if needed
if (parstate.find("ABS") != std::string::npos) {
parnom = FitBase::RWAbsToSigma( partype, parname, parnom );
parlow = FitBase::RWAbsToSigma( partype, parname, parlow );
parhigh = FitBase::RWAbsToSigma( partype, parname, parhigh );
parstep = FitBase::RWAbsToSigma( partype, parname, parstep );
} else if (parstate.find("FRAC") != std::string::npos) {
parnom = FitBase::RWFracToSigma( partype, parname, parnom );
parlow = FitBase::RWFracToSigma( partype, parname, parlow );
parhigh = FitBase::RWFracToSigma( partype, parname, parhigh );
parstep = FitBase::RWFracToSigma( partype, parname, parstep );
}
// Push into vectors
fParams.push_back(parname);
fTypeVals[parname] = FitBase::ConvDialType(partype);;
fStartVals[parname] = parnom;
fCurVals[parname] = parnom;
fErrorVals[parname] = 0.0;
fStateVals[parname] = parstate;
bool fixstate = parstate.find("FIX") != std::string::npos;
fFixVals[parname] = fixstate;
fStartFixVals[parname] = fFixVals[parname];
fMinVals[parname] = parlow;
fMaxVals[parname] = parhigh;
fStepVals[parname] = parstep;
-
}
// Setup Samples ----------------------------------------------
std::vector<nuiskey> samplekeys = Config::QueryKeys("sample");
if (!samplekeys.empty()) {
LOG(FIT) << "Number of samples : " << samplekeys.size() << std::endl;
}
for (size_t i = 0; i < samplekeys.size(); i++) {
nuiskey key = samplekeys.at(i);
// Get Sample Options
std::string samplename = key.GetS("name");
std::string samplefile = key.GetS("input");
std::string sampletype =
key.Has("type") ? key.GetS("type") : "DEFAULT";
double samplenorm =
key.Has("norm") ? key.GetD("norm") : 1.0;
// Print out
LOG(FIT) << "Read sample info " << i << " : "
<< samplename << std::endl
<< "\t\t input -> " << samplefile << std::endl
<< "\t\t state -> " << sampletype << std::endl
<< "\t\t norm -> " << samplenorm << std::endl;
// If FREE add to parameters otherwise continue
if (sampletype.find("FREE") == std::string::npos) {
continue;
}
// Form norm dial from samplename + sampletype + "_norm";
std::string normname = samplename + "_norm";
// Check normname not already present
if (fTypeVals.find(normname) != fTypeVals.end()) {
continue;
}
// Add new norm dial to list if its passed above checks
fParams.push_back(normname);
fTypeVals[normname] = kNORM;
fStateVals[normname] = sampletype;
fCurVals[normname] = samplenorm;
fErrorVals[normname] = 0.0;
fMinVals[normname] = 0.1;
fMaxVals[normname] = 10.0;
fStepVals[normname] = 0.5;
bool state = sampletype.find("FREE") == std::string::npos;
fFixVals[normname] = state;
fStartFixVals[normname] = state;
}
// Setup Fake Parameters -----------------------------
std::vector<nuiskey> fakekeys = Config::QueryKeys("fakeparameter");
if (!fakekeys.empty()) {
LOG(FIT) << "Number of fake parameters : " << fakekeys.size() << std::endl;
}
for (size_t i = 0; i < fakekeys.size(); i++) {
nuiskey key = fakekeys.at(i);
// Check for type,name,nom
if (!key.Has("name")) {
ERR(FTL) << "No name given for fakeparameter " << i << std::endl;
throw;
} else if (!key.Has("nom")) {
ERR(FTL) << "No nominal given for fakeparameter " << i << std::endl;
throw;
}
// Get Inputs
std::string parname = key.GetS("name");
double parnom = key.GetD("nom");
// Push into vectors
fFakeVals[parname] = parnom;
}
-
}
/*
Setup Functions
*/
//*************************************
void SystematicRoutines::SetupRWEngine(){
//*************************************
for (UInt_t i = 0; i < fParams.size(); i++){
std::string name = fParams[i];
FitBase::GetRW() -> IncludeDial(name, fTypeVals.at(name) );
}
UpdateRWEngine(fStartVals);
return;
}
//*************************************
void SystematicRoutines::SetupFCN(){
//*************************************
LOG(FIT)<<"Making the jointFCN"<<std::endl;
if (fSampleFCN) delete fSampleFCN;
fSampleFCN = new JointFCN(fOutputRootFile);
SetFakeData();
return;
}
//*************************************
void SystematicRoutines::SetFakeData(){
//*************************************
if (fFakeDataInput.empty()) return;
if (fFakeDataInput.compare("MC") == 0){
LOG(FIT)<<"Setting fake data from MC starting prediction." <<std::endl;
UpdateRWEngine(fFakeVals);
FitBase::GetRW()->Reconfigure();
fSampleFCN->ReconfigureAllEvents();
fSampleFCN->SetFakeData("MC");
UpdateRWEngine(fCurVals);
LOG(FIT)<<"Set all data to fake MC predictions."<<std::endl;
} else {
fSampleFCN->SetFakeData(fFakeDataInput);
}
return;
}
//*****************************************
-void SystematicRoutines::GetCovarFromFCN(){
+// Setup the parameter covariances from the FCN
+void SystematicRoutines::GetCovarFromFCN() {
//*****************************************
LOG(FIT) << "Loading ParamPull objects from FCN to build covariance..." << std::endl;
// Make helperstring
std::ostringstream helperstr;
// Keep track of what is being thrown
std::map<std::string, std::string> dialthrowhandle;
// Get Covariance Objects from FCN
std::list<ParamPull*> inputpulls = fSampleFCN->GetPullList();
for (PullListConstIter iter = inputpulls.begin(); iter != inputpulls.end(); iter++){
ParamPull* pull = (*iter);
if (pull->GetType().find("THROW") != std::string::npos){
fInputThrows.push_back(pull);
fInputCovar.push_back(pull->GetFullCovarMatrix());
fInputDials.push_back(pull->GetDataHist());
LOG(FIT) << "Read ParamPull: " << pull->GetName() << " " << pull->GetType() << std::endl;
}
TH1D dialhist = pull->GetDataHist();
TH1D minhist = pull->GetMinHist();
TH1D maxhist = pull->GetMaxHist();
TH1I typehist = pull->GetDialTypes();
for (int i = 0; i < dialhist.GetNbinsX(); i++){
std::string name = std::string(dialhist.GetXaxis()->GetBinLabel(i+1));
dialthrowhandle[name] = pull->GetName();
+ // Add to Containers
+ // Set the starting values etc to the postfit
+ fParams.push_back(name);
+ fCurVals[name] = dialhist.GetBinContent(i+1);
+ // Set the starting values to be nominal MC
+ fStartVals[name] = 0.0;
+ fMinVals[name] = minhist.GetBinContent(i+1);
+ fMaxVals[name] = maxhist.GetBinContent(i+1);
+ fStepVals[name] = 1.0;
+ fFixVals[name] = false;
+ fStartFixVals[name] = false;
+ fTypeVals[name] = typehist.GetBinContent(i+1);
+ fStateVals[name] = "FREE," + pull->GetType();
+
+
+ // If we find the string
if (fCurVals.find(name) == fCurVals.end()){
-
- // Add to Containers
- fParams.push_back(name);
- fCurVals[name] = dialhist.GetBinContent(i+1);
- fStartVals[name] = dialhist.GetBinContent(i+1);
- fMinVals[name] = minhist.GetBinContent(i+1);
- fMaxVals[name] = maxhist.GetBinContent(i+1);
- fStepVals[name] = 1.0;
- fFixVals[name] = false;
- fStartFixVals[name] = false;
- fTypeVals[name] = typehist.GetBinContent(i+1);
- fStateVals[name] = "FREE" + pull->GetType();
-
- // Maker Helper
- helperstr << std::string(16, ' ' ) << FitBase::ConvDialType(fTypeVals[name]) << " "
- << name << " " << fMinVals[name] << " "
- << fMaxVals[name] << " " << fStepVals[name] << " " << fStateVals[name]
- << std::endl;
+ // Maker Helper
+ helperstr << std::string(16, ' ' ) << FitBase::ConvDialType(fTypeVals[name]) << " "
+ << name << " " << fMinVals[name] << " "
+ << fMaxVals[name] << " " << fStepVals[name] << " " << fStateVals[name]
+ << std::endl;
}
}
}
// Check if no throws given
if (fInputThrows.empty()){
ERR(WRN) << "No covariances given to nuissyst" << std::endl;
ERR(WRN) << "Pushing back an uncorrelated gaussian throw error for each free parameter using step size" << std::endl;
for (UInt_t i = 0; i < fParams.size(); i++){
std::string syst = fParams[i];
if (fFixVals[syst]) continue;
// Make Terms
std::string name = syst + "_pull";
std::ostringstream pullterm;
pullterm << "DIAL:" << syst << ";"
- << fStartVals[syst] << ";"
- << fStepVals[syst];
+ << fStartVals[syst] << ";"
+ << fStepVals[syst];
std::string type = "GAUSTHROW/NEUT";
// Push Back Pulls
ParamPull* pull = new ParamPull( name, pullterm.str(), type );
fInputThrows.push_back(pull);
fInputCovar.push_back(pull->GetFullCovarMatrix());
fInputDials.push_back(pull->GetDataHist());
// Print Whats added
LOG(FIT) << "Added ParamPull : " << name << " " << pullterm.str() << " " << type << std::endl;
// Add helper string for future fits
helperstr << std::string(16, ' ' ) << "covar " << name << " " << pullterm.str() << " " << type << std::endl;
// Keep Track of Throws
dialthrowhandle[syst] = pull->GetName();
}
}
// Print Helper String
if (!helperstr.str().empty()){
LOG(FIT) << "To remove these statements in future studies, add the lines below to your card:" << std::endl;
// Can't use the logger properly because this can be multi-line. Use cout and added spaces to look better!
std::cout << helperstr.str();
sleep(2);
}
-
-
// Print Throw State
for (UInt_t i = 0; i < fParams.size(); i++){
std::string syst = fParams[i];
if (dialthrowhandle.find(syst) != dialthrowhandle.end()){
- LOG(FIT) << "Dial " << i << ". " << setw(40) << syst << " = THROWING with " << dialthrowhandle[syst] << std::endl;
+ LOG(FIT) << "Dial " << i << ". " << setw(20) << syst << " = THROWing with " << dialthrowhandle[syst] << std::endl;
} else {
- LOG(FIT) << "Dial " << i << ". " << setw(40) << syst << " = FIXED" << std::endl;
+ LOG(FIT) << "Dial " << i << ". " << setw(20) << syst << " = is FIXED" << std::endl;
}
}
-
- // Pause anyway
- sleep(1);
- return;
}
-
-
-
/*
- Fitting Functions
-*/
+ Fitting Functions
+ */
//*************************************
void SystematicRoutines::UpdateRWEngine(std::map<std::string,double>& updateVals){
-//*************************************
+ //*************************************
for (UInt_t i = 0; i < fParams.size(); i++){
std::string name = fParams[i];
-
if (updateVals.find(name) == updateVals.end()) continue;
FitBase::GetRW()->SetDialValue(name,updateVals.at(name));
}
FitBase::GetRW()->Reconfigure();
- return;
}
//*************************************
void SystematicRoutines::PrintState(){
-//*************************************
+ //*************************************
LOG(FIT)<<"------------"<<std::endl;
// Count max size
int maxcount = 0;
for (UInt_t i = 0; i < fParams.size(); i++){
maxcount = max(int(fParams[i].size()), maxcount);
}
// Header
LOG(FIT) << " # " << left << setw(maxcount) << "Parameter "
- << " = "
- << setw(10) << "Value" << " +- "
- << setw(10) << "Error" << " "
- << setw(8) << "(Units)" << " "
- << setw(10) << "Conv. Val" << " +- "
- << setw(10) << "Conv. Err" << " "
- << setw(8) << "(Units)" << std::endl;
+ << " = "
+ << setw(10) << "Value" << " +- "
+ << setw(10) << "Error" << " "
+ << setw(8) << "(Units)" << " "
+ << setw(10) << "Conv. Val" << " +- "
+ << setw(10) << "Conv. Err" << " "
+ << setw(8) << "(Units)" << std::endl;
// Parameters
for (UInt_t i = 0; i < fParams.size(); i++){
std::string syst = fParams.at(i);
std::string typestr = FitBase::ConvDialType(fTypeVals[syst]);
std::string curunits = "(sig.)";
double curval = fCurVals[syst];
double curerr = fErrorVals[syst];
if (fStateVals[syst].find("ABS") != std::string::npos){
curval = FitBase::RWSigmaToAbs(typestr, syst, curval);
curerr = (FitBase::RWSigmaToAbs(typestr, syst, curerr) -
- FitBase::RWSigmaToAbs(typestr, syst, 0.0));
+ FitBase::RWSigmaToAbs(typestr, syst, 0.0));
curunits = "(Abs.)";
} else if (fStateVals[syst].find("FRAC") != std::string::npos){
curval = FitBase::RWSigmaToFrac(typestr, syst, curval);
curerr = (FitBase::RWSigmaToFrac(typestr, syst, curerr) -
- FitBase::RWSigmaToFrac(typestr, syst, 0.0));
+ FitBase::RWSigmaToFrac(typestr, syst, 0.0));
curunits = "(Frac)";
}
std::string convunits = "(" + FitBase::GetRWUnits(typestr, syst) + ")";
double convval = FitBase::RWSigmaToAbs(typestr, syst, curval);
double converr = (FitBase::RWSigmaToAbs(typestr, syst, curerr) -
- FitBase::RWSigmaToAbs(typestr, syst, 0.0));
+ FitBase::RWSigmaToAbs(typestr, syst, 0.0));
std::ostringstream curparstring;
curparstring << " " << setw(3) << left
- << i << ". "
- << setw(maxcount) << syst << " = "
- << setw(10) << curval << " +- "
- << setw(10) << curerr << " "
- << setw(8) << curunits << " "
- << setw(10) << convval << " +- "
- << setw(10) << converr << " "
- << setw(8) << convunits;
+ << i << ". "
+ << setw(maxcount) << syst << " = "
+ << setw(10) << curval << " +- "
+ << setw(10) << curerr << " "
+ << setw(8) << curunits << " "
+ << setw(10) << convval << " +- "
+ << setw(10) << converr << " "
+ << setw(8) << convunits;
LOG(FIT) << curparstring.str() << std::endl;
}
LOG(FIT)<<"------------"<<std::endl;
double like = fSampleFCN->GetLikelihood();
LOG(FIT) << std::left << std::setw(46) << "Likelihood for JointFCN: " << like << std::endl;
LOG(FIT)<<"------------"<<std::endl;
}
/*
- Write Functions
-*/
+ Write Functions
+ */
//*************************************
void SystematicRoutines::SaveResults(){
-//*************************************
+ //*************************************
if (!fOutputRootFile)
fOutputRootFile = new TFile(fCompKey.GetS("outputfile").c_str(), "RECREATE");
fOutputRootFile->cd();
SaveCurrentState();
}
//*************************************
void SystematicRoutines::SaveCurrentState(std::string subdir){
-//*************************************
+ //*************************************
LOG(FIT)<<"Saving current full FCN predictions" <<std::endl;
// Setup DIRS
TDirectory* curdir = gDirectory;
if (!subdir.empty()){
TDirectory* newdir =(TDirectory*) gDirectory->mkdir(subdir.c_str());
newdir->cd();
}
FitBase::GetRW()->Reconfigure();
fSampleFCN->ReconfigureAllEvents();
fSampleFCN->Write();
// Change back to current DIR
curdir->cd();
return;
}
//*************************************
void SystematicRoutines::SaveNominal(){
-//*************************************
+ //*************************************
if (!fOutputRootFile)
fOutputRootFile = new TFile(fCompKey.GetS("outputfile").c_str(), "RECREATE");
fOutputRootFile->cd();
LOG(FIT)<<"Saving Nominal Predictions (be cautious with this)" <<std::endl;
FitBase::GetRW()->Reconfigure();
SaveCurrentState("nominal");
-
};
//*************************************
void SystematicRoutines::SavePrefit(){
-//*************************************
+ //*************************************
if (!fOutputRootFile)
fOutputRootFile = new TFile(fCompKey.GetS("outputfile").c_str(), "RECREATE");
fOutputRootFile->cd();
LOG(FIT)<<"Saving Prefit Predictions"<<std::endl;
UpdateRWEngine(fStartVals);
SaveCurrentState("prefit");
UpdateRWEngine(fCurVals);
-
};
/*
- MISC Functions
-*/
+ MISC Functions
+ */
//*************************************
int SystematicRoutines::GetStatus(){
-//*************************************
+ //*************************************
return 0;
}
//*************************************
void SystematicRoutines::SetupCovariance(){
-//*************************************
+ //*************************************
// Remove covares if they exist
if (fCovar) delete fCovar;
if (fCovarFree) delete fCovarFree;
if (fCorrel) delete fCorrel;
if (fCorrelFree) delete fCorrelFree;
if (fDecomp) delete fDecomp;
if (fDecompFree) delete fDecompFree;
int NFREE = 0;
int NDIM = 0;
// Get NFREE from min or from vals (for cases when doing throws)
NDIM = fParams.size();
for (UInt_t i = 0; i < fParams.size(); i++){
if (!fFixVals[fParams[i]]) NFREE++;
}
if (NDIM == 0) return;
fCovar = new TH2D("covariance","covariance",NDIM,0,NDIM,NDIM,0,NDIM);
if (NFREE > 0){
fCovarFree = new TH2D("covariance_free",
- "covariance_free",
- NFREE,0,NFREE,
- NFREE,0,NFREE);
+ "covariance_free",
+ NFREE,0,NFREE,
+ NFREE,0,NFREE);
}
// Set Bin Labels
int countall = 0;
int countfree = 0;
for (UInt_t i = 0; i < fParams.size(); i++){
fCovar->GetXaxis()->SetBinLabel(countall+1,fParams[i].c_str());
fCovar->GetYaxis()->SetBinLabel(countall+1,fParams[i].c_str());
countall++;
if (!fFixVals[fParams[i]] and NFREE > 0){
fCovarFree->GetXaxis()->SetBinLabel(countfree+1,fParams[i].c_str());
fCovarFree->GetYaxis()->SetBinLabel(countfree+1,fParams[i].c_str());
countfree++;
}
}
fCorrel = PlotUtils::GetCorrelationPlot(fCovar,"correlation");
fDecomp = PlotUtils::GetDecompPlot(fCovar,"decomposition");
if (NFREE > 0)fCorrelFree = PlotUtils::GetCorrelationPlot(fCovarFree, "correlation_free");
if (NFREE > 0)fDecompFree = PlotUtils::GetDecompPlot(fCovarFree,"decomposition_free");
return;
};
//*************************************
void SystematicRoutines::ThrowCovariance(bool uniformly){
-//*************************************
+ //*************************************
// Set fThrownVals to all values in currentVals
for (UInt_t i = 0; i < fParams.size(); i++){
std::string name = fParams.at(i);
fThrownVals[name] = fCurVals[name];
}
for (PullListConstIter iter = fInputThrows.begin();
- iter != fInputThrows.end(); iter++){
+ iter != fInputThrows.end(); iter++){
ParamPull* pull = *iter;
pull->ThrowCovariance();
TH1D dialhist = pull->GetDataHist();
for (int i = 0; i < dialhist.GetNbinsX(); i++){
std::string name = std::string(dialhist.GetXaxis()->GetBinLabel(i+1));
if (fCurVals.find(name) != fCurVals.end()){
- fThrownVals[name] = dialhist.GetBinContent(i+1);
+ fThrownVals[name] = dialhist.GetBinContent(i+1);
}
}
// Reset throw in case pulls are calculated.
pull->ResetToy();
}
};
//*************************************
void SystematicRoutines::PlotLimits(){
-//*************************************
+ //*************************************
std::cout << "Plotting Limits" << std::endl;
if (!fOutputRootFile)
fOutputRootFile = new TFile(fCompKey.GetS("outputfile").c_str(), "RECREATE");
TDirectory* limfolder = (TDirectory*) fOutputRootFile->mkdir("Limits");
limfolder->cd();
// Set all parameters at their starting values
for (UInt_t i = 0; i < fParams.size(); i++){
fCurVals[fParams[i]] = fStartVals[fParams[i]];
}
TDirectory* nomfolder = (TDirectory*) limfolder->mkdir("nominal");
nomfolder->cd();
UpdateRWEngine(fCurVals);
fSampleFCN->ReconfigureAllEvents();
fSampleFCN->Write();
limfolder->cd();
std::vector<std::string> allfolders;
// Loop through each parameter
for (UInt_t i = 0; i < fParams.size(); i++){
std::string syst = fParams[i];
std::cout << "Starting Param " << syst << std::endl;
if (fFixVals[syst]) continue;
// Loop Downwards
while (fCurVals[syst] > fMinVals[syst]){
fCurVals[syst] = fCurVals[syst] - fStepVals[syst];
// Check Limit
if (fCurVals[syst] < fMinVals[syst])
- fCurVals[syst] = fMinVals[syst];
+ fCurVals[syst] = fMinVals[syst];
// Check folder exists
std::string curvalstring = std::string( Form( (syst + "_%f").c_str(), fCurVals[syst] ) );
if (std::find(allfolders.begin(), allfolders.end(), curvalstring) != allfolders.end())
- break;
+ break;
// Make new folder for variation
TDirectory* minfolder = (TDirectory*) limfolder->mkdir(Form( (syst + "_%f").c_str(), fCurVals[syst] ) );
minfolder->cd();
allfolders.push_back(curvalstring);
// Update Iterations
double *vals = FitUtils::GetArrayFromMap( fParams, fCurVals );
fSampleFCN->DoEval( vals );
delete vals;
// Save to folder
fSampleFCN->Write();
}
// Reset before next loop
fCurVals[syst] = fStartVals[syst];
// Loop Upwards now
while (fCurVals[syst] < fMaxVals[syst]){
fCurVals[syst] = fCurVals[syst] + fStepVals[syst];
// Check Limit
if (fCurVals[syst] > fMaxVals[syst])
- fCurVals[syst] = fMaxVals[syst];
+ fCurVals[syst] = fMaxVals[syst];
// Check folder exists
std::string curvalstring = std::string( Form( (syst + "_%f").c_str(), fCurVals[syst] ) );
if (std::find(allfolders.begin(), allfolders.end(), curvalstring) != allfolders.end())
- break;
+ break;
// Make new folder
TDirectory* maxfolder = (TDirectory*) limfolder->mkdir(Form( (syst + "_%f").c_str(), fCurVals[syst] ) );
maxfolder->cd();
allfolders.push_back(curvalstring);
// Update Iterations
double *vals = FitUtils::GetArrayFromMap( fParams, fCurVals );
fSampleFCN->DoEval( vals );
delete vals;
// Save to file
fSampleFCN->Write();
}
// Reset before leaving
fCurVals[syst] = fStartVals[syst];
UpdateRWEngine(fCurVals);
}
return;
}
//*************************************
void SystematicRoutines::Run(){
-//*************************************
+ //*************************************
- std::cout << "Running routines "<< std::endl;
fRoutines = GeneralUtils::ParseToStr(fStrategy,",");
for (UInt_t i = 0; i < fRoutines.size(); i++){
std::string routine = fRoutines.at(i);
int fitstate = kFitUnfinished;
LOG(FIT)<<"Running Routine: "<<routine<<std::endl;
- if (routine.compare("PlotLimits") == 0) PlotLimits();
+ if (routine.compare("PlotLimits") == 0) PlotLimits();
else if (routine.compare("ErrorBands") == 0) GenerateErrorBands();
else if (routine.compare("ThrowErrors") == 0) GenerateThrows();
else if (routine.compare("MergeErrors") == 0) MergeThrows();
else if (routine.compare("EigenErrors") == 0) EigenErrors();
else {
- std::cout << "Unknown ROUTINE : " << routine << std::endl;
+ ERR(FTL) << "Unknown ROUTINE : " << routine << std::endl;
+ throw;
}
// If ending early break here
if (fitstate == kFitFinished || fitstate == kNoChange){
LOG(FIT) << "Ending fit routines loop." << std::endl;
break;
}
}
-
- return;
}
void SystematicRoutines::GenerateErrorBands(){
GenerateThrows();
MergeThrows();
}
//*************************************
void SystematicRoutines::GenerateThrows() {
-//*************************************
+ //*************************************
TFile* tempfile = new TFile((fOutputFile + ".throws.root").c_str(),"RECREATE");
tempfile->cd();
// For generating throws we check with the config
int nthrows = Config::GetParI("error_throws");
int startthrows = fStartThrows;
int endthrows = startthrows + nthrows;
if (nthrows < 0) nthrows = endthrows;
if (startthrows < 0) startthrows = 0;
if (endthrows < 0) endthrows = startthrows + nthrows;
// Setting Seed
// Matteo Mazzanti's Fix
struct timeval mytime;
gettimeofday(&mytime, NULL);
Double_t seed = time(NULL) + int(getpid())+ (mytime.tv_sec * 1000.) + (mytime.tv_usec / 1000.);
gRandom->SetSeed(seed);
// int seed = (gRandom->Uniform(0.0,1.0)*100000 + 100000000*(startthrows + endthrows) + time(NULL) + int(getpid()) );
// gRandom->SetSeed(seed);
LOG(FIT) << "Using Seed : " << seed << std::endl;
LOG(FIT) << "nthrows = " << nthrows << std::endl;
LOG(FIT) << "startthrows = " << startthrows << std::endl;
LOG(FIT) << "endthrows = " << endthrows << std::endl;
- UpdateRWEngine(fCurVals);
+ UpdateRWEngine(fStartVals);
fSampleFCN->ReconfigureAllEvents();
- if (startthrows == 0){
+ // Make the nominal
+ if (startthrows == 0) {
LOG(FIT) << "Making nominal " << std::endl;
TDirectory* nominal = (TDirectory*) tempfile->mkdir("nominal");
nominal->cd();
fSampleFCN->Write();
+
+ // Make the postfit reading from the pull
+ LOG(FIT) << "Making postfit " << std::endl;
+ TDirectory* postfit = (TDirectory*) tempfile->mkdir("postfit");
+ postfit->cd();
+ UpdateRWEngine(fCurVals);
+ fSampleFCN->ReconfigureSignal();
+ fSampleFCN->Write();
}
- TTree* parameterTree = new TTree("throws","throws");
- double chi2;
- for (UInt_t i = 0; i < fParams.size(); i++)
- parameterTree->Branch(fParams[i].c_str(), &fThrownVals[fParams[i]], (fParams[i] + "/D").c_str());
- parameterTree->Branch("chi2",&chi2,"chi2/D");
+
fSampleFCN->CreateIterationTree("error_iterations", FitBase::GetRW());
// Would anybody actually want to do uniform throws of any parameter??
bool uniformly = FitPar::Config().GetParB("error_uniform");
// Run Throws and save
for (Int_t i = 0; i < endthrows+1; i++){
+ // Generate Random Parameter Throw
ThrowCovariance(uniformly);
if (i < startthrows) continue;
if (i == 0) continue;
LOG(FIT) << "Throw " << i << "/" << endthrows << " ================================" << std::endl;
- // Generate Random Parameter Throw
- // ThrowCovariance(uniformly);
TDirectory* throwfolder = (TDirectory*)tempfile->mkdir(Form("throw_%i",i));
throwfolder->cd();
// Run Eval
double *vals = FitUtils::GetArrayFromMap( fParams, fThrownVals );
- chi2 = fSampleFCN->DoEval( vals );
+ fSampleFCN->DoEval( vals );
delete vals;
// Save the FCN
fSampleFCN->Write();
-
- parameterTree->Fill();
}
tempfile->cd();
fSampleFCN->WriteIterationTree();
tempfile->Close();
}
+// Merge throws together into one summary
void SystematicRoutines::MergeThrows() {
fOutputRootFile = new TFile(fCompKey.GetS("outputfile").c_str(), "RECREATE");
fOutputRootFile->cd();
// Make a container folder
TDirectory* errorDIR = (TDirectory*) fOutputRootFile->mkdir("error_bands");
errorDIR->cd();
TDirectory* outnominal = (TDirectory*) fOutputRootFile->mkdir("nominal_throw");
outnominal->cd();
// Split Input Files
if (!fThrowString.empty()) fThrowList = GeneralUtils::ParseToStr(fThrowString,",");
// Add default if no throwlist given
if (fThrowList.size() < 1) fThrowList.push_back( fOutputFile + ".throws.root" );
/// Save location of file containing nominal
std::string nominalfile;
bool nominalfound;
// Loop over files and check they exist.
for (uint i = 0; i < fThrowList.size(); i++){
std::string file = fThrowList[i];
bool found = false;
// normal
std::string newfile = file;
TFile* throwfile = new TFile(file.c_str(),"READ");
if (throwfile and !throwfile->IsZombie()){
found = true;
}
// normal.throws.root
if (!found){
newfile = file + ".throws.root";
throwfile = new TFile((file + ".throws.root").c_str(),"READ");
if (throwfile and !throwfile->IsZombie()) {
found = true;
}
}
// If its found save to throwlist, else save empty.
// Also search for nominal
if (found){
fThrowList[i] = newfile;
LOG(FIT) << "Throws File :" << newfile << std::endl;
// Find input which contains nominal
if (throwfile->Get("nominal")){
nominalfound = true;
nominalfile = newfile;
}
throwfile->Close();
} else {
fThrowList[i] = "";
}
delete throwfile;
}
// Make sure we have a nominal file
if (!nominalfound or nominalfile.empty()){
ERR(FTL) << "No nominal found when mergining! Exiting!" << std::endl;
throw;
}
// Get the nominal throws file
TFile* tempfile = new TFile((nominalfile).c_str(),"READ");
tempfile->cd();
TDirectory* nominal = (TDirectory*)tempfile->Get("nominal");
bool uniformly = FitPar::Config().GetParB("error_uniform");
// Check percentage of bad files is okay.
int badfilecount = 0;
for (uint i = 0; i < fThrowList.size(); i++){
if (!fThrowList[i].empty()){
LOG(FIT) << "Loading Throws From File " << i << " : "
<< fThrowList[i] << std::endl;
} else {
badfilecount++;
}
}
// Check we have at least one good file
if ((uint)badfilecount == fThrowList.size()){
ERR(FTL) << "Found no good throw files for MergeThrows" << std::endl;
throw;
} else if (badfilecount > fThrowList.size()*0.25){
ERR(WRN) << "Over 25% of your throw files are dodgy. Please check this is okay!" << std::endl;
ERR(WRN) << "Will continue for the time being..." << std::endl;
sleep(5);
}
// Now go through the keys in the temporary file and look for TH1D, and TH2D plots
TIter next(nominal->GetListOfKeys());
TKey *key;
while ((key = (TKey*)next())) {
TClass *cl = gROOT->GetClass(key->GetClassName());
if (!cl->InheritsFrom("TH1D") and !cl->InheritsFrom("TH2D")) continue;
TH1* baseplot = (TH1D*)key->ReadObj();
std::string plotname = std::string(baseplot->GetName());
LOG(FIT) << "Creating error bands for " << plotname;
if (LOG_LEVEL(FIT)){
if (!uniformly) std::cout << " : Using COVARIANCE Throws! " << std::endl;
else std::cout << " : Using UNIFORM THROWS!!! " << std::endl;
}
int nbins = 0;
if (cl->InheritsFrom("TH1D")) nbins = ((TH1D*)baseplot)->GetNbinsX();
else nbins = ((TH1D*)baseplot)->GetNbinsX()* ((TH1D*)baseplot)->GetNbinsY();
// Setup TProfile with RMS option
TProfile* tprof = new TProfile((plotname + "_prof").c_str(),(plotname + "_prof").c_str(),nbins, 0, nbins, "S");
// Setup The TTREE
double* bincontents;
bincontents = new double[nbins];
double* binlowest;
binlowest = new double[nbins];
double* binhighest;
binhighest = new double[nbins];
errorDIR->cd();
TTree* bintree = new TTree((plotname + "_tree").c_str(), (plotname + "_tree").c_str());
for (Int_t i = 0; i < nbins; i++){
bincontents[i] = 0.0;
binhighest[i] = 0.0;
binlowest[i] = 0.0;
bintree->Branch(Form("content_%i",i),&bincontents[i],Form("content_%i/D",i));
}
// Make new throw plot
TH1* newplot;
// Run Throw Merging.
for (UInt_t i = 0; i < fThrowList.size(); i++){
TFile* throwfile = new TFile(fThrowList[i].c_str(), "READ");
// Loop over all throws in a folder
TIter nextthrow(throwfile->GetListOfKeys());
TKey *throwkey;
while ((throwkey = (TKey*)nextthrow())) {
// Skip non throw folders
if (std::string(throwkey->GetName()).find("throw_") == std::string::npos) continue;
// Get Throw DIR
TDirectory* throwdir = (TDirectory*)throwkey->ReadObj();
// Get Plot From Throw
newplot = (TH1*)throwdir->Get(plotname.c_str());
if (!newplot) continue;
// Loop Over Plot
for (Int_t j = 0; j < nbins; j++){
tprof->Fill(j+0.5, newplot->GetBinContent(j+1));
bincontents[j] = newplot->GetBinContent(j+1);
if (bincontents[j] < binlowest[j] or i == 0) binlowest[j] = bincontents[j];
if (bincontents[j] > binhighest[j] or i == 0) binhighest[j] = bincontents[j];
}
errorDIR->cd();
bintree->Fill();
}
throwfile->Close();
delete throwfile;
}
errorDIR->cd();
if (uniformly){
LOG(FIT) << "Uniformly Calculating Plot Errors!" << std::endl;
}
TH1* statplot = (TH1*) baseplot->Clone();
for (Int_t j = 0; j < nbins; j++){
if (!uniformly){
// if ((baseplot->GetBinError(j+1)/baseplot->GetBinContent(j+1)) < 1.0) {
// baseplot->SetBinError(j+1,sqrt(pow(tprof->GetBinError(j+1),2) + pow(baseplot->GetBinError(j+1),2)));
// } else {
- //baseplot->SetBinContent(j+1,tprof->GetBinContent(j+1));
- baseplot->SetBinError(j+1,tprof->GetBinError(j+1));
+ baseplot->SetBinContent(j+1, tprof->GetBinContent(j+1));
+ baseplot->SetBinError(j+1, tprof->GetBinError(j+1));
// }
} else {
baseplot->SetBinContent(j+1, 0.0);//(binlowest[j] + binhighest[j]) / 2.0);
baseplot->SetBinError(j+1, 0.0); //(binhighest[j] - binlowest[j])/2.0);
}
}
errorDIR->cd();
baseplot->Write();
tprof->Write();
bintree->Write();
outnominal->cd();
for (int i = 0; i < nbins; i++){
baseplot->SetBinError(i+1, sqrt(pow(statplot->GetBinError(i+1),2) + pow(baseplot->GetBinError(i+1),2)));
}
baseplot->Write();
delete statplot;
delete baseplot;
delete tprof;
delete bintree;
delete [] bincontents;
}
-
- return;
};
void SystematicRoutines::EigenErrors() {
-
fOutputRootFile = new TFile(fCompKey.GetS("outputfile").c_str(), "RECREATE");
fOutputRootFile->cd();
// Make Covariance
TMatrixDSym* fullcovar = new TMatrixDSym( fParams.size() );
// Extract covariance from all loaded ParamPulls
for (PullListConstIter iter = fInputThrows.begin();
iter != fInputThrows.end(); iter++){
ParamPull* pull = *iter;
// Check pull is actualyl Gaussian
std::string pulltype = pull->GetType();
if (pulltype.find("GAUSTHROW") == std::string::npos){
THROW("Can only calculate EigenErrors for Gaussian pulls!");
}
// Get data and covariances
TH1D dialhist = pull->GetDataHist();
TH2D covhist = pull->GetFullCovar();
// Loop over all dials and compare names
for (size_t pari = 0; pari < fParams.size(); pari++){
for (size_t parj = 0; parj < fParams.size(); parj++){
std::string name_pari = fParams[pari];
std::string name_parj = fParams[parj];
// Compare names to those in the pull
for (int pulli = 0; pulli < dialhist.GetNbinsX(); pulli++){
for (int pullj = 0; pullj < dialhist.GetNbinsX(); pullj++){
std::string name_pulli = dialhist.GetXaxis()->GetBinLabel(pulli+1);
std::string name_pullj = dialhist.GetXaxis()->GetBinLabel(pullj+1);
if (name_pulli == name_pari && name_pullj == name_parj){
(*fullcovar)[pari][parj] = covhist.GetBinContent(pulli+1, pullj+1);
fCurVals[name_pari] = dialhist.GetBinContent(pulli+1);
fCurVals[name_parj] = dialhist.GetBinContent(pullj+1);
}
}
}
}
}
}
/*
TFile* test = new TFile("testingcovar.root","RECREATE");
test->cd();
TH2D* joinedcov = new TH2D("COVAR","COVAR",
fullcovar->GetNrows(), 0.0, float(fullcovar->GetNrows()),
fullcovar->GetNrows(), 0.0, float(fullcovar->GetNrows()));
for (int i = 0; i < fullcovar->GetNrows(); i++){
for (int j = 0; j < fullcovar->GetNcols(); j++){
joinedcov->SetBinContent(i+1, j+1, (*fullcovar)[i][j]);
}
}
joinedcov->Write("COVAR");
test->Close();
*/
// Calculator all EigenVectors and EigenValues
TMatrixDSymEigen* eigen = new TMatrixDSymEigen(*fullcovar);
const TVectorD eigenVals = eigen->GetEigenValues();
const TMatrixD eigenVect = eigen->GetEigenVectors();
eigenVals.Print();
eigenVect.Print();
TDirectory* outnominal = (TDirectory*) fOutputRootFile->mkdir("nominal");
outnominal->cd();
double *valst = FitUtils::GetArrayFromMap( fParams, fCurVals );
//double chi2 = fSampleFCN->DoEval( valst );
delete valst;
fSampleFCN->Write();
// Loop over all throws
TDirectory* throwsdir = (TDirectory*) fOutputRootFile->mkdir("throws");
throwsdir->cd();
int count = 0;
// Produce all error throws.
for (int i = 0; i < eigenVect.GetNrows(); i++){
TDirectory* throwfolder = (TDirectory*)throwsdir->mkdir(Form("throw_%i",count));
throwfolder->cd();
// Get New Parameter Vector
LOG(FIT) << "Parameter Set " << count << std::endl;
for (int j = 0; j < eigenVect.GetNrows(); j++){
std::string param = fParams[j];
LOG(FIT) << " " << j << ". " << param << " : " << fCurVals[param] + sqrt(eigenVals[i]) * eigenVect[j][i] << std::endl;
fThrownVals[param] = fCurVals[param] + sqrt(eigenVals[i]) * eigenVect[j][i];
}
// Run Eval
double *vals = FitUtils::GetArrayFromMap( fParams, fThrownVals );
double chi2 = fSampleFCN->DoEval( vals );
delete vals;
count++;
fSampleFCN->Write();
throwfolder = (TDirectory*)throwsdir->mkdir(Form("throw_%i",count));
throwfolder->cd();
// Get New Parameter Vector
LOG(FIT) << "Parameter Set " << count << std::endl;
for (int j = 0; j < eigenVect.GetNrows(); j++){
std::string param = fParams[j];
LOG(FIT) << " " << j << ". " << param << " : " <<fCurVals[param] - sqrt(eigenVals[i]) * eigenVect[j][i] << std::endl;
fThrownVals[param] = fCurVals[param] - sqrt(eigenVals[i]) * eigenVect[j][i];
}
// Run Eval
double *vals2 = FitUtils::GetArrayFromMap( fParams, fThrownVals );
chi2 = fSampleFCN->DoEval( vals2 );
delete vals2;
count++;
// Save the FCN
fSampleFCN->Write();
}
fOutputRootFile->Close();
fOutputRootFile = new TFile(fCompKey.GetS("outputfile").c_str(), "UPDATE");
fOutputRootFile->cd();
throwsdir = (TDirectory*) fOutputRootFile->Get("throws");
outnominal = (TDirectory*) fOutputRootFile->Get("nominal");
// Loop through Error DIR
TDirectory* outerr = (TDirectory*) fOutputRootFile->mkdir("errors");
outerr->cd();
TIter next(outnominal->GetListOfKeys());
TKey *key;
while ((key = (TKey*)next())) {
TClass *cl = gROOT->GetClass(key->GetClassName());
if (!cl->InheritsFrom("TH1D") and !cl->InheritsFrom("TH2D")) continue;
LOG(FIT) << "Creating error bands for " << key->GetName() << std::endl;
std::string plotname = std::string(key->GetName());
if (plotname.find("_EVT") != std::string::npos) continue;
if (plotname.find("_FLUX") != std::string::npos) continue;
if (plotname.find("_FLX") != std::string::npos) continue;
TH1* baseplot = (TH1D*)key->ReadObj()->Clone(Form("%s_ORIGINAL",key->GetName()));
TH1* errorplot_upper = (TH1D*)baseplot->Clone(Form("%s_ERROR_UPPER",key->GetName()));
TH1* errorplot_lower = (TH1D*)baseplot->Clone(Form("%s_ERROR_LOWER", key->GetName()));
TH1* meanplot = (TH1D*)baseplot->Clone(Form("%s_SET_MEAN", key->GetName()));
TH1* systplot = (TH1D*)baseplot->Clone(Form("%s_SYST", key->GetName()));
TH1* statplot = (TH1D*)baseplot->Clone(Form("%s_STAT", key->GetName()));
TH1* totlplot = (TH1D*)baseplot->Clone(Form("%s_TOTAL", key->GetName()));
int nbins = 0;
if (cl->InheritsFrom("TH1D")) nbins = ((TH1D*)baseplot)->GetNbinsX();
else nbins = ((TH1D*)baseplot)->GetNbinsX()* ((TH1D*)baseplot)->GetNbinsY();
meanplot->Reset();
errorplot_upper->Reset();
errorplot_lower->Reset();
for (int j = 0; j < nbins; j++){
errorplot_upper->SetBinError(j+1, 0.0);
errorplot_lower->SetBinError(j+1, 0.0);
}
// Loop over throws and calculate mean and error for +- throws
int addcount = 0;
// Add baseplot first to slightly bias to central value
meanplot->Add(baseplot);
addcount++;
for (int i = 0; i < count; i++){
TH1* newplot = (TH1D*) throwsdir->Get(Form("throw_%i/%s",i,plotname.c_str()));
if (!newplot){
ERR(WRN) << "Cannot find new plot : " << Form("throw_%i/%s",i,plotname.c_str()) << std::endl;
ERR(WRN) << "This plot will not have the correct errors!" << std::endl;
continue;
}
newplot->SetDirectory(0);
nbins = newplot->GetNbinsX();
for (int j = 0; j < nbins; j++){
if (i % 2 == 0){
- // std::cout << plotname<< " : upper " << errorplot_upper->GetBinContent(j+1) << " adding " << pow(baseplot->GetBinContent(j+1) - newplot->GetBinContent(j+1),2) << std::endl;
- // std::cout << " -> " << baseplot->GetBinContent(j+1) << " " <<newplot->GetBinContent(j+1) << std::endl;
errorplot_upper->SetBinContent(j+1, errorplot_upper->GetBinContent(j+1) +
pow(baseplot->GetBinContent(j+1) - newplot->GetBinContent(j+1),2));
- // newplot->Print();
} else {
- // std::cout << plotname << " : lower " << errorplot_lower->GetBinContent(j+1) << " adding " << pow(baseplot->GetBinContent(j+1) - newplot->GetBinContent(j+1),2) << std::endl;
- // std::cout << " -> " << baseplot->GetBinContent(j+1) << " " << newplot->GetBinContent(j+1) << std::endl;
errorplot_lower->SetBinContent(j+1, errorplot_lower->GetBinContent(j+1) +
pow(baseplot->GetBinContent(j+1) - newplot->GetBinContent(j+1),2));
- // newplot->Print();
}
meanplot->SetBinContent(j+1, meanplot->GetBinContent(j+1) + baseplot->GetBinContent(j+1));
}
delete newplot;
addcount++;
}
// Get mean Average
for (int j = 0; j < nbins; j++){
meanplot->SetBinContent(j+1, meanplot->GetBinContent(j+1)/double(addcount));
}
for (int j = 0; j < nbins; j++){
errorplot_upper->SetBinContent(j+1, sqrt(errorplot_upper->GetBinContent(j+1)));
errorplot_lower->SetBinContent(j+1, sqrt(errorplot_lower->GetBinContent(j+1)));
statplot->SetBinError(j+1, baseplot->GetBinError(j+1) );
systplot->SetBinError(j+1, (errorplot_upper->GetBinContent(j+1) + errorplot_lower->GetBinContent(j+1))/2.0);
totlplot->SetBinError(j+1, sqrt( pow(statplot->GetBinError(j+1),2) + pow(systplot->GetBinError(j+1),2) ) );
meanplot->SetBinError(j+1, sqrt( pow(statplot->GetBinError(j+1),2) + pow(systplot->GetBinError(j+1),2) ) );
}
outerr->cd();
errorplot_upper->Write();
errorplot_lower->Write();
baseplot->Write();
meanplot->Write();
statplot->Write();
systplot->Write();
totlplot->Write();
delete errorplot_upper;
delete errorplot_lower;
delete baseplot;
delete meanplot;
delete statplot;
delete systplot;
delete totlplot;
}
}