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diff --git a/examples/Slave.cc b/examples/Slave.cc
index cb2b512..6e891dd 100644
--- a/examples/Slave.cc
+++ b/examples/Slave.cc
@@ -1,225 +1,231 @@
#include <cstdlib>
#include <iostream>
#include <vector>
#include "TFile.h"
#include "TH2.h"
#include "TString.h"
#include "TTree.h"
#include "LauSimpleFitModel.hh"
#include "LauBkgndDPModel.hh"
#include "LauDaughters.hh"
#include "LauEffModel.hh"
#include "LauIsobarDynamics.hh"
#include "LauMagPhaseCoeffSet.hh"
#include "LauRandom.hh"
#include "LauVetoes.hh"
void usage( std::ostream& out, const TString& progName )
{
out<<"Usage:\n";
out<<progName<<" gen <category = DD or LL> [nExpt = 1] [firstExpt = 0]\n";
out<<"or\n";
- out<<progName<<" fit <category = DD or LL> <iFit> <port> [nExpt = 1] [firstExpt = 0]"<<std::endl;
+ out<<progName<<" fit <category = DD or LL> <iFit> <port> [hostname] [nExpt = 1] [firstExpt = 0]"<<std::endl;
}
int main( int argc, char** argv )
{
// Process command-line arguments
// Usage:
// ./Slave gen <category = DD or LL> [nExpt = 1] [firstExpt = 0]
// or
// ./Slave fit <category = DD or LL> <iFit> <port> [nExpt = 1] [firstExpt = 0]
if ( argc < 3 ) {
usage( std::cerr, argv[0] );
return EXIT_FAILURE;
}
TString command = argv[1];
command.ToLower();
+
TString category = argv[2];
if ( category != "DD" && category != "LL" ) {
usage( std::cerr, argv[0] );
return EXIT_FAILURE;
}
+
Int_t iFit(0);
UInt_t port(5000);
+ TString hostname("localhost");
Int_t nExpt(1);
Int_t firstExpt(0);
if ( command == "gen" ) {
if ( argc > 3 ) {
nExpt = atoi( argv[3] );
if ( argc > 4 ) {
firstExpt = atoi( argv[4] );
}
}
} else if ( command == "fit" ) {
if ( argc < 5 ) {
usage( std::cerr, argv[0] );
return EXIT_FAILURE;
}
iFit = atoi( argv[3] );
port = atoi( argv[4] );
if ( argc > 5 ) {
- nExpt = atoi( argv[5] );
+ hostname = argv[5];
if ( argc > 6 ) {
- firstExpt = atoi( argv[6] );
+ nExpt = atoi( argv[6] );
+ if ( argc > 7 ) {
+ firstExpt = atoi( argv[7] );
+ }
}
}
} else {
usage( std::cerr, argv[0] );
return EXIT_FAILURE;
}
LauRandom::setSeed(0);
// If you want to use square DP histograms for efficiency,
// backgrounds or you just want the square DP co-ordinates
// stored in the toy MC ntuple then set this to kTRUE
Bool_t squareDP = kFALSE;
// This defines the DP => decay is B0 -> pi0 pi0 K_S0
// Particle 1 = pi0
// Particle 2 = pi0
// Particle 3 = KS_0
// The DP is defined in terms of m13Sq and m23Sq
LauDaughters* daughters = new LauDaughters("B0", "pi0", "pi0", "K_S0", squareDP);
// Optionally apply some vetoes to the DP
LauVetoes* vetoes = new LauVetoes();
// Define the efficiency model (defaults to unity everywhere)
// Can optionally provide a histogram to model variation over DP
// (example syntax given in commented-out section)
LauEffModel* effModel = new LauEffModel(daughters, vetoes);
//TFile *effHistFile = TFile::Open("histoFiles/efficiency.root", "read");
//TH2* effHist = dynamic_cast<TH2*>(effHistFile->Get("effHist"));
//Bool_t useInterpolation = kTRUE;
//Bool_t fluctuateBins = kFALSE;
//Bool_t useUpperHalf = kTRUE;
//effModel->setEffHisto(effHist, useInterpolation, fluctuateBins, 0.0, 0.0, useUpperHalf, squareDP);
// Create the isobar model
LauIsobarDynamics* sigModel = new LauIsobarDynamics(daughters, effModel);
LauAbsResonance* res(0);
res = sigModel->addResonance("f_0(980)", 3, LauAbsResonance::Flatte); // resPairAmpInt = 3 => resonance mass is m12.
res = sigModel->addResonance("f_2(1270)", 3, LauAbsResonance::RelBW);
res = sigModel->addResonance("K*0(892)", 1, LauAbsResonance::RelBW);
res->fixMass(kFALSE);
res = sigModel->addResonance("K*0_0(1430)", 1, LauAbsResonance::LASS);
// Reset the maximum signal DP ASq value
// This will be automatically adjusted to avoid bias or extreme
// inefficiency if you get the value wrong but best to set this by
// hand once you've found the right value through some trial and
// error.
sigModel->setASqMaxValue(1.25);
TString integralsFileName("integ-");
integralsFileName += category;
integralsFileName += ".dat";
sigModel->setIntFileName( integralsFileName );
// Create the fit model
LauSimpleFitModel* fitModel = new LauSimpleFitModel(sigModel);
// Create the complex coefficients for the isobar model
// Here we're using the magnitude and phase form:
// c_j = a_j exp(i*delta_j)
std::vector<LauAbsCoeffSet*> coeffset;
coeffset.push_back( new LauMagPhaseCoeffSet("f_0(980)", 1.00, 0.00, kTRUE, kTRUE) );
coeffset.push_back( new LauMagPhaseCoeffSet("f_2(1270)", 0.53, 1.39, kFALSE, kFALSE) );
coeffset.push_back( new LauMagPhaseCoeffSet("K*0(892)", 0.87, 1.99, kFALSE, kFALSE) );
coeffset.push_back( new LauMagPhaseCoeffSet("K*0_0(1430)", 1.17, -1.59, kFALSE, kFALSE) );
for (std::vector<LauAbsCoeffSet*>::iterator iter=coeffset.begin(); iter!=coeffset.end(); ++iter) {
fitModel->setAmpCoeffSet(*iter);
}
// Set the signal yield and define whether it is fixed or floated
TString sigEventsName = "signalEvents" + category;
Double_t nSig(500.0);
if ( category == "DD" ) {
nSig = 750.0;
}
LauParameter * nSigEvents = new LauParameter(sigEventsName,nSig,-2.0*nSig,2.0*nSig,kFALSE);
fitModel->setNSigEvents(nSigEvents);
// Set the number of experiments to generate or fit and which
// experiment to start with
fitModel->setNExpts( nExpt, firstExpt );
// Optionally load in continuum background DP model histogram
// (example syntax given in commented-out section)
std::vector<TString> bkgndNames(1);
bkgndNames[0] = "comb" + category;
fitModel->setBkgndClassNames( bkgndNames );
Double_t nBkgnd = 1200.0;
if ( category == "DD" ) {
nBkgnd = 2500.0;
}
LauParameter* nBkgndEvents = new LauParameter(bkgndNames[0],nBkgnd,-2.0*nBkgnd,2.0*nBkgnd,kFALSE);
fitModel->setNBkgndEvents( nBkgndEvents );
//TString bkgndFileName("histoFiles/bkgndDPs.root");
//TFile* bkgndFile = TFile::Open(bkgndFileName.Data(), "read");
//TH2* bkgndDP = dynamic_cast<TH2*>(bkgndFile->Get("AllmTheta")); // m', theta'
LauBkgndDPModel* bkgndModel = new LauBkgndDPModel(daughters, vetoes);
//bkgndModel->setBkgndHisto(bkgndDP, useInterpolation, fluctuateBins, useUpperHalf, squareDP);
fitModel->setBkgndDPModel( bkgndNames[0], bkgndModel );
// Randomise initial fit values for the signal mode
fitModel->useRandomInitFitPars(kTRUE);
const Bool_t haveBkgnds = ( fitModel->nBkgndClasses() > 0 );
// Switch on/off Poissonian smearing of total number of events
fitModel->doPoissonSmearing(haveBkgnds);
// Switch on/off Extended ML Fit option
fitModel->doEMLFit(haveBkgnds);
// Activate two-stage fit
fitModel->twoStageFit(kTRUE);
// Generate toy from the fitted parameters
//TString fitToyFileName("fitToyMC_");
//fitToyFileName += category;
//fitToyFileName += "-Slave_";
//fitToyFileName += iFit;
//fitToyFileName += ".root";
//fitModel->compareFitData(100, fitToyFileName);
// Write out per-event likelihoods and sWeights
//TString splotFileName("splot_");
//splotFileName += category;
//splotFileName += "-Slave_";
//splotFileName += iFit;
//splotFileName += ".root";
//fitModel->writeSPlotData(splotFileName, "splot", kFALSE);
// Set the names of the files to read/write
TString dataFile("gen-"); dataFile += category; dataFile += "-Slave.root";
TString treeName("genResults");
TString rootFileName("");
TString tableFileName("");
if (command == "fit") {
rootFileName = "fit"; rootFileName += category; rootFileName += "-Slave_"; rootFileName += iFit;
rootFileName += "_expt_"; rootFileName += firstExpt;
rootFileName += "-"; rootFileName += (firstExpt+nExpt-1);
rootFileName += ".root";
tableFileName = "fit"; tableFileName += category; tableFileName += "SlaveResults_"; tableFileName += iFit;
} else {
rootFileName = "dummy.root";
tableFileName = "gen"; tableFileName += category; tableFileName += "SlaveResults_";
}
// Execute the generation/fit
if ( command == "fit" ) {
- fitModel->runSlave( dataFile, treeName, rootFileName, tableFileName, "localhost", port );
+ fitModel->runSlave( dataFile, treeName, rootFileName, tableFileName, hostname, port );
} else {
fitModel->run( command, dataFile, treeName, rootFileName, tableFileName );
}
return EXIT_SUCCESS;
}
diff --git a/examples/runMasterSlave.sh b/examples/runMasterSlave.sh
index 65e28c3..b65166e 100755
--- a/examples/runMasterSlave.sh
+++ b/examples/runMasterSlave.sh
@@ -1,59 +1,68 @@
#!/bin/bash
if [ $# -lt 1 ]
then
- echo "Usage: $0 <nExpt> [firstExpt = 0] [numSlaves = 2]"
+ echo "Usage: $0 <nExpt> [firstExpt = 0]"
exit 1
fi
nexpt=$1
firstexpt=0
numslaves=2
if [ $# -gt 1 ]
then
firstexpt=$2
- if [ $# -gt 2 ]
- then
- numslaves=$3
- fi
fi
# Do whatever you need to do to setup your ROOT environment
# Generate the toy MC
-echo "Generating MC"
-./Slave gen DD $nexpt $firstexpt > gen-log-DD.out 2>&1
-./Slave gen LL $nexpt $firstexpt > gen-log-LL.out 2>&1
-
+if [ ! -e gen-DD-Slave.root ]
+then
+ echo "Generating MC for DD category"
+ ./Slave gen DD $nexpt $firstexpt > gen-log-DD.out 2>&1
+fi
+if [ ! -e gen-LL-Slave.root ]
+then
+ echo "Generating MC for LL category"
+ ./Slave gen LL $nexpt $firstexpt > gen-log-LL.out 2>&1
+fi
# Do the simultaneous fit
for ifit in `seq 0 19`
do
echo "Running fit $ifit"
./Master $ifit $nexpt $firstexpt $numslaves > master-log-$ifit.out 2>&1 &
sleep 5
+ NUMOFLINES=$(wc -l < "master-log-$ifit.out")
+ while [ $NUMOFLINES -lt 1 ]
+ do
+ sleep 5
+ NUMOFLINES=$(wc -l < "master-log-$ifit.out")
+ done
+
port=`tail -1 master-log-$ifit.out | awk '{print $NF}'`
- ./Slave fit DD $ifit $port > slave-dd-log-$ifit.out 2>&1 &
+ ./Slave fit DD $ifit $port localhost > slave-dd-log-$ifit.out 2>&1 &
sleep 1
- ./Slave fit LL $ifit $port > slave-ll-log-$ifit.out 2>&1
+ ./Slave fit LL $ifit $port localhost > slave-ll-log-$ifit.out 2>&1
done
# Extract the best fit
echo "Extracting the best fit results"
ls fitDD*.root > input-list-DD.txt
ls fitLL*.root > input-list-LL.txt
ls master-ntuple-*.root > input-list-master.txt
./ResultsExtractorMain $nexpt input-list-DD.txt best-fits-DD.root > resultsextractor-DD.out 2>&1
./ResultsExtractorMain $nexpt input-list-LL.txt best-fits-LL.root > resultsextractor-LL.out 2>&1
./ResultsExtractorMain $nexpt input-list-master.txt best-fits-master.root > resultsextractor-master.out 2>&1

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