def main():
from optparse import OptionParser
parser = OptionParser()
parser.add_option("-i", "--inputfile", dest="inputfile")
parser.add_option("-l", "--logfile", dest="logfile")
parser.add_option("-N", "--multiplicity", dest="N", type="int")
parser.add_option("", "--ST", dest="ST", type="int")
(options, args) = parser.parse_args()
from ROOT import gROOT, TFile, kFALSE, TGraph, TVectorD
gROOT.ProcessLine(".L roostats_cl95.C+")
from ROOT import roostats_cl95, roostats_cla, roostats_limit
import configurations as config
ilum = config.integrated_luminosity
slum = ilum * config.relative_luminosity_uncertainty
seff = config.nominal_signal_uncertainty
infile = TFile(options.inputfile, "READ")
from HistoStore import HistoStore
store = HistoStore()
hIntBkg = infile.Get("IntegralBackground_N%dup" % options.N)
hIntData = infile.Get("IntegralData_N%dup" % options.N)
ibin = hIntData.FindBin(options.ST)
nBkg = hIntBkg.GetBinContent(ibin)
sBkg = hIntBkg.GetBinError(ibin)
nData = int(hIntData.GetBinContent(ibin))
print "%10d %10.2f +/- %10.2f" % (nData, nBkg, sBkg)
print ilum, slum, 1.0, seff, nBkg, sBkg, nData
#CLs method
rl = roostats_limit(ilum, slum, 1.0, seff, nBkg, sBkg, nData, kFALSE, 1,
"cls", "my.png", 23576)
cl95 = rl.GetObservedLimit()
cla = rl.GetExpectedLimit()
exp_up = rl.GetOneSigmaHighRange()
exp_down = rl.GetOneSigmaLowRange()
exp_2up = rl.GetTwoSigmaHighRange()
exp_2down = rl.GetTwoSigmaLowRange()
#WAS Bayesian a la:
#cl95 = roostats_cl95(ilum, slum, 1.0, seff, nBkg, sBkg, nData, kFALSE, 1, "bayesian", "")
#cla = roostats_cla(ilum, slum, 1.0, seff, nBkg, sBkg, 1)
logfile = open(options.logfile, "w")
logfile.write(
"%-10d %-10.5f %-10.5f %-10.5f %-10.5f %-10.5f %-10.5f\n" %
(options.ST, cl95, cla, exp_up, exp_down, exp_2up, exp_2down))
logfile.close()
def main():
from optparse import OptionParser
parser = OptionParser()
parser.add_option("-i", "--inputfile", dest="inputfile")
parser.add_option("-l", "--logfile", dest="logfile")
parser.add_option("-N", "--multiplicity", dest="N", type="int")
parser.add_option("", "--ST", dest="ST", type="int")
(options, args) = parser.parse_args()
from ROOT import gROOT, TFile, kFALSE, TGraph, TVectorD
gROOT.ProcessLine(".L roostats_cl95.C+")
from ROOT import roostats_cl95, roostats_cla, roostats_limit
import configurations as config
ilum = config.integrated_luminosity
slum = ilum * config.relative_luminosity_uncertainty
seff = config.nominal_signal_uncertainty
infile = TFile(options.inputfile, "READ")
from HistoStore import HistoStore
store = HistoStore()
hIntBkg = infile.Get("IntegralBackground_N%dup" % options.N)
hIntData = infile.Get("IntegralData_N%dup" % options.N)
ibin = hIntData.FindBin(options.ST)
nBkg = hIntBkg.GetBinContent(ibin)
sBkg = hIntBkg.GetBinError(ibin)
nData = int(hIntData.GetBinContent(ibin))
print "%10d %10.2f +/- %10.2f" % (nData, nBkg, sBkg)
print ilum, slum, 1.0, seff, nBkg, sBkg, nData
#CLs method
rl = roostats_limit(ilum, slum, 1.0, seff, nBkg, sBkg, nData, kFALSE, 1, "cls", "my.png",23576)
cl95 = rl.GetObservedLimit();
cla = rl.GetExpectedLimit();
exp_up = rl.GetOneSigmaHighRange();
exp_down = rl.GetOneSigmaLowRange();
exp_2up = rl.GetTwoSigmaHighRange();
exp_2down = rl.GetTwoSigmaLowRange();
#WAS Bayesian a la:
#cl95 = roostats_cl95(ilum, slum, 1.0, seff, nBkg, sBkg, nData, kFALSE, 1, "bayesian", "")
#cla = roostats_cla(ilum, slum, 1.0, seff, nBkg, sBkg, 1)
logfile = open(options.logfile, "w")
logfile.write("%-10d %-10.5f %-10.5f %-10.5f %-10.5f %-10.5f %-10.5f\n" % (options.ST, cl95, cla, exp_up, exp_down, exp_2up, exp_2down))
logfile.close()
def main():
from optparse import OptionParser
parser = OptionParser()
parser.add_option("-i", "--inputfile", dest="inputfile")
parser.add_option("-o", "--ouputfile", dest="outputfile")
parser.add_option("-l", "--logfile", dest="logfile")
parser.add_option("-m", "--model", dest="model", type="string")
(options, args) = parser.parse_args()
model = options.model
import csv
import configurations as config
from ROOT import TFile, gROOT, kFALSE
from OptimizationTools import Integral, SignificanceHistogram
gROOT.ProcessLine(".L roostats_cl95.C+")
from ROOT import roostats_cl95, roostats_cla, roostats_limit
ilum = config.integrated_luminosity
slum = ilum * config.relative_luminosity_uncertainty
seff = config.nominal_signal_uncertainty
from BHXsec import BHXsec
bhxsec =BHXsec()
xsec = bhxsec.get(model)
if xsec == -1:
return
sig_infile= TFile("%s/%s.root" % (config.bh_dir, model), "READ")
if not sig_infile.IsOpen():
return
infile = TFile(options.inputfile, "READ")
outfile = TFile(options.outputfile, "RECREATE")
logfile = open(options.logfile, "w")
outdir = outfile.mkdir(model)
hST_NoCut = sig_infile.Get("plotsNoCut/ST")
nEvents = hST_NoCut.GetEntries()
Nmin = 0
STmin = 0.0
STmin_ibin = 0
maxSignificance = 0.0
for N in config.inclusive_multiplicities:
hSig = sig_infile.Get("plotsN%dup/ST" % N)
hSig.Rebin(config.rebin)
hSig.Scale(xsec / nEvents * ilum)
Integral(hSig)
hBkg = infile.Get("IntegralBackground_N%dup" % N)
result = SignificanceHistogram(hSig, hBkg)
result.SetName("Significance_N%dup" % N)
local_max = result.GetMaximum()
if local_max > maxSignificance:
maxSignificance = local_max
Nmin = N
STmin_ibin = result.GetMaximumBin()
STmin = result.GetBinLowEdge(STmin_ibin)
outdir.cd()
result.Write()
# Calculate xsec limits
hData = infile.Get("IntegralData_N%dup" % Nmin)
hBkg = infile.Get("IntegralBackground_N%dup" % Nmin)
hSig = sig_infile.Get("plotsN%dup/ST" % Nmin)
nData = int(hData.GetBinContent(STmin_ibin))
nBkg = hBkg.GetBinContent(STmin_ibin)
nBkgErr = hBkg.GetBinError(STmin_ibin)
nSig = hSig.GetBinContent(STmin_ibin)
eff = nSig/xsec/ilum
seff *= eff
print "Printing numbers "
print ilum, slum, eff, seff, nBkg, nBkgErr, nData
#CLs method
rl = roostats_limit(ilum, slum, eff, seff, nBkg, nBkgErr, nData, kFALSE, 1, "cls", "my.png",12345)
cl95 = rl.GetObservedLimit();
cla = rl.GetExpectedLimit();
exp_up = rl.GetOneSigmaHighRange();
exp_down = rl.GetOneSigmaLowRange();
exp_2up = rl.GetTwoSigmaHighRange();
exp_2down = rl.GetTwoSigmaLowRange();
#WAS Bayesian a la:
#cl95 = roostats_cl95(ilum, slum, eff, seff, nBkg, nBkgErr,
# nData, kFALSE, 1, "bayesian", "")
#cla = roostats_cla(ilum, slum, 1.0, seff, nBkg, nBkgErr, 1)
#logfile.write("%-25s %10.3e %d %d %8.3f %10.5e %8d %10.3e %10.3e %10.3e %10.3e\n"\
# % (model, xsec, Nmin, STmin, eff, nSig, nData, nBkg, nBkgErr, cl95, cla))
logfile.write("%-25s %10.3e %d %d %8.3f %10.5e %8d %10.3e %10.3e %10.3e %10.3e %10.3e %10.3e %10.3e %10.3e\n"\
% (model, xsec, Nmin, STmin, eff, nSig, nData, nBkg, nBkgErr, cl95, cla, exp_up, exp_down, exp_2up, exp_2down))
sig_infile.Close()
infile.Close()
outfile.Close()
logfile.close()
def main():
from optparse import OptionParser
parser = OptionParser()
parser.add_option("-i", "--inputfile", dest="inputfile")
parser.add_option("-o", "--ouputfile", dest="outputfile")
parser.add_option("-l", "--logfile", dest="logfile")
parser.add_option("-m", "--model", dest="model", type="string")
(options, args) = parser.parse_args()
model = options.model
import csv
import configurations as config
from ROOT import TFile, gROOT, kFALSE
from OptimizationTools import Integral, SignificanceHistogram
gROOT.ProcessLine(".L roostats_cl95.C+")
from ROOT import roostats_cl95, roostats_cla, roostats_limit
ilum = config.integrated_luminosity
slum = ilum * config.relative_luminosity_uncertainty
seff = config.nominal_signal_uncertainty
from BHXsec import BHXsec
bhxsec = BHXsec()
xsec = bhxsec.get(model)
if xsec == -1:
return
sig_infile = TFile("%s/%s.root" % (config.bh_dir, model), "READ")
if not sig_infile.IsOpen():
return
infile = TFile(options.inputfile, "READ")
outfile = TFile(options.outputfile, "RECREATE")
logfile = open(options.logfile, "w")
outdir = outfile.mkdir(model)
hST_NoCut = sig_infile.Get("plotsNoCut/ST")
nEvents = hST_NoCut.GetEntries()
Nmin = 0
STmin = 0.0
STmin_ibin = 0
maxSignificance = 0.0
for N in config.inclusive_multiplicities:
hSig = sig_infile.Get("plotsN%dup/ST" % N)
hSig.Rebin(config.rebin)
hSig.Scale(xsec / nEvents * ilum)
Integral(hSig)
hBkg = infile.Get("IntegralBackground_N%dup" % N)
result = SignificanceHistogram(hSig, hBkg)
result.SetName("Significance_N%dup" % N)
local_max = result.GetMaximum()
if local_max > maxSignificance:
maxSignificance = local_max
Nmin = N
STmin_ibin = result.GetMaximumBin()
STmin = result.GetBinLowEdge(STmin_ibin)
outdir.cd()
result.Write()
# Calculate xsec limits
hData = infile.Get("IntegralData_N%dup" % Nmin)
hBkg = infile.Get("IntegralBackground_N%dup" % Nmin)
hSig = sig_infile.Get("plotsN%dup/ST" % Nmin)
nData = int(hData.GetBinContent(STmin_ibin))
nBkg = hBkg.GetBinContent(STmin_ibin)
nBkgErr = hBkg.GetBinError(STmin_ibin)
nSig = hSig.GetBinContent(STmin_ibin)
eff = nSig / xsec / ilum
seff *= eff
print "Printing numbers "
print ilum, slum, eff, seff, nBkg, nBkgErr, nData
#CLs method
rl = roostats_limit(ilum, slum, eff, seff, nBkg, nBkgErr, nData, kFALSE, 1,
"cls", "my.png", 12345)
cl95 = rl.GetObservedLimit()
cla = rl.GetExpectedLimit()
exp_up = rl.GetOneSigmaHighRange()
exp_down = rl.GetOneSigmaLowRange()
exp_2up = rl.GetTwoSigmaHighRange()
exp_2down = rl.GetTwoSigmaLowRange()
#WAS Bayesian a la:
#cl95 = roostats_cl95(ilum, slum, eff, seff, nBkg, nBkgErr,
# nData, kFALSE, 1, "bayesian", "")
#cla = roostats_cla(ilum, slum, 1.0, seff, nBkg, nBkgErr, 1)
#logfile.write("%-25s %10.3e %d %d %8.3f %10.5e %8d %10.3e %10.3e %10.3e %10.3e\n"\
# % (model, xsec, Nmin, STmin, eff, nSig, nData, nBkg, nBkgErr, cl95, cla))
logfile.write("%-25s %10.3e %d %d %8.3f %10.5e %8d %10.3e %10.3e %10.3e %10.3e %10.3e %10.3e %10.3e %10.3e\n"\
% (model, xsec, Nmin, STmin, eff, nSig, nData, nBkg, nBkgErr, cl95, cla, exp_up, exp_down, exp_2up, exp_2down))
sig_infile.Close()
infile.Close()
outfile.Close()
logfile.close()
