forked from cms-egamma/egm_tnp_analysis
/
fitUtils.py
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fitUtils.py
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import ROOT as rt
rt.gROOT.LoadMacro('./libCpp/histFitter.C+')
rt.gROOT.LoadMacro('./libCpp/RooCBExGaussShape.cc+')
rt.gROOT.LoadMacro('./libCpp/RooCMSShape.cc+')
rt.gROOT.SetBatch(1)
from ROOT import tnpFitter
import re
import math
minPtForSwitch = 70
def ptMin( tnpBin ):
ptmin = 1
if tnpBin['name'].find('pt_') >= 0:
ptmin = float(tnpBin['name'].split('pt_')[1].split('p')[0])
elif tnpBin['name'].find('et_') >= 0:
ptmin = float(tnpBin['name'].split('et_')[1].split('p')[0])
return ptmin
def createWorkspaceForAltSig( sample, tnpBin, tnpWorkspaceParam ):
### tricky: use n < 0 for high pT bin (so need to remove param and add it back)
cbNList = ['tailLeft']
ptmin = ptMin(tnpBin)
if ptmin >= 35 :
for par in cbNList:
for ip in range(len(tnpWorkspaceParam)):
x=re.compile('%s.*?' % par)
listToRM = filter(x.match, tnpWorkspaceParam)
for ir in listToRM :
print '**** remove', ir
tnpWorkspaceParam.remove(ir)
tnpWorkspaceParam.append( 'tailLeft[-1]' )
if sample.isMC:
return tnpWorkspaceParam
fileref = sample.mcRef.altSigFit
filemc = rt.TFile(fileref,'read')
from ROOT import RooFit,RooFitResult
fitresP = filemc.Get( '%s_resP' % tnpBin['name'] )
fitresF = filemc.Get( '%s_resF' % tnpBin['name'] )
listOfParam = ['nF','alphaF','nP','alphaP','sigmaP','sigmaF','sigmaP_2','sigmaF_2']
fitPar = fitresF.floatParsFinal()
for ipar in range(len(fitPar)):
pName = fitPar[ipar].GetName()
print '%s[%2.3f]' % (pName,fitPar[ipar].getVal())
for par in listOfParam:
if pName == par:
x=re.compile('%s.*?' % pName)
listToRM = filter(x.match, tnpWorkspaceParam)
for ir in listToRM :
tnpWorkspaceParam.remove(ir)
tnpWorkspaceParam.append( '%s[%2.3f]' % (pName,fitPar[ipar].getVal()) )
fitPar = fitresP.floatParsFinal()
for ipar in range(len(fitPar)):
pName = fitPar[ipar].GetName()
print '%s[%2.3f]' % (pName,fitPar[ipar].getVal())
for par in listOfParam:
if pName == par:
x=re.compile('%s.*?' % pName)
listToRM = filter(x.match, tnpWorkspaceParam)
for ir in listToRM :
tnpWorkspaceParam.remove(ir)
tnpWorkspaceParam.append( '%s[%2.3f]' % (pName,fitPar[ipar].getVal()) )
filemc.Close()
return tnpWorkspaceParam
#############################################################
########## nominal fitter
#############################################################
def histFitterNominal( sample, tnpBin, tnpWorkspaceParam ):
tnpWorkspaceFunc = [
"Gaussian::sigResPass(x,meanP,sigmaP)",
"Gaussian::sigResFail(x,meanF,sigmaF)",
"RooCMSShape::bkgPass(x, acmsP, betaP, gammaP, peakP)",
"RooCMSShape::bkgFail(x, acmsF, betaF, gammaF, peakF)",
]
tnpWorkspace = []
tnpWorkspace.extend(tnpWorkspaceParam)
tnpWorkspace.extend(tnpWorkspaceFunc)
## init fitter
infile = rt.TFile( sample.histFile, "read")
hP = infile.Get('%s_Pass' % tnpBin['name'] )
hF = infile.Get('%s_Fail' % tnpBin['name'] )
fitter = tnpFitter( hP, hF, tnpBin['name'] )
infile.Close()
## setup
fitter.useMinos()
rootfile = rt.TFile(sample.nominalFit,'update')
fitter.setOutputFile( rootfile )
## generated Z LineShape
## for high pT change the failing spectra to any probe to get statistics
fileTruth = rt.TFile(sample.mcRef.histFile,'read')
histZLineShapeP = fileTruth.Get('%s_Pass'%tnpBin['name'])
histZLineShapeF = fileTruth.Get('%s_Fail'%tnpBin['name'])
if ptMin( tnpBin ) > minPtForSwitch:
histZLineShapeF = fileTruth.Get('%s_Pass'%tnpBin['name'])
# fitter.fixSigmaFtoSigmaP()
fitter.setZLineShapes(histZLineShapeP,histZLineShapeF)
fileTruth.Close()
### set workspace
workspace = rt.vector("string")()
for iw in tnpWorkspace:
workspace.push_back(iw)
fitter.setWorkspace( workspace )
title = tnpBin['title'].replace(';',' - ')
title = title.replace('probe_sc_eta','#eta_{SC}')
title = title.replace('probe_Ele_pt','p_{T}')
fitter.fits(sample.mcTruth,title)
rootfile.Close()
#############################################################
########## alternate signal fitter
#############################################################
def histFitterAltSig( sample, tnpBin, tnpWorkspaceParam ):
tnpWorkspacePar = createWorkspaceForAltSig( sample, tnpBin, tnpWorkspaceParam )
tnpWorkspaceFunc = [
"tailLeft[1]",
"RooCBExGaussShape::sigResPass(x,meanP,expr('sqrt(sigmaP*sigmaP+sosP*sosP)',{sigmaP,sosP}),alphaP,nP, expr('sqrt(sigmaP_2*sigmaP_2+sosP*sosP)',{sigmaP_2,sosP}),tailLeft)",
"RooCBExGaussShape::sigResFail(x,meanF,expr('sqrt(sigmaF*sigmaF+sosF*sosF)',{sigmaF,sosF}),alphaF,nF, expr('sqrt(sigmaF_2*sigmaF_2+sosF*sosF)',{sigmaF_2,sosF}),tailLeft)",
"RooCMSShape::bkgPass(x, acmsP, betaP, gammaP, peakP)",
"RooCMSShape::bkgFail(x, acmsF, betaF, gammaF, peakF)",
]
tnpWorkspace = []
tnpWorkspace.extend(tnpWorkspacePar)
tnpWorkspace.extend(tnpWorkspaceFunc)
## init fitter
infile = rt.TFile( sample.histFile, "read")
hP = infile.Get('%s_Pass' % tnpBin['name'] )
hF = infile.Get('%s_Fail' % tnpBin['name'] )
## for high pT change the failing spectra to passing probe to get statistics
## MC only: this is to get MC parameters in data fit!
if sample.isMC and ptMin( tnpBin ) > minPtForSwitch:
hF = infile.Get('%s_Pass' % tnpBin['name'] )
fitter = tnpFitter( hP, hF, tnpBin['name'] )
# fitter.fixSigmaFtoSigmaP()
infile.Close()
## setup
rootfile = rt.TFile(sample.altSigFit,'update')
fitter.setOutputFile( rootfile )
## generated Z LineShape
fileTruth = rt.TFile('etc/inputs/ZeeGenLevel.root','read')
histZLineShape = fileTruth.Get('Mass')
fitter.setZLineShapes(histZLineShape,histZLineShape)
fileTruth.Close()
### set workspace
workspace = rt.vector("string")()
for iw in tnpWorkspace:
workspace.push_back(iw)
fitter.setWorkspace( workspace )
title = tnpBin['title'].replace(';',' - ')
title = title.replace('probe_sc_eta','#eta_{SC}')
title = title.replace('probe_Ele_pt','p_{T}')
fitter.fits(sample.mcTruth,title)
rootfile.Close()
#############################################################
########## alternate background fitter
#############################################################
def histFitterAltBkg( sample, tnpBin, tnpWorkspaceParam ):
tnpWorkspaceFunc = [
"Gaussian::sigResPass(x,meanP,sigmaP)",
"Gaussian::sigResFail(x,meanF,sigmaF)",
"Exponential::bkgPass(x, alphaP)",
"Exponential::bkgFail(x, alphaF)",
]
tnpWorkspace = []
tnpWorkspace.extend(tnpWorkspaceParam)
tnpWorkspace.extend(tnpWorkspaceFunc)
## init fitter
infile = rt.TFile(sample.histFile,'read')
hP = infile.Get('%s_Pass' % tnpBin['name'] )
hF = infile.Get('%s_Fail' % tnpBin['name'] )
fitter = tnpFitter( hP, hF, tnpBin['name'] )
infile.Close()
## setup
rootfile = rt.TFile(sample.altBkgFit,'update')
fitter.setOutputFile( rootfile )
# fitter.setFitRange(65,115)
## generated Z LineShape
## for high pT change the failing spectra to any probe to get statistics
fileTruth = rt.TFile(sample.mcRef.histFile,'read')
histZLineShapeP = fileTruth.Get('%s_Pass'%tnpBin['name'])
histZLineShapeF = fileTruth.Get('%s_Fail'%tnpBin['name'])
if ptMin( tnpBin ) > minPtForSwitch:
histZLineShapeF = fileTruth.Get('%s_Pass'%tnpBin['name'])
# fitter.fixSigmaFtoSigmaP()
fitter.setZLineShapes(histZLineShapeP,histZLineShapeF)
fileTruth.Close()
### set workspace
workspace = rt.vector("string")()
for iw in tnpWorkspace:
workspace.push_back(iw)
fitter.setWorkspace( workspace )
title = tnpBin['title'].replace(';',' - ')
title = title.replace('probe_sc_eta','#eta_{SC}')
title = title.replace('probe_Ele_pt','p_{T}')
fitter.fits(sample.mcTruth,title)
rootfile.Close()