def latexfitresults(filename,regionList,sampleList,exactRegionNames=False,dataname='obsData',showSum=False, doAsym=True, blinded=False):
workspacename = 'w'
w = Util.GetWorkspaceFromFile(filename,'w')
if w==None:
print "ERROR : Cannot open workspace : ", workspacename
sys.exit(1)
resultAfterFit = w.obj('RooExpandedFitResult_afterFit')
if resultAfterFit==None:
print "ERROR : Cannot open fit result after fit RooExpandedFitResult_afterFit"
sys.exit(1)
resultBeforeFit = w.obj('RooExpandedFitResult_beforeFit')
if resultBeforeFit==None:
print "ERROR : Cannot open fit result before fit RooExpandedFitResult_beforeFit"
sys.exit(1)
data_set = w.data(dataname)
if data_set==None:
print "ERROR : Cannot open dataset : ", "data_set"+suffix
sys.exit(1)
regionCat = w.obj("channelCat")
data_set.table(regionCat).Print("v");
regionFullNameList = [ Util.GetFullRegionName(regionCat, region) for region in regionList]
print regionFullNameList
######
snapshot = 'snapshot_paramsVals_RooExpandedFitResult_afterFit'
w.loadSnapshot(snapshot)
if not w.loadSnapshot(snapshot):
print "ERROR : Cannot load snapshot : ", snapshot
sys.exit(1)
tablenumbers = {}
# SUM ALL REGIONS
sumName = ""
for index, reg in enumerate(regionList):
if index == 0:
sumName = reg
else:
sumName = sumName + " + " + reg
regionListWithSum = list(regionList)
if showSum:
regionListWithSum.append(sumName)
tablenumbers['names'] = regionListWithSum
regionCatList = [ 'channelCat==channelCat::' +region.Data() for region in regionFullNameList]
regionDatasetList = [data_set.reduce(regioncat) for regioncat in regionCatList]
for index, data in enumerate(regionDatasetList):
data.SetName("data_" + regionList[index])
data.SetTitle("data_" + regionList[index])
nobs_regionList = [ data.sumEntries() for data in regionDatasetList]
#SUM
sumNobs = 0.
for nobs in nobs_regionList:
sumNobs += nobs
## print " \n XXX nobs = ", nobs, " sumNobs = ", sumNobs
if showSum:
nobs_regionList.append(sumNobs)
tablenumbers['nobs'] = nobs_regionList
######
######
###### FROM HERE ON OUT WE CALCULATE THE FITTED NUMBER OF EVENTS __AFTER__ THE FIT
######
######
# total pdf, not splitting in components
pdfinRegionList = [ Util.GetRegionPdf(w, region) for region in regionList]
varinRegionList = [ Util.GetRegionVar(w, region) for region in regionList]
rrspdfinRegionList = []
for index,pdf in enumerate(pdfinRegionList):
# pdf.Print("t")
prodList = pdf.pdfList()
foundRRS = 0
for idx in range(prodList.getSize()):
# if "BG" in prodList[idx].GetName():
# prodList[idx].Print("t")
if prodList[idx].InheritsFrom("RooRealSumPdf"):
rrspdfInt = prodList[idx].createIntegral(RooArgSet(varinRegionList[index]));
rrspdfinRegionList.append(rrspdfInt)
foundRRS += 1
if foundRRS >1 or foundRRS==0:
print " \n\n WARNING: ", pdf.GetName(), " has ", foundRRS, " instances of RooRealSumPdf"
print pdf.GetName(), " component list:", prodList.Print("v")
nFittedInRegionList = [ pdf.getVal() for index, pdf in enumerate(rrspdfinRegionList)]
pdfFittedErrInRegionList = [ Util.GetPropagatedError(pdf, resultAfterFit, doAsym) for pdf in rrspdfinRegionList]
if showSum:
pdfInAllRegions = RooArgSet()
for index, pdf in enumerate(rrspdfinRegionList):
pdfInAllRegions.add(pdf)
pdfSumInAllRegions = RooAddition( "pdf_AllRegions_AFTER", "pdf_AllRegions_AFTER", RooArgList(pdfInAllRegions))
pdfSumInAllRegions.Print()
nPdfSumVal = pdfSumInAllRegions.getVal()
nPdfSumError = Util.GetPropagatedError(pdfSumInAllRegions, resultAfterFit, doAsym)
nFittedInRegionList.append(nPdfSumVal)
pdfFittedErrInRegionList.append(nPdfSumError)
tablenumbers['TOTAL_FITTED_bkg_events'] = nFittedInRegionList
tablenumbers['TOTAL_FITTED_bkg_events_err'] = pdfFittedErrInRegionList
if blinded:
nobs_regionList = [ data.sumEntries() for data in regionDatasetList]
nobs_regionList[-1] = -1
tablenumbers['nobs'] = nobs_regionList
# components
for isam, sample in enumerate(sampleList):
sampleName=getName(sample)
nSampleInRegionVal = []
nSampleInRegionError = []
sampleInAllRegions = RooArgSet()
for ireg, region in enumerate(regionList):
sampleInRegion=getPdfInRegions(w,sample,region)
#sampleInRegion = Util.GetComponent(w,sample,region,exactRegionNames)
sampleInRegionVal = 0.
sampleInRegionError = 0.
if not sampleInRegion==None:
sampleInRegion.Print()
sampleInRegionVal = sampleInRegion.getVal()
sampleInRegionError = Util.GetPropagatedError(sampleInRegion, resultAfterFit, doAsym)
sampleInAllRegions.add(sampleInRegion)
else:
print " \n YieldsTable.py: WARNING: sample =", sampleName, " non-existent (empty) in region =",region, "\n"
nSampleInRegionVal.append(sampleInRegionVal)
nSampleInRegionError.append(sampleInRegionError)
if showSum:
sampleSumInAllRegions = RooAddition( (sampleName+"_AllRegions_FITTED"), (sampleName+"_AllRegions_FITTED"), RooArgList(sampleInAllRegions))
sampleSumInAllRegions.Print()
nSampleSumVal = sampleSumInAllRegions.getVal()
nSampleSumError = Util.GetPropagatedError(sampleSumInAllRegions, resultAfterFit, doAsym)
nSampleInRegionVal.append(nSampleSumVal)
nSampleInRegionError.append(nSampleSumError)
tablenumbers['Fitted_events_'+sampleName] = nSampleInRegionVal
tablenumbers['Fitted_err_'+sampleName] = nSampleInRegionError
print tablenumbers
######
######
###### FROM HERE ON OUT WE CALCULATE THE EXPECTED NUMBER OF EVENTS __BEFORE__ THE FIT
######
######
# FROM HERE ON OUT WE CALCULATE THE EXPECTED NUMBER OF EVENTS BEFORE THE FIT
w.loadSnapshot('snapshot_paramsVals_RooExpandedFitResult_beforeFit')
# check if any of the initial scaling factors is != 1
_result = w.obj('RooExpandedFitResult_beforeFit')
_muFacs = _result.floatParsFinal()
for i in range(len(_muFacs)):
if "mu_" in _muFacs[i].GetName() and _muFacs[i].getVal() != 1.0:
print " \n WARNING: scaling factor %s != 1.0 (%g) expected MC yield WILL BE WRONG!" % (_muFacs[i].GetName(), _muFacs[i].getVal())
pdfinRegionList = [ Util.GetRegionPdf(w, region) for region in regionList]
varinRegionList = [ Util.GetRegionVar(w, region) for region in regionList]
rrspdfinRegionList = []
for index,pdf in enumerate(pdfinRegionList):
prodList = pdf.pdfList()
foundRRS = 0
for idx in range(prodList.getSize()):
if prodList[idx].InheritsFrom("RooRealSumPdf"):
prodList[idx].Print()
rrspdfInt = prodList[idx].createIntegral(RooArgSet(varinRegionList[index]))
rrspdfinRegionList.append(rrspdfInt)
foundRRS += 1
if foundRRS >1 or foundRRS==0:
print " \n\n WARNING: ", pdf.GetName(), " has ", foundRRS, " instances of RooRealSumPdf"
print pdf.GetName(), " component list:", prodList.Print("v")
nExpInRegionList = [ pdf.getVal() for index, pdf in enumerate(rrspdfinRegionList)]
pdfExpErrInRegionList = [ Util.GetPropagatedError(pdf, resultBeforeFit, doAsym) for pdf in rrspdfinRegionList]
if showSum:
pdfInAllRegions = RooArgSet()
for index, pdf in enumerate(rrspdfinRegionList):
pdfInAllRegions.add(pdf)
pdfSumInAllRegions = RooAddition( "pdf_AllRegions_BEFORE", "pdf_AllRegions_BEFORE", RooArgList(pdfInAllRegions))
nPdfSumVal = pdfSumInAllRegions.getVal()
nPdfSumError = Util.GetPropagatedError(pdfSumInAllRegions, resultBeforeFit, doAsym)
nExpInRegionList.append(nPdfSumVal)
pdfExpErrInRegionList.append(nPdfSumError)
tablenumbers['TOTAL_MC_EXP_BKG_events'] = nExpInRegionList
tablenumbers['TOTAL_MC_EXP_BKG_err'] = pdfExpErrInRegionList
for isam, sample in enumerate(sampleList):
sampleName=getName(sample)
nMCSampleInRegionVal = []
nMCSampleInRegionError = []
MCSampleInAllRegions = RooArgSet()
for ireg, region in enumerate(regionList):
MCSampleInRegion = getPdfInRegions(w,sample,region)
#MCSampleInRegion = Util.GetComponent(w,sample,region,exactRegionNames)
MCSampleInRegionVal = 0.
MCSampleInRegionError = 0.
if not MCSampleInRegion==None:
MCSampleInRegionVal = MCSampleInRegion.getVal()
MCSampleInRegionError = Util.GetPropagatedError(MCSampleInRegion, resultBeforeFit, doAsym)
MCSampleInAllRegions.add(MCSampleInRegion)
else:
print " \n WARNING: sample=", sampleName, " non-existent (empty) in region=",region
nMCSampleInRegionVal.append(MCSampleInRegionVal)
nMCSampleInRegionError.append(MCSampleInRegionError)
if showSum:
MCSampleSumInAllRegions = RooAddition( (sampleName+"_AllRegions_MC"), (sampleName+"_AllRegions_MC"), RooArgList(MCSampleInAllRegions))
nMCSampleSumVal = MCSampleSumInAllRegions.getVal()
nMCSampleSumError = Util.GetPropagatedError(MCSampleSumInAllRegions, resultBeforeFit, doAsym)
nMCSampleInRegionVal.append(nMCSampleSumVal)
nMCSampleInRegionError.append(nMCSampleSumError)
tablenumbers['MC_exp_events_'+sampleName] = nMCSampleInRegionVal
tablenumbers['MC_exp_err_'+sampleName] = nMCSampleInRegionError
# sorted(tablenumbers, key=lambda sample: sample[1]) # sort by age
map_listofkeys = tablenumbers.keys()
map_listofkeys.sort()
for name in map_listofkeys:
if tablenumbers.has_key(name) :
print name, ": ", tablenumbers[name]
###
return tablenumbers
def latexfitresults(filename,
regionList,
sampleList,
dataname='obsData',
showSum=False,
doAsym=True,
blinded=False,
splitBins=False):
"""
Calculate before/after-fit yields in all channels given
@param filename The filename containing afterFit workspace
@param regionList A list of regions to be considered
@param sampleList A list of samples to be considered
@param dataname The name of dataset (default='obsData')
@param showSum Calculates sum of all regions if set to true (default=False)
@param doAsym Calculates asymmetric errors taken from MINOS (default=True)
@param blinded Observed event count will not be shown if set to True (default=False)
@param splitBins Calculates bin-by-bin yields for all regions if set to True (default=False)
"""
"""
pick up workspace from file
"""
workspacename = 'w'
w = Util.GetWorkspaceFromFile(filename, 'w')
if w == None:
print "ERROR : Cannot open workspace : ", workspacename
sys.exit(1)
"""
pick up after-fit RooExpandedFitResult from workspace
"""
resultAfterFit = w.obj('RooExpandedFitResult_afterFit')
if resultAfterFit == None:
print "ERROR : Cannot open fit result after fit RooExpandedFitResult_afterFit"
sys.exit(1)
"""
pick up before-fit RooExpandedFitResult from workspace
"""
resultBeforeFit = w.obj('RooExpandedFitResult_beforeFit')
if resultBeforeFit == None:
print "ERROR : Cannot open fit result before fit RooExpandedFitResult_beforeFit"
sys.exit(1)
"""
pick up dataset from workspace
"""
data_set = w.data(dataname)
if data_set == None:
print "ERROR : Cannot open dataset : ", "data_set" + suffix
sys.exit(1)
"""
pick up channel category (RooCategory) from workspace
"""
regionCat = w.obj("channelCat")
if not blinded:
data_set.table(regionCat).Print("v")
"""
find full (long) name list of regions (i.e. short=SR3J, long=SR3J_meffInc30_JVF25pt50)
"""
regionFullNameList = [
Util.GetFullRegionName(regionCat, region) for region in regionList
]
"""
load afterFit workspace snapshot (=set all parameters to values after fit)
"""
snapshot = 'snapshot_paramsVals_RooExpandedFitResult_afterFit'
w.loadSnapshot(snapshot)
if not w.loadSnapshot(snapshot):
print "ERROR : Cannot load snapshot : ", snapshot
sys.exit(1)
"""
define set, for all names/yields to be saved in
"""
tablenumbers = {}
"""
if showSum=True define names for sum of all regions and add to regionList
"""
sumName = ""
for index, reg in enumerate(regionList):
if index == 0:
sumName = reg
else:
sumName = sumName + " + " + reg
regionListWithSum = list(regionList)
if showSum:
regionListWithSum.append(sumName)
tablenumbers['names'] = regionListWithSum
"""
make a list of channelCat calls for every region
"""
regionCatList = [
'channelCat==channelCat::' + region.Data()
for region in regionFullNameList
]
"""
retrieve number of observed (=data) events per region
"""
regionDatasetList = [
data_set.reduce(regioncat) for regioncat in regionCatList
]
for index, data in enumerate(regionDatasetList):
data.SetName("data_" + regionList[index])
data.SetTitle("data_" + regionList[index])
nobs_regionList = [data.sumEntries() for data in regionDatasetList]
"""
if showSum=True calculate the total number of observed events in all regions
"""
sumNobs = 0.
for nobs in nobs_regionList:
sumNobs += nobs
if showSum:
nobs_regionList.append(sumNobs)
tablenumbers['nobs'] = nobs_regionList
"""
FROM HERE ON OUT WE CALCULATE THE FITTED NUMBER OF EVENTS __AFTER__ THE FIT
"""
"""
get a list of pdf's and variables per region
"""
pdfinRegionList = [Util.GetRegionPdf(w, region) for region in regionList]
varinRegionList = [Util.GetRegionVar(w, region) for region in regionList]
"""
if splitBins=True get the list of Nbins, binMax and binMin; make a list of new region names for each bin
"""
varNbinsInRegionList = []
varBinLowInRegionList = []
varBinHighInRegionList = []
rangeNameBinsInRegionList = []
if splitBins:
varNbinsInRegionList = [
Util.GetRegionVar(w, region).getBinning().numBins()
for region in regionList
]
varBinLowInRegionList = [[
Util.GetRegionVar(w, region).getBinning(
(region + "binning")).binLow(ibin)
for ibin in range(0, varNbinsInRegionList[idx])
] for idx, region in enumerate(regionList)]
varBinHighInRegionList = [[
Util.GetRegionVar(w, region).getBinning(
(region + "binning")).binHigh(ibin)
for ibin in range(0, varNbinsInRegionList[idx])
] for idx, region in enumerate(regionList)]
rangeNameBinsInRegionList = [[
regionList[idx] + "_bin" + str(ibin)
for ibin in range(0, varNbinsInRegionList[idx])
] for idx, region in enumerate(regionList)]
for index, region in enumerate(regionList):
if varNbinsInRegionList[index] == 1:
print " \n YieldsTable.py: WARNING: you have called -P (= per-bin yields) but this region ", region, " has only 1 bin \n"
"""
if splitBins=True reshuffle the regionName list; each region name is followed by names of each bin (i.e. regionNameList=['SR3J','SR3J_bin1','SR3j_bin2','SR4J','SR4J_bin1'])
"""
regionListWithBins = []
if splitBins:
for index, region in enumerate(regionList):
regionListWithBins.append(region)
for ibin in range(0, varNbinsInRegionList[index]):
regionListWithBins.append(
rangeNameBinsInRegionList[index][ibin])
tablenumbers['names'] = regionListWithBins
"""
calculate number of observed(=data) events per bin
"""
nobs_regionListWithBins = []
if splitBins:
binFuncInRegionList = [
RooBinningCategory("bin_" + region, "bin_" + region,
varinRegionList[index])
for index, region in enumerate(regionList)
]
for index, data in enumerate(regionDatasetList):
data.addColumn(binFuncInRegionList[index])
if not blinded:
data.table(binFuncInRegionList[index]).Print("v")
nobs_regionListWithBins.append(data.sumEntries())
for ibin in range(0, varNbinsInRegionList[index]):
nobs_regionListWithBins.append(
(data.reduce(binFuncInRegionList[index].GetName() + "==" +
binFuncInRegionList[index].GetName() + "::" +
varinRegionList[index].GetName() + "_bin" +
str(ibin))).sumEntries())
tablenumbers['nobs'] = nobs_regionListWithBins
"""
if blinded=True, set all numbers of observed events to -1
"""
if blinded:
for index, nobs in enumerate(nobs_regionListWithBins):
nobs_regionListWithBins[index] = -1
tablenumbers['nobs'] = nobs_regionListWithBins
"""
get a list of RooRealSumPdf per region (RooRealSumPdf is the top-pdf per region containing all samples)
"""
rrspdfinRegionList = []
for index, pdf in enumerate(pdfinRegionList):
prodList = pdf.pdfList()
foundRRS = 0
for idx in range(prodList.getSize()):
if prodList[idx].InheritsFrom("RooRealSumPdf"):
rrspdfInt = prodList[idx].createIntegral(
RooArgSet(varinRegionList[index]))
rrspdfinRegionList.append(rrspdfInt)
if splitBins:
origMin = varinRegionList[index].getMin()
origMax = varinRegionList[index].getMax()
for ibin in range(0, varNbinsInRegionList[index]):
rangeName = rangeNameBinsInRegionList[index][ibin]
varinRegionList[index].setRange(
rangeName, varBinLowInRegionList[index][ibin],
varBinHighInRegionList[index][ibin])
rrspdfInt = prodList[idx].createIntegral(
RooArgSet(varinRegionList[index]), rangeName)
rrspdfinRegionList.append(rrspdfInt)
varinRegionList[index].setRange(origMin, origMax)
foundRRS += 1
if foundRRS > 1 or foundRRS == 0:
print " \n\n WARNING: ", pdf.GetName(
), " has ", foundRRS, " instances of RooRealSumPdf"
print pdf.GetName(), " component list:", prodList.Print("v")
"""
calculate total pdf number of fitted events and error
"""
nFittedInRegionList = [
pdf.getVal() for index, pdf in enumerate(rrspdfinRegionList)
]
pdfFittedErrInRegionList = [
Util.GetPropagatedError(pdf, resultAfterFit, doAsym)
for pdf in rrspdfinRegionList
]
"""
if showSum=True calculate the total number of fitted events in all regions
"""
if showSum:
pdfInAllRegions = RooArgSet()
for index, pdf in enumerate(rrspdfinRegionList):
pdfInAllRegions.add(pdf)
pdfSumInAllRegions = RooAddition("pdf_AllRegions_AFTER",
"pdf_AllRegions_AFTER",
RooArgList(pdfInAllRegions))
nPdfSumVal = pdfSumInAllRegions.getVal()
nPdfSumError = Util.GetPropagatedError(pdfSumInAllRegions,
resultAfterFit, doAsym)
nFittedInRegionList.append(nPdfSumVal)
pdfFittedErrInRegionList.append(nPdfSumError)
tablenumbers['TOTAL_FITTED_bkg_events'] = nFittedInRegionList
tablenumbers['TOTAL_FITTED_bkg_events_err'] = pdfFittedErrInRegionList
"""
calculate the fitted number of events and propagated error for each requested sample, by splitting off each sample pdf
"""
for isam, sample in enumerate(sampleList):
sampleName = getName(sample)
nSampleInRegionVal = []
nSampleInRegionError = []
sampleInAllRegions = RooArgSet()
for ireg, region in enumerate(regionList):
sampleInRegion = getPdfInRegions(w, sample, region)
sampleInRegionVal = 0.
sampleInRegionError = 0.
if not sampleInRegion == None:
sampleInRegionVal = sampleInRegion.getVal()
sampleInRegionError = Util.GetPropagatedError(
sampleInRegion, resultAfterFit, doAsym)
sampleInAllRegions.add(sampleInRegion)
else:
print " \n YieldsTable.py: WARNING: sample =", sampleName, " non-existent (empty) in region =", region, "\n"
nSampleInRegionVal.append(sampleInRegionVal)
nSampleInRegionError.append(sampleInRegionError)
"""
if splitBins=True calculate numbers of fitted events plus error per bin
"""
if splitBins:
origMin = varinRegionList[ireg].getMin()
origMax = varinRegionList[ireg].getMax()
for ibin in range(0, varNbinsInRegionList[ireg]):
rangeName = rangeNameBinsInRegionList[ireg][ibin]
sampleInRegion = getPdfInRegionsWithRangeName(
w, sample, region, rangeName)
sampleInRegionVal = 0.
sampleInRegionError = 0.
if not sampleInRegion == None:
varinRegionList[ireg].setRange(
rangeName, varBinLowInRegionList[ireg][ibin],
varBinHighInRegionList[ireg][ibin])
sampleInRegionVal = sampleInRegion.getVal()
sampleInRegionError = Util.GetPropagatedError(
sampleInRegion, resultAfterFit, doAsym)
else:
print " \n YieldsTable.py: WARNING: sample =", sampleName, " non-existent (empty) in region=", region, " bin=", ibin, " \n"
nSampleInRegionVal.append(sampleInRegionVal)
nSampleInRegionError.append(sampleInRegionError)
varinRegionList[ireg].setRange(origMin, origMax)
"""
if showSum=True calculate the total number of fitted events in all regions
"""
if showSum:
sampleSumInAllRegions = RooAddition(
(sampleName + "_AllRegions_FITTED"),
(sampleName + "_AllRegions_FITTED"),
RooArgList(sampleInAllRegions))
nSampleSumVal = sampleSumInAllRegions.getVal()
nSampleSumError = Util.GetPropagatedError(sampleSumInAllRegions,
resultAfterFit, doAsym)
nSampleInRegionVal.append(nSampleSumVal)
nSampleInRegionError.append(nSampleSumError)
tablenumbers['Fitted_events_' + sampleName] = nSampleInRegionVal
tablenumbers['Fitted_err_' + sampleName] = nSampleInRegionError
print "\n starting BEFORE-FIT calculations \n"
"""
FROM HERE ON OUT WE CALCULATE THE EXPECTED NUMBER OF EVENTS __BEFORRE__ THE FIT
"""
"""
load beforeFit workspace snapshot (=set all parameters to values before fit)
"""
w.loadSnapshot('snapshot_paramsVals_RooExpandedFitResult_beforeFit')
"""
check if any of the initial scaling factors is != 1
"""
_result = w.obj('RooExpandedFitResult_beforeFit')
_muFacs = _result.floatParsFinal()
for i in range(len(_muFacs)):
if "mu_" in _muFacs[i].GetName() and _muFacs[i].getVal() != 1.0:
print " \n WARNING: scaling factor %s != 1.0 (%g) expected MC yield WILL BE WRONG!" % (
_muFacs[i].GetName(), _muFacs[i].getVal())
"""
get a list of pdf's and variables per region
"""
pdfinRegionList = [Util.GetRegionPdf(w, region) for region in regionList]
varinRegionList = [Util.GetRegionVar(w, region) for region in regionList]
"""
get a list of RooRealSumPdf per region (RooRealSumPdf is the top-pdf per region containing all samples)
"""
rrspdfinRegionList = []
for index, pdf in enumerate(pdfinRegionList):
prodList = pdf.pdfList()
foundRRS = 0
for idx in range(prodList.getSize()):
if prodList[idx].InheritsFrom("RooRealSumPdf"):
rrspdfInt = prodList[idx].createIntegral(
RooArgSet(varinRegionList[index]))
rrspdfinRegionList.append(rrspdfInt)
if splitBins:
origMin = varinRegionList[index].getMin()
origMax = varinRegionList[index].getMax()
for ibin in range(0, varNbinsInRegionList[index]):
rangeName = rangeNameBinsInRegionList[index][ibin]
varinRegionList[index].setRange(
rangeName, varBinLowInRegionList[index][ibin],
varBinHighInRegionList[index][ibin])
rrspdfInt = prodList[idx].createIntegral(
RooArgSet(varinRegionList[index]), rangeName)
rrspdfinRegionList.append(rrspdfInt)
varinRegionList[index].setRange(origMin, origMax)
foundRRS += 1
if foundRRS > 1 or foundRRS == 0:
print " \n\n WARNING: ", pdf.GetName(
), " has ", foundRRS, " instances of RooRealSumPdf"
print pdf.GetName(), " component list:", prodList.Print("v")
"""
calculate total pdf number of expected events and error
"""
nExpInRegionList = [
pdf.getVal() for index, pdf in enumerate(rrspdfinRegionList)
]
pdfExpErrInRegionList = [
Util.GetPropagatedError(pdf, resultBeforeFit, doAsym)
for pdf in rrspdfinRegionList
]
"""
if showSum=True calculate the total number of expected events in all regions
"""
if showSum:
pdfInAllRegions = RooArgSet()
for index, pdf in enumerate(rrspdfinRegionList):
pdfInAllRegions.add(pdf)
pdfSumInAllRegions = RooAddition("pdf_AllRegions_BEFORE",
"pdf_AllRegions_BEFORE",
RooArgList(pdfInAllRegions))
nPdfSumVal = pdfSumInAllRegions.getVal()
nPdfSumError = Util.GetPropagatedError(pdfSumInAllRegions,
resultBeforeFit, doAsym)
nExpInRegionList.append(nPdfSumVal)
pdfExpErrInRegionList.append(nPdfSumError)
tablenumbers['TOTAL_MC_EXP_BKG_events'] = nExpInRegionList
tablenumbers['TOTAL_MC_EXP_BKG_err'] = pdfExpErrInRegionList
"""
calculate the fitted number of events and propagated error for each requested sample, by splitting off each sample pdf
"""
for isam, sample in enumerate(sampleList):
sampleName = getName(sample)
nMCSampleInRegionVal = []
nMCSampleInRegionError = []
MCSampleInAllRegions = RooArgSet()
for ireg, region in enumerate(regionList):
MCSampleInRegion = getPdfInRegions(w, sample, region)
MCSampleInRegionVal = 0.
MCSampleInRegionError = 0.
if not MCSampleInRegion == None:
MCSampleInRegionVal = MCSampleInRegion.getVal()
MCSampleInRegionError = Util.GetPropagatedError(
MCSampleInRegion, resultBeforeFit, doAsym)
MCSampleInAllRegions.add(MCSampleInRegion)
else:
print " \n WARNING: sample=", sampleName, " non-existent (empty) in region=", region
nMCSampleInRegionVal.append(MCSampleInRegionVal)
nMCSampleInRegionError.append(MCSampleInRegionError)
"""
if splitBins=True calculate numbers of fitted events plus error per bin
"""
if splitBins:
origMin = varinRegionList[ireg].getMin()
origMax = varinRegionList[ireg].getMax()
for ibin in range(0, varNbinsInRegionList[ireg]):
rangeName = rangeNameBinsInRegionList[ireg][ibin]
MCSampleInRegion = getPdfInRegionsWithRangeName(
w, sample, region, rangeName)
MCSampleInRegionVal = 0.
MCSampleInRegionError = 0.
if not MCSampleInRegion == None:
varinRegionList[ireg].setRange(
rangeName, varBinLowInRegionList[ireg][ibin],
varBinHighInRegionList[ireg][ibin])
MCSampleInRegionVal = MCSampleInRegion.getVal()
MCSampleInRegionError = Util.GetPropagatedError(
MCSampleInRegion, resultBeforeFit, doAsym)
else:
print " \n YieldsTable.py: WARNING: sample =", sampleName, " non-existent (empty) in region=", region, " bin=", ibin, " \n"
nMCSampleInRegionVal.append(MCSampleInRegionVal)
nMCSampleInRegionError.append(MCSampleInRegionError)
varinRegionList[ireg].setRange(origMin, origMax)
"""
if showSum=True calculate the total number of fitted events in all regions
"""
if showSum:
MCSampleSumInAllRegions = RooAddition(
(sampleName + "_AllRegions_MC"),
(sampleName + "_AllRegions_MC"),
RooArgList(MCSampleInAllRegions))
nMCSampleSumVal = MCSampleSumInAllRegions.getVal()
nMCSampleSumError = Util.GetPropagatedError(
MCSampleSumInAllRegions, resultBeforeFit, doAsym)
nMCSampleInRegionVal.append(nMCSampleSumVal)
nMCSampleInRegionError.append(nMCSampleSumError)
tablenumbers['MC_exp_events_' + sampleName] = nMCSampleInRegionVal
tablenumbers['MC_exp_err_' + sampleName] = nMCSampleInRegionError
"""
sort the tablenumbers set
"""
map_listofkeys = tablenumbers.keys()
map_listofkeys.sort()
"""
print the sorted tablenumbers set
"""
for name in map_listofkeys:
if tablenumbers.has_key(name):
print name, ": ", tablenumbers[name]
return tablenumbers
calculate the systematics breakdown for each channel/region given in chanList
choose whether to use method-1 or method-2
choose whether calculate systematic for full model or just a sample chosen by user
"""
for chan in origChanList:
if not chosenSample:
if method == "2":
regSys = latexfitresults_method2(wsFileName,resultName,chan,'',fitRegionsStr,'obsData',doAsym)
else:
regSys = latexfitresults(wsFileName,namemap,chan,'',resultName,'obsData',doAsym)
chanSys[chan] = regSys
chanList.append(chan)
else:
for sample in sampleList:
sampleName=getName(sample)
if method == "2":
regSys = latexfitresults_method2(wsFileName,resultName,chan,sample,fitRegionsStr,'obsData',doAsym)
else:
regSys = latexfitresults(wsFileName,namemap,chan,sample,resultName,'obsData',doAsym)
chanSys[chan+"_"+sampleName] = regSys
chanList.append(chan+"_"+sampleName)
pass
pass
"""
write out LaTeX table by calling function from SysTableTex.py function tablefragment
"""
line_chanSysTight = tablefragment(chanSys,chanList,skiplist,chanStr,showPercent)
f = open(outputFileName, 'w')
def latexfitresults(filename,regionList,sampleList,dataname='obsData',showSum=False, doAsym=True, blinded=False, splitBins=False):
"""
Calculate before/after-fit yields in all channels given
@param filename The filename containing afterFit workspace
@param regionList A list of regions to be considered
@param sampleList A list of samples to be considered
@param dataname The name of dataset (default='obsData')
@param showSum Calculates sum of all regions if set to true (default=False)
@param doAsym Calculates asymmetric errors taken from MINOS (default=True)
@param blinded Observed event count will not be shown if set to True (default=False)
@param splitBins Calculates bin-by-bin yields for all regions if set to True (default=False)
"""
"""
pick up workspace from file
"""
workspacename = 'w'
w = Util.GetWorkspaceFromFile(filename,'w')
if w==None:
print "ERROR : Cannot open workspace : ", workspacename
sys.exit(1)
"""
pick up after-fit RooExpandedFitResult from workspace
"""
resultAfterFit = w.obj('RooExpandedFitResult_afterFit')
if resultAfterFit==None:
print "ERROR : Cannot open fit result after fit RooExpandedFitResult_afterFit"
sys.exit(1)
"""
pick up before-fit RooExpandedFitResult from workspace
"""
resultBeforeFit = w.obj('RooExpandedFitResult_beforeFit')
if resultBeforeFit==None:
print "ERROR : Cannot open fit result before fit RooExpandedFitResult_beforeFit"
sys.exit(1)
"""
pick up dataset from workspace
"""
data_set = w.data(dataname)
if data_set==None:
print "ERROR : Cannot open dataset : ", "data_set"+suffix
sys.exit(1)
"""
pick up channel category (RooCategory) from workspace
"""
regionCat = w.obj("channelCat")
if not blinded:
data_set.table(regionCat).Print("v")
"""
find full (long) name list of regions (i.e. short=SR3J, long=SR3J_meffInc30_JVF25pt50)
"""
regionFullNameList = [ Util.GetFullRegionName(regionCat, region) for region in regionList]
"""
load afterFit workspace snapshot (=set all parameters to values after fit)
"""
snapshot = 'snapshot_paramsVals_RooExpandedFitResult_afterFit'
w.loadSnapshot(snapshot)
if not w.loadSnapshot(snapshot):
print "ERROR : Cannot load snapshot : ", snapshot
sys.exit(1)
"""
define set, for all names/yields to be saved in
"""
tablenumbers = {}
"""
if showSum=True define names for sum of all regions and add to regionList
"""
sumName = ""
for index, reg in enumerate(regionList):
if index == 0:
sumName = reg
else:
sumName = sumName + " + " + reg
regionListWithSum = list(regionList)
if showSum:
regionListWithSum.append(sumName)
tablenumbers['names'] = regionListWithSum
"""
make a list of channelCat calls for every region
"""
regionCatList = [ 'channelCat==channelCat::' +region.Data() for region in regionFullNameList]
"""
retrieve number of observed (=data) events per region
"""
print regionCatList
srindex = regionFullNameList.index("SR_cuts")
regionDatasetList = [data_set.reduce(regioncat) for regioncat in regionCatList]
for index, data in enumerate(regionDatasetList):
print "data," , data, data.GetName()
data.SetName("data_" + regionList[index])
data.SetTitle("data_" + regionList[index])
nobs_regionList = [ data.sumEntries() for data in regionDatasetList]
if blinded : nobs_regionList[srindex] = -1
"""
if showSum=True calculate the total number of observed events in all regions
"""
sumNobs = 0.
for nobs in nobs_regionList:
sumNobs += nobs
if showSum:
nobs_regionList.append(sumNobs)
tablenumbers['nobs'] = nobs_regionList
"""
FROM HERE ON OUT WE CALCULATE THE FITTED NUMBER OF EVENTS __AFTER__ THE FIT
"""
"""
get a list of pdf's and variables per region
"""
pdfinRegionList = [ Util.GetRegionPdf(w, region) for region in regionList]
varinRegionList = [ Util.GetRegionVar(w, region) for region in regionList]
"""
if splitBins=True get the list of Nbins, binMax and binMin; make a list of new region names for each bin
"""
varNbinsInRegionList = []
varBinLowInRegionList = []
varBinHighInRegionList = []
rangeNameBinsInRegionList = []
if splitBins:
varNbinsInRegionList = [Util.GetRegionVar(w, region).getBinning().numBins() for region in regionList]
varBinLowInRegionList = [[Util.GetRegionVar(w, region).getBinning((region+"binning")).binLow(ibin) for ibin in range(0, varNbinsInRegionList[idx]) ] for idx,region in enumerate(regionList)]
varBinHighInRegionList = [[Util.GetRegionVar(w, region).getBinning((region+"binning")).binHigh(ibin) for ibin in range(0, varNbinsInRegionList[idx]) ] for idx,region in enumerate(regionList)]
rangeNameBinsInRegionList = [[regionList[idx]+"_bin"+str(ibin) for ibin in range(0, varNbinsInRegionList[idx]) ] for idx,region in enumerate(regionList)]
for index,region in enumerate(regionList):
if varNbinsInRegionList[index]==1:
print " \n YieldsTable.py: WARNING: you have called -P (= per-bin yields) but this region ", region, " has only 1 bin \n"
"""
if splitBins=True reshuffle the regionName list; each region name is followed by names of each bin (i.e. regionNameList=['SR3J','SR3J_bin1','SR3j_bin2','SR4J','SR4J_bin1'])
"""
regionListWithBins = []
if splitBins:
for index,region in enumerate(regionList):
regionListWithBins.append(region)
for ibin in range(0,varNbinsInRegionList[index]):
regionListWithBins.append(rangeNameBinsInRegionList[index][ibin])
tablenumbers['names'] = regionListWithBins
"""
calculate number of observed(=data) events per bin
"""
nobs_regionListWithBins = []
if splitBins:
binFuncInRegionList = [RooBinningCategory("bin_"+region,"bin_"+region,varinRegionList[index]) for index,region in enumerate(regionList)]
for index, data in enumerate(regionDatasetList):
data.addColumn(binFuncInRegionList[index])
if not blinded:
data.table(binFuncInRegionList[index]).Print("v")
nobs_regionListWithBins.append(data.sumEntries())
for ibin in range(0,varNbinsInRegionList[index]):
nobs_regionListWithBins.append((data.reduce(binFuncInRegionList[index].GetName()+"=="+binFuncInRegionList[index].GetName()+"::"+varinRegionList[index].GetName()+"_bin"+str(ibin))).sumEntries())
tablenumbers['nobs'] = nobs_regionListWithBins
"""
if blinded=True, set all numbers of observed events to -1
"""
if blinded and splitBins:
for index, nobs in enumerate(nobs_regionListWithBins):
nobs_regionListWithBins[index] = -1
tablenumbers['nobs'] = nobs_regionListWithBins
"""
get a list of RooRealSumPdf per region (RooRealSumPdf is the top-pdf per region containing all samples)
"""
rrspdfinRegionList = []
for index,pdf in enumerate(pdfinRegionList):
prodList = pdf.pdfList()
foundRRS = 0
for idx in range(prodList.getSize()):
if prodList[idx].InheritsFrom("RooRealSumPdf"):
rrspdfInt = prodList[idx].createIntegral(RooArgSet(varinRegionList[index]))
rrspdfinRegionList.append(rrspdfInt)
if splitBins:
origMin = varinRegionList[index].getMin()
origMax = varinRegionList[index].getMax()
for ibin in range(0,varNbinsInRegionList[index]):
rangeName = rangeNameBinsInRegionList[index][ibin]
varinRegionList[index].setRange(rangeName,varBinLowInRegionList[index][ibin],varBinHighInRegionList[index][ibin])
rrspdfInt = prodList[idx].createIntegral(RooArgSet(varinRegionList[index]),rangeName)
rrspdfinRegionList.append(rrspdfInt)
varinRegionList[index].setRange(origMin,origMax)
foundRRS += 1
if foundRRS >1 or foundRRS==0:
print " \n\n WARNING: ", pdf.GetName(), " has ", foundRRS, " instances of RooRealSumPdf"
print pdf.GetName(), " component list:", prodList.Print("v")
"""
calculate total pdf number of fitted events and error
"""
nFittedInRegionList = [ pdf.getVal() for index, pdf in enumerate(rrspdfinRegionList)]
pdfFittedErrInRegionList = [ Util.GetPropagatedError(pdf, resultAfterFit, doAsym) for pdf in rrspdfinRegionList]
"""
if showSum=True calculate the total number of fitted events in all regions
"""
if showSum:
pdfInAllRegions = RooArgSet()
for index, pdf in enumerate(rrspdfinRegionList):
pdfInAllRegions.add(pdf)
pdfSumInAllRegions = RooAddition( "pdf_AllRegions_AFTER", "pdf_AllRegions_AFTER", RooArgList(pdfInAllRegions))
nPdfSumVal = pdfSumInAllRegions.getVal()
nPdfSumError = Util.GetPropagatedError(pdfSumInAllRegions, resultAfterFit, doAsym)
nFittedInRegionList.append(nPdfSumVal)
pdfFittedErrInRegionList.append(nPdfSumError)
tablenumbers['TOTAL_FITTED_bkg_events'] = nFittedInRegionList
tablenumbers['TOTAL_FITTED_bkg_events_err'] = pdfFittedErrInRegionList
"""
calculate the fitted number of events and propagated error for each requested sample, by splitting off each sample pdf
"""
for isam, sample in enumerate(sampleList):
sampleName=getName(sample)
nSampleInRegionVal = []
nSampleInRegionError = []
sampleInAllRegions = RooArgSet()
for ireg, region in enumerate(regionList):
sampleInRegion=getPdfInRegions(w,sample,region)
sampleInRegionVal = 0.
sampleInRegionError = 0.
if not sampleInRegion==None:
sampleInRegionVal = sampleInRegion.getVal()
sampleInRegionError = Util.GetPropagatedError(sampleInRegion, resultAfterFit, doAsym)
sampleInAllRegions.add(sampleInRegion)
else:
print " \n YieldsTable.py: WARNING: sample =", sampleName, " non-existent (empty) in region =",region, "\n"
nSampleInRegionVal.append(sampleInRegionVal)
nSampleInRegionError.append(sampleInRegionError)
"""
if splitBins=True calculate numbers of fitted events plus error per bin
"""
if splitBins:
origMin = varinRegionList[ireg].getMin()
origMax = varinRegionList[ireg].getMax()
for ibin in range(0,varNbinsInRegionList[ireg]):
rangeName = rangeNameBinsInRegionList[ireg][ibin]
sampleInRegion=getPdfInRegionsWithRangeName(w,sample,region,rangeName)
sampleInRegionVal = 0.
sampleInRegionError = 0.
if not sampleInRegion==None:
varinRegionList[ireg].setRange(rangeName,varBinLowInRegionList[ireg][ibin],varBinHighInRegionList[ireg][ibin])
sampleInRegionVal = sampleInRegion.getVal()
sampleInRegionError = Util.GetPropagatedError(sampleInRegion, resultAfterFit, doAsym)
else:
print " \n YieldsTable.py: WARNING: sample =", sampleName, " non-existent (empty) in region=",region, " bin=",ibin, " \n"
nSampleInRegionVal.append(sampleInRegionVal)
nSampleInRegionError.append(sampleInRegionError)
varinRegionList[ireg].setRange(origMin,origMax)
"""
if showSum=True calculate the total number of fitted events in all regions
"""
if showSum:
sampleSumInAllRegions = RooAddition( (sampleName+"_AllRegions_FITTED"), (sampleName+"_AllRegions_FITTED"), RooArgList(sampleInAllRegions))
nSampleSumVal = sampleSumInAllRegions.getVal()
nSampleSumError = Util.GetPropagatedError(sampleSumInAllRegions, resultAfterFit, doAsym)
nSampleInRegionVal.append(nSampleSumVal)
nSampleInRegionError.append(nSampleSumError)
tablenumbers['Fitted_events_'+sampleName] = nSampleInRegionVal
tablenumbers['Fitted_err_'+sampleName] = nSampleInRegionError
print "\n starting BEFORE-FIT calculations \n"
"""
FROM HERE ON OUT WE CALCULATE THE EXPECTED NUMBER OF EVENTS __BEFORRE__ THE FIT
"""
"""
load beforeFit workspace snapshot (=set all parameters to values before fit)
"""
w.loadSnapshot('snapshot_paramsVals_RooExpandedFitResult_beforeFit')
"""
check if any of the initial scaling factors is != 1
"""
_result = w.obj('RooExpandedFitResult_beforeFit')
_muFacs = _result.floatParsFinal()
for i in range(len(_muFacs)):
if "mu_" in _muFacs[i].GetName() and _muFacs[i].getVal() != 1.0:
print " \n WARNING: scaling factor %s != 1.0 (%g) expected MC yield WILL BE WRONG!" % (_muFacs[i].GetName(), _muFacs[i].getVal())
"""
get a list of pdf's and variables per region
"""
pdfinRegionList = [ Util.GetRegionPdf(w, region) for region in regionList]
varinRegionList = [ Util.GetRegionVar(w, region) for region in regionList]
"""
get a list of RooRealSumPdf per region (RooRealSumPdf is the top-pdf per region containing all samples)
"""
rrspdfinRegionList = []
for index,pdf in enumerate(pdfinRegionList):
prodList = pdf.pdfList()
foundRRS = 0
for idx in range(prodList.getSize()):
if prodList[idx].InheritsFrom("RooRealSumPdf"):
rrspdfInt = prodList[idx].createIntegral(RooArgSet(varinRegionList[index]))
rrspdfinRegionList.append(rrspdfInt)
if splitBins:
origMin = varinRegionList[index].getMin()
origMax = varinRegionList[index].getMax()
for ibin in range(0,varNbinsInRegionList[index]):
rangeName = rangeNameBinsInRegionList[index][ibin]
varinRegionList[index].setRange(rangeName,varBinLowInRegionList[index][ibin],varBinHighInRegionList[index][ibin])
rrspdfInt = prodList[idx].createIntegral(RooArgSet(varinRegionList[index]),rangeName)
rrspdfinRegionList.append(rrspdfInt)
varinRegionList[index].setRange(origMin,origMax)
foundRRS += 1
if foundRRS >1 or foundRRS==0:
print " \n\n WARNING: ", pdf.GetName(), " has ", foundRRS, " instances of RooRealSumPdf"
print pdf.GetName(), " component list:", prodList.Print("v")
"""
calculate total pdf number of expected events and error
"""
nExpInRegionList = [ pdf.getVal() for index, pdf in enumerate(rrspdfinRegionList)]
pdfExpErrInRegionList = [ Util.GetPropagatedError(pdf, resultBeforeFit, doAsym) for pdf in rrspdfinRegionList]
"""
if showSum=True calculate the total number of expected events in all regions
"""
if showSum:
pdfInAllRegions = RooArgSet()
for index, pdf in enumerate(rrspdfinRegionList):
pdfInAllRegions.add(pdf)
pdfSumInAllRegions = RooAddition( "pdf_AllRegions_BEFORE", "pdf_AllRegions_BEFORE", RooArgList(pdfInAllRegions))
nPdfSumVal = pdfSumInAllRegions.getVal()
nPdfSumError = Util.GetPropagatedError(pdfSumInAllRegions, resultBeforeFit, doAsym)
nExpInRegionList.append(nPdfSumVal)
pdfExpErrInRegionList.append(nPdfSumError)
tablenumbers['TOTAL_MC_EXP_BKG_events'] = nExpInRegionList
tablenumbers['TOTAL_MC_EXP_BKG_err'] = pdfExpErrInRegionList
"""
calculate the fitted number of events and propagated error for each requested sample, by splitting off each sample pdf
"""
for isam, sample in enumerate(sampleList):
sampleName=getName(sample)
nMCSampleInRegionVal = []
nMCSampleInRegionError = []
MCSampleInAllRegions = RooArgSet()
for ireg, region in enumerate(regionList):
MCSampleInRegion = getPdfInRegions(w,sample,region)
MCSampleInRegionVal = 0.
MCSampleInRegionError = 0.
if not MCSampleInRegion==None:
MCSampleInRegionVal = MCSampleInRegion.getVal()
MCSampleInRegionError = Util.GetPropagatedError(MCSampleInRegion, resultBeforeFit, doAsym)
MCSampleInAllRegions.add(MCSampleInRegion)
else:
print " \n WARNING: sample=", sampleName, " non-existent (empty) in region=",region
nMCSampleInRegionVal.append(MCSampleInRegionVal)
nMCSampleInRegionError.append(MCSampleInRegionError)
"""
if splitBins=True calculate numbers of fitted events plus error per bin
"""
if splitBins:
origMin = varinRegionList[ireg].getMin()
origMax = varinRegionList[ireg].getMax()
for ibin in range(0,varNbinsInRegionList[ireg]):
rangeName = rangeNameBinsInRegionList[ireg][ibin]
MCSampleInRegion=getPdfInRegionsWithRangeName(w,sample,region,rangeName)
MCSampleInRegionVal = 0.
MCSampleInRegionError = 0.
if not MCSampleInRegion==None:
varinRegionList[ireg].setRange(rangeName,varBinLowInRegionList[ireg][ibin],varBinHighInRegionList[ireg][ibin])
MCSampleInRegionVal = MCSampleInRegion.getVal()
MCSampleInRegionError = Util.GetPropagatedError(MCSampleInRegion, resultBeforeFit, doAsym)
else:
print " \n YieldsTable.py: WARNING: sample =", sampleName, " non-existent (empty) in region=",region, " bin=",ibin, " \n"
nMCSampleInRegionVal.append(MCSampleInRegionVal)
nMCSampleInRegionError.append(MCSampleInRegionError)
varinRegionList[ireg].setRange(origMin,origMax)
"""
if showSum=True calculate the total number of fitted events in all regions
"""
if showSum:
MCSampleSumInAllRegions = RooAddition( (sampleName+"_AllRegions_MC"), (sampleName+"_AllRegions_MC"), RooArgList(MCSampleInAllRegions))
nMCSampleSumVal = MCSampleSumInAllRegions.getVal()
nMCSampleSumError = Util.GetPropagatedError(MCSampleSumInAllRegions, resultBeforeFit, doAsym)
nMCSampleInRegionVal.append(nMCSampleSumVal)
nMCSampleInRegionError.append(nMCSampleSumError)
tablenumbers['MC_exp_events_'+sampleName] = nMCSampleInRegionVal
tablenumbers['MC_exp_err_'+sampleName] = nMCSampleInRegionError
"""
sort the tablenumbers set
"""
map_listofkeys = tablenumbers.keys()
map_listofkeys.sort()
"""
print the sorted tablenumbers set
"""
for name in map_listofkeys:
if tablenumbers.has_key(name) :
print name, ": ", tablenumbers[name]
return tablenumbers
f = open(wsFileName, 'r')
m3 = pickle.load(f)
f.close()
else:
m3 = latexfitresults(wsFileName, chanList, sampleList, dataname,
showSumAllRegions, doAsym, blinded, splitBins)
f = open(outputFileName.replace(".tex", ".pickle"), 'w')
pickle.dump(m3, f)
f.close()
"""
when multiple samples to be evaluated together (for example [SingleTop,ttbarV] when calling with -s 'TTbar,[SingleTop,ttbarV]')
the names need to re-specified, as a set of samples produces only one number
"""
sampleList_decoded = []
for isam, sample in enumerate(sampleList):
sampleName = getName(sample)
sampleList_decoded.append(sampleName)
"""
write out LaTeX table by calling function from YieldsTableTex.py
"""
f = open(outputFileName, 'w')
f.write(tablestart())
f.write(
tablefragment(m3, tableName, regionsList_2Digits, sampleList_decoded,
showBeforeFitError))
if tableCaption != "" or tableLabel != "":
f.write(tableEndWithCaptionAndLabel(tableCaption, tableLabel))
else:
f.write(tableend(userString, tableName))
f.close()
print "\nResult written in:"
def systable(workspace, samples, channels, output_name):
chan_str = channels.replace(",","_")
chan_list = channels.split(",")
chosen_sample = False
if samples:
sample_str = samples.replace(",","_") + "_"
from cmdLineUtils import cmdStringToListOfLists
sample_list = cmdStringToListOfLists(samples)
chosen_sample = True
show_percent = True
doAsym = True
result_name = 'RooExpandedFitResult_afterFit'
skip_list = ['sqrtnobsa', 'totbkgsysa', 'poisqcderr','sqrtnfitted','totsyserr','nfitted']
chan_sys = {}
orig_chan_list = list(chan_list)
chan_list = []
# calculate the systematics breakdown for each channel/region given in chanList
# choose whether to use method-1 or method-2
# choose whether calculate systematic for full model or just a sample chosen by user
for chan in orig_chan_list:
if not chosen_sample:
reg_sys = latexfitresults(workspace, chan, '', result_name, 'obsData', doAsym)
chan_sys[chan] = reg_sys
chan_list.append(chan)
else:
for sample in sample_list:
sample_name = getName(sample)
reg_sys = latexfitresults(workspace, chan, sample, result_name, 'obsData', doAsym)
chan_sys[chan+"_"+sample_name] = reg_sys
chan_list.append(chan+"_"+sample_name)
# write out LaTeX table by calling function from SysTableTex.py function tablefragment
#line_chan_sys_tight = tablefragment(chanSys,chanList,skiplist,chanStr,showPercent)
if not chosen_sample:
field_names = ['\\textbf{Uncertainties}',] + [ '\\textbf{%s}' % reg for reg in chan_list ]
elif len(sample_list) == 1:
sample_label = labels_latex_dict.get(getName(sample_list[0]), getName(sample_list[0]))
field_names = ['\\textbf{Uncertainties (%s)}' % sample_label ] + [ '\\textbf{%s}' % (reg.split('_')[0]) for reg in chan_list ]
else:
field_names = ['\\textbf{Uncertainties}',] + [ '\\textbf{%s (%s)}' % (reg.split('_')[0], reg.split('_')[1]) for reg in chan_list ]
align = ['l',] + [ 'r' for i in chan_list ]
tablel = LatexTable(field_names, align=align, env=True)
# print the total fitted (after fit) number of events
row = ['Total background expectation',]
for region in chan_list:
row.append("$%.2f$" % chan_sys[region]['nfitted'])
tablel.add_row(row)
tablel.add_line()
# print sqrt(N_obs) - for comparison with total systematic
row = ['Total statistical $(\\sqrt{N_\\mathrm{exp}})$',]
for region in chan_list:
row.append("$\\pm %.2f$" % chan_sys[region]['sqrtnfitted'])
tablel.add_row(row)
# print total systematic uncertainty
row = [ 'Total background systematic', ]
for region in chan_list:
percentage = chan_sys[region]['totsyserr']/chan_sys[region]['nfitted'] * 100.0
row.append("$\\pm %.2f\ [%.2f\%%]$" % (chan_sys[region]['totsyserr'], percentage))
tablel.add_row(row)
tablel.add_line()
tablel.add_line()
# print systematic uncertainty per floated parameter (or set of parameters, if requested)
d = chan_sys[chan_list[0]]
m_listofkeys = sorted(d.iterkeys(), key=lambda k: d[k], reverse=True)
# uncertanties dict
unc_dict = dict()
unc_order = []
for name in m_listofkeys:
if name in skip_list:
continue
printname = name.replace('syserr_','')
#slabel = label.split('_')
#label = 'MC stat. (%s)' % slabel[2]
# skip negligible uncertainties in all requested regions:
zero = True
for index, region in enumerate(chan_list):
percentage = chan_sys[region][name]/chan_sys[region]['nfitted'] * 100.0
if ('%.4f' % chan_sys[region][name]) != '0.0000' and ('%.2f' % percentage) != '0.00':
zero = False
if zero:
continue
# Parameter name -> parameter label
if printname.startswith('gamma_stat'):
label = 'MC stat.'
elif printname.startswith('gamma_shape_JFAKE_STAT_jfake'):
label = 'jet $\\to\\gamma$ fakes stat.'
elif printname.startswith('gamma_shape_EFAKE_STAT_efake'):
label = '$e\\to\\gamma$ fakes stat.'
else:
if printname in systdict and systdict[printname]:
label = systdict[printname]
else:
label = printname
# Fill dict
for index, region in enumerate(chan_list):
if printname.startswith('gamma') and not region.split('_')[0] in printname:
continue
if not label in unc_dict:
unc_dict[label] = []
unc_order.append(label)
if not show_percent:
unc_dict[label].append("$\\pm %.2f$" % chan_sys[region][name])
else:
percentage = chan_sys[region][name]/chan_sys[region]['nfitted'] * 100.0
if percentage < 1:
unc_dict[label].append("$\\pm %.2f\ [%.2f\%%]$" % (chan_sys[region][name], percentage))
else:
unc_dict[label].append("$\\pm %.2f\ [%.1f\%%]$" % (chan_sys[region][name], percentage))
# fill table
for label in unc_order:
tablel.add_row([label,] + unc_dict[label])
tablel.add_line()
tablel.save_tex(output_name)
def latexfitresults(filename, region_list, sample_list):
f = ROOT.TFile.Open(filename)
w = f.Get('w')
if w is None:
print "ERROR : Cannot open workspace : w"
sys.exit(1)
resultAfterFit = w.obj('RooExpandedFitResult_afterFit')
if resultAfterFit is None:
print "ERROR : Cannot open fit result after fit RooExpandedFitResult_afterFit"
sys.exit(1)
resultBeforeFit = w.obj('RooExpandedFitResult_beforeFit')
if resultBeforeFit is None:
print "ERROR : Cannot open fit result before fit RooExpandedFitResult_beforeFit"
sys.exit(1)
# pick up dataset from workspace
data_set = w.data('obsData')
# pick up channel category (RooCategory) from workspace
regionCat = w.obj("channelCat")
# if not blinded:
# data_set.table(regionCat).Print("v")
# find full (long) name list of regions (i.e. short=SR3J, long=SR3J_meffInc30_JVF25pt50)
regionFullNameList = [ Util.GetFullRegionName(regionCat, region) for region in region_list ]
# load afterFit workspace snapshot (=set all parameters to values after fit)
snapshot = 'snapshot_paramsVals_RooExpandedFitResult_afterFit'
w.loadSnapshot(snapshot)
if not w.loadSnapshot(snapshot):
print "ERROR : Cannot load snapshot : ", snapshot
sys.exit(1)
# define set, for all names/yields to be saved in
tablenumbers = {}
tablenumbers['names'] = region_list
# make a list of channelCat calls for every region
regionCatList = [ 'channelCat==channelCat::' + region.Data() for region in regionFullNameList]
# retrieve number of observed (=data) events per region
regionDatasetList = [data_set.reduce(regioncat) for regioncat in regionCatList]
for index, data in enumerate(regionDatasetList):
data.SetName("data_" + region_list[index])
data.SetTitle("data_" + region_list[index])
nobs_regionList = [ data.sumEntries() for data in regionDatasetList]
tablenumbers['nobs'] = nobs_regionList
# FROM HERE ON OUT WE CALCULATE THE FITTED NUMBER OF EVENTS __AFTER__ THE FIT
#get a list of pdf's and variables per region
pdfinRegionList = [ Util.GetRegionPdf(w, region) for region in region_list]
varinRegionList = [ Util.GetRegionVar(w, region) for region in region_list]
# if splitBins=True get the list of Nbins, binMax and binMin; make a list of new region names for each bin
varNbinsInRegionList = []
varBinLowInRegionList = []
varBinHighInRegionList = []
rangeNameBinsInRegionList = []
# if blinded=True, set all numbers of observed events to -1
# if blinded:
# for index, nobs in enumerate(nobs_regionListWithBins):
# nobs_regionListWithBins[index] = -1
# tablenumbers['nobs'] = nobs_regionListWithBins
# get a list of RooRealSumPdf per region (RooRealSumPdf is the top-pdf per region containing all samples)
rrspdfinRegionList = []
for index, pdf in enumerate(pdfinRegionList):
if not pdf:
print "WARNING: pdf is NULL for index {0}".format(index)
continue
prodList = pdf.pdfList()
foundRRS = 0
for idx in range(prodList.getSize()):
if prodList[idx].InheritsFrom("RooRealSumPdf"):
rrspdfInt = prodList[idx].createIntegral(ROOT.RooArgSet(varinRegionList[index]))
rrspdfinRegionList.append(rrspdfInt)
foundRRS += 1
if foundRRS >1 or foundRRS==0:
print " \n\n WARNING: ", pdf.GetName(), " has ", foundRRS, " instances of RooRealSumPdf"
print pdf.GetName(), " component list:", prodList.Print("v")
# calculate total pdf number of fitted events and error
nFittedInRegionList = [ pdf.getVal() for index, pdf in enumerate(rrspdfinRegionList)]
pdfFittedErrInRegionList = [ Util.GetPropagatedError(pdf, resultAfterFit, True) for pdf in rrspdfinRegionList]
tablenumbers['TOTAL_FITTED_bkg_events'] = nFittedInRegionList
tablenumbers['TOTAL_FITTED_bkg_events_err'] = pdfFittedErrInRegionList
# calculate the fitted number of events and propagated error for each requested sample, by splitting off each sample pdf
for isam, sample in enumerate(sample_list):
sampleName = getName(sample)
nSampleInRegionVal = []
nSampleInRegionError = []
sampleInAllRegions = ROOT.RooArgSet()
for ireg, region in enumerate(region_list):
sampleInRegion = getPdfInRegions(w, sample, region)
sampleInRegionVal = 0.
sampleInRegionError = 0.
try: ##if sampleInRegion is not None:
sampleInRegionVal = sampleInRegion.getVal()
sampleInRegionError = Util.GetPropagatedError(sampleInRegion, resultAfterFit, True)
sampleInAllRegions.add(sampleInRegion)
except:
print " \n YieldsTable.py: WARNING: sample =", sampleName, " non-existent (empty) in region =", region, "\n"
nSampleInRegionVal.append(sampleInRegionVal)
nSampleInRegionError.append(sampleInRegionError)
tablenumbers['Fitted_events_'+sampleName] = nSampleInRegionVal
tablenumbers['Fitted_err_'+sampleName] = nSampleInRegionError
# FROM HERE ON OUT WE CALCULATE THE EXPECTED NUMBER OF EVENTS __BEFORRE__ THE FIT
#load beforeFit workspace snapshot (=set all parameters to values before fit)
w.loadSnapshot('snapshot_paramsVals_RooExpandedFitResult_beforeFit')
# check if any of the initial scaling factors is != 1
_result = w.obj('RooExpandedFitResult_beforeFit')
_muFacs = _result.floatParsFinal()
for i in xrange(len(_muFacs)):
if "mu_" in _muFacs[i].GetName() and _muFacs[i].getVal() != 1.0:
print " \n WARNING: scaling factor %s != 1.0 (%g) expected MC yield WILL BE WRONG!" % (_muFacs[i].GetName(), _muFacs[i].getVal())
# get a list of pdf's and variables per region
pdfinRegionList = [ Util.GetRegionPdf(w, region) for region in region_list]
varinRegionList = [ Util.GetRegionVar(w, region) for region in region_list]
# get a list of RooRealSumPdf per region (RooRealSumPdf is the top-pdf per region containing all samples)
rrspdfinRegionList = []
for index,pdf in enumerate(pdfinRegionList):
if not pdf:
print "WARNING: pdf is NULL for index {0}".format(index)
continue
prodList = pdf.pdfList()
foundRRS = 0
for idx in range(prodList.getSize()):
if prodList[idx].InheritsFrom("RooRealSumPdf"):
rrspdfInt = prodList[idx].createIntegral(ROOT.RooArgSet(varinRegionList[index]))
rrspdfinRegionList.append(rrspdfInt)
foundRRS += 1
if foundRRS >1 or foundRRS==0:
print " \n\n WARNING: ", pdf.GetName(), " has ", foundRRS, " instances of RooRealSumPdf"
print pdf.GetName(), " component list:", prodList.Print("v")
# calculate total pdf number of expected events and error
nExpInRegionList = [ pdf.getVal() for index, pdf in enumerate(rrspdfinRegionList)]
pdfExpErrInRegionList = [ Util.GetPropagatedError(pdf, resultBeforeFit, True) for pdf in rrspdfinRegionList]
tablenumbers['TOTAL_MC_EXP_BKG_events'] = nExpInRegionList
tablenumbers['TOTAL_MC_EXP_BKG_err'] = pdfExpErrInRegionList
# calculate the fitted number of events and propagated error for each requested sample, by splitting off each sample pdf
for isam, sample in enumerate(sample_list):
sampleName = getName(sample)
nMCSampleInRegionVal = []
nMCSampleInRegionError = []
MCSampleInAllRegions = ROOT.RooArgSet()
for ireg, region in enumerate(region_list):
MCSampleInRegion = getPdfInRegions(w,sample,region)
MCSampleInRegionVal = 0.
MCSampleInRegionError = 0.
try:
MCSampleInRegionVal = MCSampleInRegion.getVal()
MCSampleInRegionError = Util.GetPropagatedError(MCSampleInRegion, resultBeforeFit, True)
MCSampleInAllRegions.add(MCSampleInRegion)
except:
print " \n WARNING: sample=", sampleName, " non-existent (empty) in region=",region
nMCSampleInRegionVal.append(MCSampleInRegionVal)
nMCSampleInRegionError.append(MCSampleInRegionError)
tablenumbers['MC_exp_events_'+sampleName] = nMCSampleInRegionVal
tablenumbers['MC_exp_err_'+sampleName] = nMCSampleInRegionError
# sort the tablenumbers set
map_listofkeys = tablenumbers.keys()
map_listofkeys.sort()
return tablenumbers
def yieldstable(workspace, samples, channels, output_name, table_name, is_cr=False, show_before_fit=False, unblind=True):
if is_cr:
show_before_fit=True
normalization_factors = get_normalization_factors(workspace)
#sample_str = samples.replace(",","_")
from cmdLineUtils import cmdStringToListOfLists
samples_list = cmdStringToListOfLists(samples)
regions_list = [ '%s_cuts' % r for r in channels.split(",") ]
#samples_list = samples.split(",")
# call the function to calculate the numbers, or take numbers from pickle file
if workspace.endswith(".pickle"):
print "READING PICKLE FILE"
f = open(workspace, 'r')
m = pickle.load(f)
f.close()
else:
#m = YieldsTable.latexfitresults(workspace, regions_list, samples_list, 'obsData')
m = latexfitresults(workspace, regions_list, samples_list)
with open(output_name.replace('.tex', '.pickle'), 'w') as f:
pickle.dump(m, f)
regions_names = [ region.replace("_cuts", "").replace('_','\_') for region in m['names'] ]
field_names = [table_name,] + regions_names
align = ['l',] + [ 'r' for i in regions_names ]
samples_list_decoded = []
for isam, sample in enumerate(samples_list):
sampleName = getName(sample)
samples_list_decoded.append(sampleName)
samples_list = samples_list_decoded
tablel = LatexTable(field_names, align=align, env=True)
tablep = PrettyTable(field_names, align=align)
# number of observed events
if unblind:
row = ['Observed events',] + [ '%d' % n for n in m['nobs'] ]
else:
row = ['Observed events',] + [ '-' for n in m['nobs'] ]
tablel.add_row(row)
tablep.add_row(row)
tablel.add_line()
tablep.add_line()
#print the total fitted (after fit) number of events
# if the N_fit - N_error extends below 0, make the error physical , meaning extend to 0
rowl = ['Expected SM events', ]
rowp = ['Expected SM events', ]
for index, n in enumerate(m['TOTAL_FITTED_bkg_events']):
if (n - m['TOTAL_FITTED_bkg_events_err'][index]) > 0. :
rowl.append('$%.2f \pm %.2f$' % (n, m['TOTAL_FITTED_bkg_events_err'][index]))
rowp.append('%.2f ± %.2f' % (n, m['TOTAL_FITTED_bkg_events_err'][index]))
else:
#print "WARNING: negative symmetric error after fit extends below 0. for total bkg pdf: will print asymmetric error w/ truncated negative error reaching to 0."
rowl.append('$%.2f_{-%.2f}^{+%.2f}$' % (n, n, m['TOTAL_FITTED_bkg_events_err'][index]))
rowp.append('%.2f -%.2f +%.2f' % (n, n, m['TOTAL_FITTED_bkg_events_err'][index]))
tablel.add_row(rowl)
tablel.add_line()
tablep.add_row(rowp)
tablep.add_line()
map_listofkeys = m.keys()
# print fitted number of events per sample
# if the N_fit - N_error extends below 0, make the error physical , meaning extend to 0
for sample in samples_list:
for name in map_listofkeys:
rowl = []
rowp = []
if not "Fitted_events_" in name:
continue
sample_name = name.replace("Fitted_events_", "")
if sample_name != sample:
continue
rowl.append('%s' % labels_latex_dict.get(sample_name, sample_name).replace('_', '\_'))
rowp.append('%s' % labels_html_dict.get(sample_name, sample_name))
for index, n in enumerate(m[name]):
if ((n - m['Fitted_err_'+sample][index]) > 0.) or not abs(n) > 0.00001:
rowl.append('$%.2f \\pm %.2f$' % (n, m['Fitted_err_'+sample][index]))
rowp.append('%.2f ± %.2f' % (n, m['Fitted_err_'+sample][index]))
else:
#print "WARNING: negative symmetric error after fit extends below 0. for sample", sample, " will print asymmetric error w/ truncated negative error reaching to 0."
rowl.append('$%.2f_{-%.2f}^{+%.2f}$' % (n, n, m['Fitted_err_'+sample][index]))
rowp.append('%.2f -%.2f +%.2f' % (n, n, m['Fitted_err_'+sample][index]))
tablel.add_row(rowl)
tablep.add_row(rowp)
tablel.add_line()
tablep.add_line()
# print the total expected (before fit) number of events
if show_before_fit:
# if the N_fit - N_error extends below 0, make the error physical , meaning extend to 0
rowl = ['Before SM events',]
rowp = ['(before fit) SM events',]
total_before = []
purity_before = []
for index, n in enumerate(m['TOTAL_MC_EXP_BKG_events']):
if regions_names[index].startswith('CR'):
total_before.append(n)
rowl.append('$%.2f$' % n)
rowp.append('%.2f' % n)
tablel.add_row(rowl)
tablel.add_line()
tablep.add_row(rowp)
tablep.add_line()
map_listofkeys = m.keys()
# print expected number of events per sample
# if the N_fit - N_error extends below 0, make the error physical , meaning extend to 0
for sample in samples_list:
for name in map_listofkeys:
rowl = []
rowp = []
if "MC_exp_events_" in name and sample in name:
sample_name = name.replace("MC_exp_events_","")
if sample_name != sample:
continue
rowl.append('(before fit) %s' % labels_latex_dict.get(sample_name, sample_name).replace('_', '\_'))
rowp.append('(before fit) %s' % labels_html_dict.get(sample_name, sample_name))
for index, n in enumerate(m[name]):
if regions_names[index] == 'CRQ' and sample == 'photonjet':
purity_before.append(n)
if regions_names[index] == 'CRW' and sample == 'wgamma':
purity_before.append(n)
if regions_names[index] == 'CRT' and sample == 'ttbarg':
purity_before.append(n)
rowl.append('$%.2f$' % n)
rowp.append('%.2f' % n)
tablel.add_row(rowl)
tablep.add_row(rowp)
tablel.add_line()
tablep.add_line()
if show_before_fit and all([r.startswith('CR') for r in regions_names]) and normalization_factors is not None:
tablel.add_row(['', '', '', ''])
tablel.add_line()
tablep.add_row(['', '', '', ''])
tablep.add_line()
# purity
rowl = ['Background purity',]
rowp = ['Background purity',]
for index, region in enumerate(regions_names):
purity = int(purity_before[index]/total_before[index] * 100.)
rowl.append('$%i\%%$' % purity)
rowp.append('%i%%' % purity)
tablel.add_row(rowl)
tablel.add_line()
tablep.add_row(rowp)
tablep.add_line()
# normalization
rowl = ['Normalization factor ($\mu$)',]
rowp = ['Normalization factor (mu)',]
for region in regions_names:
rowl.append('$%.2f \pm %.2f$' % normalization_factors[region])
rowp.append('%.2f ± %.2f' % normalization_factors[region])
tablel.add_row(rowl)
tablel.add_line()
tablep.add_row(rowp)
tablep.add_line()
tablel.save_tex(output_name)
with open(output_name.replace('.tex', '.html'), 'w+') as f:
f.write(tablep.get_html_string())
def latexfitresults(filename, region_list, sample_list):
f = ROOT.TFile.Open(filename)
w = f.Get('w')
doAsym = True
if w is None:
print "ERROR : Cannot open workspace : w"
sys.exit(1)
resultAfterFit = w.obj('RooExpandedFitResult_afterFit')
if resultAfterFit is None:
print(
"ERROR : Cannot open fit result after fit RooExpandedFitResult_afterFit"
)
sys.exit(1)
resultBeforeFit = w.obj('RooExpandedFitResult_beforeFit')
if resultBeforeFit is None:
print(
"ERROR : Cannot open fit result before fit RooExpandedFitResult_beforeFit"
)
sys.exit(1)
# pick up dataset from workspace
data_set = w.data('obsData')
# pick up channel category (RooCategory) from workspace
regionCat = w.obj("channelCat")
# find full (long) name list of regions (i.e. short=SR3J, long=SR3J_meffInc30_JVF25pt50)
regionFullNameList = [
Util.GetFullRegionName(regionCat, region) for region in region_list
]
# load afterFit workspace snapshot (=set all parameters to values after fit)
snapshot = 'snapshot_paramsVals_RooExpandedFitResult_afterFit'
w.loadSnapshot(snapshot)
if not w.loadSnapshot(snapshot):
print "ERROR : Cannot load snapshot : ", snapshot
sys.exit(1)
# define set, for all names/yields to be saved in
tablenumbers = dict()
tablenumbers['names'] = region_list
# make a list of channelCat calls for every region
regionCatList = [
'channelCat==channelCat::' + region.Data()
for region in regionFullNameList
]
# retrieve number of observed (=data) events per region
regionDatasetList = [
data_set.reduce(regioncat) for regioncat in regionCatList
]
for index, data in enumerate(regionDatasetList):
data.SetName("data_" + region_list[index])
data.SetTitle("data_" + region_list[index])
nobs_regionList = [data.sumEntries() for data in regionDatasetList]
tablenumbers['nobs'] = nobs_regionList
# FROM HERE ON OUT WE CALCULATE THE FITTED NUMBER OF EVENTS __AFTER__ THE FIT
#get a list of pdf's and variables per region
pdfinRegionList = [Util.GetRegionPdf(w, region) for region in region_list]
varinRegionList = [Util.GetRegionVar(w, region) for region in region_list]
varNbinsInRegionList = []
varBinLowInRegionList = []
varBinHighInRegionList = []
rangeNameBinsInRegionList = []
# get a list of RooRealSumPdf per region (RooRealSumPdf is the top-pdf per region containing all samples)
rrspdfinRegionList = []
for index, pdf in enumerate(pdfinRegionList):
if not pdf:
print "WARNING: pdf is NULL for index {0}".format(index)
continue
prodList = pdf.pdfList()
foundRRS = 0
for idx in range(prodList.getSize()):
if prodList[idx].InheritsFrom("RooRealSumPdf"):
rrspdfInt = prodList[idx].createIntegral(
ROOT.RooArgSet(varinRegionList[index]))
rrspdfinRegionList.append(rrspdfInt)
foundRRS += 1
if foundRRS > 1 or foundRRS == 0:
print " \n\n WARNING: ", pdf.GetName(
), " has ", foundRRS, " instances of RooRealSumPdf"
print pdf.GetName(), " component list:", prodList.Print("v")
# calculate total pdf number of fitted events and error
nFittedInRegionList = [
pdf.getVal() for index, pdf in enumerate(rrspdfinRegionList)
]
pdfFittedErrInRegionList = [
Util.GetPropagatedError(pdf, resultAfterFit, doAsym)
for pdf in rrspdfinRegionList
]
tablenumbers['TOTAL_FITTED_bkg_events'] = nFittedInRegionList
tablenumbers['TOTAL_FITTED_bkg_events_err'] = pdfFittedErrInRegionList
# calculate the fitted number of events and propagated error for each requested sample, by splitting off each sample pdf
for isam, sample in enumerate(sample_list):
sampleName = getName(sample)
nSampleInRegionVal = []
nSampleInRegionError = []
sampleInAllRegions = ROOT.RooArgSet()
for ireg, region in enumerate(region_list):
sampleInRegion = getPdfInRegions(w, sample, region)
sampleInRegionVal = 0.
sampleInRegionError = 0.
try:
sampleInRegionVal = sampleInRegion.getVal()
sampleInRegionError = Util.GetPropagatedError(
sampleInRegion, resultAfterFit, doAsym)
sampleInAllRegions.add(sampleInRegion)
except:
print " \n YieldsTable.py: WARNING: sample =", sampleName, " non-existent (empty) in region =", region, "\n"
nSampleInRegionVal.append(sampleInRegionVal)
nSampleInRegionError.append(sampleInRegionError)
tablenumbers['Fitted_events_' + sampleName] = nSampleInRegionVal
tablenumbers['Fitted_err_' + sampleName] = nSampleInRegionError
# FROM HERE ON OUT WE CALCULATE THE EXPECTED NUMBER OF EVENTS __BEFORRE__ THE FIT
#load beforeFit workspace snapshot (=set all parameters to values before fit)
w.loadSnapshot('snapshot_paramsVals_RooExpandedFitResult_beforeFit')
# check if any of the initial scaling factors is != 1
_result = w.obj('RooExpandedFitResult_beforeFit')
_muFacs = _result.floatParsFinal()
for i in xrange(len(_muFacs)):
if "mu_" in _muFacs[i].GetName() and _muFacs[i].getVal() != 1.0:
print " \n WARNING: scaling factor %s != 1.0 (%g) expected MC yield WILL BE WRONG!" % (
_muFacs[i].GetName(), _muFacs[i].getVal())
# get a list of pdf's and variables per region
pdfinRegionList = [Util.GetRegionPdf(w, region) for region in region_list]
varinRegionList = [Util.GetRegionVar(w, region) for region in region_list]
# get a list of RooRealSumPdf per region (RooRealSumPdf is the top-pdf per region containing all samples)
rrspdfinRegionList = []
for index, pdf in enumerate(pdfinRegionList):
if not pdf:
print "WARNING: pdf is NULL for index {0}".format(index)
continue
prodList = pdf.pdfList()
foundRRS = 0
for idx in xrange(prodList.getSize()):
if prodList[idx].InheritsFrom("RooRealSumPdf"):
rrspdfInt = prodList[idx].createIntegral(
ROOT.RooArgSet(varinRegionList[index]))
rrspdfinRegionList.append(rrspdfInt)
foundRRS += 1
if foundRRS > 1 or foundRRS == 0:
print " \n\n WARNING: ", pdf.GetName(
), " has ", foundRRS, " instances of RooRealSumPdf"
print pdf.GetName(), " component list:", prodList.Print("v")
# calculate total pdf number of expected events and error
nExpInRegionList = [
pdf.getVal() for index, pdf in enumerate(rrspdfinRegionList)
]
pdfExpErrInRegionList = [
Util.GetPropagatedError(pdf, resultBeforeFit, doAsym)
for pdf in rrspdfinRegionList
]
tablenumbers['TOTAL_MC_EXP_BKG_events'] = nExpInRegionList
tablenumbers['TOTAL_MC_EXP_BKG_err'] = pdfExpErrInRegionList
# calculate the fitted number of events and propagated error for each requested sample, by splitting off each sample pdf
for isam, sample in enumerate(sample_list):
sampleName = getName(sample)
nMCSampleInRegionVal = []
nMCSampleInRegionError = []
MCSampleInAllRegions = ROOT.RooArgSet()
for ireg, region in enumerate(region_list):
MCSampleInRegion = getPdfInRegions(w, sample, region)
MCSampleInRegionVal = 0.
MCSampleInRegionError = 0.
try:
MCSampleInRegionVal = MCSampleInRegion.getVal()
MCSampleInRegionError = Util.GetPropagatedError(
MCSampleInRegion, resultBeforeFit, doAsym)
MCSampleInAllRegions.add(MCSampleInRegion)
except:
print " \n WARNING: sample=", sampleName, " non-existent (empty) in region=", region
nMCSampleInRegionVal.append(MCSampleInRegionVal)
nMCSampleInRegionError.append(MCSampleInRegionError)
tablenumbers['MC_exp_events_' + sampleName] = nMCSampleInRegionVal
tablenumbers['MC_exp_err_' + sampleName] = nMCSampleInRegionError
# sort the tablenumbers set
map_listofkeys = tablenumbers.keys()
map_listofkeys.sort()
f.Close()
return tablenumbers
def yieldstable(workspace,
samples,
channels,
output_name,
table_name='',
show_before_fit=False,
unblind=True,
show_cr_info=False,
cr_dict={}):
if show_cr_info:
show_before_fit = True
normalization_factors = get_normalization_factors(workspace)
samples_list = cmdStringToListOfLists(samples)
regions_list = ['%s_cuts' % r for r in channels.split(",")]
# call the function to calculate the numbers, or take numbers from pickle file
if workspace.endswith(".pickle"):
print "Reading from pickle file"
f = open(workspace, 'r')
m = pickle.load(f)
f.close()
else:
#m = YieldsTable.latexfitresults(workspace, regions_list, samples_list, 'obsData')
m = latexfitresults(workspace, regions_list, samples_list)
with open(output_name.replace('.tex', '.pickle'), 'w') as f:
pickle.dump(m, f)
regions_names = [
region.replace("_cuts", "").replace('_', '\_') for region in m['names']
]
field_names = [
table_name,
] + regions_names
align = [
'l',
] + ['r' for i in regions_names]
samples_list_decoded = []
for isam, sample in enumerate(samples_list):
sampleName = getName(sample)
samples_list_decoded.append(sampleName)
samples_list = samples_list_decoded
tablel = LatexTable(field_names, align=align, env=True)
# number of observed events
if unblind:
row = [
'Observed events',
] + ['%d' % n for n in m['nobs']]
else:
row = [
'Observed events',
] + ['-' for n in m['nobs']]
tablel.add_row(row)
tablel.add_line()
# Total fitted (after fit) number of events
# if the N_fit - N_error extends below 0, make the error physical, meaning extend to 0
rowl = [
'Expected SM events',
]
for index, n in enumerate(m['TOTAL_FITTED_bkg_events']):
if (n - m['TOTAL_FITTED_bkg_events_err'][index]) > 0.:
rowl.append('$%.2f \pm %.2f$' %
(n, m['TOTAL_FITTED_bkg_events_err'][index]))
else:
rowl.append('$%.2f_{-%.2f}^{+%.2f}$' %
(n, n, m['TOTAL_FITTED_bkg_events_err'][index]))
tablel.add_row(rowl)
tablel.add_line()
map_listofkeys = m.keys()
# After fit number of events per sample (if the N_fit-N_error extends below 0, make the error physical, meaning extend to 0)
for sample in samples_list:
for name in map_listofkeys:
rowl = []
if not "Fitted_events_" in name:
continue
sample_name = name.replace("Fitted_events_", "")
if sample_name != sample:
continue
rowl.append('%s' % labels_latex_dict.get(
sample_name, sample_name).replace('_', '\_'))
for index, n in enumerate(m[name]):
if ((n - m['Fitted_err_' + sample][index]) >
0.) or not abs(n) > 0.00001:
rowl.append('$%.2f \\pm %.2f$' %
(n, m['Fitted_err_' + sample][index]))
else:
rowl.append('$%.2f_{-%.2f}^{+%.2f}$' %
(n, n, m['Fitted_err_' + sample][index]))
tablel.add_row(rowl)
tablel.add_line()
# Total expected (before fit) number of events
if show_before_fit:
# if the N_fit - N_error extends below 0, make the error physical, meaning extend to 0
rowl = [
'Before fit SM events',
]
total_before = {}
purity_before = {}
for index, n in enumerate(m['TOTAL_MC_EXP_BKG_events']):
reg_name = regions_names[index]
if cr_dict and reg_name in cr_dict:
total_before[reg_name] = n
rowl.append('$%.2f$' % n)
tablel.add_row(rowl)
tablel.add_line()
map_listofkeys = m.keys()
# Expected number of events per sample (if the N_fit - N_error extends below 0, make the error physical, meaning extend to 0)
for sample in samples_list:
for name in map_listofkeys:
rowl = []
if "MC_exp_events_" in name and sample in name:
sample_name = name.replace("MC_exp_events_", "")
if sample_name != sample:
continue
rowl.append('Before fit %s' % labels_latex_dict.get(
sample_name, sample_name).replace('_', '\_'))
for index, n in enumerate(m[name]):
reg_name = regions_names[index]
if cr_dict and reg_name in cr_dict and sample == cr_dict[
reg_name]:
purity_before[reg_name] = n
rowl.append('$%.2f$' % n)
tablel.add_row(rowl)
tablel.add_line()
if show_cr_info and normalization_factors is not None:
tablel.add_row(['' for i in range(len(regions_names) + 1)])
tablel.add_line()
# purity
rowl = [
'Background purity',
]
for region in regions_names:
try:
purity = int(
round(purity_before[region] / total_before[region] * 100.))
rowl.append('$%i\%%$' % purity)
except:
rowl.append('-')
tablel.add_row(rowl)
tablel.add_line()
# normalization
rowl = [
'Normalization factor ($\mu$)',
]
for region in regions_names:
try:
rowl.append('$%.2f \pm %.2f$' % normalization_factors[region])
except:
rowl.append('-')
tablel.add_row(rowl)
tablel.add_line()
tablel.save_tex(output_name)
