treeLumi = 1.0 # 1/fb
newLumi = 5.0 # 1/fb
masses = [3000, 2500, 2000, 1500]
poi_setups = { 3000 : [60, 0, 30],
2500 : [60, 0, 30],
2000 : [60, 0, 20],
1500 : [75, 0, 20],
}
# createDirectoryStructure('/afs/cern.ch/work/y/yygao/public/VVJJStatInput/results_CamKt12SplitFilteredMu100SmallR30YCut4/syst_100', releaseDir, analysis, inputListTag, outputWSTag)
createDirectoryStructure('/afs/cern.ch/work/v/vippolit/public//VVJJ/test_inputs_20150508', releaseDir, analysis, inputListTag, outputWSTag)
vvjj = RF.VVJJAnalysisRunner(analysis)
vvjj.setTreeObs('x')
vvjj.setTreeWeight('weight')
vvjj.setLumiRescale(treeLumi, newLumi)
vvjj.setNbins(25)
vvjj.setObsMin(1050)
vvjj.setObsMax(3550)
vvjj.setReleaseDir(releaseDir)
vvjj.setInputListTag(inputListTag)
vvjj.setOutputWSTag(outputWSTag)
vvjj.doPull(producePull);
if not doExternalPdf:
# set up function for up/down shape variations
run1_input_path_format = '/afs/cern.ch/work/k/kalliopi/public/forValerio/test0/lvqq_{mass}.root'
masses = [3000, 2500, 2000, 1500, 1000 ]
dest = createEmptyDirs(releaseDir, analysis, inputTag)
poi_setups = { 3000 : [60, 0, 30],
2500 : [60, 0, 30],
2000 : [60, 0, 20],
1500 : [75, 0, 20],
1000 : [80, 0, 20],
1000 : [60, 0, 20]
}
getInputFromRunI(run1_input_path_format, masses, dest)
# run over them
lvqq = RF.VVlvqqValidationRunner(analysis)
lvqq.setReleaseDir(releaseDir)
lvqq.setInputListTag(inputTag)
lvqq.setOutputWSTag(wsTag)
# create a new channel (will automatically add data)
lvqq.addChannel('SR', '') # not using selection criteria
# loop over signals
for mass in masses:
thisSig = 'signal_{mass}'.format(mass=mass)
lvqq.channel('SR').addSample(thisSig)
lvqq.channel('SR').sample(thisSig).setNormByLumi(True)
lvqq.channel('SR').sample(thisSig).multiplyBy('mu', 1.0, 0, 20)
# declare signal mass value
if "TCR" in sys.argv[1]: useTCR=True
if "Merg" in sys.argv[1]: useMerg=True
if "Res" in sys.argv[1]: useRes=True
if "PNN" in sys.argv[1]: usePNN=True
if usePNN: tag=tag+"PNN"
masses=[]
if len(sys.argv) > 2:
masses=[int(sys.argv[2])]
signalType="HVTWZ"
if len(sys.argv) > 3:
signalType=sys.argv[3]
#Start analysis and define directory structure
RFAnalysis = RF.DefaultTreeAnalysisRunner("ExampleVV")
RFAnalysis.setOutputDir("./output/M4f/")
RFAnalysis.setOutputWSTag(tag)
#Set up/down variation names to your convention
RFAnalysis.setCollectionTagNominal("Nominal")
RFAnalysis.setCollectionTagUp("_up")
RFAnalysis.setCollectionTagDown("_dn")
#Example of defining some variables ahead of time. Example here for lumi uncertainities
lumi_scale=1.
lumi_uncert=0.017
####################################################
# Now define your fit model and where to find the data
####################################################
#doInjection = False
doToys = False
#doToys = True
doOptimisedScan = True
poi_setups = { # for optimised mu scan
2500 : [1000, 0, 100],
2000 : [1000, 0, 100],
1800 : [400, 0, 50],
1600 : [200, 0, 30],
1500 : [50, 0, 30],
1400 : [50, 0, 30],
1100 : [50, 0, 20],
1000 : [50, 0, 20],
}
llqq = RF.VVllqqAnalysisRunner(analysis)
# define input tree format
llqq.setTreeObs('Mlljj')
llqq.setTreeWeight('weight')
# define histogram binning
llqq.setNbins(7)
llqq.setObsMin(400)
llqq.setObsMax(2500)
# apply directory structure
llqq.setReleaseDir(releaseDir)
llqq.setInputListTag(inputListTag)
llqq.setOutputWSTag(outputWSTag)
# define channels
run1_input_path_format = '/afs/cern.ch/work/v/vippolit/public/VVJJ/DibjetStatPL_lite/Input/WPRIME/WPRIME_{mass}_measurement_combined_meas_model.root'
masses = [3000, 2500, 2000, 1500]
dest = '{releaseDir}/{analysis}/data/{inputTag}'.format(
releaseDir=releaseDir, analysis=analysis, inputTag=inputTag)
poi_setups = {
3000: [60, 0, 30],
2500: [60, 0, 30],
2000: [60, 0, 20],
1500: [75, 0, 20],
}
getInputFromRunI(run1_input_path_format, masses, dest)
# run over them
vvjj = RF.VVJJValidationRunner(analysis)
vvjj.setReleaseDir(releaseDir)
vvjj.setInputListTag(inputTag)
vvjj.setOutputWSTag(wsTag)
# create a new channel (will automatically add data)
vvjj.addChannel('SR', '') # not using selection criteria
# loop over signals
for mass in masses:
thisSig = 'signal_{mass}'.format(mass=mass)
vvjj.channel('SR').addSample(thisSig)
vvjj.channel('SR').sample(thisSig).setNormByLumi(True)
vvjj.channel('SR').sample(thisSig).multiplyBy('mu', 1.0, 0, 300)
vvjj.channel('SR').sample(thisSig).addVariation('ptr')
vvjj.channel('SR').sample(thisSig).addVariation('pts')
# import histograms from Run-I code
# run1_input_path_format = '/afs/cern.ch/user/w/wfisher/work/VHreso/VHlvbb_WPRIME/test00/lvbb_sL00_{mass}.root'
#/afs/cern.ch/work/k/kalliopi/public/forValerio/test0/lvbb_{mass}.root'
masses = [1000, 1500 ]
#dest = createEmptyDirs(releaseDir, analysis, inputTag)
poi_setups = {
1000 : [100, 0, 100],
1500 : [100, 0, 100]
}
# getInputFromRunI(run1_input_path_format, masses, dest)
# run over them
lvbb = RF.VHlvbbAnalysisRunner(analysis)
lvbb.setReleaseDir(releaseDir)
lvbb.setInputListTag(inputTag)
lvbb.setOutputWSTag(wsTag)
# create a new channel (will automatically add data)
lvbb.addChannel('J2T2E', '') # not using selection criteria
# loop over signals
for mass in masses:
thisSig = 'WpL{mass}'.format(mass=mass)
lvbb.channel('J2T2E').addSample(thisSig)
lvbb.channel('J2T2E').sample(thisSig).setNormByLumi(True)
lvbb.channel('J2T2E').sample(thisSig).multiplyBy('mu', 1.0, 0, 100)
# declare signal mass value
treeLumi = 1.01148 # 1/fb
newLumi = 4.0 # 1/fb
masses = [3000, 2500, 2000, 1500]
poi_setups = {
3000: [60, 0, 30],
2500: [60, 0, 30],
2000: [60, 0, 20],
1500: [75, 0, 20],
}
createDirectoryStructure(inputdir, releaseDir, analysis, inputListTag,
outputWSTag)
vvjj = RF.VVJJAnalysisRunner(analysis)
vvjj.setTreeObs('x')
vvjj.setTreeWeight('weight')
vvjj.setLumiRescale(treeLumi, newLumi)
vvjj.setNbins(23)
vvjj.setObsMin(1200)
vvjj.setObsMax(3500)
vvjj.setReleaseDir(releaseDir)
vvjj.setInputListTag(inputListTag)
vvjj.setOutputWSTag(outputWSTag)
vvjj.doPull(producePull)
if not doExternalPdf:
# set up function for up/down shape variations
# syntax is the same as RooWorkspace::factory
import ROOT
ROOT.gROOT.SetBatch(True)
ROOT.gROOT.ProcessLine(
ROOT.gSystem.ExpandPathName('.x $ROOTCOREDIR/scripts/load_packages.C'))
from ROOT import RF as RF
if __name__ == '__main__':
releaseDir = './test_v4'
analysis = 'VVlvqq_HVT'
outputWSTag = 'wtest01'
# retrieve results
results = RF.StatisticalResultsCollection('lvqqtest')
filename = '{releaseDir}/{analysis}/ws/results_{analysis}_{outputWSTag}.root'.format(
releaseDir=releaseDir, analysis=analysis, outputWSTag=outputWSTag)
results.retrieve(filename)
# plot
ROOT.gROOT.ProcessLine('.L atlasstyle/AtlasStyle.C+')
ROOT.SetAtlasStyle()
plotter = RF.Plotter('lvqqtest', '.')
plotter.setVarName('m_{HVT}')
plotter.setVarUnit('GeV')
plotter.setSpec(500, 1.0,
1.765000 * 1.0 * 0.75616 * 1e3) # note the 1000x factor
plotter.setSpec(1000, 1.0, 0.098950 * 1.0 * 0.79607 * 1e3)
plotter.setSpec(1500, 1.0, 0.016110 * 1.0 * 0.81147 * 1e3)
plotter.setSpec(2000, 1.0, 0.003810 * 1.0 * 0.81310 * 1e3)