def __init__(self,
box,
tag="Razor2016_MoriondRereco",
weights=None,
isData=True,
configFile="config/run2_2016.config",
full=False):
self.sideband = None
self.weights = weights
self.isData = isData
if full:
self.fitRegion = 'Full'
else:
self.fitRegion = 'LowMR,LowRsq'
self.analysis = Analysis(box, tag=tag)
self.filename = self.getFilename()
self.dirname = os.path.dirname(self.filename)
self.toysFile = self.getToysFilename(noStat=False, doFreq=False)
self.sysFile = self.getToysFilename(noStat=True, doFreq=False)
self.freqToysFile = self.getToysFilename(noStat=False, doFreq=True)
self.freqSysFile = self.getToysFilename(noStat=True, doFreq=True)
if not os.path.isdir(self.dirname):
os.makedirs(self.dirname)
self.config = Config.Config(configFile)
self.x = array('d', self.config.getBinning(box)[0]) # MR binning
self.y = array('d', self.config.getBinning(box)[1]) # Rsq binning
self.z = array('d', self.config.getBinning(box)[2]) # nBtag binning
self.workspace = rt.RooWorkspace("w" + box)
initializeWorkspace(self.workspace, self.config, box)
setStyle()
def __init__(self, box, tag="Razor2016_MoriondRereco",
weights=None, isData=True, configFile="config/run2_2016.config",
full=False):
self.sideband = None
self.weights = weights
self.isData = isData
if full:
self.fitRegion = 'Full'
else:
self.fitRegion = 'LowMR,LowRsq'
self.analysis = Analysis(box, tag=tag)
self.filename = self.getFilename()
self.dirname = os.path.dirname(self.filename)
self.toysFile = self.getToysFilename(noStat=False, doFreq=False)
self.sysFile = self.getToysFilename(noStat=True, doFreq=False)
self.freqToysFile = self.getToysFilename(noStat=False, doFreq=True)
self.freqSysFile = self.getToysFilename(noStat=True, doFreq=True)
if not os.path.isdir(self.dirname):
os.makedirs(self.dirname)
self.config = Config.Config(configFile)
self.x = array('d', self.config.getBinning(box)[0]) # MR binning
self.y = array('d', self.config.getBinning(box)[1]) # Rsq binning
self.z = array('d', self.config.getBinning(box)[2]) # nBtag binning
self.workspace = rt.RooWorkspace("w"+box)
initializeWorkspace(self.workspace, self.config, box)
setStyle()
dest="box",
default="MultiJet",
type="string",
help="box name")
(options, args) = parser.parse_args()
cfg = Config.Config(options.config)
box = options.box
ids = 0
w = rt.RooWorkspace("w" + box)
initializeWorkspace(w, cfg, box)
ds = []
for f in args:
if f.lower().endswith('.root'):
rootFile = rt.TFile(f)
workspace = rootFile.Get('w' + box)
ds.append(workspace.data('RMRTree').Clone('RMRTree_%i' % ids))
ids += 1
wdata = ds[0].Clone('RMRTree')
for ids in range(1, len(ds)):
wdata.append(ds[ids])
uwdata = convertDataset2UnweightedToy(wdata, cfg, box, w, uwName='uw')
#if a list of physics processes is given, the uncertainty will be applied to each process in the list, assumed uncorrelated from process to process.
#if an empty list is given, the uncertainty will be applied (correlated) to all processes, including signal.
shapes = {
#"norm" : ['dyjetstoll', 'qcd', 'ttjets', 'zjetstonunu', 'multiboson', 'singletop', 'wjetstolnu', 'ttv']
}
#specify which systematics should be treated as uncorrelated bin-by-bin
uncorrShapes = [
#"norm"
]
print 'Input files are %s' % ', '.join(args)
#create workspace
w = rt.RooWorkspace("w"+box)
variables = initializeWorkspace(w,cfg,box)
#list of histograms
ds = []
btagMin = w.var('nBtag').getMin()
btagMax = w.var('nBtag').getMax()
if removeQCD:
# first get sum of weights for each background per b-tag bin ( sumW[label] )
sumW = {}
sumWQCD = 0.
for f in args:
if f.lower().endswith('.root'):
rootFile = rt.TFile(f)
tree = rootFile.Get('RazorInclusive')
parser.add_option('-d','--dir',dest="outDir",default="./",type="string",
help="Output directory to store datasets")
parser.add_option('-b','--box',dest="box", default="MultiJet",type="string",
help="box name")
(options,args) = parser.parse_args()
cfg = Config.Config(options.config)
box = options.box
ids = 0
w = rt.RooWorkspace("w"+box)
initializeWorkspace(w,cfg,box)
ds = []
for f in args:
if f.lower().endswith('.root'):
rootFile = rt.TFile(f)
workspace = rootFile.Get('w'+box)
ds.append(workspace.data('RMRTree').Clone('RMRTree_%i'%ids))
ids+=1
wdata = ds[0].Clone('RMRTree')
for ids in range(1,len(ds)):
wdata.append(ds[ids])
uwdata = convertDataset2UnweightedToy(wdata, cfg, box, w, uwName = 'uw')
unrollBins = None
if options.mergeBins:
btagBins = cfg.getBinning(box)[2][:-1]
unrollBins = [(xbinsSignal[box][str(int(btags))+'B'], colsSignal[box][str(int(btags))+'B']) for btags in btagBins]
if options.noSignalSys:
shapes = []
else:
shapes = ['tightmuoneff','tighteleeff','vetomuoneff','vetoeleeff','jes','muontrig','eletrig','btag','tightmuonfastsim','tightelefastsim','vetomuonfastsim','vetoelefastsim','btagfastsim','facscale','renscale','facrenscale','ees','mes','pileup','isr','mcstat%s'%box.lower()]
#shapes.append('pdf%s'%box.lower()) #this is for the flat 10% PDF uncertainty (uncorrelated across bins)
#shapes.extend(['n'+str(n)+'pdf' for n in range(options.numPdfWeights)])
for curBox in boxList:
#create workspace
w = rt.RooWorkspace("w"+curBox)
variables = initializeWorkspace(w,cfg,curBox)
btagMin = w.var('nBtag').getMin()
btagMax = w.var('nBtag').getMax()
z = array('d', cfg.getBinning(curBox)[2]) # nBtag binning
#list of histograms
ds = []
#dictionary of histograms (same content - useful for uncorrelate function)
dsDict = {}
#make MC histograms
model = ''
if f.lower().endswith('.root'):
rootFile = rt.TFile.Open(f) #open file
tree = rootFile.Get('RazorInclusive') #get tree
else:
shapes = [
'tightmuoneff', 'tighteleeff', 'vetomuoneff', 'vetoeleeff', 'jes',
'muontrig', 'eletrig', 'btag', 'tightmuonfastsim',
'tightelefastsim', 'vetomuonfastsim', 'vetoelefastsim',
'btagfastsim', 'facscale', 'renscale', 'facrenscale', 'ees', 'mes',
'pileup', 'isr',
'mcstat%s' % box.lower()
]
#shapes.append('pdf%s'%box.lower()) #this is for the flat 10% PDF uncertainty (uncorrelated across bins)
#shapes.extend(['n'+str(n)+'pdf' for n in range(options.numPdfWeights)])
for curBox in boxList:
#create workspace
w = rt.RooWorkspace("w" + curBox)
variables = initializeWorkspace(w, cfg, curBox)
btagMin = w.var('nBtag').getMin()
btagMax = w.var('nBtag').getMax()
z = array('d', cfg.getBinning(curBox)[2]) # nBtag binning
#list of histograms
ds = []
#dictionary of histograms (same content - useful for uncorrelate function)
dsDict = {}
#make MC histograms
model = ''
if f.lower().endswith('.root'):
rootFile = rt.TFile.Open(f) #open file
tree = rootFile.Get('RazorInclusive') #get tree
