def make_weighted_dataset(subproc,ws,tree,mc_events):
data = RooDataSet('%s_shape_data'%subproc,
'M_{ll#gamma} Shape Data for %s'%subproc,
tree,
ws.set('vars_with_weights')
)
mc_yield_var = RooConstVar('temp','temp',mc_events)
weighter = RooFormulaVar('weight','weight','@0*@1/@2',
RooArgList( ws.var('procWeight'),
ws.var('puWeight'),
mc_yield_var )
)
data.addColumn(weighter)
data_total_weight = RooDataSet('%s_shape_data'%subproc,
'M_{ll#gamma} Shape Data for %s'%subproc,
data,
ws.set('vars_with_weights_final'),
'','weight')
data_pu_weight = RooDataSet('%s_shape_data_puonly'%subproc,
'M_{ll#gamma} Shape Data for %s'%subproc,
data,
ws.set('vars_with_weights_final'),
'','puWeight')
return data_total_weight, data_pu_weight
def get_dataset(varargset, ftree, cut='', wt='', scale=1):
"""Return a dataset.
Return a dataset from the ntuple `ftree'. Apply a selection cut
using the `cutVar' variable and the selection `cut'.
"""
from rplot.fixes import ROOT
from rplot.tselect import Tsplice
splice = Tsplice(ftree)
splice.make_splice('sel', cut)
from ROOT import RooDataSet, RooFit, RooFormulaVar, RooArgList
tmpdst = RooDataSet('tmpdataset', '', varargset, RooFit.Import(ftree))
if wt:
wtvar = RooFormulaVar('wt', '{}*@0'.format(scale),
RooArgList(varargset[wt]))
wtvar = tmpdst.addColumn(wtvar)
varargset.remove(varargset[wt])
varargset.add(wtvar)
dst = RooDataSet('dataset', 'Dataset', varargset,
RooFit.Import(tmpdst), RooFit.WeightVar(wtvar))
varargset.remove(wtvar)
dst = dst.reduce(varargset)
return dst
def File2Dataset(self,
fnames,
dsName,
ws,
noCuts=False,
weighted=False,
CPweight=False,
cutOverride=None,
interference=0,
additionalWgt=1.0):
if ws.data(dsName):
return ws.data(dsName)
cols = RooArgSet(ws.set('obsSet'))
# print 'interference weight flag:',interference
if (weighted):
evtWgt = RooRealVar('evtWgt', 'evtWgt', 1.0)
cols.add(evtWgt)
ds = RooDataSet(dsName, dsName, cols, 'evtWgt')
print 'including weights for eff',
if CPweight:
print 'and CP weight',
if interference in [1, 2, 3]:
print 'and interference',
print
else:
ds = RooDataSet(dsName, dsName, cols)
if not (type(fnames) == type([])):
fnames = [fnames]
try:
obs = [self.pars.varNames[x] for x in self.pars.var]
except AttributeError:
obs = self.pars.var
for fname in fnames:
for (row, effWgt, cpw, iwt) in \
self.TreeLoopFromFile(fname, noCuts,
CPweight = CPweight,
cutOverride = cutOverride,
interference = interference):
inRange = True
for (i, v) in enumerate(obs):
inRange = (inRange and ws.var(v).inRange(row[i], ''))
cols.setRealValue(v, row[i])
if CPweight:
effWgt *= cpw
if interference in [1, 2, 3]:
effWgt *= iwt
if inRange:
ds.add(cols, effWgt * additionalWgt)
getattr(ws, 'import')(ds)
return ws.data(dsName)
def predict(self, x, theta_true):
"""
Run the unbinned ML fit
"""
# Data
roodata = RooDataSet('data', 'data', RooArgSet(self.phistar))
for xval in x:
self.phistar.setVal(xval)
roodata.add(RooArgSet(self.phistar))
theta = RooRealVar('theta', 'theta', 0.5, self.theta_min, self.theta_max)
# The combined pdf
model = RooAddPdf('model', 'model',
RooArgList(self.pdfs['A'], self.pdfs['H']),
RooArgList(theta))
with stdout_redirected_to('%s/minuit_output.log' % self.outdir):
res = model.fitTo(roodata, Save(True))
nll = res.minNll()
fitted_theta = theta.getValV()
# Get Lambda(theta_true | theta_best)
with stdout_redirected_to():
logl = model.createNLL(roodata)
theta.setVal(theta_true)
nll_theta_true = logl.getValV()
nll_ratio = nll_theta_true - nll
return fitted_theta, nll, nll_ratio
def createDataSet(self, dname, dcut):
"""Create named dataset"""
if dname in self.dataset.keys():
return self.dataset[dname]
data = RooDataSet(dname, "", self.ch, self.argset, dcut)
self.dataset[dname] = data
return data
def main():
# setting for reduced DS to be used on the output of makeSubset.C
inputfile_name = "small.root"
tree_name = "upsTree"
# settings for full dataset (long processing time for unbinned lh)
#inputfile_name = "/data1/chibdata/collision/v2/2012_AllData_v2.root"
#tree_name = "rootuple/upsTree"
print "Opening file"
inputfile = TFile.Open(inputfile_name, "READ")
print "Importing tree"
tree = TTree()
inputfile.GetObject(tree_name, tree)
mass = RooRealVar("ups_mass", "ups_mass", 7, 11)
y = RooRealVar("ups_rapidity", "ups_rapidity", -3, 3)
pt = RooRealVar("ups_pt", "ups_pt", 0, 100)
print "Assigning dataset"
dataArgSet = RooArgSet(mass, y, pt)
dataSet = RooDataSet("yds", "Y data set", tree, dataArgSet)
cuts= "abs(ups_rapidity) < 1.25"+\
"&& ups_pt>9.5"\
reduced_ds = dataSet.reduce(RooFit.Cut(cuts))
print "Performing likelihood analysis"
dofit(reduced_ds, "Y3S")
def main1():
m = RooRealVar('evt.m', 'evt.m', 1.92, 2.02)
mode = RooCategory('evt.mode', 'evt.mode')
mode.defineType('phipi', 0)
aset = RooArgSet('aset')
aset.add(m)
aset.add(mode)
tuplist = tupleList('genmc', 7)
dst, _, f = getTree('/'.join([tuplePath(), tuplist[-1]]))
# dst.Print()
# dst.Show(0)
# for evt in dst:
# print('Hello!')
# print(evt.evt.m)
# break
ds = RooDataSet('ds', 'ds', dst, aset)
print(ds.numEntries())
mean = RooRealVar('mean', 'mean', 1.96, 1.92, 2.0)
width = RooRealVar('width', 'width', 0.006, 0.001, 0.010)
pdf = RooGaussian('pdf', 'pdf', m, mean, width)
pdf.fitTo(ds, Verbose(), Timer(True))
makePlot(m, ds, pdf)
raw_input("Press Enter to continue...")
def RooFitSig(mbbarray, bdtarray, weightarray, TC_mass, binstart, binend):
fitstart = 40
fitend = 150
mbbarray = range(200)
bdtarray = range(200)
weightarray = range(200)
mass = RooRealVar("X", "m(bb)[GeV]", fitstart, fitend)
BDT = RooRealVar("BDT", "BDT", -1, 100)
weight = RooRealVar("weight", "weight", -100, 200)
branchnames = ["X", "BDT", "weight"]
dtype = np.dtype([(branchnames[idx], np.float64)
for idx in range(len(branchnames))])
treearray = np.array([(mbbarray[idx], bdtarray[idx], weightarray[idx])
for idx in range(len(mbbarray))], dtype)
tree = rnp.array2tree(treearray)
m0 = RooRealVar("m0", "m0", TC_mass * 1., TC_mass * 1. - 60.,
TC_mass * 1. + 60.)
m02 = RooRealVar("m02", "m02", TC_mass * 1., TC_mass * 1. - 60.,
TC_mass * 1. + 60.)
alpha = RooRealVar("alpha", "alpha", 1.295, 1.0, 1.6)
sigma2 = RooRealVar("sigma2", "sigma2", 35, 8., 100)
n = RooRealVar("n", "n", 5, 1, 35)
mean = RooRealVar("mean", "mean of gaussian", 750, 0, 6000)
sigma = RooRealVar("sigma", "width of gaussian", 90, 38, 300)
gauss = RooGaussian("gauss", "gaussian PDF", mass, m0, sigma)
gauss2 = RooGaussian("gauss2", "gaussian PDF", mass, m02, sigma2)
CBshape = RooCBShape("CBshape", "Crystal Ball PDF", mass, m0, sigma2,
alpha, n)
##PDF normalization
num1 = RooRealVar("num1", "number of events", 400, 0, 5000)
##relative weight of 2 PDFs
f = RooRealVar("f", "f", 0.95, 0.6, 1)
sigPdf = RooAddPdf("sigPdf", "Signal PDF", RooArgList(CBshape, gauss),
RooArgList(f))
extPdf = RooExtendPdf("extPdf", "extPdf", sigPdf, num1)
data = RooDataSet("data", "data", tree, RooArgSet(mass, BDT, weight),
"BDT>0", "weight")
xframe = mass.frame()
mass.setBins(20)
data.plotOn(xframe)
extPdf.plotOn(
xframe) #,Normalization(1.0,RooAbsReal.RelativeExpected),LineColor(1))
hist = extPdf.createHistogram("X", fitend - fitstart)
hist.SetAxisRange(binstart, binend)
return deepcopy(hist)
def addData(self,
ws,
ds_name,
item_title,
var_set_name,
var_set_type,
ds_object,
weight_var_name=None,
debug=0,
sets=None):
legend = '[exostConfig::addData]:'
arg_set = RooArgSet()
if (var_set_type == 'set'):
#arg_set.add(ws.set(var_set_name))
arg_set.add(sets[var_set_name])
elif (var_set_type == 'var'):
arg_set.add(ws.var(var_set_name))
else:
print legend, 'error: unknown var_set_type, cannot create dataset', ds_name
return -1
#create the dataset
if weight_var_name == None: #no weight
if (debug > 0):
print legend, 'no weight branch given'
ds = RooDataSet(ds_name, item_title, ds_object, arg_set)
else:
if (debug > 0):
print legend, 'using weight branch', weight_var_name
# old and incorrect(?) way of applying weights
#
#_var_formula = RooFormulaVar('f'+weight_var_name,
# 'f'+weight_var_name,
# '@0',
# RooArgList(ws.var(weight_var_name)))
#ds = RooDataSet(ds_name, item_title, ds_object, arg_set, _var_formula, weight_var_name)
arg_set.add(ws.var(weight_var_name))
ds = RooDataSet(ds_name, item_title, ds_object, arg_set, "1>0",
weight_var_name)
# import the datahist. Note workaround 'import' being a reserved word
getattr(ws, 'import')(ds)
def fillDataSet(data, x, N, dsName='ds'):
cols = RooArgSet(x)
ds = RooDataSet(dsName, dsName, cols)
#ds.Print()
print 'length data:', N
for datum in range(0, N):
if (data[datum] < x.getMax()) and (data[datum] > x.getMin()):
x.setVal(data[datum])
ds.add(cols)
ds.Print()
return ds
def main():
inputfile_name = "/data1/chibdata/collision/v2/2012_AllData_v2.root"
chibTree_name = "rootuple/chibTree"
print "Opening file"
inputfile = TFile.Open(inputfile_name, "READ")
print "Importing tree"
tree = TTree()
inputfile.GetObject(chibTree_name, tree)
invm1S = RooRealVar("invm1S", "invm1S", 9.5, 11.5)
invm2S = RooRealVar("invm2S", "invm2S", 9.5, 20.0)
invm3S = RooRealVar("invm3S", "invm3S", 9.5, 20.0)
dimuon_mass = RooRealVar("dimuon_mass", "dimuon_mass", 8.0, 12.0)
dimuon_rapidity = RooRealVar("dimuon_rapidity", "dimuon_rapidity", -5.0,
5.0)
dimuon_pt = RooRealVar("dimuon_pt", "dimuon_pt", 0.0, 100.0)
photon_eta = RooRealVar("photon_eta", "photon_eta", -5.0, 5.0)
photon_pt = RooRealVar("photon_pt", "photon_pt", 0.0, 100.0)
ctpv = RooRealVar("ctpv", "ctpv", -5.0, 5.0)
ctpv_error = RooRealVar("ctpv_err", "ctpv_err", -5.0, 5.0)
pi0_abs_mass = RooRealVar("pi0_abs_mass", "pi0_abs_mass", 0.0, 2.0)
Y1S_nsigma = RooRealVar("Y1S_nsigma", "Y1S_nsigma", 0.0, 30.0)
Y2S_nsigma = RooRealVar("Y2S_nsigma", "Y2S_nsigma", 0.0, 30.0)
Y3S_nsigma = RooRealVar("Y3S_nsigma", "Y3S_nsigma", 0.0, 35.0)
conv_vertex = RooRealVar("conv_vertex", "conv_vertex", 0.0, 70.0)
dz = RooRealVar("dz", "dz", -50.0, 50.0)
print "Assigning argset"
dataArgSet = RooArgSet(invm1S, invm2S, invm3S, dimuon_mass,
dimuon_rapidity, dimuon_pt, photon_eta, photon_pt,
ctpv)
dataArgSet.add(ctpv_error)
dataArgSet.add(pi0_abs_mass)
dataArgSet.add(Y1S_nsigma)
dataArgSet.add(Y2S_nsigma)
dataArgSet.add(Y3S_nsigma)
dataArgSet.add(conv_vertex)
dataArgSet.add(dz)
print "Creating DataSet"
dataSet = RooDataSet("chibds", "Chib RooDataSet", tree, dataArgSet)
# Selection
cuts = "photon_pt > 0.0" + \
"&&dimuon_pt > 9.5" \
"&& abs(photon_eta) < 1.4" + \
"&& abs(dimuon_rapidity) < 1.25 " + \
"&& Y3S_nsigma < 2.5 " + \
"&& abs(dz) < 0.1"
rds_cutted = dataSet.reduce(RooFit.Cut(cuts))
dofit(rds_cutted, "Chib_fit_2012_3S")
def gendata(filename, q2Bin, nSample):
time_start = time.time()
obs = getobs()
file = TFile.Open(filename)
if not file:
print('File not found: ', filename)
return
wspname = 'ws_b{}p0'.format(q2Bin)
wsp = file.Get(wspname)
if not wsp:
print('Workspace not found in file: ', filename)
return
ctK = wsp.var('ctK')
ctL = wsp.var('ctL')
phi = wsp.var('phi')
rand = wsp.var("rand")
if (not ctK) or (not ctL) or (not phi):
print('Variables not found in file: ', filename)
return
vars = RooArgList(ctK, ctL, phi)
datasetname = 'data_genDen_ev_b{}'.format(q2Bin)
fullData = wsp.data(datasetname)
if not fullData:
print('DataSet ', datasetname, ' not found in file: ', filename)
return
data = RooDataSet()
if nSample == 0:
data = fullData
elif nSample <= 10000:
data = fullData.reduce(
RooArgSet(vars), 'rand > {:1.6f} && rand < {:1.6f}'.format(
(nSample - 1) * dataStat[q2Bin] / genStat[q2Bin],
nSample * dataStat[q2Bin] / genStat[q2Bin]))
else:
data = fullData.reduce(
RooArgSet(vars), 'rand > {:.6f} && rand < {:.6f}'.format(
(nSample % 10000 - 1) * d16Stat[q2Bin] / genStat[q2Bin],
(nSample % 10000) * d16Stat[q2Bin] / genStat[q2Bin]))
print('Fit ', data.numEntries(), ' events')
###convert the final dataset to pd.dataframe
datalist = []
for i in range(0, data.numEntries()):
ctKvalue = data.get(i).getRealValue('ctK')
ctLvalue = data.get(i).getRealValue('ctL')
phivalue = data.get(i).getRealValue('phi')
datalist.append([ctKvalue, ctLvalue, phivalue])
#print ('lenth of datalist ', len(datalist))
finaldata_df = pd.DataFrame(datalist, columns=['costk', 'costl', 'phi'])
finaldata = zfit.Data.from_pandas(finaldata_df, obs=obs)
print('generate zfit data from RooDataSet successfully')
time_end = time.time()
time_c = time_end - time_start
print('time used in fit data import: ', time_c, 's')
return finaldata
def makeRooDataset(inputfile_name):
chibTree_name = 'rootuple/chibTree'
inputfile = TFile.Open(inputfile_name,"READ")
tree = TTree()
inputfile.GetObject(chibTree_name, tree)
invm1S = RooRealVar("invm1S", "invm1S", 9.7, 10.1)#9.5, 11.5
invm2S = RooRealVar("invm2S", "invm2S", 9.5, 13.0)
invm3S = RooRealVar("invm3S", "invm3S", 9.5, 13.0)
chib_mass = RooRealVar("chib_mass", "chib_mass", 5., 15.0)
chib_pt = RooRealVar("chib_pt", "chib_pt", 0., 300.)
chib_eta = RooRealVar("chib_eta", "chib_eta", -5., 5.)
chib_phi = RooRealVar("chib_phi", "chib_phi", -3.2, 3.2)
dimuon_mass = RooRealVar("dimuon_mass","dimuon_mass", 8.0, 12.0)
dimuon_rapidity = RooRealVar("dimuon_rapidity", "dimuon_rapidity", -5.0, 5.0)
dimuon_pt = RooRealVar("dimuon_pt","dimuon_pt", 0.0, 100.0)
photon_eta = RooRealVar("photon_eta","photon_eta", -5.0, 5.0)
photon_pt = RooRealVar("photon_pt","photon_pt", 0.0, 100.0)
ctpv = RooRealVar("ctpv","ctpv", -5.0, 5.0)
ctpv_error = RooRealVar("ctpv_err","ctpv_err", -5.0, 5.0)
pi0_abs_mass = RooRealVar("pi0_abs_mass","pi0_abs_mass", 0.0, 2.0)
Y1S_nsigma = RooRealVar("Y1S_nsigma","Y1S_nsigma",0.0,30.0)
Y2S_nsigma = RooRealVar("Y2S_nsigma","Y2S_nsigma",0.0,30.0)
Y3S_nsigma = RooRealVar("Y3S_nsigma","Y3S_nsigma",0.0,35.0)
conv_vertex = RooRealVar("conv_vertex", "conv_vertex", 0.0, 70.0)
dz = RooRealVar("dz","dz", 0., 0.6)
numPrimaryVertices = RooRealVar("numPrimaryVertices","numPrimaryVertices",0,60)
rf1S_chib_mass = RooRealVar("rf1S_chib_mass","rf1S_chib_mass",9.5,60.)
probFit1S = RooRealVar("probFit1S","probFit1S",0.,1.)
rf1S_chib_pt = RooRealVar("rf1S_chib_pt", "rf1S_chib_pt", 0., 300.)
rf1S_chib_eta = RooRealVar("rf1S_chib_eta", "rf1S_chib_eta", -5., 5.)
rf1S_dimuon_mass = RooRealVar("rf1S_dimuon_mass","rf1S_dimuon_mass", 8.0, 12.0)
rf1S_dimuon_rapidity = RooRealVar("rf1S_dimuon_rapidity", "rf1S_dimuon_rapidity", -5.0, 5.0)
rf1S_dimuon_pt = RooRealVar("rf1S_dimuon_pt","rf1S_dimuon_pt", 0.0, 230.0)
rf1S_photon_eta = RooRealVar("rf1S_photon_eta","rf1S_photon_eta", -5.0, 5.0)
rf1S_photon_pt = RooRealVar("rf1S_photon_pt","rf1S_photon_pt", 0.0, 100.0)
rf2S_chib_mass = RooRealVar("rf2S_chib_mass","rf2S_chib_mass",9.5,60.)
probFit2S = RooRealVar("probFit2S","probFit2S",0.,1.)
rf3S_chib_mass = RooRealVar("rf3S_chib_mass","rf3S_chib_mass",9.5,60.)
probFit3S = RooRealVar("probFit3S","probFit3S",0.,1.)
muonP_pt = RooRealVar("muonP_pt","muonP_pt",0.,100.)
muonM_pt = RooRealVar("muonM_pt","muonM_pt",0.,100.)
muonP_eta = RooRealVar("muonP_eta","muonP_eta",-5.,5.)
muonM_eta = RooRealVar("muonM_eta","muonM_eta",-5.,5.)
dataArgSet = RooArgSet(invm1S, invm2S, invm3S, dimuon_mass, dimuon_rapidity, dimuon_pt, photon_eta, photon_pt, ctpv)
dataArgSet.add(RooArgSet(chib_mass, chib_pt, chib_eta, chib_phi))
dataArgSet.add(RooArgSet(ctpv_error, pi0_abs_mass, Y1S_nsigma, Y2S_nsigma, Y3S_nsigma, conv_vertex, dz, numPrimaryVertices))
dataArgSet.add(RooArgSet(rf1S_chib_mass, probFit1S, rf2S_chib_mass, probFit2S, rf3S_chib_mass, probFit3S))
dataArgSet.add(RooArgSet(rf1S_chib_pt, rf1S_chib_eta, rf1S_dimuon_mass, rf1S_dimuon_rapidity, rf1S_dimuon_pt, rf1S_photon_eta, rf1S_photon_pt))
dataArgSet.add(RooArgSet(muonP_pt, muonM_pt, muonP_eta, muonM_eta))
dataSet = RooDataSet("chibds","Chib RooDataSet", tree, dataArgSet)
return dataSet
def CreateSimData(w, typ):
sim_dsets = []
for c in range(w.cat("cat").numTypes()):
w.cat("cat").setIndex(c)
samp = w.cat("cat").getLabel()
name = "%s_sim_data" % typ if c == 0 else "%s_sim_data%d" % (typ, c)
sim_dsets.append(
RooDataSet(name, "", RooArgSet(w.var("B_DTFDict_D0_B_M")),
rf.Index(w.cat("cat")),
rf.Import(samp, w.data("%s_%s" % (typ, samp)))))
if c != 0:
sim_dsets[0].append(sim_dsets[c])
getattr(w, 'import')(sim_dsets[0])
def createDataSet(self, dname, dcut):
"""Return named dataset, create if not exist"""
if dname in self.dataset.keys() and not self.process.cfg['args'].force:
self.logger.logINFO("\033[0;34;47m Dataset: ", dname,
" Already Exists! \033[0m. Total Entries:",
self.dataset[dname].sumEntries())
self.logger.logDEBUG(dcut, Stamp=False)
return 1
tempfile_preload = ROOT.TFile(tempfile.gettempdir() + "/temp.root",
'RECREATE') #Pritam
RooCut = ROOT.RooFit.Cut(dcut)
Import = ROOT.RooFit.Import(self.ch)
Range = ROOT.RooFit.CutRange(
dname.split(".")[2]) # Not taking effect, need review
Weight = ROOT.RooFit.WeightVar(self.cfg['weight'])
if self.argset.find("Bmass"):
self.argset.find(
"Bmass").removeRange() # Analysis specific line introduced
if "dataReader" in dname or "sigMCGENReader" in dname:
data = RooDataSet(dname, "", self.argset, Import, RooCut, Range)
else:
data = RooDataSet(dname, "Weighted dataset", self.argset, Import,
RooCut, Range, Weight)
if self.argset.find("Phimass"):
self.argset.find("Phimass").setBins(20)
datahist = ROOT.RooDataHist(
dname + ".hist", "",
ROOT.RooArgSet(self.argset.find("Phimass")), data)
self.dataset[dname + ".hist"] = deepcopy(datahist)
data.Write()
self.dataset[dname] = deepcopy(data)
self.logger.logINFO("\033[0;34;47m Creating Dataset: ", dname,
": \033[0m. Total Entries:", data.sumEntries())
self.logger.logDEBUG(dcut, Stamp=False)
tempfile_preload.Close() #Pritam
return 1
def predict(self, x, theta_true):
"""
Run an unbinned ML fit to make predictions
"""
# Create RooDataSet
xs = self.scaler.transform(x)
preds = self.model.predict(xs)[:, 1]
min_nn_output_local, max_nn_output_local = np.min(preds), np.max(preds)
if min_nn_output_local < self.min_nn_output:
self.min_nn_output = min_nn_output_local
if max_nn_output_local > self.max_nn_output:
self.max_nn_output = max_nn_output_local
roodata = RooDataSet('data', 'data', RooArgSet(self.roopred))
for pred in preds:
self.roopred.setVal(pred)
roodata.add(RooArgSet(self.roopred))
# Fit
theta = RooRealVar('theta', 'theta', 0.5, self.theta_min, self.theta_max)
model = RooAddPdf('model', 'model',
RooArgList(self.pdfs['A'], self.pdfs['H']),
RooArgList(theta))
with stdout_redirected_to('%s/minuit_output.log' % self.outdir):
res = model.fitTo(roodata, Save(True))
nll = res.minNll()
fitstatus = res.status()
fitstatus |= (not subprocess.call(['grep', 'p.d.f value is less than zero', 'output_MLE_unbinned/minuit_output.log']))
fitted_theta = theta.getValV()
# Get Lambda(theta_true | theta_best)
logl = model.createNLL(roodata)
theta.setVal(theta_true)
nll_theta_true = logl.getValV()
nll_ratio = nll_theta_true - nll
return fitted_theta, nll, nll_ratio, fitstatus
def fit(self):
pdfs = RooArgList()
obsvars = RooArgSet('set')
for constraint in self.constraint.values():
pdfs.add(constraint.pdf_constraint)
if hasattr(constraint, 'var_obs'):
obsvars.add(constraint.var_obs)
self.model = RooProdPdf('model', 'model', pdfs)
self.data = RooDataSet('data', 'data', obsvars)
self.data.add(obsvars)
self.data.Print()
self.fit_result = self.model.fitTo(self.data,
ROOT.RooFit.PrintLevel(3),
ROOT.RooFit.Optimize(1),
ROOT.RooFit.Hesse(1),
ROOT.RooFit.Minos(1),
ROOT.RooFit.Strategy(2),
ROOT.RooFit.Save(1))
def extract_data_in_categories(channel,input_file,ws):
pwd = gDirectory.GetPath()
fin = TFile.Open(input_file,'read')
gDirectory.cd(pwd)
tree = fin.Get('selected_zg')
data = RooDataSet('%s_data'%channel,
'real data %s channel'%channel,
tree,
ws.set('vars') )
data_in_ws = ws.data('%s_data'%channel)
if not not data_in_ws:
data_in_ws.append(data)
else:
getattr(ws,'import')(data)
def Split_DS(Save_DS=False):
ws_file = TFile(
"/afs/cern.ch/user/" + prefix + "/" + user + "/eos/lhcb/user/" +
prefix + "/" + user +
"/WrongSign/2015/WorkSpaces/Merged_Merged_WS.root", "read")
wsp = ws_file.Get("wspace")
ws_file.Close()
LOG_D0_IPCHI2_OWNPV = wsp.var("LOG_D0_IPCHI2_OWNPV")
Dst_DTF_D0_CTAU = wsp.var("Dst_DTF_D0_CTAU")
Dst_DTF_D0_M = wsp.var("Dst_DTF_D0_M")
DTF_D0sPi_M = wsp.var("DTF_D0sPi_M")
DTF_D0sPi_M.setMax(2020)
DTF_D0sPi_M.setMin(2000)
dataset_RS_tot = wsp.data("dataset_RS")
dataset_RS_tot.SetName("dataset_RS_tot")
varset = RooArgSet("varset")
varset.add(LOG_D0_IPCHI2_OWNPV)
varset.add(DTF_D0sPi_M)
varset.add(Dst_DTF_D0_CTAU)
varset.add(Dst_DTF_D0_M)
for i, bin in enumerate(decaytime_binnning):
start = datetime.now()
dataset_RS_dtb_init = RooDataSet(
"dataset_RS_dtb_init", "Decaytime bin" + str(i), dataset_RS_tot,
varset, "Dst_DTF_D0_CTAU>" + str(bin[0] * ctau) +
"&&Dst_DTF_D0_CTAU<" + str(bin[1] * ctau) + "&&" + offline_cut)
dataset_RS = Subtract_Distribution(dataset_RS_dtb_init, DTF_D0sPi_M,
LOG_D0_IPCHI2_OWNPV, str(i))
dataset_RS.SetName("dataset_RS")
wspace = RooWorkspace("wspace")
wsfile2 = TFile(
"~/eos/lhcb/user/" + prefix + "/" + user +
"/WrongSign/2015/WorkSpaces/Merged_WS_Bin_" + str(i) + ".root",
"recreate")
wspace.rfimport(varset)
wspace.rfimport(dataset_RS)
wspace.Write("wspace")
wsfile2.Close()
print "Dataset " + str(i) + " creation took " + str(datetime.now() -
start) + " \n"
return True
def fill_dataset(varargset, ftree, wt, wtvar, cut=''):
"""Return a dataset (slow, get_dataset is more efficient).
Return a dataset from the ntuple `ftree', also apply `cut'. Use
`wt' as the weight expression in the tree. `wtvar' is the
corresponding RooRealVar weight. Note, varargset should contain
wtvar.
The dataset is filled by iterating over the tree. This is needed
when you want to ensure different datasets have the same weight
variable names, so that they can be combined later on. This is
needed even if they are combined as different categories.
"""
from rplot.fixes import ROOT
from ROOT import RooDataSet, RooFit, TTreeFormula
from helpers import suppress_warnings
suppress_warnings()
from rplot.tselect import Tsplice
splice = Tsplice(ftree)
splice.make_splice('sel', cut)
formulae = {}
wtname = wtvar.GetName()
for var in varargset:
name = var.GetName()
expr = wt if name == wtname else name
formulae[name] = TTreeFormula(name, expr, ftree)
dataset = RooDataSet('dataset', 'Dataset', varargset,
RooFit.WeightVar(wtvar))
for i in xrange(ftree.GetEntries()):
ftree.GetEntry(i)
for var, expr in formulae.iteritems():
realvar = varargset.find(var)
realvar.setVal(expr.EvalInstance())
dataset.add(varargset, varargset[wtname].getVal())
return dataset
def make_fit():
adc_bin = 12 #18 for low-m gg, 24 for jpsi
adc_min = 0. #10.
adc_max = 400.
#adc_max = 1200
ptmax = 0.18
#mmin = 1.6
#mmin = 2.1
#mmax = 2.6
#mmin = 1.5
#mmax = 5.
mmin = 2.9
mmax = 3.2
#mmin = 3.4
#mmax = 4.6
#east/west projections and 2D plot
ew = 1
p2d = 2 # 0: single projection by 'ew', 1: 2D plot, 2: both projections
#plot colors
model_col = rt.kMagenta
model_col = rt.kBlue
out = open("out.txt", "w")
lmg = 6
ut.log_results(out, "in " + infile, lmg)
strlog = "adc_bin " + str(adc_bin) + " adc_min " + str(
adc_min) + " adc_max " + str(adc_max)
strlog += " ptmax " + str(ptmax) + " mmin " + str(mmin) + " mmax " + str(
mmax)
ut.log_results(out, strlog, lmg)
#adc distributions
adc_east = RooRealVar("jZDCUnAttEast", "ZDC ADC east", adc_min, adc_max)
adc_west = RooRealVar("jZDCUnAttWest", "ZDC ADC west", adc_min, adc_max)
#kinematics variables
m = RooRealVar("jRecM", "e^{+}e^{-} mass (GeV)", 0., 10.)
y = RooRealVar("jRecY", "rapidity", -1., 1.)
pT = RooRealVar("jRecPt", "pT", 0., 10.)
#adc distributions
#adc_east = RooRealVar("zdce", "ZDC ADC east", adc_min, adc_max)
#adc_west = RooRealVar("zdcw", "ZDC ADC west", adc_min, adc_max)
#kinematics variables
#m = RooRealVar("mee", "e^{+}e^{-} mass (GeV)", 0., 10.)
#y = RooRealVar("rapee", "rapidity", -1., 1.)
#pT = RooRealVar("ptpair", "pT", 0., 10.)
strsel = "jRecPt<{0:.3f} && jRecM>{1:.3f} && jRecM<{2:.3f}".format(
ptmax, mmin, mmax)
#strsel = "ptpair<{0:.3f} && mee>{1:.3f} && mee<{2:.3f}".format(ptmax, mmin, mmax)
data_all = RooDataSet("data", "data", tree,
RooArgSet(adc_east, adc_west, m, y, pT))
print "All input:", data_all.numEntries()
data = data_all.reduce(strsel)
print "Sel input:", data.numEntries()
model = Model2D(adc_east, adc_west)
r1 = model.model.fitTo(data, rf.Save())
ut.log_results(out, ut.log_fit_result(r1), lmg)
ut.log_results(out, "Fit parameters:\n", lmg)
out.write(ut.log_fit_parameters(r1, lmg + 2) + "\n")
#out.write(ut.table_fit_parameters(r1))
#print ut.table_fit_parameters(r1)
#create the plot
if p2d != 2: can = ut.box_canvas()
nbins, adc_max = ut.get_nbins(adc_bin, adc_min, adc_max)
adc_east.setMax(adc_max)
adc_west.setMax(adc_max)
frame_east = adc_east.frame(rf.Bins(nbins), rf.Title(""))
frame_west = adc_west.frame(rf.Bins(nbins), rf.Title(""))
data.plotOn(frame_east, rf.Name("data"))
model.model.plotOn(frame_east, rf.Precision(1e-6), rf.Name("model"),
rf.LineColor(model_col))
data.plotOn(frame_west, rf.Name("data"))
model.model.plotOn(frame_west, rf.Precision(1e-6), rf.Name("model"),
rf.LineColor(model_col))
#reduced chi^2 in east and west projections
ut.log_results(out, "chi2/ndf:\n", lmg)
ut.log_results(
out,
" East chi2/ndf: " + str(frame_east.chiSquare("model", "data", 16)),
lmg)
ut.log_results(
out,
" West chi2/ndf: " + str(frame_west.chiSquare("model", "data", 16)),
lmg)
ut.log_results(out, "", 0)
ytit = "Events / ({0:.0f} ADC units)".format(adc_bin)
frame_east.SetYTitle(ytit)
frame_west.SetYTitle(ytit)
frame_east.SetTitle("")
frame_west.SetTitle("")
frame = [frame_east, frame_west]
if p2d == 0: plot_projection(frame[ew], ew)
plot_pdf = PlotPdf(model, adc_east, adc_west)
if p2d == 1: plot_2d(plot_pdf)
if p2d == 2:
frame2 = ut.prepare_TH1D("frame2", adc_bin, adc_min,
2. * adc_max + 4.1 * adc_bin)
plot_proj_both(frame2, frame_east, frame_west, adc_bin, adc_min,
adc_max, ptmax, mmin, mmax)
lhead = ["east ZDC", "west ZDC"]
if p2d == 1:
leg = ut.prepare_leg(0.003, 0.9, 0.3, 0.1, 0.035)
else:
leg = ut.prepare_leg(0.66, 0.8, 0.32, 0.13, 0.03)
if p2d == 0: leg.AddEntry(None, "#bf{Projection to " + lhead[ew] + "}", "")
leg.SetMargin(0.05)
leg.AddEntry(None,
"#bf{#it{p}_{T} < " + "{0:.2f}".format(ptmax) + " GeV/c}", "")
mmin_fmt = "{0:.1f}".format(mmin)
mmax_fmt = "{0:.1f}".format(mmax)
leg.AddEntry(
None, "#bf{" + mmin_fmt + " < #it{m}_{e^{+}e^{-}} < " + mmax_fmt +
" GeV/c^{2}}", "")
leg.Draw("same")
pleg = ut.prepare_leg(0.99, 0.87, -0.4, 0.11, 0.035)
pleg.SetFillStyle(1001)
#pleg.AddEntry(None, "STAR Preliminary", "")
pleg.AddEntry(None, "AuAu@200 GeV", "")
pleg.AddEntry(None, "UPC sample", "")
#pleg.Draw("same")
#ut.print_pad(gPad)
#b3d = TBuffer3D(0)
#b3d = None
#gPad.GetViewer3D().OpenComposite(b3d)
#print b3d
#print "All input: ", data.numEntries()
#print "All input: 858"
#all input data
nall = float(tree.Draw("", strsel))
print "All input: ", nall
n_1n1n = float(model.num_1n1n.getVal())
print "1n1n events: ", n_1n1n
ratio_1n1n = n_1n1n / nall
sigma_ratio_1n1n = ratio_1n1n * TMath.Sqrt(
(nall - n_1n1n) / (nall * n_1n1n))
print "Ratio 1n1n / all: ", ratio_1n1n, "+/-", sigma_ratio_1n1n
ut.log_results(out, "Fraction of 1n1n events:\n", lmg)
ut.log_results(out, "All input: " + str(nall), lmg)
ut.log_results(out, "1n1n events: " + str(model.num_1n1n.getVal()), lmg)
ratio_str = "Ratio 1n1n / all: " + str(ratio_1n1n) + " +/- " + str(
sigma_ratio_1n1n)
ut.log_results(out, ratio_str, lmg)
if p2d != 2:
#ut.print_pad(gPad)
ut.invert_col(gPad)
can.SaveAs("01fig.pdf")
if interactive == True: start_interactive()
def signal(channel, stype):
if 'VBF' in channel:
stype = 'XZHVBF'
else:
stype = 'XZH'
# HVT model
if stype.startswith('X'):
signalType = 'HVT'
genPoints = [800, 1000, 1200, 1400, 1600, 1800, 2000, 2500, 3000, 3500, 4000, 4500, 5000]
massPoints = [x for x in range(800, 5000+1, 100)]
interPar = True
else:
print "Signal type", stype, "not recognized"
return
n = len(genPoints)
category = channel
cColor = color[category] if category in color else 1
nElec = channel.count('e')
nMuon = channel.count('m')
nLept = nElec + nMuon
nBtag = channel.count('b')
if '0b' in channel:
nBtag = 0
X_name = "VH_mass"
if not os.path.exists(PLOTDIR+stype+category): os.makedirs(PLOTDIR+stype+category)
#*******************************************************#
# #
# Variables and selections #
# #
#*******************************************************#
X_mass = RooRealVar( "X_mass", "m_{ZH}", XBINMIN, XBINMAX, "GeV")
J_mass = RooRealVar( "H_mass", "jet mass", LOWMIN, HIGMAX, "GeV")
V_mass = RooRealVar( "V_mass", "V jet mass", -9., 1.e6, "GeV")
CSV1 = RooRealVar( "H_csv1", "", -999., 2. )
CSV2 = RooRealVar( "H_csv2", "", -999., 2. )
DeepCSV1= RooRealVar( "H_deepcsv1", "", -999., 2. )
DeepCSV2= RooRealVar( "H_deepcsv2", "", -999., 2. )
H_ntag = RooRealVar( "H_ntag", "", -9., 9. )
H_dbt = RooRealVar( "H_dbt", "", -2., 2. )
H_tau21 = RooRealVar( "H_tau21", "", -9., 2. )
H_eta = RooRealVar( "H_eta", "", -9., 9. )
H_tau21_ddt = RooRealVar( "H_ddt", "", -9., 2. )
MaxBTag = RooRealVar( "MaxBTag", "", -10., 2. )
H_chf = RooRealVar( "H_chf", "", -1., 2. )
MinDPhi = RooRealVar( "MinDPhi", "", -1., 99. )
DPhi = RooRealVar( "DPhi", "", -1., 99. )
DEta = RooRealVar( "DEta", "", -1., 99. )
Mu1_relIso = RooRealVar( "Mu1_relIso", "", -1., 99. )
Mu2_relIso = RooRealVar( "Mu2_relIso", "", -1., 99. )
nTaus = RooRealVar( "nTaus", "", -1., 99. )
Vpt = RooRealVar( "V.Pt()", "", -1., 1.e6 )
V_pt = RooRealVar( "V_pt", "", -1., 1.e6 )
H_pt = RooRealVar( "H_pt", "", -1., 1.e6 )
VH_deltaR=RooRealVar( "VH_deltaR", "", -1., 99. )
isZtoNN = RooRealVar( "isZtoNN", "", 0., 2. )
isZtoEE = RooRealVar( "isZtoEE", "", 0., 2. )
isZtoMM = RooRealVar( "isZtoMM", "", 0., 2. )
isHtobb = RooRealVar( "isHtobb", "", 0., 2. )
isVBF = RooRealVar( "isVBF", "", 0., 2. )
isMaxBTag_loose = RooRealVar( "isMaxBTag_loose", "", 0., 2. )
weight = RooRealVar( "eventWeightLumi", "", -1.e9, 1.e9 )
Xmin = XBINMIN
Xmax = XBINMAX
# Define the RooArgSet which will include all the variables defined before
# there is a maximum of 9 variables in the declaration, so the others need to be added with 'add'
variables = RooArgSet(X_mass, J_mass, V_mass, CSV1, CSV2, H_ntag, H_dbt, H_tau21)
variables.add(RooArgSet(DEta, DPhi, MaxBTag, MinDPhi, nTaus, Vpt))
variables.add(RooArgSet(DeepCSV1, DeepCSV2,VH_deltaR, H_tau21_ddt))
variables.add(RooArgSet(isZtoNN, isZtoEE, isZtoMM, isHtobb, isMaxBTag_loose, weight))
variables.add(RooArgSet(isVBF, Mu1_relIso, Mu2_relIso, H_chf, H_pt, V_pt,H_eta))
#X_mass.setRange("X_extended_range", X_mass.getMin(), X_mass.getMax())
X_mass.setRange("X_reasonable_range", X_mass.getMin(), X_mass.getMax())
X_mass.setRange("X_integration_range", Xmin, Xmax)
X_mass.setBins(int((X_mass.getMax() - X_mass.getMin())/100))
binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin())/100), X_mass.getMin(), X_mass.getMax())
X_mass.setBinning(binsXmass, "PLOT")
massArg = RooArgSet(X_mass)
# Cuts
SRcut = selection[category]+selection['SR']
print " Cut:\t", SRcut
#*******************************************************#
# #
# Signal fits #
# #
#*******************************************************#
treeSign = {}
setSignal = {}
vmean = {}
vsigma = {}
valpha1 = {}
vslope1 = {}
smean = {}
ssigma = {}
salpha1 = {}
sslope1 = {}
salpha2 = {}
sslope2 = {}
a1 = {}
a2 = {}
sbrwig = {}
signal = {}
signalExt = {}
signalYield = {}
signalIntegral = {}
signalNorm = {}
signalXS = {}
frSignal = {}
frSignal1 = {}
frSignal2 = {}
frSignal3 = {}
# Signal shape uncertainties (common amongst all mass points)
xmean_fit = RooRealVar("sig_p1_fit", "Variation of the resonance position with the fit uncertainty", 0.005, -1., 1.)
smean_fit = RooRealVar("CMSRunII_sig_p1_fit", "Change of the resonance position with the fit uncertainty", 0., -10, 10)
xmean_jes = RooRealVar("sig_p1_scale_jes", "Variation of the resonance position with the jet energy scale", 0.010, -1., 1.) #0.001
smean_jes = RooRealVar("CMSRunII_sig_p1_jes", "Change of the resonance position with the jet energy scale", 0., -10, 10)
xmean_e = RooRealVar("sig_p1_scale_e", "Variation of the resonance position with the electron energy scale", 0.001, -1., 1.)
smean_e = RooRealVar("CMSRunII_sig_p1_scale_e", "Change of the resonance position with the electron energy scale", 0., -10, 10)
xmean_m = RooRealVar("sig_p1_scale_m", "Variation of the resonance position with the muon energy scale", 0.001, -1., 1.)
smean_m = RooRealVar("CMSRunII_sig_p1_scale_m", "Change of the resonance position with the muon energy scale", 0., -10, 10)
xsigma_fit = RooRealVar("sig_p2_fit", "Variation of the resonance width with the fit uncertainty", 0.02, -1., 1.)
ssigma_fit = RooRealVar("CMSRunII_sig_p2_fit", "Change of the resonance width with the fit uncertainty", 0., -10, 10)
xsigma_jes = RooRealVar("sig_p2_scale_jes", "Variation of the resonance width with the jet energy scale", 0.010, -1., 1.) #0.001
ssigma_jes = RooRealVar("CMSRunII_sig_p2_jes", "Change of the resonance width with the jet energy scale", 0., -10, 10)
xsigma_jer = RooRealVar("sig_p2_scale_jer", "Variation of the resonance width with the jet energy resolution", 0.020, -1., 1.)
ssigma_jer = RooRealVar("CMSRunII_sig_p2_jer", "Change of the resonance width with the jet energy resolution", 0., -10, 10)
xsigma_e = RooRealVar("sig_p2_scale_e", "Variation of the resonance width with the electron energy scale", 0.001, -1., 1.)
ssigma_e = RooRealVar("CMSRunII_sig_p2_scale_e", "Change of the resonance width with the electron energy scale", 0., -10, 10)
xsigma_m = RooRealVar("sig_p2_scale_m", "Variation of the resonance width with the muon energy scale", 0.040, -1., 1.)
ssigma_m = RooRealVar("CMSRunII_sig_p2_scale_m", "Change of the resonance width with the muon energy scale", 0., -10, 10)
xalpha1_fit = RooRealVar("sig_p3_fit", "Variation of the resonance alpha with the fit uncertainty", 0.03, -1., 1.)
salpha1_fit = RooRealVar("CMSRunII_sig_p3_fit", "Change of the resonance alpha with the fit uncertainty", 0., -10, 10)
xslope1_fit = RooRealVar("sig_p4_fit", "Variation of the resonance slope with the fit uncertainty", 0.10, -1., 1.)
sslope1_fit = RooRealVar("CMSRunII_sig_p4_fit", "Change of the resonance slope with the fit uncertainty", 0., -10, 10)
xmean_fit.setConstant(True)
smean_fit.setConstant(True)
xmean_jes.setConstant(True)
smean_jes.setConstant(True)
xmean_e.setConstant(True)
smean_e.setConstant(True)
xmean_m.setConstant(True)
smean_m.setConstant(True)
xsigma_fit.setConstant(True)
ssigma_fit.setConstant(True)
xsigma_jes.setConstant(True)
ssigma_jes.setConstant(True)
xsigma_jer.setConstant(True)
ssigma_jer.setConstant(True)
xsigma_e.setConstant(True)
ssigma_e.setConstant(True)
xsigma_m.setConstant(True)
ssigma_m.setConstant(True)
xalpha1_fit.setConstant(True)
salpha1_fit.setConstant(True)
xslope1_fit.setConstant(True)
sslope1_fit.setConstant(True)
# the alpha method is now done.
for m in massPoints:
signalString = "M%d" % m
signalMass = "%s_M%d" % (stype, m)
signalName = "%s%s_M%d" % (stype, category, m)
signalColor = sample[signalMass]['linecolor'] if signalName in sample else 1
# define the signal PDF
vmean[m] = RooRealVar(signalName + "_vmean", "Crystal Ball mean", m, m*0.5, m*1.25)
smean[m] = RooFormulaVar(signalName + "_mean", "@0*(1+@1*@2)*(1+@3*@4)*(1+@5*@6)*(1+@7*@8)", RooArgList(vmean[m], xmean_e, smean_e, xmean_m, smean_m, xmean_jes, smean_jes, xmean_fit, smean_fit))
vsigma[m] = RooRealVar(signalName + "_vsigma", "Crystal Ball sigma", m*0.035, m*0.01, m*0.4)
sigmaList = RooArgList(vsigma[m], xsigma_e, ssigma_e, xsigma_m, ssigma_m, xsigma_jes, ssigma_jes, xsigma_jer, ssigma_jer)
sigmaList.add(RooArgList(xsigma_fit, ssigma_fit))
ssigma[m] = RooFormulaVar(signalName + "_sigma", "@0*(1+@1*@2)*(1+@3*@4)*(1+@5*@6)*(1+@7*@8)*(1+@9*@10)", sigmaList)
valpha1[m] = RooRealVar(signalName + "_valpha1", "Crystal Ball alpha", 1., 0., 5.) # number of sigmas where the exp is attached to the gaussian core. >0 left, <0 right
salpha1[m] = RooFormulaVar(signalName + "_alpha1", "@0*(1+@1*@2)", RooArgList(valpha1[m], xalpha1_fit, salpha1_fit))
vslope1[m] = RooRealVar(signalName + "_vslope1", "Crystal Ball slope", 10., 1., 60.) # slope of the power tail #10 1 60
sslope1[m] = RooFormulaVar(signalName + "_slope1", "@0*(1+@1*@2)", RooArgList(vslope1[m], xslope1_fit, sslope1_fit))
salpha2[m] = RooRealVar(signalName + "_alpha2", "Crystal Ball alpha", 2, 1., 5.) # number of sigmas where the exp is attached to the gaussian core. >0 left, <0 right
sslope2[m] = RooRealVar(signalName + "_slope2", "Crystal Ball slope", 10, 1.e-1, 115.) # slope of the power tail
#define polynomial
#a1[m] = RooRealVar(signalName + "_a1", "par 1 for polynomial", m, 0.5*m, 2*m)
a1[m] = RooRealVar(signalName + "_a1", "par 1 for polynomial", 0.001*m, 0.0005*m, 0.01*m)
a2[m] = RooRealVar(signalName + "_a2", "par 2 for polynomial", 0.05, -1.,1.)
#if channel=='nnbbVBF' or channel=='nn0bVBF':
# signal[m] = RooPolynomial(signalName,"m_{%s'} = %d GeV" % (stype[1], m) , X_mass, RooArgList(a1[m],a2[m]))
#else:
# signal[m] = RooCBShape(signalName, "m_{%s'} = %d GeV" % (stype[1], m), X_mass, smean[m], ssigma[m], salpha1[m], sslope1[m]) # Signal name does not have the channel
signal[m] = RooCBShape(signalName, "m_{%s'} = %d GeV" % (stype[1], m), X_mass, smean[m], ssigma[m], salpha1[m], sslope1[m]) # Signal name does not have the channel
# extend the PDF with the yield to perform an extended likelihood fit
signalYield[m] = RooRealVar(signalName+"_yield", "signalYield", 100, 0., 1.e6)
signalNorm[m] = RooRealVar(signalName+"_norm", "signalNorm", 1., 0., 1.e6)
signalXS[m] = RooRealVar(signalName+"_xs", "signalXS", 1., 0., 1.e6)
signalExt[m] = RooExtendPdf(signalName+"_ext", "extended p.d.f", signal[m], signalYield[m])
vslope1[m].setMax(50.)
vslope1[m].setVal(20.)
#valpha1[m].setVal(1.0)
#valpha1[m].setConstant(True)
if 'bb' in channel and 'VBF' not in channel:
if 'nn' in channel:
valpha1[m].setVal(0.5)
elif '0b' in channel and 'VBF' not in channel:
if 'nn' in channel:
if m==800:
valpha1[m].setVal(2.)
vsigma[m].setVal(m*0.04)
elif 'ee' in channel:
valpha1[m].setVal(0.8)
if m==800:
#valpha1[m].setVal(1.2)
valpha1[m].setVal(2.5)
vslope1[m].setVal(50.)
elif 'mm' in channel:
if m==800:
valpha1[m].setVal(2.)
vsigma[m].setVal(m*0.03)
else:
vmean[m].setVal(m*0.9)
vsigma[m].setVal(m*0.08)
elif 'bb' in channel and 'VBF' in channel:
if 'nn' in channel:
if m!=1800:
vmean[m].setVal(m*0.8)
vsigma[m].setVal(m*0.08)
valpha1[m].setMin(1.)
elif 'ee' in channel:
valpha1[m].setVal(0.7)
elif 'mm' in channel:
if m==800:
vslope1[m].setVal(50.)
valpha1[m].setVal(0.7)
elif '0b' in channel and 'VBF' in channel:
if 'nn' in channel:
valpha1[m].setVal(3.)
vmean[m].setVal(m*0.8)
vsigma[m].setVal(m*0.08)
valpha1[m].setMin(1.)
elif 'ee' in channel:
if m<2500:
valpha1[m].setVal(2.)
if m==800:
vsigma[m].setVal(m*0.05)
elif m==1000:
vsigma[m].setVal(m*0.03)
elif m>1000 and m<1800:
vsigma[m].setVal(m*0.04)
elif 'mm' in channel:
if m<2000:
valpha1[m].setVal(2.)
if m==1000 or m==1800:
vsigma[m].setVal(m*0.03)
elif m==1200 or m==1600:
vsigma[m].setVal(m*0.04)
#if m < 1000: vsigma[m].setVal(m*0.06)
# If it's not the proper channel, make it a gaussian
#if nLept==0 and 'VBF' in channel:
# valpha1[m].setVal(5)
# valpha1[m].setConstant(True)
# vslope1[m].setConstant(True)
# salpha2[m].setConstant(True)
# sslope2[m].setConstant(True)
# ---------- if there is no simulated signal, skip this mass point ----------
if m in genPoints:
if VERBOSE: print " - Mass point", m
# define the dataset for the signal applying the SR cuts
treeSign[m] = TChain("tree")
for j, ss in enumerate(sample[signalMass]['files']):
treeSign[m].Add(NTUPLEDIR + ss + ".root")
if treeSign[m].GetEntries() <= 0.:
if VERBOSE: print " - 0 events available for mass", m, "skipping mass point..."
signalNorm[m].setVal(-1)
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
salpha1[m].setConstant(True)
sslope1[m].setConstant(True)
salpha2[m].setConstant(True)
sslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
continue
setSignal[m] = RooDataSet("setSignal_"+signalName, "setSignal", variables, RooFit.Cut(SRcut), RooFit.WeightVar(weight), RooFit.Import(treeSign[m]))
if VERBOSE: print " - Dataset with", setSignal[m].sumEntries(), "events loaded"
# FIT
signalYield[m].setVal(setSignal[m].sumEntries())
if treeSign[m].GetEntries(SRcut) > 5:
if VERBOSE: print " - Running fit"
frSignal[m] = signalExt[m].fitTo(setSignal[m], RooFit.Save(1), RooFit.Extended(True), RooFit.SumW2Error(True), RooFit.PrintLevel(-1))
if VERBOSE: print "********** Fit result [", m, "] **", category, "*"*40, "\n", frSignal[m].Print(), "\n", "*"*80
if VERBOSE: frSignal[m].correlationMatrix().Print()
drawPlot(signalMass, stype+channel, X_mass, signal[m], setSignal[m], frSignal[m])
else:
print " WARNING: signal", stype, "and mass point", m, "in channel", channel, "has 0 entries or does not exist"
# Remove HVT cross section (which is the same for Zlep and Zinv)
if stype == "XZHVBF":
sample_name = 'Zprime_VBF_Zh_Zlephinc_narrow_M-%d' % m
else:
sample_name = 'ZprimeToZHToZlepHinc_narrow_M%d' % m
xs = xsection[sample_name]['xsec']
signalXS[m].setVal(xs * 1000.)
signalIntegral[m] = signalExt[m].createIntegral(massArg, RooFit.NormSet(massArg), RooFit.Range("X_integration_range"))
boundaryFactor = signalIntegral[m].getVal()
if VERBOSE:
print " - Fit normalization vs integral:", signalYield[m].getVal(), "/", boundaryFactor, "events"
if channel=='nnbb' and m==5000:
signalNorm[m].setVal(2.5)
elif channel=='nn0b' and m==5000:
signalNorm[m].setVal(6.7)
else:
signalNorm[m].setVal( boundaryFactor * signalYield[m].getVal() / signalXS[m].getVal()) # here normalize to sigma(X) x Br(X->VH) = 1 [fb]
a1[m].setConstant(True)
a2[m].setConstant(True)
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
valpha1[m].setConstant(True)
vslope1[m].setConstant(True)
salpha2[m].setConstant(True)
sslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
#*******************************************************#
# #
# Signal interpolation #
# #
#*******************************************************#
# ====== CONTROL PLOT ======
c_signal = TCanvas("c_signal", "c_signal", 800, 600)
c_signal.cd()
frame_signal = X_mass.frame()
for m in genPoints[:-2]:
if m in signalExt.keys():
signal[m].plotOn(frame_signal, RooFit.LineColor(sample["%s_M%d" % (stype, m)]['linecolor']), RooFit.Normalization(signalNorm[m].getVal(), RooAbsReal.NumEvent), RooFit.Range("X_reasonable_range"))
frame_signal.GetXaxis().SetRangeUser(0, 6500)
frame_signal.Draw()
drawCMS(-1, YEAR, "Simulation")
drawAnalysis(channel)
drawRegion(channel)
c_signal.SaveAs(PLOTDIR+"/"+stype+category+"/"+stype+"_Signal.pdf")
c_signal.SaveAs(PLOTDIR+"/"+stype+category+"/"+stype+"_Signal.png")
#if VERBOSE: raw_input("Press Enter to continue...")
# ====== CONTROL PLOT ======
# Normalization
gnorm = TGraphErrors()
gnorm.SetTitle(";m_{X} (GeV);integral (GeV)")
gnorm.SetMarkerStyle(20)
gnorm.SetMarkerColor(1)
gnorm.SetMaximum(0)
inorm = TGraphErrors()
inorm.SetMarkerStyle(24)
fnorm = TF1("fnorm", "pol9", 800, 5000) #"pol5" if not channel=="XZHnnbb" else "pol6" #pol5*TMath::Floor(x-1800) + ([5]*x + [6]*x*x)*(1-TMath::Floor(x-1800))
fnorm.SetLineColor(920)
fnorm.SetLineStyle(7)
fnorm.SetFillColor(2)
fnorm.SetLineColor(cColor)
# Mean
gmean = TGraphErrors()
gmean.SetTitle(";m_{X} (GeV);gaussian mean (GeV)")
gmean.SetMarkerStyle(20)
gmean.SetMarkerColor(cColor)
gmean.SetLineColor(cColor)
imean = TGraphErrors()
imean.SetMarkerStyle(24)
fmean = TF1("fmean", "pol1", 0, 5000)
fmean.SetLineColor(2)
fmean.SetFillColor(2)
# Width
gsigma = TGraphErrors()
gsigma.SetTitle(";m_{X} (GeV);gaussian width (GeV)")
gsigma.SetMarkerStyle(20)
gsigma.SetMarkerColor(cColor)
gsigma.SetLineColor(cColor)
isigma = TGraphErrors()
isigma.SetMarkerStyle(24)
fsigma = TF1("fsigma", "pol1", 0, 5000)
fsigma.SetLineColor(2)
fsigma.SetFillColor(2)
# Alpha1
galpha1 = TGraphErrors()
galpha1.SetTitle(";m_{X} (GeV);crystal ball lower alpha")
galpha1.SetMarkerStyle(20)
galpha1.SetMarkerColor(cColor)
galpha1.SetLineColor(cColor)
ialpha1 = TGraphErrors()
ialpha1.SetMarkerStyle(24)
falpha1 = TF1("falpha", "pol0", 0, 5000)
falpha1.SetLineColor(2)
falpha1.SetFillColor(2)
# Slope1
gslope1 = TGraphErrors()
gslope1.SetTitle(";m_{X} (GeV);exponential lower slope (1/Gev)")
gslope1.SetMarkerStyle(20)
gslope1.SetMarkerColor(cColor)
gslope1.SetLineColor(cColor)
islope1 = TGraphErrors()
islope1.SetMarkerStyle(24)
fslope1 = TF1("fslope", "pol0", 0, 5000)
fslope1.SetLineColor(2)
fslope1.SetFillColor(2)
# Alpha2
galpha2 = TGraphErrors()
galpha2.SetTitle(";m_{X} (GeV);crystal ball upper alpha")
galpha2.SetMarkerStyle(20)
galpha2.SetMarkerColor(cColor)
galpha2.SetLineColor(cColor)
ialpha2 = TGraphErrors()
ialpha2.SetMarkerStyle(24)
falpha2 = TF1("falpha", "pol0", 0, 5000)
falpha2.SetLineColor(2)
falpha2.SetFillColor(2)
# Slope2
gslope2 = TGraphErrors()
gslope2.SetTitle(";m_{X} (GeV);exponential upper slope (1/Gev)")
gslope2.SetMarkerStyle(20)
gslope2.SetMarkerColor(cColor)
gslope2.SetLineColor(cColor)
islope2 = TGraphErrors()
islope2.SetMarkerStyle(24)
fslope2 = TF1("fslope", "pol0", 0, 5000)
fslope2.SetLineColor(2)
fslope2.SetFillColor(2)
n = 0
for i, m in enumerate(genPoints):
if not m in signalNorm.keys(): continue
if signalNorm[m].getVal() < 1.e-6: continue
signalString = "M%d" % m
signalName = "%s_M%d" % (stype, m)
if gnorm.GetMaximum() < signalNorm[m].getVal(): gnorm.SetMaximum(signalNorm[m].getVal())
gnorm.SetPoint(n, m, signalNorm[m].getVal())
gmean.SetPoint(n, m, vmean[m].getVal())
gmean.SetPointError(n, 0, min(vmean[m].getError(), vmean[m].getVal()*0.02))
gsigma.SetPoint(n, m, vsigma[m].getVal())
gsigma.SetPointError(n, 0, min(vsigma[m].getError(), vsigma[m].getVal()*0.05))
galpha1.SetPoint(n, m, valpha1[m].getVal())
galpha1.SetPointError(n, 0, min(valpha1[m].getError(), valpha1[m].getVal()*0.10))
gslope1.SetPoint(n, m, vslope1[m].getVal())
gslope1.SetPointError(n, 0, min(vslope1[m].getError(), vslope1[m].getVal()*0.10))
galpha2.SetPoint(n, m, salpha2[m].getVal())
galpha2.SetPointError(n, 0, min(salpha2[m].getError(), salpha2[m].getVal()*0.10))
gslope2.SetPoint(n, m, sslope2[m].getVal())
gslope2.SetPointError(n, 0, min(sslope2[m].getError(), sslope2[m].getVal()*0.10))
n = n + 1
print "fit on gmean:"
gmean.Fit(fmean, "Q0", "SAME")
print "fit on gsigma:"
gsigma.Fit(fsigma, "Q0", "SAME")
print "fit on galpha:"
galpha1.Fit(falpha1, "Q0", "SAME")
print "fit on gslope:"
gslope1.Fit(fslope1, "Q0", "SAME")
galpha2.Fit(falpha2, "Q0", "SAME")
gslope2.Fit(fslope2, "Q0", "SAME")
#for m in [5000, 5500]: gnorm.SetPoint(gnorm.GetN(), m, gnorm.Eval(m, 0, "S"))
gnorm.Fit(fnorm, "Q", "SAME", 700, 5000)
for m in massPoints:
signalName = "%s_M%d" % (stype, m)
if vsigma[m].getVal() < 10.: vsigma[m].setVal(10.)
# Interpolation method
syield = gnorm.Eval(m)
spline = gnorm.Eval(m, 0, "S")
sfunct = fnorm.Eval(m)
#delta = min(abs(1.-spline/sfunct), abs(1.-spline/syield))
delta = abs(1.-spline/sfunct) if sfunct > 0 else 0
syield = spline
if interPar:
jmean = gmean.Eval(m)
jsigma = gsigma.Eval(m)
jalpha1 = galpha1.Eval(m)
jslope1 = gslope1.Eval(m)
else:
jmean = fmean.GetParameter(0) + fmean.GetParameter(1)*m + fmean.GetParameter(2)*m*m
jsigma = fsigma.GetParameter(0) + fsigma.GetParameter(1)*m + fsigma.GetParameter(2)*m*m
jalpha1 = falpha1.GetParameter(0) + falpha1.GetParameter(1)*m + falpha1.GetParameter(2)*m*m
jslope1 = fslope1.GetParameter(0) + fslope1.GetParameter(1)*m + fslope1.GetParameter(2)*m*m
inorm.SetPoint(inorm.GetN(), m, syield)
signalNorm[m].setVal(syield)
imean.SetPoint(imean.GetN(), m, jmean)
if jmean > 0: vmean[m].setVal(jmean)
isigma.SetPoint(isigma.GetN(), m, jsigma)
if jsigma > 0: vsigma[m].setVal(jsigma)
ialpha1.SetPoint(ialpha1.GetN(), m, jalpha1)
if not jalpha1==0: valpha1[m].setVal(jalpha1)
islope1.SetPoint(islope1.GetN(), m, jslope1)
if jslope1 > 0: vslope1[m].setVal(jslope1)
c1 = TCanvas("c1", "Crystal Ball", 1200, 800)
c1.Divide(2, 2)
c1.cd(1)
gmean.SetMinimum(0.)
gmean.Draw("APL")
imean.Draw("P, SAME")
drawRegion(channel)
c1.cd(2)
gsigma.SetMinimum(0.)
gsigma.Draw("APL")
isigma.Draw("P, SAME")
drawRegion(channel)
c1.cd(3)
galpha1.Draw("APL")
ialpha1.Draw("P, SAME")
drawRegion(channel)
galpha1.GetYaxis().SetRangeUser(0., 5.)
c1.cd(4)
gslope1.Draw("APL")
islope1.Draw("P, SAME")
drawRegion(channel)
gslope1.GetYaxis().SetRangeUser(0., 125.)
if False:
c1.cd(5)
galpha2.Draw("APL")
ialpha2.Draw("P, SAME")
drawRegion(channel)
c1.cd(6)
gslope2.Draw("APL")
islope2.Draw("P, SAME")
drawRegion(channel)
gslope2.GetYaxis().SetRangeUser(0., 10.)
c1.Print(PLOTDIR+stype+category+"/"+stype+"_SignalShape.pdf")
c1.Print(PLOTDIR+stype+category+"/"+stype+"_SignalShape.png")
c2 = TCanvas("c2", "Signal Efficiency", 800, 600)
c2.cd(1)
gnorm.SetMarkerColor(cColor)
gnorm.SetMarkerStyle(20)
gnorm.SetLineColor(cColor)
gnorm.SetLineWidth(2)
gnorm.Draw("APL")
inorm.Draw("P, SAME")
gnorm.GetXaxis().SetRangeUser(genPoints[0]-100, genPoints[-1]+100)
gnorm.GetYaxis().SetRangeUser(0., gnorm.GetMaximum()*1.25)
drawCMS(-1,YEAR , "Simulation")
drawAnalysis(channel)
drawRegion(channel)
c2.Print(PLOTDIR+stype+category+"/"+stype+"_SignalNorm.pdf")
c2.Print(PLOTDIR+stype+category+"/"+stype+"_SignalNorm.png")
#*******************************************************#
# #
# Generate workspace #
# #
#*******************************************************#
# create workspace
w = RooWorkspace("ZH_RunII", "workspace")
for m in massPoints:
getattr(w, "import")(signal[m], RooFit.Rename(signal[m].GetName()))
getattr(w, "import")(signalNorm[m], RooFit.Rename(signalNorm[m].GetName()))
getattr(w, "import")(signalXS[m], RooFit.Rename(signalXS[m].GetName()))
w.writeToFile("%s%s.root" % (WORKDIR, stype+channel), True)
print "Workspace", "%s%s.root" % (WORKDIR, stype+channel), "saved successfully"
sys.exit()
mmtt_mass = RooRealVar("xM", "xM" + region + "m(#mu#muKK)[GeV]", xmin, xmax)
tt_pt = RooRealVar("ttPt", "ttPt" + region + "p_{t}(KK)[GeV]", 0.0, ptmax)
mm_pt = RooRealVar("mmPt", "mmPt" + region + "p{t}(#mu#mu)[GeV]", 0.0, ptmax)
mmtt_pt = RooRealVar("xPt", "xPt" + region + "p_{t}(#mu#muKK)[GeV]", 0.0,
ptmax)
lxysig = RooRealVar("xL", "l_{xy} sign." + region + "l_{xy} / #sigma(l_{xy})",
-1000.0, 1000.0)
massvars = RooArgSet(tt_mass, mm_mass, mmtt_mass)
ptvars = RooArgSet(tt_pt, mm_pt, mmtt_pt)
kinvars = RooArgSet(massvars, ptvars)
extravars = RooArgSet(lxysig)
theData = RooDataSet("theData", "theData", theTree,
RooArgSet(kinvars, extravars))
c = TCanvas("canvas", "canvas", 1200, 800)
if args.nonprompt:
theData = theData.reduce("xL > 3.0")
if args.prompt:
theData = theData.reduce("xL < 1.5")
if args.ptcuts is not None:
theData = theData.reduce("trigp_pT > " + str(args.ptcuts))
theData = theData.reduce("trign_pT > " + str(args.ptcuts))
cuts += "P_t_" + str(args.ptcuts) + "_"
#### #### Plotting variables
#### TrakTrak Data
LbStabilityFit = False
inFile16 = TFile.Open(dataFileName)
inN16 = inFile16.Get('pLbL0/2016Data')
inn16 = inFile16.Get('nLbL0/2016Data')
# load parameters from other workspace and import them in current workspace
massLb = space.var('lbl0Mass')
massTkTk = space.var('tktkMass')
massLb.setBins(50)
massLb.setRange('sigRangeLb', 5.4, 5.9)
loadDatasetLb = RooDataSet(
'loadDatasetLb', 'loadData16', inN16,
RooArgSet(massLb, massTkTk, space.var('lbl0Pt'))).reduce(
'tktkMass>1.110&&tktkMass<1.120 &&{0}'.format(ptCut))
loadDatasetlB = RooDataSet(
'loadDatasetlB', 'loadData16', inn16,
RooArgSet(massLb, massTkTk, space.var('lbl0Pt'))).reduce(
'tktkMass>1.110&&tktkMass<1.120 &&{0}'.format(ptCut))
totNum = loadDatasetLb.sumEntries()
space.factory('numLb[{0},-1000.,{1}]'.format(
space1st.var('numLb').getVal(), totNum))
simulComponents = []
# scan Lb likelihood {{{
# jack's profile likelihood scan
if sysFitLb:
label = 'sysLbFit'
sample = ws.cat("sample")
simPdf = ws.pdf("simPdf")
efficiency = ws.var("efficiency")
meanB = ws.var("meanB")
# prepare_datasets(ws, p)
# Prepare datasets
datasetAllMap = {}
datasetPassMap = {}
hAllMap = {}
hPassMap = {}
for ptBin1 in range(0, len(ptBinsX)):
for ptBin2 in range(0, len(ptBinsY)):
datasetAllMap[(ptBin1, ptBin2)] = RooDataSet(
buildName("datasetAll_", ptBin1, ptBin2, ptBinsX, ptBinsY),
buildName("datasetAll_", ptBin1, ptBin2, ptBinsX, ptBinsY),
RooArgSet(mass))
datasetPassMap[(ptBin1, ptBin2)] = RooDataSet(
buildName("datasetPass_", ptBin1, ptBin2, ptBinsX, ptBinsY),
buildName("datasetPass_", ptBin1, ptBin2, ptBinsX, ptBinsY),
RooArgSet(mass))
hAllMap[(ptBin1, ptBin2)] = ROOT.TH1F(
buildName("hAll_", ptBin1, ptBin2, ptBinsX, ptBinsY),
buildName("All events passing old trigger ", ptBin1, ptBin2,
ptBinsX, ptBinsY), 100, 60, 120)
hPassMap[(ptBin1, ptBin2)] = ROOT.TH1F(
buildName("hPass_", ptBin1, ptBin2, ptBinsX, ptBinsY),
buildName("All events passing old trigger and new trigger ",
ptBin1, ptBin2, ptBinsX, ptBinsY), 100, 60, 120)
# Event loop
def fitMass(rangem=[0.4, 2.0], iflag=1, iopt_bkgshape=0, CBpar=[0., 0., 0.]):
global myc, fitres
m0 = sum(rangem) / 2
#w0=(rangem[1]-rangem[0])/10
w0 = 0.004
mass = RooRealVar("ee_ivm", "ee_ivm", rangem[0], rangem[1])
if iflag == 1:
###Construct signal pdf with gaus
mean = RooRealVar("mean", "mean", m0)
sigma = RooRealVar("sigma", "sigma", w0)
signal = RooGaussian("signal", "signal", mass, mean, sigma)
elif iflag == 2 or iflag == 3:
## Construct signal pdf with CB function
##print "outinfo",x,CBpar[0],CBpar[1],CBpar[2],CBpar[3]
cbmean = RooRealVar("cbmean", "cbmean", m0)
cbsigma = RooRealVar("cbsigma", "cbsigma", CBpar[0])
n1 = RooRealVar("n1", "", CBpar[1])
alpha = RooRealVar("alpha", "", CBpar[2])
cbsigma.setConstant(ROOT.kTRUE)
n1.setConstant(ROOT.kTRUE)
alpha.setConstant(ROOT.kTRUE)
signal = RooCBShape("cball", "crystal ball1", mass, cbmean, cbsigma,
alpha, n1)
# elif iflag ==3:
# pass
else:
print "ERROR, please specify signal shape for fitting!!"
sys.exit()
# Construct background pdf
a0 = RooRealVar("a0", "a0", 0.1, -1, 1)
a1 = RooRealVar("a1", "a1", 0.004, -1, 1)
a2 = RooRealVar("a2", "a2", 0.001, -1, 1)
if iopt_bkgshape == 0:
background = RooChebychev("background", "background", mass,
RooArgList(a0, a1))
else:
background = RooChebychev("background", "background", mass,
RooArgList(a0, a1, a2))
# Construct composite pdf
if iflag == 1:
up_nsig = 40
else:
up_nsig = 60
nsig = RooRealVar("nsig", "nsig", 5, 0.0, up_nsig)
nbkg = RooRealVar("nbkg", "nbkg", 800, 0, 3000)
#frac = RooRealVar("frac", "frac", 0.001, 0.0001, 0.1)
model = RooAddPdf("model", "model", RooArgList(signal, background),
RooArgList(nsig, nbkg))
#model = RooAddPdf("model", "model", RooArgList(signal, background), RooArgList(frac))
mcdata = RooDataSet(
"ds", "ds", RooArgSet(mass), RooFit.Import(data),
RooFit.Cut("ee_ivm<" + str(rangem[1]) + "&&ee_ivm>" + str(rangem[0])))
if optp == 1:
ipr = 1
verbose = 0
elif optp == 2:
ipr = 1
verbose = 1
else:
ipr = -1
verbose = 0
fitres=model.fitTo(mcdata,RooFit.Save(),RooFit.Minos(1), RooFit.Strategy(2),\
RooFit.PrintLevel(ipr), RooFit.Verbose(verbose))
nll = RooNLLVar("nll", "nll", model, mcdata,
RooFit.Range(rangem[0], rangem[1]))
pll = nll.createProfile(RooArgSet(nsig))
Profile = RooProfileLL("Profile", "Profile", nll, RooArgSet(nsig))
llhoodP = RooFormulaVar("llhoodP", "exp(-0.5*Profile)",
RooArgList(Profile))
xframe2 = nsig.frame(RooFit.Title("number of signal"))
nllplot = nll.plotOn(xframe2, RooFit.ShiftToZero())
themin = RooConstVar("themin", "themin", nllplot.GetMinimum())
llhood = RooFormulaVar("llhood", "exp(-0.5*(nll-themin*0.95))",
RooArgList(nll, themin))
if optp:
xframe = mass.frame(RooFit.Title("mass of ee pair"))
xframe3 = nsig.frame(RooFit.Title("number of signal"))
xframe3.SetYTitle("Likelihood")
mcdata.plotOn(xframe)
model.plotOn(xframe)
model.plotOn(xframe, RooFit.Components("background"),
RooFit.LineStyle(ROOT.kDashed),
RooFit.LineColor(ROOT.kRed))
model.plotOn(xframe, RooFit.Components("cball"),
RooFit.LineStyle(ROOT.kDashed),
RooFit.LineColor(ROOT.kGreen))
myc.cd(1)
xframe.Draw()
#pll.plotOn(xframe2,RooFit.LineColor(ROOT.kRed))
if optp: print "***** archmin ", themin.Print()
#llhoodP.plotOn(xframe3, RooFit.LineColor(ROOT.kRed))
llhood.plotOn(xframe3)
myc.cd(2)
xframe2.SetMinimum(0)
xframe2.Draw()
myc.cd(3)
xframe3.Draw()
myc.Update()
raw_input()
nsig.setRange("IntRange1", 0, 1000.)
Int1 = llhood.createIntegral(RooArgSet(nsig),
ROOT.RooFit.Range('IntRange1'))
Int1Val = Int1.getVal()
i = 0
hit = False
while not (hit):
i = i + 1
nsig.setRange("IntRange2", 0, float(i))
Int2 = llhood.createIntegral(RooArgSet(nsig),
ROOT.RooFit.Range('IntRange2'))
if Int2.getVal() >= Int1Val * 0.9:
if optp: print "&&& ", i
hit = True
return i
def fit_mass(data,
column,
x,
sig_pdf=None,
bkg_pdf=None,
n_sig=None,
n_bkg=None,
blind=False,
nll_profile=False,
second_storage=None,
log_plot=False,
pulls=True,
sPlot=False,
bkg_in_region=False,
importance=3,
plot_importance=3):
"""Fit a given pdf to a variable distribution
Parameter
---------
data : |hepds_type|
The data containing the variable to fit to
column : str
The name of the column to fit the pdf to
sig_pdf : RooFit pdf
The signal Probability Density Function. The variable to fit to has
to be named 'x'.
bkg_pdf : RooFit pdf
The background Probability Density Function. The variable to fit to has
to be named 'x'.
n_sig : None or numeric
The number of signals in the data. If it should be fitted, use None.
n_bkg : None or numeric
The number of background events in the data.
If it should be fitted, use None.
blind : boolean or tuple(numberic, numberic)
If False, the data is fitted. If a tuple is provided, the values are
used as the lower (the first value) and the upper (the second value)
limit of a blinding region, which will be omitted in plots.
Additionally, no true number of signal will be returned but only fake.
nll_profile : boolean
If True, a Negative Log-Likelihood Profile will be generated. Does not
work with blind fits.
second_storage : |hepds_type|
A second data-storage that will be concatenated with the first one.
importance : |importance_type|
|importance_docstring|
plot_importance : |plot_importance_type|
|plot_importance_docstring|
Return
------
tuple(numerical, numerical)
Return the number of signals and the number of backgrounds in the
signal-region. If a blind fit is performed, the signal will be a fake
number. If no number of background events is required, -999 will be
returned.
"""
if not (isinstance(column, str) or len(column) == 1):
raise ValueError("Fitting to several columns " + str(column) +
" not supported.")
if type(sig_pdf) == type(bkg_pdf) == None:
raise ValueError("sig_pdf and bkg_pdf are both None-> no fit possible")
if blind is not False:
lower_blind, upper_blind = blind
blind = True
n_bkg_below_sig = -999
# create data
data_name = data.name
data_array, _t1, _t2 = data.make_dataset(second_storage, columns=column)
del _t1, _t2
# double crystalball variables
min_x, max_x = min(data_array[column]), max(data_array[column])
# x = RooRealVar("x", "x variable", min_x, max_x)
# create data
data_array = np.array([i[0] for i in data_array.as_matrix()])
data_array.dtype = [('x', np.float64)]
tree1 = array2tree(data_array, "x")
data = RooDataSet("data", "Data", RooArgSet(x), RooFit.Import(tree1))
# # TODO: export somewhere? does not need to be defined inside...
# mean = RooRealVar("mean", "Mean of Double CB PDF", 5280, 5100, 5600)#, 5300, 5500)
# sigma = RooRealVar("sigma", "Sigma of Double CB PDF", 40, 0.001, 200)
# alpha_0 = RooRealVar("alpha_0", "alpha_0 of one side", 5.715)#, 0, 150)
# alpha_1 = RooRealVar("alpha_1", "alpha_1 of other side", -4.019)#, -200, 0.)
# lambda_0 = RooRealVar("lambda_0", "Exponent of one side", 3.42)#, 0, 150)
# lambda_1 = RooRealVar("lambda_1", "Exponent of other side", 3.7914)#, 0, 500)
#
# # TODO: export somewhere? pdf construction
# frac = RooRealVar("frac", "Fraction of crystal ball pdfs", 0.479, 0.01, 0.99)
#
# crystalball1 = RooCBShape("crystallball1", "First CrystalBall PDF", x,
# mean, sigma, alpha_0, lambda_0)
# crystalball2 = RooCBShape("crystallball2", "Second CrystalBall PDF", x,
# mean, sigma, alpha_1, lambda_1)
# doubleCB = RooAddPdf("doubleCB", "Double CrystalBall PDF",
# crystalball1, crystalball2, frac)
# n_sig = RooRealVar("n_sig", "Number of signals events", 10000, 0, 1000000)
# test input
if n_sig == n_bkg == 0:
raise ValueError("n_sig as well as n_bkg is 0...")
if n_bkg is None:
n_bkg = RooRealVar("n_bkg", "Number of background events", 10000, 0,
500000)
elif n_bkg >= 0:
n_bkg = RooRealVar("n_bkg", "Number of background events", int(n_bkg))
else:
raise ValueError("n_bkg is not >= 0 or None")
if n_sig is None:
n_sig = RooRealVar("n_sig", "Number of signal events", 1050, 0, 200000)
# START BLINDING
blind_cat = RooCategory("blind_cat", "blind state category")
blind_cat.defineType("unblind", 0)
blind_cat.defineType("blind", 1)
if blind:
blind_cat.setLabel("blind")
blind_n_sig = RooUnblindPrecision("blind_n_sig",
"blind number of signals",
"wasistdas", n_sig.getVal(),
10000, n_sig, blind_cat)
else:
# blind_cat.setLabel("unblind")
blind_n_sig = n_sig
print "n_sig value " + str(n_sig.getVal())
# raw_input("blind value " + str(blind_n_sig.getVal()))
# n_sig = blind_n_sig
# END BLINDING
elif n_sig >= 0:
n_sig = RooRealVar("n_sig", "Number of signal events", int(n_sig))
else:
raise ValueError("n_sig is not >= 0")
# if not blind:
# blind_n_sig = n_sig
# # create bkg-pdf
# lambda_exp = RooRealVar("lambda_exp", "lambda exp pdf bkg", -0.00025, -1., 1.)
# bkg_pdf = RooExponential("bkg_pdf", "Background PDF exp", x, lambda_exp)
if blind:
comb_pdf = RooAddPdf("comb_pdf", "Combined DoubleCB and bkg PDF",
RooArgList(sig_pdf, bkg_pdf),
RooArgList(blind_n_sig, n_bkg))
else:
comb_pdf = RooAddPdf("comb_pdf", "Combined DoubleCB and bkg PDF",
RooArgList(sig_pdf, bkg_pdf),
RooArgList(n_sig, n_bkg))
# create test dataset
# mean_gauss = RooRealVar("mean_gauss", "Mean of Gaussian", 5553, -10000, 10000)
# sigma_gauss = RooRealVar("sigma_gauss", "Width of Gaussian", 20, 0.0001, 300)
# gauss1 = RooGaussian("gauss1", "Gaussian test dist", x, mean_gauss, sigma_gauss)
# lambda_data = RooRealVar("lambda_data", "lambda exp data", -.002)
# exp_data = RooExponential("exp_data", "data example exp", x, lambda_data)
# frac_data = RooRealVar("frac_data", "Fraction PDF of data", 0.15)
#
# data_pdf = RooAddPdf("data_pdf", "Data PDF", gauss1, exp_data, frac_data)
# data = data_pdf.generate(RooArgSet(x), 30000)
# data.printValue()
# xframe = x.frame()
# data_pdf.plotOn(xframe)
# print "n_cpu:", meta_config.get_n_cpu()
# input("test")
# comb_pdf.fitTo(data, RooFit.Extended(ROOT.kTRUE), RooFit.NumCPU(meta_config.get_n_cpu()))
# HACK to get 8 cores in testing
c5 = TCanvas("c5", "RooFit pdf not fit vs " + data_name)
c5.cd()
x_frame1 = x.frame()
# data.plotOn(x_frame1)
# comb_pdf.pdfList()[1].plotOn(x_frame1)
if __name__ == "__main__":
n_cpu = 8
else:
n_cpu = meta_config.get_n_cpu()
print "n_cpu = ", n_cpu
# HACK
# n_cpu = 8
result_fit = comb_pdf.fitTo(data, RooFit.Minos(ROOT.kTRUE),
RooFit.Extended(ROOT.kTRUE),
RooFit.NumCPU(n_cpu))
# HACK end
if bkg_in_region:
x.setRange("signal", bkg_in_region[0], bkg_in_region[1])
bkg_pdf_fitted = comb_pdf.pdfList()[1]
int_argset = RooArgSet(x)
# int_argset = x
# int_argset.setRange("signal", bkg_in_region[0], bkg_in_region[1])
integral = bkg_pdf_fitted.createIntegral(int_argset,
RooFit.NormSet(int_argset),
RooFit.Range("signal"))
bkg_cdf = bkg_pdf_fitted.createCdf(int_argset, RooFit.Range("signal"))
bkg_cdf.plotOn(x_frame1)
# integral.plotOn(x_frame1)
n_bkg_below_sig = integral.getVal(int_argset) * n_bkg.getVal()
x_frame1.Draw()
if plot_importance >= 3:
c2 = TCanvas("c2", "RooFit pdf fit vs " + data_name)
c2.cd()
x_frame = x.frame()
# if log_plot:
# c2.SetLogy()
# x_frame.SetTitle("RooFit pdf vs " + data_name)
x_frame.SetTitle(data_name)
if pulls:
pad_data = ROOT.TPad("pad_data", "Pad with data and fit", 0, 0.33,
1, 1)
pad_pulls = ROOT.TPad("pad_pulls", "Pad with data and fit", 0, 0,
1, 0.33)
pad_data.SetBottomMargin(0.00001)
pad_data.SetBorderMode(0)
if log_plot:
pad_data.SetLogy()
pad_pulls.SetTopMargin(0.00001)
pad_pulls.SetBottomMargin(0.2)
pad_pulls.SetBorderMode(0)
pad_data.Draw()
pad_pulls.Draw()
pad_data.cd()
else:
if log_plot:
c2.SetLogy()
if blind:
# HACK
column = 'x'
# END HACK
x.setRange("lower", min_x, lower_blind)
x.setRange("upper", upper_blind, max_x)
range_str = "lower,upper"
lower_cut_str = str(
min_x) + "<=" + column + "&&" + column + "<=" + str(lower_blind)
upper_cut_str = str(
upper_blind) + "<=" + column + "&&" + column + "<=" + str(max_x)
sideband_cut_str = "(" + lower_cut_str + ")" + "||" + "(" + upper_cut_str + ")"
n_entries = data.reduce(
sideband_cut_str).numEntries() / data.numEntries()
# raw_input("n_entries: " + str(n_entries))
if plot_importance >= 3:
data.plotOn(x_frame, RooFit.CutRange(range_str),
RooFit.NormRange(range_str))
comb_pdf.plotOn(
x_frame, RooFit.Range(range_str),
RooFit.Normalization(n_entries, RooAbsReal.Relative),
RooFit.NormRange(range_str))
if pulls:
# pull_hist(pull_frame=x_frame, pad_data=pad_data, pad_pulls=pad_pulls)
x_frame_pullhist = x_frame.pullHist()
else:
if plot_importance >= 3:
data.plotOn(x_frame)
comb_pdf.plotOn(x_frame)
if pulls:
pad_pulls.cd()
x_frame_pullhist = x_frame.pullHist()
pad_data.cd()
comb_pdf.plotOn(x_frame,
RooFit.Components(sig_pdf.namePtr().GetName()),
RooFit.LineStyle(ROOT.kDashed))
comb_pdf.plotOn(x_frame,
RooFit.Components(bkg_pdf.namePtr().GetName()),
RooFit.LineStyle(ROOT.kDotted))
# comb_pdf.plotPull(n_sig)
if plot_importance >= 3:
x_frame.Draw()
if pulls:
pad_pulls.cd()
x_frame.SetTitleSize(0.05, 'Y')
x_frame.SetTitleOffset(0.7, 'Y')
x_frame.SetLabelSize(0.04, 'Y')
# c11 = TCanvas("c11", "RooFit\ pulls" + data_name)
# c11.cd()
# frame_tmp = x_frame
frame_tmp = x.frame()
# frame_tmp.SetTitle("significance")
frame_tmp.SetTitle("Roofit\ pulls\ " + data_name)
frame_tmp.addObject(x_frame_pullhist)
frame_tmp.SetMinimum(-5)
frame_tmp.SetMaximum(5)
# frame_tmp.GetYaxis().SetTitle("significance")
frame_tmp.GetYaxis().SetNdivisions(5)
frame_tmp.SetTitleSize(0.1, 'X')
frame_tmp.SetTitleOffset(1, 'X')
frame_tmp.SetLabelSize(0.1, 'X')
frame_tmp.SetTitleSize(0.1, 'Y')
frame_tmp.SetTitleOffset(0.5, 'Y')
frame_tmp.SetLabelSize(0.1, 'Y')
frame_tmp.Draw()
# raw_input("")
if not blind and nll_profile:
# nll_range = RooRealVar("nll_range", "Signal for nLL", n_sig.getVal(),
# -10, 2 * n_sig.getVal())
sframe = n_sig.frame(RooFit.Bins(20), RooFit.Range(1, 1000))
# HACK for best n_cpu
lnL = comb_pdf.createNLL(data, RooFit.NumCPU(8))
# HACK end
lnProfileL = lnL.createProfile(ROOT.RooArgSet(n_sig))
lnProfileL.plotOn(sframe, RooFit.ShiftToZero())
c4 = TCanvas("c4", "NLL Profile")
c4.cd()
# input("press ENTER to show plot")
sframe.Draw()
if plot_importance >= 3:
pass
params = comb_pdf.getVariables()
params.Print("v")
# print bkg_cdf.getVal()
if sPlot:
sPlotData = ROOT.RooStats.SPlot(
"sPlotData",
"sPlotData",
data, # variable fitted to, RooDataSet
comb_pdf, # fitted pdf
ROOT.RooArgList(
n_sig,
n_bkg,
# NSigB0s
))
sweights = np.array([
sPlotData.GetSWeight(i, 'n_sig') for i in range(data.numEntries())
])
return n_sig.getVal(), n_bkg_below_sig, sweights
if blind:
return blind_n_sig.getVal(), n_bkg_below_sig, comb_pdf
else:
return n_sig.getVal(), n_bkg_below_sig, comb_pdf
for i in range(len(tupleDict)):
varName = tupleDict.keys()[i]
varList += [
RooRealVar(varName,
tree1.GetBranch(varName).GetTitle(),
tree1.GetMinimum(varName), tree1.GetMaximum(varName))
]
varRenamedList += [RooFit.RenameVariable(varName, tupleDict[varName])]
#RooFit.RenameVariable(varName, tupleDict[varName]);
#varList[i].SetName(tupleDict.keys()[i]);
#tupleDataSet = RooDataSet("treeData","treeData",tree1,RooArgSet(*varList));
weightvar = RooRealVar("weight", "weight", -1e7, 1e7)
tupleDataSet = RooDataSet("tupleDataSet", "tupleDataSet",
RooArgSet(*varList), RooFit.Import(tree1),
RooFit.WeightVar(weightvar))
ws = RooWorkspace('ws_FIT')
tupleDataSet = WS(ws, tupleDataSet, varRenamedList)
tupleDataSet.Print()
sys.exit(0)
#qt needs to be a category?
# Manuel's shit...
#weightvar = RooRealVar("weight", "weight", -1e7, 1e7)
#tupleDataSet = RooDataSet("tupleDataSet", "tupleDataSet",
# RooArgSet(treetime, treeqt, treeqf, treeeta),
# RooFit.Import(tree1), RooFit.WeightVar(weightvar))
#tupleDS = WS(ws, tupleDS, [
# RooFit.RenameVariable("Bs_ctau", "time"), ... ])
def rooFit207():
print ">>> setup model signal components: gaussians..."
x = RooRealVar("x","x",0,10)
mean = RooRealVar("mean","mean of gaussians",5)
sigma = RooRealVar("sigma","width of gaussians",0.5)
sig = RooGaussian("sig","Signal",x,mean,sigma)
print ">>> setup model background components: Chebychev polynomial plus exponential..."
a0 = RooRealVar("a0","a0",0.5,0.,1.)
a1 = RooRealVar("a1","a1",-0.2,0.,1.)
bkg1 = RooChebychev("bkg1","Background 1",x,RooArgList(a0,a1))
alpha = RooRealVar("alpha","alpha",-1)
bkg2 = RooExponential("bkg2","Background 2",x,alpha)
bkg1frac = RooRealVar("bkg1frac","fraction of component 1 in background",0.2,0.,1.)
bkg = RooAddPdf("bkg","Signal",RooArgList(bkg1,bkg2),RooArgList(bkg1frac))
print ">>> sum signal and background component..."
bkgfrac = RooRealVar("bkgfrac","fraction of background",0.5,0.,1.)
model = RooAddPdf("model","g1+g2+a",RooArgList(bkg,sig),RooArgList(bkgfrac))
# Create dummy dataset that has more observables than the above pdf
y = RooRealVar("y","y",-10,10)
data = RooDataSet("data","data",RooArgSet(x,y))
# Basic information requests:"
print ">>> get list of observables of pdf in context of a dataset..."
# Observables are define each context as the variables
# shared between a model and a dataset. In this case
# that is the variable 'x'
model_obs = model.getObservables(data) # RooArgSet
model_obs.Print('v')
print "\n>>> get list of parameters..."
# Get list of parameters, given list of observables
model_params = model.getParameters(RooArgSet(x)) # RooArgSet
print ">>> model_params.getStringValue(\"a0\") = %s" % (model_params.getStringValue("a0"))
print ">>> model_params.getRealValue(\"a0\") = %s" % (model_params.getRealValue("a0"))
print ">>> model_params.find(\"a0\").GetName() = %s" % (model_params.find("a0").GetName())
print ">>> model_params.find(\"a0\").getVal() = %s" % (model_params.find("a0").getVal())
# print ">>> for param in model_params:"
# for param in model_params.():
# print ">>> %s"%(model_params.first())
# print ">>> %s"%(model_params.first())
# model_params.selectByName("a*").Print('v')
model_params.Print('v')
print "\n>>> get list of parameters of a dataset..."
# Gives identical results to operation above
model_params2 = model.getParameters(data) # RooArgSet
model_params2.Print()
print "\n>>> get list of components..."
# Get list of component objects, including top-level node
model_comps = model.getComponents() # RooArgSet
model_comps.Print('v')
print "\n>>> modifications to structure of composites..."
sigma2 = RooRealVar("sigma2","width of gaussians",1)
sig2 = RooGaussian("sig2","Signal component 1",x,mean,sigma2)
sig1frac = RooRealVar("sig1frac","fraction of component 1 in signal",0.8,0.,1.)
sigsum = RooAddPdf("sigsum","sig+sig2",RooArgList(sig,sig2),RooArgList(sig1frac))
print ">>> construct a customizer utility to customize model..."
cust = RooCustomizer(model,"cust")
print ">>> instruct the customizer to replace node 'sig' with node 'sigsum'..."
cust.replaceArg(sig,sigsum)
# Build a clone of the input pdf according to the above customization
# instructions. Each node that requires modified is clone so that the
# original pdf remained untouched. The name of each cloned node is that
# of the original node suffixed by the name of the customizer object
#
# The returned head node own all nodes that were cloned as part of
# the build process so when cust_clone is deleted so will all other
# nodes that were created in the process.
cust_clone = cust.build(kTRUE) # RooAbsPdf
# Print structure of clone of model with sig->sigsum replacement.
cust_clone.Print("t")
# delete clone
del cust_clone
def signal(category):
interPar = True
n = len(genPoints)
cColor = color[category] if category in color else 4
nBtag = category.count('b')
isAH = False #relict from using Alberto's more complex script
if not os.path.exists(PLOTDIR + "MC_signal_" + YEAR):
os.makedirs(PLOTDIR + "MC_signal_" + YEAR)
#*******************************************************#
# #
# Variables and selections #
# #
#*******************************************************#
X_mass = RooRealVar("jj_mass_widejet", "m_{jj}", X_min, X_max, "GeV")
j1_pt = RooRealVar("jpt_1", "jet1 pt", 0., 13000., "GeV")
jj_deltaEta = RooRealVar("jj_deltaEta_widejet", "", 0., 5.)
jbtag_WP_1 = RooRealVar("jbtag_WP_1", "", -1., 4.)
jbtag_WP_2 = RooRealVar("jbtag_WP_2", "", -1., 4.)
fatjetmass_1 = RooRealVar("fatjetmass_1", "", -1., 2500.)
fatjetmass_2 = RooRealVar("fatjetmass_2", "", -1., 2500.)
jid_1 = RooRealVar("jid_1", "j1 ID", -1., 8.)
jid_2 = RooRealVar("jid_2", "j2 ID", -1., 8.)
jnmuons_1 = RooRealVar("jnmuons_1", "j1 n_{#mu}", -1., 8.)
jnmuons_2 = RooRealVar("jnmuons_2", "j2 n_{#mu}", -1., 8.)
jnmuons_loose_1 = RooRealVar("jnmuons_loose_1", "jnmuons_loose_1", -1., 8.)
jnmuons_loose_2 = RooRealVar("jnmuons_loose_2", "jnmuons_loose_2", -1., 8.)
nmuons = RooRealVar("nmuons", "n_{#mu}", -1., 10.)
nelectrons = RooRealVar("nelectrons", "n_{e}", -1., 10.)
HLT_AK8PFJet500 = RooRealVar("HLT_AK8PFJet500", "", -1., 1.)
HLT_PFJet500 = RooRealVar("HLT_PFJet500", "", -1., 1.)
HLT_CaloJet500_NoJetID = RooRealVar("HLT_CaloJet500_NoJetID", "", -1., 1.)
HLT_PFHT900 = RooRealVar("HLT_PFHT900", "", -1., 1.)
HLT_AK8PFJet550 = RooRealVar("HLT_AK8PFJet550", "", -1., 1.)
HLT_PFJet550 = RooRealVar("HLT_PFJet550", "", -1., 1.)
HLT_CaloJet550_NoJetID = RooRealVar("HLT_CaloJet550_NoJetID", "", -1., 1.)
HLT_PFHT1050 = RooRealVar("HLT_PFHT1050", "", -1., 1.)
#HLT_DoublePFJets100_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets100_CaloBTagDeepCSV_p71" , "", -1., 1. )
#HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71", "", -1., 1. )
#HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71", "", -1., 1. )
#HLT_DoublePFJets200_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets200_CaloBTagDeepCSV_p71" , "", -1., 1. )
#HLT_DoublePFJets350_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets350_CaloBTagDeepCSV_p71" , "", -1., 1. )
#HLT_DoublePFJets40_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets40_CaloBTagDeepCSV_p71" , "", -1., 1. )
weight = RooRealVar("eventWeightLumi", "", -1.e9, 1.e9)
# Define the RooArgSet which will include all the variables defined before
# there is a maximum of 9 variables in the declaration, so the others need to be added with 'add'
variables = RooArgSet(X_mass)
variables.add(
RooArgSet(j1_pt, jj_deltaEta, jbtag_WP_1, jbtag_WP_2, fatjetmass_1,
fatjetmass_2, jnmuons_1, jnmuons_2, weight))
variables.add(
RooArgSet(nmuons, nelectrons, jid_1, jid_2, jnmuons_loose_1,
jnmuons_loose_2))
variables.add(
RooArgSet(HLT_AK8PFJet500, HLT_PFJet500, HLT_CaloJet500_NoJetID,
HLT_PFHT900, HLT_AK8PFJet550, HLT_PFJet550,
HLT_CaloJet550_NoJetID, HLT_PFHT1050))
#variables.add(RooArgSet(HLT_DoublePFJets100_CaloBTagDeepCSV_p71, HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71, HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71, HLT_DoublePFJets200_CaloBTagDeepCSV_p71, HLT_DoublePFJets350_CaloBTagDeepCSV_p71, HLT_DoublePFJets40_CaloBTagDeepCSV_p71))
X_mass.setRange("X_reasonable_range", X_mass.getMin(), X_mass.getMax())
X_mass.setRange("X_integration_range", X_mass.getMin(), X_mass.getMax())
if VARBINS:
binsXmass = RooBinning(len(abins) - 1, abins)
X_mass.setBinning(binsXmass)
plot_binning = RooBinning(
int((X_mass.getMax() - X_mass.getMin()) / 100.), X_mass.getMin(),
X_mass.getMax())
else:
X_mass.setBins(int((X_mass.getMax() - X_mass.getMin()) / 10))
binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin()) / 100.),
X_mass.getMin(), X_mass.getMax())
plot_binning = binsXmass
X_mass.setBinning(plot_binning, "PLOT")
#X_mass.setBins(int((X_mass.getMax() - X_mass.getMin())/10))
#binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin())/100), X_mass.getMin(), X_mass.getMax())
#X_mass.setBinning(binsXmass, "PLOT")
massArg = RooArgSet(X_mass)
# Cuts
if BTAGGING == 'semimedium':
SRcut = aliasSM[category]
#SRcut = aliasSM[category+"_vetoAK8"]
else:
SRcut = alias[category].format(WP=working_points[BTAGGING])
#SRcut = alias[category+"_vetoAK8"].format(WP=working_points[BTAGGING])
if ADDSELECTION: SRcut += SELECTIONS[options.selection]
print " Cut:\t", SRcut
#*******************************************************#
# #
# Signal fits #
# #
#*******************************************************#
treeSign = {}
setSignal = {}
vmean = {}
vsigma = {}
valpha1 = {}
vslope1 = {}
valpha2 = {}
vslope2 = {}
smean = {}
ssigma = {}
salpha1 = {}
sslope1 = {}
salpha2 = {}
sslope2 = {}
sbrwig = {}
signal = {}
signalExt = {}
signalYield = {}
signalIntegral = {}
signalNorm = {}
signalXS = {}
frSignal = {}
frSignal1 = {}
frSignal2 = {}
frSignal3 = {}
# Signal shape uncertainties (common amongst all mass points)
xmean_jes = RooRealVar(
"CMS" + YEAR + "_sig_" + category + "_p1_scale_jes",
"Variation of the resonance position with the jet energy scale", 0.02,
-1., 1.) #0.001
smean_jes = RooRealVar(
"CMS" + YEAR + "_sig_" + category + "_p1_jes",
"Change of the resonance position with the jet energy scale", 0., -10,
10)
xsigma_jer = RooRealVar(
"CMS" + YEAR + "_sig_" + category + "_p2_scale_jer",
"Variation of the resonance width with the jet energy resolution",
0.10, -1., 1.)
ssigma_jer = RooRealVar(
"CMS" + YEAR + "_sig_" + category + "_p2_jer",
"Change of the resonance width with the jet energy resolution", 0.,
-10, 10)
xmean_jes.setConstant(True)
smean_jes.setConstant(True)
xsigma_jer.setConstant(True)
ssigma_jer.setConstant(True)
for m in massPoints:
signalMass = "%s_M%d" % (stype, m)
signalName = "ZpBB_{}_{}_M{}".format(YEAR, category, m)
sampleName = "ZpBB_M{}".format(m)
signalColor = sample[sampleName][
'linecolor'] if signalName in sample else 1
# define the signal PDF
vmean[m] = RooRealVar(signalName + "_vmean", "Crystal Ball mean", m,
m * 0.96, m * 1.05)
smean[m] = RooFormulaVar(signalName + "_mean", "@0*(1+@1*@2)",
RooArgList(vmean[m], xmean_jes, smean_jes))
vsigma[m] = RooRealVar(signalName + "_vsigma", "Crystal Ball sigma",
m * 0.0233, m * 0.019, m * 0.025)
ssigma[m] = RooFormulaVar(
signalName + "_sigma", "@0*(1+@1*@2)",
RooArgList(vsigma[m], xsigma_jer, ssigma_jer))
valpha1[m] = RooRealVar(
signalName + "_valpha1", "Crystal Ball alpha 1", 0.2, 0.05, 0.28
) # number of sigmas where the exp is attached to the gaussian core. >0 left, <0 right
salpha1[m] = RooFormulaVar(signalName + "_alpha1", "@0",
RooArgList(valpha1[m]))
#vslope1[m] = RooRealVar(signalName + "_vslope1", "Crystal Ball slope 1", 10., 0.1, 20.) # slope of the power tail
vslope1[m] = RooRealVar(signalName + "_vslope1",
"Crystal Ball slope 1", 13., 10.,
20.) # slope of the power tail
sslope1[m] = RooFormulaVar(signalName + "_slope1", "@0",
RooArgList(vslope1[m]))
valpha2[m] = RooRealVar(signalName + "_valpha2",
"Crystal Ball alpha 2", 1.)
valpha2[m].setConstant(True)
salpha2[m] = RooFormulaVar(signalName + "_alpha2", "@0",
RooArgList(valpha2[m]))
#vslope2[m] = RooRealVar(signalName + "_vslope2", "Crystal Ball slope 2", 6., 2.5, 15.) # slope of the higher power tail
## FIXME test FIXME
vslope2_estimation = -5.88111436852 + m * 0.00728809389442 + m * m * (
-1.65059568762e-06) + m * m * m * (1.25128996309e-10)
vslope2[m] = RooRealVar(signalName + "_vslope2",
"Crystal Ball slope 2", vslope2_estimation,
vslope2_estimation * 0.9, vslope2_estimation *
1.1) # slope of the higher power tail
## FIXME end FIXME
sslope2[m] = RooFormulaVar(
signalName + "_slope2", "@0",
RooArgList(vslope2[m])) # slope of the higher power tail
signal[m] = RooDoubleCrystalBall(signalName,
"m_{%s'} = %d GeV" % ('X', m), X_mass,
smean[m], ssigma[m], salpha1[m],
sslope1[m], salpha2[m], sslope2[m])
# extend the PDF with the yield to perform an extended likelihood fit
signalYield[m] = RooRealVar(signalName + "_yield", "signalYield", 50,
0., 1.e15)
signalNorm[m] = RooRealVar(signalName + "_norm", "signalNorm", 1., 0.,
1.e15)
signalXS[m] = RooRealVar(signalName + "_xs", "signalXS", 1., 0., 1.e15)
signalExt[m] = RooExtendPdf(signalName + "_ext", "extended p.d.f",
signal[m], signalYield[m])
# ---------- if there is no simulated signal, skip this mass point ----------
if m in genPoints:
if VERBOSE: print " - Mass point", m
# define the dataset for the signal applying the SR cuts
treeSign[m] = TChain("tree")
if YEAR == 'run2':
pd = sample[sampleName]['files']
if len(pd) > 3:
print "multiple files given than years for a single masspoint:", pd
sys.exit()
for ss in pd:
if not '2016' in ss and not '2017' in ss and not '2018' in ss:
print "unknown year given in:", ss
sys.exit()
else:
pd = [x for x in sample[sampleName]['files'] if YEAR in x]
if len(pd) > 1:
print "multiple files given for a single masspoint/year:", pd
sys.exit()
for ss in pd:
if options.unskimmed:
j = 0
while True:
if os.path.exists(NTUPLEDIR + ss + "/" + ss +
"_flatTuple_{}.root".format(j)):
treeSign[m].Add(NTUPLEDIR + ss + "/" + ss +
"_flatTuple_{}.root".format(j))
j += 1
else:
print "found {} files for sample:".format(j), ss
break
else:
if os.path.exists(NTUPLEDIR + ss + ".root"):
treeSign[m].Add(NTUPLEDIR + ss + ".root")
else:
print "found no file for sample:", ss
if treeSign[m].GetEntries() <= 0.:
if VERBOSE:
print " - 0 events available for mass", m, "skipping mass point..."
signalNorm[m].setVal(-1)
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
salpha1[m].setConstant(True)
sslope1[m].setConstant(True)
salpha2[m].setConstant(True)
sslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
continue
#setSignal[m] = RooDataSet("setSignal_"+signalName, "setSignal", variables, RooFit.Cut(SRcut), RooFit.WeightVar("eventWeightLumi*BTagAK4Weight_deepJet"), RooFit.Import(treeSign[m]))
setSignal[m] = RooDataSet("setSignal_" + signalName, "setSignal",
variables, RooFit.Cut(SRcut),
RooFit.WeightVar(weight),
RooFit.Import(treeSign[m]))
if VERBOSE:
print " - Dataset with", setSignal[m].sumEntries(
), "events loaded"
# FIT
entries = setSignal[m].sumEntries()
if entries < 0. or entries != entries: entries = 0
signalYield[m].setVal(entries)
# Instead of eventWeightLumi
#signalYield[m].setVal(entries * LUMI / (300000 if YEAR=='run2' else 100000) )
if treeSign[m].GetEntries(SRcut) > 5:
if VERBOSE: print " - Running fit"
frSignal[m] = signalExt[m].fitTo(setSignal[m], RooFit.Save(1),
RooFit.Extended(True),
RooFit.SumW2Error(True),
RooFit.PrintLevel(-1))
if VERBOSE:
print "********** Fit result [", m, "] **", category, "*" * 40, "\n", frSignal[
m].Print(), "\n", "*" * 80
if VERBOSE: frSignal[m].correlationMatrix().Print()
drawPlot(signalMass + "_" + category, stype + category, X_mass,
signal[m], setSignal[m], frSignal[m])
else:
print " WARNING: signal", stype, "and mass point", m, "in category", category, "has 0 entries or does not exist"
# Remove HVT cross sections
#xs = getCrossSection(stype, channel, m)
xs = 1.
signalXS[m].setVal(xs * 1000.)
signalIntegral[m] = signalExt[m].createIntegral(
massArg, RooFit.NormSet(massArg),
RooFit.Range("X_integration_range"))
boundaryFactor = signalIntegral[m].getVal()
if boundaryFactor < 0. or boundaryFactor != boundaryFactor:
boundaryFactor = 0
if VERBOSE:
print " - Fit normalization vs integral:", signalYield[
m].getVal(), "/", boundaryFactor, "events"
signalNorm[m].setVal(boundaryFactor * signalYield[m].getVal() /
signalXS[m].getVal()
) # here normalize to sigma(X) x Br = 1 [fb]
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
valpha1[m].setConstant(True)
vslope1[m].setConstant(True)
valpha2[m].setConstant(True)
vslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
#*******************************************************#
# #
# Signal interpolation #
# #
#*******************************************************#
### FIXME FIXME just for a test FIXME FIXME
#print
#print
#print "slope2 fit results:"
#print
#y_vals = []
#for m in genPoints:
# y_vals.append(vslope2[m].getVal())
#print "m =", genPoints
#print "y =", y_vals
#sys.exit()
### FIXME FIXME test end FIXME FIXME
# ====== CONTROL PLOT ======
color_scheme = [
636, 635, 634, 633, 632, 633, 636, 635, 634, 633, 632, 633, 636, 635,
634, 633, 632, 633, 636, 635, 634, 633, 632, 633, 636, 635, 634, 633,
632, 633, 636, 635, 634, 633, 632, 633, 636, 635, 634, 633, 632, 633
]
c_signal = TCanvas("c_signal", "c_signal", 800, 600)
c_signal.cd()
frame_signal = X_mass.frame()
for j, m in enumerate(genPoints):
if m in signalExt.keys():
#print "color:",(j%9)+1
#print "signalNorm[m].getVal() =", signalNorm[m].getVal()
#print "RooAbsReal.NumEvent =", RooAbsReal.NumEvent
signal[m].plotOn(
frame_signal, RooFit.LineColor(color_scheme[j]),
RooFit.Normalization(signalNorm[m].getVal(),
RooAbsReal.NumEvent),
RooFit.Range("X_reasonable_range"))
frame_signal.GetXaxis().SetRangeUser(0, 10000)
frame_signal.Draw()
drawCMS(-1, "Simulation Preliminary", year=YEAR)
#drawCMS(-1, "Work in Progress", year=YEAR, suppressCMS=True)
#drawCMS(-1, "", year=YEAR, suppressCMS=True)
drawAnalysis(category)
drawRegion(category)
c_signal.SaveAs(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" +
category + "_Signal.pdf")
c_signal.SaveAs(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" +
category + "_Signal.png")
#if VERBOSE: raw_input("Press Enter to continue...")
# ====== CONTROL PLOT ======
# Normalization
gnorm = TGraphErrors()
gnorm.SetTitle(";m_{X} (GeV);integral (GeV)")
gnorm.SetMarkerStyle(20)
gnorm.SetMarkerColor(1)
gnorm.SetMaximum(0)
inorm = TGraphErrors()
inorm.SetMarkerStyle(24)
fnorm = TF1("fnorm", "pol9", 700, 3000)
fnorm.SetLineColor(920)
fnorm.SetLineStyle(7)
fnorm.SetFillColor(2)
fnorm.SetLineColor(cColor)
# Mean
gmean = TGraphErrors()
gmean.SetTitle(";m_{X} (GeV);gaussian mean (GeV)")
gmean.SetMarkerStyle(20)
gmean.SetMarkerColor(cColor)
gmean.SetLineColor(cColor)
imean = TGraphErrors()
imean.SetMarkerStyle(24)
fmean = TF1("fmean", "pol1", 0, 10000)
fmean.SetLineColor(2)
fmean.SetFillColor(2)
# Width
gsigma = TGraphErrors()
gsigma.SetTitle(";m_{X} (GeV);gaussian width (GeV)")
gsigma.SetMarkerStyle(20)
gsigma.SetMarkerColor(cColor)
gsigma.SetLineColor(cColor)
isigma = TGraphErrors()
isigma.SetMarkerStyle(24)
fsigma = TF1("fsigma", "pol1", 0, 10000)
fsigma.SetLineColor(2)
fsigma.SetFillColor(2)
# Alpha1
galpha1 = TGraphErrors()
galpha1.SetTitle(";m_{X} (GeV);crystal ball lower alpha")
galpha1.SetMarkerStyle(20)
galpha1.SetMarkerColor(cColor)
galpha1.SetLineColor(cColor)
ialpha1 = TGraphErrors()
ialpha1.SetMarkerStyle(24)
falpha1 = TF1("falpha", "pol1", 0, 10000) #pol0
falpha1.SetLineColor(2)
falpha1.SetFillColor(2)
# Slope1
gslope1 = TGraphErrors()
gslope1.SetTitle(";m_{X} (GeV);exponential lower slope (1/Gev)")
gslope1.SetMarkerStyle(20)
gslope1.SetMarkerColor(cColor)
gslope1.SetLineColor(cColor)
islope1 = TGraphErrors()
islope1.SetMarkerStyle(24)
fslope1 = TF1("fslope", "pol1", 0, 10000) #pol0
fslope1.SetLineColor(2)
fslope1.SetFillColor(2)
# Alpha2
galpha2 = TGraphErrors()
galpha2.SetTitle(";m_{X} (GeV);crystal ball upper alpha")
galpha2.SetMarkerStyle(20)
galpha2.SetMarkerColor(cColor)
galpha2.SetLineColor(cColor)
ialpha2 = TGraphErrors()
ialpha2.SetMarkerStyle(24)
falpha2 = TF1("falpha", "pol1", 0, 10000) #pol0
falpha2.SetLineColor(2)
falpha2.SetFillColor(2)
# Slope2
gslope2 = TGraphErrors()
gslope2.SetTitle(";m_{X} (GeV);exponential upper slope (1/Gev)")
gslope2.SetMarkerStyle(20)
gslope2.SetMarkerColor(cColor)
gslope2.SetLineColor(cColor)
islope2 = TGraphErrors()
islope2.SetMarkerStyle(24)
fslope2 = TF1("fslope", "pol1", 0, 10000) #pol0
fslope2.SetLineColor(2)
fslope2.SetFillColor(2)
n = 0
for i, m in enumerate(genPoints):
if not m in signalNorm.keys(): continue
if signalNorm[m].getVal() < 1.e-6: continue
if gnorm.GetMaximum() < signalNorm[m].getVal():
gnorm.SetMaximum(signalNorm[m].getVal())
gnorm.SetPoint(n, m, signalNorm[m].getVal())
#gnorm.SetPointError(i, 0, signalNorm[m].getVal()/math.sqrt(treeSign[m].GetEntriesFast()))
gmean.SetPoint(n, m, vmean[m].getVal())
gmean.SetPointError(n, 0,
min(vmean[m].getError(), vmean[m].getVal() * 0.02))
gsigma.SetPoint(n, m, vsigma[m].getVal())
gsigma.SetPointError(
n, 0, min(vsigma[m].getError(), vsigma[m].getVal() * 0.05))
galpha1.SetPoint(n, m, valpha1[m].getVal())
galpha1.SetPointError(
n, 0, min(valpha1[m].getError(), valpha1[m].getVal() * 0.10))
gslope1.SetPoint(n, m, vslope1[m].getVal())
gslope1.SetPointError(
n, 0, min(vslope1[m].getError(), vslope1[m].getVal() * 0.10))
galpha2.SetPoint(n, m, salpha2[m].getVal())
galpha2.SetPointError(
n, 0, min(valpha2[m].getError(), valpha2[m].getVal() * 0.10))
gslope2.SetPoint(n, m, sslope2[m].getVal())
gslope2.SetPointError(
n, 0, min(vslope2[m].getError(), vslope2[m].getVal() * 0.10))
#tmpVar = w.var(var+"_"+signalString)
#print m, tmpVar.getVal(), tmpVar.getError()
n = n + 1
gmean.Fit(fmean, "Q0", "SAME")
gsigma.Fit(fsigma, "Q0", "SAME")
galpha1.Fit(falpha1, "Q0", "SAME")
gslope1.Fit(fslope1, "Q0", "SAME")
galpha2.Fit(falpha2, "Q0", "SAME")
gslope2.Fit(fslope2, "Q0", "SAME")
# gnorm.Fit(fnorm, "Q0", "", 700, 5000)
#for m in [5000, 5500]: gnorm.SetPoint(gnorm.GetN(), m, gnorm.Eval(m, 0, "S"))
#gnorm.Fit(fnorm, "Q", "SAME", 700, 6000)
gnorm.Fit(fnorm, "Q", "SAME", 1800, 8000) ## adjusted recently
for m in massPoints:
if vsigma[m].getVal() < 10.: vsigma[m].setVal(10.)
# Interpolation method
syield = gnorm.Eval(m)
spline = gnorm.Eval(m, 0, "S")
sfunct = fnorm.Eval(m)
#delta = min(abs(1.-spline/sfunct), abs(1.-spline/syield))
delta = abs(1. - spline / sfunct) if sfunct > 0 else 0
syield = spline
if interPar:
#jmean = gmean.Eval(m)
#jsigma = gsigma.Eval(m)
#jalpha1 = galpha1.Eval(m)
#jslope1 = gslope1.Eval(m)
#jalpha2 = galpha2.Eval(m)
#jslope2 = gslope2.Eval(m)
jmean = gmean.Eval(m, 0, "S")
jsigma = gsigma.Eval(m, 0, "S")
jalpha1 = galpha1.Eval(m, 0, "S")
jslope1 = gslope1.Eval(m, 0, "S")
jalpha2 = galpha2.Eval(m, 0, "S")
jslope2 = gslope2.Eval(m, 0, "S")
else:
jmean = fmean.GetParameter(
0) + fmean.GetParameter(1) * m + fmean.GetParameter(2) * m * m
jsigma = fsigma.GetParameter(0) + fsigma.GetParameter(
1) * m + fsigma.GetParameter(2) * m * m
jalpha1 = falpha1.GetParameter(0) + falpha1.GetParameter(
1) * m + falpha1.GetParameter(2) * m * m
jslope1 = fslope1.GetParameter(0) + fslope1.GetParameter(
1) * m + fslope1.GetParameter(2) * m * m
jalpha2 = falpha2.GetParameter(0) + falpha2.GetParameter(
1) * m + falpha2.GetParameter(2) * m * m
jslope2 = fslope2.GetParameter(0) + fslope2.GetParameter(
1) * m + fslope2.GetParameter(2) * m * m
inorm.SetPoint(inorm.GetN(), m, syield)
signalNorm[m].setVal(max(0., syield))
imean.SetPoint(imean.GetN(), m, jmean)
if jmean > 0: vmean[m].setVal(jmean)
isigma.SetPoint(isigma.GetN(), m, jsigma)
if jsigma > 0: vsigma[m].setVal(jsigma)
ialpha1.SetPoint(ialpha1.GetN(), m, jalpha1)
if not jalpha1 == 0: valpha1[m].setVal(jalpha1)
islope1.SetPoint(islope1.GetN(), m, jslope1)
if jslope1 > 0: vslope1[m].setVal(jslope1)
ialpha2.SetPoint(ialpha2.GetN(), m, jalpha2)
if not jalpha2 == 0: valpha2[m].setVal(jalpha2)
islope2.SetPoint(islope2.GetN(), m, jslope2)
if jslope2 > 0: vslope2[m].setVal(jslope2)
#### newly introduced, not yet sure if helpful:
vmean[m].removeError()
vsigma[m].removeError()
valpha1[m].removeError()
valpha2[m].removeError()
vslope1[m].removeError()
vslope2[m].removeError()
#signalNorm[m].setConstant(False) ## newly put here to ensure it's freely floating in the combine fit
#c1 = TCanvas("c1", "Crystal Ball", 1200, 1200) #if not isAH else 1200
#c1.Divide(2, 3)
c1 = TCanvas("c1", "Crystal Ball", 1800, 800)
c1.Divide(3, 2)
c1.cd(1)
gmean.SetMinimum(0.)
gmean.Draw("APL")
imean.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
c1.cd(2)
gsigma.SetMinimum(0.)
gsigma.Draw("APL")
isigma.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
c1.cd(3)
galpha1.Draw("APL")
ialpha1.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
galpha1.GetYaxis().SetRangeUser(0., 1.1) #adjusted upper limit from 5 to 2
c1.cd(4)
gslope1.Draw("APL")
islope1.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
gslope1.GetYaxis().SetRangeUser(0.,
150.) #adjusted upper limit from 125 to 60
if True: #isAH:
c1.cd(5)
galpha2.Draw("APL")
ialpha2.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
galpha2.GetYaxis().SetRangeUser(0., 2.)
c1.cd(6)
gslope2.Draw("APL")
islope2.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
gslope2.GetYaxis().SetRangeUser(0., 20.)
c1.Print(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category +
"_SignalShape.pdf")
c1.Print(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category +
"_SignalShape.png")
c2 = TCanvas("c2", "Signal Efficiency", 800, 600)
c2.cd(1)
gnorm.SetMarkerColor(cColor)
gnorm.SetMarkerStyle(20)
gnorm.SetLineColor(cColor)
gnorm.SetLineWidth(2)
gnorm.Draw("APL")
inorm.Draw("P, SAME")
gnorm.GetXaxis().SetRangeUser(genPoints[0] - 100, genPoints[-1] + 100)
gnorm.GetYaxis().SetRangeUser(0., gnorm.GetMaximum() * 1.25)
drawCMS(-1, "Simulation Preliminary", year=YEAR)
#drawCMS(-1, "Work in Progress", year=YEAR, suppressCMS=True)
#drawCMS(-1, "", year=YEAR, suppressCMS=True)
drawAnalysis(category)
drawRegion(category)
c2.Print(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category +
"_SignalNorm.pdf")
c2.Print(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category +
"_SignalNorm.png")
#*******************************************************#
# #
# Generate workspace #
# #
#*******************************************************#
# create workspace
w = RooWorkspace("Zprime_" + YEAR, "workspace")
for m in massPoints:
getattr(w, "import")(signal[m], RooFit.Rename(signal[m].GetName()))
getattr(w, "import")(signalNorm[m],
RooFit.Rename(signalNorm[m].GetName()))
getattr(w, "import")(signalXS[m], RooFit.Rename(signalXS[m].GetName()))
w.writeToFile(WORKDIR + "MC_signal_%s_%s.root" % (YEAR, category), True)
print "Workspace", WORKDIR + "MC_signal_%s_%s.root" % (
YEAR, category), "saved successfully"
