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 dump_simple():
# try poisson
roo_lam = RooRealVar("lambda", ".", lam, 0.0, 1.5)
roo_x = RooRealVar('x', 'x range', 0, bins)
model = RooPoisson("poisson", "Poisson Model", roo_x, roo_lam)
#data = model.generate(RooArgSet(roo_x), n_events)
data = RooDataSet('data', 'Data', RooArgSet(roo_x))
for val in unbinned_from_binned(xdata, ydata):
roo_x.setVal(val)
data.add(RooArgSet(roo_x))
#// --- Perform extended ML fit of composite PDF to toy data ---
fitresult = model.fitTo(data, RooFit.Save(), RooFit.PrintLevel(-1)) ;
#// --- Plot toy data and composite PDF overlaid ---
mesframe = roo_x.frame(bins) ;
data.plotOn(mesframe) ;
model.plotOn(mesframe) ;
mesframe.Draw()
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
if args.noplot:
print("TrakTrak data plotting . . .")
print("All : " + str(theData.numEntries()))
ttFrame = tt_mass.frame(Title("KK mass"))
theData.plotOn(ttFrame)
ttFrame.Draw()
c.SaveAs(region + "/tt_mass" + cuts + ".png")
#### MuMu Data
print("MuMu data plotting . . .")
print("All : " + str(theData.numEntries()))
mumuFrame = mm_mass.frame(Title("KK mass"))
theData.plotOn(mumuFrame)
mumuFrame.Draw()
c.SaveAs(region + "/mm_mass.png")
#### X Data
print("TrakTrakMuMu data plotting . . .")
tp.SetBranchStatus('Bid', 1)
tp.SetBranchStatus('hid', 1)
tp.SetBranchStatus('time', 1)
for i in range(tp.GetEntries()):
tp.GetEntry(i)
# choose decay equation (1 of 2 for Dspi)
if (tp.Bid == 531 and tp.hid == 211):
time.setVal(tp.time)
dst.add(RooArgSet(time), tp.wt)
dst.Print()
# model.fitTo(dst)
# plot
tfr = time.frame()
dst.plotOn(tfr)
model.plotOn(tfr)
tfr.Draw()
mode_title = get_title_from_mode(mode)
if nokfactor:
k_title = 'no'
else:
k_title = 'with'
tfr.SetTitle('{} ({} #it{{k}}-factor smearing)'.format(mode_title, k_title))
tfr.GetYaxis().SetTitle('')
# FIXME: this is a hack, fix the real issue in splines
if trange:
rescale_roofit_pad(gPad, trange[0], trange[1])
canvas.Print(plotfile)
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
def rf501_simultaneouspdf():
# C r e a t e m o d e l f o r p h y s i c s s a m p l e
# -------------------------------------------------------------
# Create observables
x = RooRealVar( "x", "x", -8, 8 )
# Construct signal pdf
mean = RooRealVar( "mean", "mean", 0, -8, 8 )
sigma = RooRealVar( "sigma", "sigma", 0.3, 0.1, 10 )
gx = RooGaussian( "gx", "gx", x, mean, sigma )
# Construct background pdf
a0 = RooRealVar( "a0", "a0", -0.1, -1, 1 )
a1 = RooRealVar( "a1", "a1", 0.004, -1, 1 )
px = RooChebychev( "px", "px", x, RooArgList( a0, a1 ) )
# Construct composite pdf
f = RooRealVar( "f", "f", 0.2, 0., 1. )
model = RooAddPdf( "model", "model", RooArgList( gx, px ), RooArgList( f ) )
# C r e a t e m o d e l f o r c o n t r o l s a m p l e
# --------------------------------------------------------------
# Construct signal pdf.
# NOTE that sigma is shared with the signal sample model
mean_ctl = RooRealVar( "mean_ctl", "mean_ctl", -3, -8, 8 )
gx_ctl = RooGaussian( "gx_ctl", "gx_ctl", x, mean_ctl, sigma )
# Construct the background pdf
a0_ctl = RooRealVar( "a0_ctl", "a0_ctl", -0.1, -1, 1 )
a1_ctl = RooRealVar( "a1_ctl", "a1_ctl", 0.5, -0.1, 1 )
px_ctl = RooChebychev( "px_ctl", "px_ctl", x, RooArgList( a0_ctl, a1_ctl ) )
# Construct the composite model
f_ctl = RooRealVar( "f_ctl", "f_ctl", 0.5, 0., 1. )
model_ctl = RooAddPdf( "model_ctl", "model_ctl", RooArgList( gx_ctl, px_ctl ),
RooArgList( f_ctl ) )
# G e n e r a t e e v e n t s f o r b o t h s a m p l e s
# ---------------------------------------------------------------
# Generate 1000 events in x and y from model
data = model.generate( RooArgSet( x ), 100 )
data_ctl = model_ctl.generate( RooArgSet( x ), 2000 )
# C r e a t e i n d e x c a t e g o r y a n d j o i n s a m p l e s
# ---------------------------------------------------------------------------
# Define category to distinguish physics and control samples events
sample = RooCategory( "sample", "sample" )
sample.defineType( "physics" )
sample.defineType( "control" )
# Construct combined dataset in (x,sample)
combData = RooDataSet( "combData", "combined data", RooArgSet(x), RooFit.Index( sample ),
RooFit.Import( "physics", data ),
RooFit.Import( "control", data_ctl ) )
# C o n s t r u c t a s i m u l t a n e o u s p d f i n ( x , s a m p l e )
# -----------------------------------------------------------------------------------
# Construct a simultaneous pdf using category sample as index
simPdf = RooSimultaneous( "simPdf", "simultaneous pdf", sample )
# Associate model with the physics state and model_ctl with the control state
simPdf.addPdf( model, "physics" )
simPdf.addPdf( model_ctl, "control" )
# P e r f o r m a s i m u l t a n e o u s f i t
# ---------------------------------------------------
# Perform simultaneous fit of model to data and model_ctl to data_ctl
simPdf.fitTo( combData )
# P l o t m o d e l s l i c e s o n d a t a s l i c e s
# ----------------------------------------------------------------
# Make a frame for the physics sample
frame1 = x.frame( RooFit.Bins( 30 ), RooFit.Title( "Physics sample" ) )
# Plot all data tagged as physics sample
combData.plotOn( frame1, RooFit.Cut( "sample==sample::physics" ) )
# Plot "physics" slice of simultaneous pdf.
# NBL You _must_ project the sample index category with data using ProjWData
# as a RooSimultaneous makes no prediction on the shape in the index category
# and can thus not be integrated
simPdf.plotOn( frame1, RooFit.Slice( sample, "physics" ),
RooFit.ProjWData( RooArgSet(sample), combData ) )
simPdf.plotOn( frame1, RooFit.Slice( sample, "physics" ),
RooFit.Components( "px" ),
RooFit.ProjWData( RooArgSet(sample), combData ),
RooFit.LineStyle( kDashed ) )
# The same plot for the control sample slice
frame2 = x.frame( RooFit.Bins( 30 ), RooFit.Title( "Control sample" ) )
combData.plotOn( frame2, RooFit.Cut( "sample==sample::control" ) )
simPdf.plotOn( frame2, RooFit.Slice( sample, "control" ),
RooFit.ProjWData( RooArgSet(sample), combData ) )
simPdf.plotOn( frame2, RooFit.Slice( sample, "control" ),
RooFit.Components( "px_ctl" ),
RooFit.ProjWData( RooArgSet(sample), combData ),
RooFit.LineStyle( kDashed ) )
c = TCanvas( "rf501_simultaneouspdf", "rf403_simultaneouspdf", 800, 400 )
c.Divide( 2 )
c.cd( 1 )
gPad.SetLeftMargin( 0.15 )
frame1.GetYaxis().SetTitleOffset( 1.4 )
frame1.Draw()
c.cd( 2 )
gPad.SetLeftMargin( 0.15 )
frame2.GetYaxis().SetTitleOffset( 1.4 )
frame2.Draw()
raw_input()
#gamma.setConstant(False)
#sigma.setConstant(False)
#rPhifit = tot.fitTo(splotData,Range(phimin,phimax),RooFit.NumCPU(args.ncpu),RooFit.Verbose(False))
#nfits = nfits + 1
mean.setConstant(False)
gamma.setConstant(False)
sigma_1.setConstant(False)
sigma_2.setConstant(False)
rPhifit = tot.fitTo(splotData,Range(phimin,phimax),RooFit.NumCPU(args.ncpu),RooFit.Verbose(False))
nfits = nfits + 1
c = TCanvas("canvas","canvas",1200,900)
phiFrame = masskk.frame(Range(phimin,phimax),Normalization((nSig.getValV() + nBkg.getValV())), Title("#phi Mass"))
splotData.plotOn(phiFrame)
ratio = 1.0/float(nfits)
tot.plotOn(phiFrame,Normalization(ratio))
bFrac = (nBkg.getValV())/(nSig.getValV() + nBkg.getValV())
bkg.plotOn(phiFrame,LineColor(kRed),Normalization(bFrac),LineStyle(kDashed))
signal.plotOn(phiFrame,LineColor(kGreen),Normalization(1.0-bFrac))
a0.setConstant(True)
a1.setConstant(True)
a2.setConstant(True)
a3.setConstant(True)
a4.setConstant(True)
nBkg.setConstant(True)
tot.paramOn(phiFrame,RooFit.Layout(0.57,0.99,0.65))
def FitMassPoint(filename, massin, massmin, massmax, rwCPS=1, nbins=50):
### take the input tree from the file
inputFile = ROOT.TFile(filename);
tree = inputFile.Get("WJet");
print "Lineshape Higgs mass --> n entries: ", tree.GetEntries();
# RooFitting
rrv_mass = RooRealVar("rrv_mass","rrv_mass",massin,massmin,massmax);
rrv_weight = RooRealVar("rrv_weight","rrv_weight",0. ,10000000.);
rrv_mH1 = RooRealVar("rrv_mH1","rrv_mH1", massin, massmin, massmax );
rrv_gamma1 = RooRealVar("rrv_gamma1","rrv_gamma1",20.,0.,massmax);
rrv_mH2 = RooRealVar("rrv_mH2","rrv_mH2", massin, massmin, massmax )
rrv_gamma2 = RooRealVar("rrv_gamma2","rrv_gamma2",20.,0.,massmax)
rrv_mH3 = RooRealVar("rrv_mH3","rrv_mH3", massin, massmin, massmax );
rrv_gamma3 = RooRealVar("rrv_gamma3","rrv_gamma3",20.,0.,massmax);
rds_raw = RooDataSet("rds_raw","rds_raw",RooArgSet(rrv_mass,rrv_weight),RooFit.WeightVar(rrv_weight)); ## created the raw dataset --> raw lineshape
rds_cps = RooDataSet("rds_cps","rds_cps",RooArgSet(rrv_mass,rrv_weight),RooFit.WeightVar(rrv_weight)); ## create the cps dataset --> cps lineshape
rds_cps_intf = ROOT.RooDataSet("rds_cps_intf","rds_cps_intf",RooArgSet(rrv_mass,rrv_weight),RooFit.WeightVar(rrv_weight)); ## create the cps + interference dataset
model1_pdf = ROOT.RooRelBWRunningWidth("model1_pdf","model1_pdf",rrv_mass,rrv_mH1,rrv_gamma1); ## BWRunningWidth Pdf from the dedicated library
model2_pdf = ROOT.RooRelBWRunningWidth("model2_pdf","model2_pdf",rrv_mass,rrv_mH2,rrv_gamma2); ## BWRunningWidth Pdf from the dedicated library
model3_pdf = ROOT.RooRelBWRunningWidth("model3_pdf","model3_pdf",rrv_mass,rrv_mH3,rrv_gamma3); ## BWRunningWidth Pdf from the dedicated library
### loop on Higgs signal events
for i in range(tree.GetEntries()):
if i % 10000 == 0: print "reweighter, i: ", i;
tree.GetEntry(i);
curmass = getattr(tree,"W_H_mass_gen"); ## take the higgs generated mass inside the window; point not in the window are neglected
if curmass < massmax and curmass > massmin:
rrv_mass.setVal( curmass ); ## set the value of the RooRealVar
tmpweight_cps = getattr(tree,"complexpolewtggH"+str(massin))*rwCPS/getattr(tree,"avecomplexpolewtggH"+str(massin)); ## take the cps weight from the tree
tmpweight_cps_intf = getattr(tree,"complexpolewtggH"+str(massin))*rwCPS*getattr(tree,"interferencewtggH"+str(massin))/getattr(tree,"avecomplexpolewtggH"+str(massin)); ## cps*int
rds_raw.add( RooArgSet( rrv_mass ), 1. );
rds_cps.add( RooArgSet( rrv_mass ), tmpweight_cps );
rds_cps_intf.add( RooArgSet( rrv_mass ), tmpweight_cps_intf );
print ">>>>"
RooTrace.dump(ROOT.cout,ROOT.kTRUE);
RooTrace.mark();
print "<<<<"
#### final fit
model1_pdf.fitTo(rds_raw,RooFit.Save(1), RooFit.SumW2Error(kTRUE));
model1_pdf.fitTo(rds_raw,RooFit.Save(1), RooFit.SumW2Error(kTRUE), RooFit.Minimizer("Minuit2"));
model2_pdf.fitTo(rds_cps,RooFit.Save(1), RooFit.SumW2Error(kTRUE));
model2_pdf.fitTo(rds_cps,RooFit.Save(1), RooFit.SumW2Error(kTRUE), RooFit.Minimizer("Minuit2"));
model3_pdf.fitTo(rds_cps_intf,RooFit.Save(1), RooFit.SumW2Error(kTRUE));
model3_pdf.fitTo(rds_cps_intf,RooFit.Save(1), RooFit.SumW2Error(kTRUE), RooFit.Minimizer("Minuit2"));
## plot of the fits
mplot = rrv_mass.frame(RooFit.Title("mass plot"));
rds_raw.plotOn(mplot, RooFit.MarkerColor(kBlack), RooFit.LineColor(kBlack), RooFit.Binning(nbins,massmin,massmax), RooFit.DataError(RooAbsData.SumW2) );
rds_cps.plotOn(mplot, RooFit.MarkerColor(kRed), RooFit.LineColor(kRed), RooFit.Binning(nbins,massmin,massmax), RooFit.DataError(RooAbsData.SumW2) );
rds_cps_intf.plotOn(mplot, RooFit.MarkerColor(kBlue), RooFit.LineColor(kBlue), RooFit.Binning(nbins,massmin,massmax), RooFit.DataError(RooAbsData.SumW2) );
model1_pdf.plotOn(mplot, RooFit.LineColor(kBlack));
model2_pdf.plotOn(mplot, RooFit.LineColor(kRed), RooFit.LineStyle(2) );
model3_pdf.plotOn(mplot, RooFit.LineColor(kBlue), RooFit.LineStyle(3) );
rds_raw.plotOn(mplot, RooFit.MarkerColor(kBlack), RooFit.LineColor(kBlack), RooFit.Binning(nbins,massmin,massmax), RooFit.DataError(RooAbsData.SumW2) );
rds_cps.plotOn(mplot, RooFit.MarkerColor(kRed), RooFit.LineColor(kRed), RooFit.Binning(nbins,massmin,massmax), RooFit.DataError(RooAbsData.SumW2) );
rds_cps_intf.plotOn(mplot, RooFit.MarkerColor(kBlue), RooFit.LineColor(kBlue), RooFit.Binning(nbins,massmin,massmax), RooFit.DataError(RooAbsData.SumW2) );
print "rds_raw.sumEntries() = ", rds_raw.sumEntries()
print "model1_pdf: mH = ", rrv_mH1.getVal(), ", gamma = ", rrv_gamma1.getVal();
print "rds_cps.sumEntries() = ", rds_cps.sumEntries()
print "model2_pdf: mH = ", rrv_mH2.getVal(), ", gamma = ", rrv_gamma2.getVal();
print "rds_cps_intf.sumEntries() = ", rds_cps_intf.sumEntries()
print "model3_pdf: mH = ", rrv_mH3.getVal(), ", gamma = ", rrv_gamma3.getVal();
dummy_h1 = ROOT.TH1F("dummy_h1","dummy_h1",1,0,1);
dummy_h1.SetMarkerColor( ROOT.kBlack );
dummy_h2 = ROOT.TH1F("dummy_h2","dummy_h2",1,0,1);
dummy_h2.SetMarkerColor( ROOT.kRed );
dummy_h3 = ROOT.TH1F("dummy_h3","dummy_h3",1,0,1);
dummy_h3.SetMarkerColor( ROOT.kBlue );
L = TLegend(0.65,0.60,0.93,0.85);
L.SetFillStyle(0);
L.AddEntry(dummy_h1,"Powheg","p");
L.AddEntry(dummy_h2,"w/CPS weight","p");
L.AddEntry(dummy_h3,"w/CPS,Intf weight","p");
can2 = ROOT.TCanvas("can2","can2",800,800);
mplot.Draw();
L.Draw();
os.system("mkdir -p massFits");
can2.SaveAs("massFits/mass_rf_"+str(massin)+".pdf");
can2.SaveAs("massFits/mass_rf_"+str(massin)+".png");
outputpar_1 = [];
outputpar_1.append( rrv_mH1.getVal() );
outputpar_1.append( rrv_gamma1.getVal() );
outputpar_2 = [];
outputpar_2.append( rrv_mH2.getVal() );
outputpar_2.append( rrv_gamma2.getVal() );
outputpar_3 = [];
outputpar_3.append( rrv_mH3.getVal() );
outputpar_3.append( rrv_gamma3.getVal() );
model1_pdf.Delete();
model2_pdf.Delete();
model3_pdf.Delete();
rds_raw.Delete(),
rds_cps.Delete(),
rds_cps_intf.Delete();
rrv_mH1.Delete(),
rrv_gamma1.Delete();
rrv_mH2.Delete(),
rrv_gamma2.Delete();
rrv_mH3.Delete(),
rrv_gamma3.Delete();
rrv_mass.Delete();
return outputpar_2
tktkPt=RooRealVar('tktkPt', 'tktkPt', 0., 160., "GeV/c")
fitMC=RooDataSet()
fitdata=RooDataSet('2016Data', 'RunB', ntuple, RooArgSet(lbtkMass,bsMass,bdMass,bdbarMass,phiMass,kstarMass,kstarbarMass,tktkPt))
subdataset=fitdata.reduce( RooFit.Cut("tktkPt>20.&&lbtkMass>5.45&&lbtkMass<5.8") )
canv=TCanvas('c1', 'c1', 1600,1000)
canv.SetFillColor(4000)
canv.SetFillStyle(4000)
lbtkmassFrame=lbtkMass.frame(RooFit.Range(5.,6.))
bsmassFrame=bsMass.frame(RooFit.Range(5.,6.))
bdmassFrame=bdMass.frame(RooFit.Range(5.,6.))
bdbarmassFrame=bdbarMass.frame(RooFit.Range(5.,6.))
fitdata.plotOn(lbtkmassFrame, RooFit.Cut("tktkPt>20."))
subdataset.plotOn(bsmassFrame)
subdataset.plotOn(bdmassFrame)
subdataset.plotOn(bdbarmassFrame)
lbtkmassFrame.Draw()
canv.SaveAs('h_checkPlot_lbtkMass_tktkPtB20.eps')
bsmassFrame.Draw()
canv.SaveAs('h_checkPlot_BsMass_lbtkCriticalRegion_tktkPtB20.eps')
bdmassFrame.Draw()
canv.SaveAs('h_checkPlot_BdMass_lbtkCriticalRegion_tktkPtB20.eps')
bdbarmassFrame.Draw()
canv.SaveAs('h_checkPlot_BdBarMass_lbtkCriticalRegion_tktkPtB20.eps')
tupleDataSet = RooDataSet("treeData", "treeData", tree1, RooArgSet(*varList))
tupleDataSet.get().find(varName).Print()
for i in range(len(tupleDict)):
varName = tupleDict.keys()[i]
a = tupleDataSet.get().find(varName)
a.SetName(tupleDict[varName])
a.SetTitle(tupleDict[varName])
tupleDataSet.get().find("SSKaon").SetMaximum(2)
tupleDataSet.get().find("SSKaon").SetMinimum(-2)
aFrame = tupleDataSet.get().find("SSKaon").frame()
tupleDataSet.get().find("SSKaon").Print()
tupleDataSet.plotOn(aFrame)
aFrame.Draw()
raw_input("Press Enter to continue")
'''
tupleDataSet = tupleDataSet.reduce("fake_Bs>=0&&true_Bs>=0");
tupleDataSet.get().find("fake_Bs").setMin(0.0);
tupleDataSet.Print();
aFrame = tupleDataSet.get().find("fake_Bs").frame();
tupleDataSet.get().find("fake_Bs").Print();
tupleDataSet.plotOn(aFrame);
bFrame = tupleDataSet.get().find("true_Bs").frame();
#For combining binned data look rf_combine_binned_data.py
data_pass = gauss1.generate(RooArgSet(Mkk), 6000)
data_fail = gauss1.generate(RooArgSet(Mkk), 4000)
combData = RooDataSet("combData", "combined data", RooArgSet(Mkk),
RooFit.Index(myFitCat), RooFit.Import("pass", data_pass),
RooFit.Import("fail", data_fail))
#FIT
totalPdf.fitTo(combData)
#PLOT
sampleSet = RooArgSet(myFitCat)
frame1 = Mkk.frame(RooFit.Bins(50), RooFit.Title("Passed sample"))
# Plot all data tagged as passed sample
combData.plotOn(frame1, RooFit.Cut("myFitCat==myFitCat::pass"))
totalPdf.plotOn(frame1, RooFit.Slice(myFitCat, "pass"),
RooFit.ProjWData(sampleSet, combData),
RooFit.LineStyle(kDashed))
totalPdf.paramOn(frame1, RooFit.Layout(0.55, 0.9, 0.9),
RooFit.Format("NEU", RooFit.AutoPrecision(1)))
# The same plot for the failled sample slice
frame2 = Mkk.frame(RooFit.Bins(50), RooFit.Title("Failed sample"))
combData.plotOn(frame2, RooFit.Cut("myFitCat==myFitCat::fail"))
totalPdf.plotOn(frame2, RooFit.Slice(myFitCat, "fail"),
RooFit.ProjWData(sampleSet, combData))
#simPdf.plotOn(frame2, RooFit.Slice(myFitCat, "fail"), RooFit.ProjWData(sampleSet, combData), RooFit.LineStyle(kDashed))
c = TCanvas("simultaneouspdf", "simultaneouspdf", 800, 400)
c.Divide(2)
region = "_promt_"
if args.ptcuts is not None:
trakData = trakData.reduce("trigp_pT > " + str(args.ptcuts))
trakData = trakData.reduce("trign_pT > " + str(args.ptcuts))
cuts += "pt_" + str(args.ptcuts) + "_"
#### #### Plotting variables
#### TrakTrak Data
if args.noplot:
print("TrakTrak data plotting . . .")
print("All : " + str(trakData.numEntries()))
ttFrame = tt_mass.frame()
trakData.plotOn(ttFrame)
ttFrame.Draw()
c.SaveAs("tt_mass" + cuts + ".png")
tt_trig_Frame = tt_mass_trig.frame()
trakData.plotOn(tt_trig_Frame)
tt_trig_Frame.Draw()
c.SaveAs("tt_mass" + cuts + "trigger.png")
#### MuMu Data
print("MuMu data plotting . . .")
print("All : " + str(mumuData.numEntries()))
mumuFrame = mm_mass.frame()
mumuData.plotOn(mumuFrame)
mumuFrame.Draw()
event = 2
xTuple.SetBranchAddress("event", AddressOf(milk, 'price5'))
newfile = TFile("small.root", "recreate")
newtree = xTuple.CloneTree()
newtree.CopyEntries(xTuple)
newtree.Write()
sys.exit()
# In[16]:
file = TFile("newFile.root", "RECREATE")
canvas = TCanvas("canvas", "canvas", 1200, 1000)
mass = RooRealVar("xM", "M(#mu#mu#mu#mu)[GeV]", 5.15, 5.55)
trigger = RooRealVar("trigger", "trigger", 0.0, 10000)
vProb = RooRealVar("vProb", "vProb", -1.0, 1.0)
alldata = RooDataSet("alldata", "alldata", xTuple, RooArgSet(mass),
RooFormulaVar("vProb", "vProb", "vProb>0.01",
RooArgList(vProb))) #,cutFormula)
frame = mass.frame(Range(5.15, 5.55))
alldata.plotOn(frame, RooLinkedList())
alldata.Write()
frame.Draw()
# In[ ]:
canvas.SaveAs("testCanvas.eps")
print "Tree entries %d"%(splotData.numEntries())
print "PHSP fit"
BkgTotalMPdf = RooGenericPdf("BkgPdf","BkgPdf","sqrt( pow(dimuonditrk_m_rf_c,4) + pow(3.0967,4) + pow(1.01946,4) - 2*pow(dimuonditrk_m_rf_c,2)*pow(3.0967,2) - 2*pow(3.0967,2)*pow(1.01946,2) - 2*pow(dimuonditrk_m_rf_c,2)*pow(1.01946,2) ) * sqrt( pow(5.279,4) + pow(dimuonditrk_m_rf_c,4) + pow(0.493677,4) - 2*pow(5.279,2)*pow(dimuonditrk_m_rf_c,2) - 2*pow(5.279,2)*pow(0.493677,2) - 2*pow(dimuonditrk_m_rf_c,2)*pow(0.493677,2) ) / (dimuonditrk_m_rf_c)", RooArgList(dimuonditrk_m_rf_c));
dimuonditrk_m_rf_c.setBins(80)
dimuonditrk_m_rf_c.setRange("baserange",4.0,5.0)
s = BkgTotalMPdf.createIntegral(RooArgSet(dimuonditrk_m_rf_c),"baserange").getVal()
#bkgFit = BkgTotalMPdf.fitTo(splotBkgData,Range(4.0,5.0),RooFit.NumCPU(args.ncpu),RooFit.Verbose(False))
cb = TCanvas("canvas_b","canvas_b",1200,800)
print s
mumukkFrame = dimuonditrk_m_rf_c.frame(Title("Phase Space Fit"),Range(4.0,5.0),Normalization(1.0))
splotData.plotOn(mumukkFrame)
BkgTotalMPdf.plotOn(mumukkFrame,Normalization(1.65))
mumukkFrame.Draw()
if args.phsps:
cb.SaveAs(args.path[:-5] + '_bu_phsp_plot.root')
cb.SaveAs(args.path[:-5] + '_bu_phsp_plot.png')
sys.exit()
print "SPLOT FIT"
a0 = RooRealVar("a0","a0",0.001,-10.,10.)
a1 = RooRealVar("a1","a1",0.001,-5.0,5.0)
# +mistag (average mistag of omega = .35, 100% tagging efficiency)
model2 = RooBDecay('model2', 'model', time, tau, dGamma, one, zero, zero, zero,
dM, gresdt, RooBDecay.SingleSided)
# + acceptance
model3 = RooBDecay('model3', 'model', time, tau, dGamma, one, zero, zero, zero,
dM, gresacc, RooBDecay.SingleSided)
model3.fitTo(ds, RooFit.Strategy(2), RooFit.Verbose(), RooFit.SumW2Error(True),
RooFit.Optimize(1))
tfr = time.frame()
tfr.SetTitle('Acceptance function and knot positions (%s)' %
('data' if originSuffix == 'DATA' else 'MC'))
ds.plotOn(tfr)
model3.plotOn(tfr, RooFit.Precision(1e-5), RooFit.Name(''))
tfr.Draw()
tfr.SetMinimum(0)
from ROOT import TLine
aLine = []
print "XMAX ---> ", tfr.GetMaximum()
for i in spline_knots:
aLine += [TLine(i, 0.0, i, tfr.GetMaximum())]
aLine[-1].SetLineWidth(2)
aLine[-1].SetLineColor(ROOT.kRed)
aLine[-1].Draw()
def rooFit501():
print ">>> setup model for physics sample..."
x = RooRealVar("x", "x", -8, 8)
mean = RooRealVar("mean", "mean", 0, -8, 8)
sigma = RooRealVar("sigma", "sigma", 0.3, 0.1, 10)
gauss = RooGaussian("gx", "gx", x, mean, sigma)
a0 = RooRealVar("a0", "a0", -0.1, -1, 1)
a1 = RooRealVar("a1", "a1", 0.004, -1, 1)
px = RooChebychev("px", "px", x, RooArgList(a0, a1))
f = RooRealVar("f", "f", 0.2, 0., 1.)
model = RooAddPdf("model", "model", RooArgList(gauss, px), RooArgList(f))
print ">>> setup model for control sample..."
# NOTE: sigma is shared with the signal sample model
mean_ctrl = RooRealVar("mean_ctrl", "mean_ctrl", -3, -8, 8)
gauss_ctrl = RooGaussian("gauss_ctrl", "gauss_ctrl", x, mean_ctrl, sigma)
a0_ctrl = RooRealVar("a0_ctrl", "a0_ctrl", -0.1, -1, 1)
a1_ctrl = RooRealVar("a1_ctrl", "a1_ctrl", 0.5, -0.1, 1)
px_ctrl = RooChebychev("px_ctrl", "px_ctrl", x,
RooArgList(a0_ctrl, a1_ctrl))
f_ctrl = RooRealVar("f_ctrl", "f_ctrl", 0.5, 0., 1.)
model_ctrl = RooAddPdf("model_ctrl", "model_ctrl",
RooArgList(gauss_ctrl, px_ctrl), RooArgList(f_ctrl))
print ">>> generate events for both samples..."
data = model.generate(RooArgSet(x), 100) # RooDataSet
data_ctrl = model_ctrl.generate(RooArgSet(x), 2000) # RooDataSet
print ">>> create index category and join samples..."
# Define category to distinguish physics and control samples events
sample = RooCategory("sample", "sample")
sample.defineType("physics")
sample.defineType("control")
print ">>> construct combined dataset in (x,sample)..."
combData = RooDataSet("combData", "combined data", RooArgSet(x),
Index(sample), Import("physics", data),
Import("control", data_ctrl))
print ">>> construct a simultaneous pdf in (x,sample)..."
# Construct a simultaneous pdf using category sample as index
simPdf = RooSimultaneous("simPdf", "simultaneous pdf", sample)
# Associate model with the physics state and model_ctrl with the control state
simPdf.addPdf(model, "physics")
simPdf.addPdf(model_ctrl, "control")
print ">>> perform a simultaneous fit..."
# Perform simultaneous fit of model to data and model_ctrl to data_ctrl
simPdf.fitTo(combData)
print "\n>>> plot model slices on data slices..."
frame1 = x.frame(Bins(30), Title("Physics sample")) # RooPlot
combData.plotOn(frame1, Cut("sample==sample::physics"))
# Plot "physics" slice of simultaneous pdf.
# NBL You _must_ project the sample index category with data using ProjWData
# as a RooSimultaneous makes no prediction on the shape in the index category
# and can thus not be integrated
simPdf.plotOn(frame1, Slice(sample, "physics"),
ProjWData(RooArgSet(sample), combData))
simPdf.plotOn(frame1, Slice(sample, "physics"), Components("px"),
ProjWData(RooArgSet(sample), combData), LineStyle(kDashed))
print "\n>>> plot control sample slices..."
frame2 = x.frame(Bins(30), Title("Control sample")) # RooPlot
combData.plotOn(frame2, Cut("sample==sample::control"))
simPdf.plotOn(frame2, Slice(sample, "control"),
ProjWData(RooArgSet(sample), combData))
simPdf.plotOn(frame2, Slice(sample, "control"), Components("px_ctrl"),
ProjWData(RooArgSet(sample), combData), LineStyle(kDashed))
print "\n>>> draw on canvas..."
canvas = TCanvas("canvas", "canvas", 100, 100, 1400, 600)
canvas.Divide(2)
canvas.cd(1)
gPad.SetLeftMargin(0.15)
gPad.SetRightMargin(0.02)
frame1.GetYaxis().SetLabelOffset(0.008)
frame1.GetYaxis().SetTitleOffset(1.6)
frame1.GetYaxis().SetTitleSize(0.045)
frame1.GetXaxis().SetTitleSize(0.045)
frame1.Draw()
canvas.cd(2)
gPad.SetLeftMargin(0.15)
gPad.SetRightMargin(0.02)
frame2.GetYaxis().SetLabelOffset(0.008)
frame2.GetYaxis().SetTitleOffset(1.6)
frame2.GetYaxis().SetTitleSize(0.045)
frame2.GetXaxis().SetTitleSize(0.045)
frame2.Draw()
canvas.SaveAs("rooFit501.png")
def rooFit102():
print ">>> import TH1 into RooDataHist..."
hist = makeTH1()
x = RooRealVar("x", "x", -10, 10)
data_hist = RooDataHist("data_hist", "data_hist", RooArgList(x),
Import(hist))
print ">>> plot and fit RooDataHist...\n"
mean = RooRealVar("mean", "mean of gaussian", 1, -10, 10)
sigma = RooRealVar("sigma", "width of gaussian", 1, 0.1, 10)
gauss = RooGaussian("gauss", "gaussian PDF", x, mean, sigma)
gauss.fitTo(data_hist)
frame1 = x.frame(Title("Imported TH1 with Poisson error bars")) # RooPlot
data_hist.plotOn(frame1)
gauss.plotOn(frame1)
print "\n>>> plot and fit RooDataHist with internal errors..."
# If histogram has custom error (i.e. its contents is does not originate from a
# Poisson process but e.g. is a sum of weighted events) you can create data with
# symmetric 'sum-of-weights' error instead (i.e. same error bars as shown by ROOT)
frame2 = x.frame(Title("Imported TH1 with internal errors"))
data_hist.plotOn(frame2, DataError(RooAbsData.SumW2))
gauss.plotOn(frame2)
# Please note that error bars shown (Poisson or SumW2) are for visualization only,
# the are NOT used in a maximum likelihood (ML) fit
#
# A (binned) ML fit will ALWAYS assume the Poisson error interpretation of data
# (the mathematical definition of likelihood does not take any external definition
# of errors). Data with non-unit weights can only be correctly fitted with a chi^2
# fit (see rf602_chi2fit.C)
print ">>> import TTree into RooDataHist..."
# Construct unbinned dataset importing tree branches x and y matching between
# branches and RooRealVars is done by name of the branch/RRV
#
# Note that ONLY entries for which x,y have values within their allowed ranges as
# defined in RooRealVar x and y are imported. Since the y values in the import tree
# are in the range [-15,15] and RRV y defines a range [-10,10] this means that the
# RooDataSet below will have less entries than the TTree 'tree'
tree = makeTTree()
px = RooRealVar("px", "px", -10, 10)
py = RooRealVar("py", "py", -10, 10)
data_set = RooDataSet("data_set", "data_set", RooArgSet(px, py),
Import(tree))
data_set.Print()
frame3 = py.frame(Title("Unbinned data shown in default frame binning"))
frame4 = py.frame(Title("Unbinned data shown with custom binning"))
data_set.plotOn(frame3) # default frame binning of 100 bins
data_set.plotOn(frame4, Binning(20)) # custom binning choice
print ">>> draw pfds and fits on canvas..."
canvas = TCanvas("canvas", "canvas", 100, 100, 1000, 1200)
canvas.Divide(2, 2)
for i, frame in enumerate([frame1, frame2, frame3, frame4], 1):
canvas.cd(i)
gPad.SetLeftMargin(0.15)
gPad.SetRightMargin(0.05)
frame.GetYaxis().SetTitleOffset(1.6)
frame.GetYaxis().SetLabelOffset(0.010)
frame.GetYaxis().SetTitleSize(0.045)
frame.GetYaxis().SetLabelSize(0.042)
frame.GetXaxis().SetTitleSize(0.045)
frame.GetXaxis().SetLabelSize(0.042)
frame.Draw()
canvas.SaveAs("rooFit102.png")
#print "without NormSet"
#print "iLeft: ", iLeft.getVal()
#print "iTotal: ", iTotal.getVal()
n_total_evaluate = nGauss11.getVal() / iLeft.getVal()
print "n_total_evaluate: ", n_total_evaluate
#gauss11_ext.plotOn(xframe7,RooFit.Normalization( n_total_evaluate , RooAbsReal.NumEvent), RooFit.Range("whole_range"), RooFit.LineColor(RooFit.kRed))
#gauss11_ext.plotOn(xframe7,RooFit.Normalization( nGauss11.getVal() , RooAbsReal.NumEvent), RooFit.Range("whole_range"), RooFit.LineColor(RooFit.kRed))
gauss11_ext.plotOn(xframe7,RooFit.Normalization( nGauss11.getVal() , RooAbsReal.NumEvent), RooFit.Range("left_gaussian_range"), RooFit.LineColor(RooFit.kRed))
#gauss11_ext.plotOn(xframe7,RooFit.Normalization( nGauss11.getVal() , RooAbsReal.NumEvent), RooFit.Range("whole_range"), RooFit.LineColor(RooFit.kRed),RooFit.DrawOption("F"), RooFit.FillColor(RooFit.kRed), RooFit.FillStyle(1001) )
#data3.plotOn(xframe7)
data3_half.plotOn(xframe7)
#gauss11.plotOn(xframe7,RooFit.LineColor(RooFit.kRed))
# -------------------------------------------
# 8. use combined PDF to fit SB only and test fixAddCoefRange
xframe8 = x.frame(RooFit.Title("8. use combined PDF to fit SB only and test fixAddCoefRange"))
# gauss 12
mean12 = RooRealVar("mean12","mean of gaussian",-1,-10,10)
sigma12 = RooRealVar("sigma12","width of gaussian",2,0.1,10)
gauss12 = RooGaussian("gauss12","gaussian PDF",x,mean12,sigma12)
# gauss 13
# In[49]:
alldata = RooDataSet("alldata", "alldata", xTree,
RooArgSet(masskk, mass, massmumu))
# In[50]:
alldata.numEntries()
# In[51]:
alldata.numEntries()
c = TCanvas("canvas", "canvas", 1200, 800)
massFrame = mass.frame()
alldata.plotOn(massFrame)
massFrame.Draw()
c.SaveAs("testmass.png")
kkFrame = masskk.frame()
alldata.plotOn(kkFrame)
kkFrame.Draw()
c.SaveAs("testkk.png")
mumuFrame = massmumu.frame()
alldata.plotOn(mumuFrame)
mumuFrame.Draw()
c.SaveAs("testmm.png")
def dijet(category):
channel = 'bb'
stype = channel
isSB = True # relict from using Alberto's more complex script
isData = not ISMC
nTupleDir = NTUPLEDIR
samples = data if isData else back
pd = []
for sample_name in samples:
if YEAR == 'run2':
pd += sample[sample_name]['files']
else:
pd += [x for x in sample[sample_name]['files'] if YEAR in x]
print "datasets:", pd
if not os.path.exists(PLOTDIR): os.makedirs(PLOTDIR)
if BIAS: print "Running in BIAS mode"
order = 0
RSS = {}
X_mass = RooRealVar("jj_mass_widejet", "m_{jj}", X_min, X_max, "GeV")
weight = RooRealVar("MANtag_weight", "", -1.e9, 1.e9)
variables = RooArgSet(X_mass)
variables.add(RooArgSet(weight))
if VARBINS:
binsXmass = RooBinning(len(abins) - 1, abins)
X_mass.setBinning(RooBinning(len(abins_narrow) - 1, abins_narrow))
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
baseCut = ""
print stype, "|", baseCut
print " - Reading from Tree"
treeBkg = TChain("tree")
for ss in pd:
if os.path.exists(nTupleDir + ss + "_" + BTAGGING + ".root"):
treeBkg.Add(nTupleDir + ss + "_" + BTAGGING + ".root")
else:
print "found no file for sample:", ss
setData = RooDataSet("setData", "Data (QCD+TTbar MC)", variables,
RooFit.Cut(baseCut), RooFit.WeightVar(weight),
RooFit.Import(treeBkg))
nevents = setData.sumEntries()
dataMin, dataMax = array('d', [0.]), array('d', [0.])
setData.getRange(X_mass, dataMin, dataMax)
xmin, xmax = dataMin[0], dataMax[0]
lastBin = X_mass.getMax()
if VARBINS:
for b in narrow_bins:
if b > xmax:
lastBin = b
break
print "Imported", (
"data" if isData else "MC"
), "RooDataSet with", nevents, "events between [%.1f, %.1f]" % (xmin, xmax)
#xmax = xmax+binsXmass.averageBinWidth() # start form next bin
# 1 parameter
print "fitting 1 parameter model"
p1_1 = RooRealVar("CMS" + YEAR + "_" + category + "_p1_1", "p1", 7.0, 0.,
2000.)
modelBkg1 = RooGenericPdf("Bkg1", "Bkg. fit (2 par.)",
"1./pow(@0/13000, @1)", RooArgList(X_mass, p1_1))
normzBkg1 = RooRealVar(
modelBkg1.GetName() + "_norm", "Number of background events", nevents,
0., 5. * nevents) #range dependent of actual number of events!
modelExt1 = RooExtendPdf(modelBkg1.GetName() + "_ext",
modelBkg1.GetTitle(), modelBkg1, normzBkg1)
fitRes1 = modelExt1.fitTo(setData, RooFit.Extended(True), RooFit.Save(1),
RooFit.SumW2Error(not isData),
RooFit.Strategy(2), RooFit.Minimizer("Minuit2"),
RooFit.PrintLevel(1 if VERBOSE else -1))
fitRes1.Print()
RSS[1] = drawFit("Bkg1", category, X_mass, modelBkg1, setData, binsXmass,
[fitRes1], normzBkg1.getVal())
# 2 parameters
print "fitting 2 parameter model"
p2_1 = RooRealVar("CMS" + YEAR + "_" + category + "_p2_1", "p1", 0., -100.,
1000.)
p2_2 = RooRealVar("CMS" + YEAR + "_" + category + "_p2_2", "p2",
p1_1.getVal(), -100., 600.)
modelBkg2 = RooGenericPdf("Bkg2", "Bkg. fit (3 par.)",
"pow(1-@0/13000, @1) / pow(@0/13000, @2)",
RooArgList(X_mass, p2_1, p2_2))
normzBkg2 = RooRealVar(modelBkg2.GetName() + "_norm",
"Number of background events", nevents, 0.,
5. * nevents)
modelExt2 = RooExtendPdf(modelBkg2.GetName() + "_ext",
modelBkg2.GetTitle(), modelBkg2, normzBkg2)
fitRes2 = modelExt2.fitTo(setData, RooFit.Extended(True), RooFit.Save(1),
RooFit.SumW2Error(not isData),
RooFit.Strategy(2), RooFit.Minimizer("Minuit2"),
RooFit.PrintLevel(1 if VERBOSE else -1))
fitRes2.Print()
RSS[2] = drawFit("Bkg2", category, X_mass, modelBkg2, setData, binsXmass,
[fitRes2], normzBkg2.getVal())
# 3 parameters
print "fitting 3 parameter model"
p3_1 = RooRealVar("CMS" + YEAR + "_" + category + "_p3_1", "p1",
p2_1.getVal(), -2000., 2000.)
p3_2 = RooRealVar("CMS" + YEAR + "_" + category + "_p3_2", "p2",
p2_2.getVal(), -400., 2000.)
p3_3 = RooRealVar("CMS" + YEAR + "_" + category + "_p3_3", "p3", -2.5,
-500., 500.)
modelBkg3 = RooGenericPdf(
"Bkg3", "Bkg. fit (4 par.)",
"pow(1-@0/13000, @1) / pow(@0/13000, @2+@3*log(@0/13000))",
RooArgList(X_mass, p3_1, p3_2, p3_3))
normzBkg3 = RooRealVar(modelBkg3.GetName() + "_norm",
"Number of background events", nevents, 0.,
5. * nevents)
modelExt3 = RooExtendPdf(modelBkg3.GetName() + "_ext",
modelBkg3.GetTitle(), modelBkg3, normzBkg3)
fitRes3 = modelExt3.fitTo(setData, RooFit.Extended(True), RooFit.Save(1),
RooFit.SumW2Error(not isData),
RooFit.Strategy(2), RooFit.Minimizer("Minuit2"),
RooFit.PrintLevel(1 if VERBOSE else -1))
fitRes3.Print()
RSS[3] = drawFit("Bkg3", category, X_mass, modelBkg3, setData, binsXmass,
[fitRes3], normzBkg3.getVal())
# 4 parameters
print "fitting 4 parameter model"
p4_1 = RooRealVar("CMS" + YEAR + "_" + category + "_p4_1", "p1",
p3_1.getVal(), -2000., 2000.)
p4_2 = RooRealVar("CMS" + YEAR + "_" + category + "_p4_2", "p2",
p3_2.getVal(), -2000., 2000.)
p4_3 = RooRealVar("CMS" + YEAR + "_" + category + "_p4_3", "p3",
p3_3.getVal(), -50., 50.)
p4_4 = RooRealVar("CMS" + YEAR + "_" + category + "_p4_4", "p4", 0.1, -50.,
50.)
modelBkg4 = RooGenericPdf(
"Bkg4", "Bkg. fit (5 par.)",
"pow(1 - @0/13000, @1) / pow(@0/13000, @2+@3*log(@0/13000)+@4*pow(log(@0/13000), 2))",
RooArgList(X_mass, p4_1, p4_2, p4_3, p4_4))
normzBkg4 = RooRealVar(modelBkg4.GetName() + "_norm",
"Number of background events", nevents, 0.,
5. * nevents)
modelExt4 = RooExtendPdf(modelBkg4.GetName() + "_ext",
modelBkg4.GetTitle(), modelBkg4, normzBkg4)
fitRes4 = modelExt4.fitTo(setData, RooFit.Extended(True), RooFit.Save(1),
RooFit.SumW2Error(not isData),
RooFit.Strategy(2), RooFit.Minimizer("Minuit2"),
RooFit.PrintLevel(1 if VERBOSE else -1))
fitRes4.Print()
RSS[4] = drawFit("Bkg4", category, X_mass, modelBkg4, setData, binsXmass,
[fitRes4], normzBkg4.getVal())
# Normalization parameters are should be set constant, but shape ones should not
# if BIAS:
# p1_1.setConstant(True)
# p2_1.setConstant(True)
# p2_2.setConstant(True)
# p3_1.setConstant(True)
# p3_2.setConstant(True)
# p3_3.setConstant(True)
# p4_1.setConstant(True)
# p4_2.setConstant(True)
# p4_3.setConstant(True)
# p4_4.setConstant(True)
normzBkg1.setConstant(True)
normzBkg2.setConstant(True)
normzBkg3.setConstant(True)
normzBkg4.setConstant(True)
#*******************************************************#
# #
# Fisher #
# #
#*******************************************************#
# Fisher test
with open(PLOTDIR + "/Fisher_" + category + ".tex", 'w') as fout:
fout.write(r"\begin{tabular}{c|c|c|c|c}")
fout.write("\n")
fout.write(r"function & $\chi^2$ & RSS & ndof & F-test \\")
fout.write("\n")
fout.write("\hline")
fout.write("\n")
CL_high = False
for o1 in range(1, 5):
o2 = min(o1 + 1, 5)
fout.write("%d par & %.2f & %.2f & %d & " %
(o1 + 1, RSS[o1]["chi2"], RSS[o1]["rss"],
RSS[o1]["nbins"] - RSS[o1]["npar"]))
if o2 > len(RSS):
fout.write(r"\\")
fout.write("\n")
continue #order==0 and
CL = fisherTest(RSS[o1]['rss'], RSS[o2]['rss'], o1 + 1., o2 + 1.,
RSS[o1]["nbins"])
fout.write("CL=%.3f " % (CL))
if CL > 0.10: # The function with less parameters is enough
if not CL_high:
order = o1
#fout.write( "%d par are sufficient " % (o1+1))
CL_high = True
else:
#fout.write( "%d par are needed " % (o2+1))
if not CL_high:
order = o2
fout.write(r"\\")
fout.write("\n")
fout.write("\hline")
fout.write("\n")
fout.write(r"\end{tabular}")
print "saved F-test table as", PLOTDIR + "/Fisher_" + category + ".tex"
#print "-"*25
#print "function & $\\chi^2$ & RSS & ndof & F-test & result \\\\"
#print "\\multicolumn{6}{c}{", "Zprime_to_bb", "} \\\\"
#print "\\hline"
#CL_high = False
#for o1 in range(1, 5):
# o2 = min(o1 + 1, 5)
# print "%d par & %.2f & %.2f & %d & " % (o1+1, RSS[o1]["chi2"], RSS[o1]["rss"], RSS[o1]["nbins"]-RSS[o1]["npar"]),
# if o2 > len(RSS):
# print "\\\\"
# continue #order==0 and
# CL = fisherTest(RSS[o1]['rss'], RSS[o2]['rss'], o1+1., o2+1., RSS[o1]["nbins"])
# print "%d par vs %d par CL=%f & " % (o1+1, o2+1, CL),
# if CL > 0.10: # The function with less parameters is enough
# if not CL_high:
# order = o1
# print "%d par are sufficient" % (o1+1),
# CL_high=True
# else:
# print "%d par are needed" % (o2+1),
# if not CL_high:
# order = o2
# print "\\\\"
#print "\\hline"
#print "-"*25
#print "@ Order is", order, "("+category+")"
#order = min(3, order)
#order = 2
if order == 1:
modelBkg = modelBkg1 #.Clone("Bkg")
modelAlt = modelBkg2 #.Clone("BkgAlt")
normzBkg = normzBkg1 #.Clone("Bkg_norm")
fitRes = fitRes1
elif order == 2:
modelBkg = modelBkg2 #.Clone("Bkg")
modelAlt = modelBkg3 #.Clone("BkgAlt")
normzBkg = normzBkg2 #.Clone("Bkg_norm")
fitRes = fitRes2
elif order == 3:
modelBkg = modelBkg3 #.Clone("Bkg")
modelAlt = modelBkg4 #.Clone("BkgAlt")
normzBkg = normzBkg3 #.Clone("Bkg_norm")
fitRes = fitRes3
elif order == 4:
modelBkg = modelBkg4 #.Clone("Bkg")
modelAlt = modelBkg3 #.Clone("BkgAlt")
normzBkg = normzBkg4 #.Clone("Bkg_norm")
fitRes = fitRes4
else:
print "Functions with", order + 1, "or more parameters are needed to fit the background"
exit()
modelBkg.SetName("Bkg_" + YEAR + "_" + category)
modelAlt.SetName("Alt_" + YEAR + "_" + category)
normzBkg.SetName("Bkg_" + YEAR + "_" + category + "_norm")
print "-" * 25
# Generate pseudo data
setToys = RooDataSet()
setToys.SetName("data_toys")
setToys.SetTitle("Data (toys)")
if not isData:
print " - Generating", nevents, "events for toy data"
setToys = modelBkg.generate(RooArgSet(X_mass), nevents)
#setToys = modelAlt.generate(RooArgSet(X_mass), nevents)
print "toy data generated"
if VERBOSE: raw_input("Press Enter to continue...")
#*******************************************************#
# #
# Plot #
# #
#*******************************************************#
print "starting to plot"
c = TCanvas("c_" + category, category, 800, 800)
c.Divide(1, 2)
setTopPad(c.GetPad(1), RATIO)
setBotPad(c.GetPad(2), RATIO)
c.cd(1)
frame = X_mass.frame()
setPadStyle(frame, 1.25, True)
if VARBINS: frame.GetXaxis().SetRangeUser(X_mass.getMin(), lastBin)
signal = getSignal(
category, stype,
2000) #replacing Alberto's getSignal by own dummy function
graphData = setData.plotOn(frame, RooFit.Binning(plot_binning),
RooFit.Scaling(False), RooFit.Invisible())
modelBkg.plotOn(frame, RooFit.VisualizeError(fitRes, 1, False),
RooFit.LineColor(602), RooFit.FillColor(590),
RooFit.FillStyle(1001), RooFit.DrawOption("FL"),
RooFit.Name("1sigma"))
modelBkg.plotOn(frame, RooFit.LineColor(602), RooFit.FillColor(590),
RooFit.FillStyle(1001), RooFit.DrawOption("L"),
RooFit.Name(modelBkg.GetName()))
modelAlt.plotOn(frame, RooFit.LineStyle(7), RooFit.LineColor(613),
RooFit.FillColor(609), RooFit.FillStyle(1001),
RooFit.DrawOption("L"), RooFit.Name(modelAlt.GetName()))
if not isSB and signal[0] is not None: # FIXME remove /(2./3.)
signal[0].plotOn(
frame,
RooFit.Normalization(signal[1] * signal[2], RooAbsReal.NumEvent),
RooFit.LineStyle(3), RooFit.LineWidth(6), RooFit.LineColor(629),
RooFit.DrawOption("L"), RooFit.Name("Signal"))
graphData = setData.plotOn(
frame, RooFit.Binning(plot_binning), RooFit.Scaling(False),
RooFit.XErrorSize(0 if not VARBINS else 1),
RooFit.DataError(RooAbsData.Poisson if isData else RooAbsData.SumW2),
RooFit.DrawOption("PE0"), RooFit.Name(setData.GetName()))
fixData(graphData.getHist(), True, True, not isData)
pulls = frame.pullHist(setData.GetName(), modelBkg.GetName(), True)
chi = frame.chiSquare(setData.GetName(), modelBkg.GetName(), True)
#setToys.plotOn(frame, RooFit.DataError(RooAbsData.Poisson), RooFit.DrawOption("PE0"), RooFit.MarkerColor(2))
frame.GetYaxis().SetTitle("Events / ( 100 GeV )")
frame.GetYaxis().SetTitleOffset(1.05)
frame.Draw()
#print "frame drawn"
# Get Chi2
# chi2[1] = frame.chiSquare(modelBkg1.GetName(), setData.GetName())
# chi2[2] = frame.chiSquare(modelBkg2.GetName(), setData.GetName())
# chi2[3] = frame.chiSquare(modelBkg3.GetName(), setData.GetName())
# chi2[4] = frame.chiSquare(modelBkg4.GetName(), setData.GetName())
frame.SetMaximum(frame.GetMaximum() * 10)
frame.SetMinimum(max(frame.GetMinimum(), 1.e-1))
c.GetPad(1).SetLogy()
drawAnalysis(category)
drawRegion(category, True)
#drawCMS(LUMI, "Simulation Preliminary")
drawCMS(LUMI, "Work in Progress", suppressCMS=True)
leg = TLegend(0.575, 0.6, 0.95, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.AddEntry(setData.GetName(),
setData.GetTitle() + " (%d events)" % nevents, "PEL")
leg.AddEntry(modelBkg.GetName(), modelBkg.GetTitle(),
"FL") #.SetTextColor(629)
leg.AddEntry(modelAlt.GetName(), modelAlt.GetTitle(), "L")
if not isSB and signal[0] is not None:
leg.AddEntry("Signal", signal[0].GetTitle(), "L")
leg.SetY1(0.9 - leg.GetNRows() * 0.05)
leg.Draw()
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.04)
latex.SetTextFont(42)
if not isSB:
latex.DrawLatex(leg.GetX1() * 1.16,
leg.GetY1() - 0.04, "HVT model B (g_{V}=3)")
# latex.DrawLatex(0.67, leg.GetY1()-0.045, "#sigma_{X} = 1.0 pb")
c.cd(2)
frame_res = X_mass.frame()
setPadStyle(frame_res, 1.25)
frame_res.addPlotable(pulls, "P")
setBotStyle(frame_res, RATIO, False)
if VARBINS: frame_res.GetXaxis().SetRangeUser(X_mass.getMin(), lastBin)
frame_res.GetYaxis().SetRangeUser(-5, 5)
frame_res.GetYaxis().SetTitle("pulls(#sigma)")
frame_res.GetYaxis().SetTitleOffset(0.3)
frame_res.Draw()
fixData(pulls, False, True, False)
drawChi2(RSS[order]["chi2"], RSS[order]["nbins"] - (order + 1), True)
line = drawLine(X_mass.getMin(), 0, lastBin, 0)
if VARBINS:
c.SaveAs(PLOTDIR + "/BkgSR_" + category + ".pdf")
c.SaveAs(PLOTDIR + "/BkgSR_" + category + ".png")
else:
c.SaveAs(PLOTDIR + "/BkgSR_" + category + ".pdf")
c.SaveAs(PLOTDIR + "/BkgSR_" + category + ".png")
#*******************************************************#
# #
# Generate workspace #
# #
#*******************************************************#
if BIAS:
gSystem.Load("libHiggsAnalysisCombinedLimit.so")
from ROOT import RooMultiPdf
cat = RooCategory("pdf_index", "Index of Pdf which is active")
pdfs = RooArgList(modelBkg, modelAlt)
roomultipdf = RooMultiPdf("roomultipdf", "All Pdfs", cat, pdfs)
normulti = RooRealVar("roomultipdf_norm",
"Number of background events", nevents, 0., 1.e6)
normzBkg.setConstant(
False
) ## newly put here to ensure it's freely floating in the combine fit
# create workspace
w = RooWorkspace("Zprime_" + YEAR, "workspace")
# Dataset
if isData: getattr(w, "import")(setData, RooFit.Rename("data_obs"))
else: getattr(w, "import")(setToys, RooFit.Rename("data_obs"))
#getattr(w, "import")(setData, RooFit.Rename("data_obs"))
if BIAS:
getattr(w, "import")(cat, RooFit.Rename(cat.GetName()))
getattr(w, "import")(normulti, RooFit.Rename(normulti.GetName()))
getattr(w, "import")(roomultipdf, RooFit.Rename(roomultipdf.GetName()))
getattr(w, "import")(modelBkg, RooFit.Rename(modelBkg.GetName()))
getattr(w, "import")(modelAlt, RooFit.Rename(modelAlt.GetName()))
getattr(w, "import")(normzBkg, RooFit.Rename(normzBkg.GetName()))
w.writeToFile(WORKDIR + "%s_%s.root" % (DATA_TYPE + "_" + YEAR, category),
True)
print "Workspace", WORKDIR + "%s_%s.root" % (
DATA_TYPE + "_" + YEAR, category), "saved successfully"
if VERBOSE: raw_input("Press Enter to continue...")
space.factory('Product::sigma_%s1({data_MC_factor,sigma_MCfit_%s1})' % (fitLabel,fitLabel))
space.factory('Product::sigma_%s2({data_MC_factor,sigma_MCfit_%s2})' % (fitLabel,fitLabel))
space.factory('Gaussian::gPDF_{0}1(lbl0Mass,mean_{0},sigma_{0}1)'.format(fitLabel))
space.factory('Gaussian::gPDF_{0}2(lbl0Mass,mean_{0},sigma_{0}2)'.format(fitLabel))
space.factory('SUM::gPDF_{0}(frac1_{0}*gPDF_{0}1,gPDF_{0}2)'.format(fitLabel))
#space.factory('SUM::gPDF_{0}(gPDF_{0}2)'.format(fitLabel))
ConstraintVar( space, 'pLbL0', space.var('mean_{0}'.format(fitLabel)) )
ConstraintVar( space, 'pLbL0', space.var('sigma_MCfit_{0}1'.format(fitLabel)) )
ConstraintVar( space, 'pLbL0', space.var('sigma_MCfit_{0}2'.format(fitLabel)) )
ConstraintVar( space, 'pLbL0', space.var('frac1_{0}'.format(fitLabel)) )
myFrame=mass.frame(RooFit.Title(fitLabel))
myFrame.GetXaxis().SetTitle('J/#psi #bar{p} #pi^{+} Mass(GeV)')
dataset.plotOn(myFrame, RooFit.Name('data'))
myPDF.plotOn(myFrame,RooFit.LineColor(2),RooFit.LineWidth(1), RooFit.Name('totModel'))
myFrame.Draw()
canv.SaveAs('tmpLbL0.pdf')
SaveResult(
origPlot=myFrame,
origVar=mass,
data={'content':dataset},
totPDF={'content':myPDF},
fitres=fitres,
label=fitLabel,
fitDir=fitDir,
figDir=figDir,
absNumNormalize=dataset.sumEntries()
def Subtract_Distribution(dataset, DTF_D0sPi_M, LOG_D0_IPCHI2_OWNPV, bin = "undefined", silent = False):
dataset_sig = RooDataSet("dataset_sig", "Signal region",dataset, RooArgSet(DTF_D0sPi_M, LOG_D0_IPCHI2_OWNPV) ," ( DTF_D0sPi_M < 2015 ) ")
dataset_bckg = RooDataSet("dataset_bckg", "Background region",dataset, RooArgSet(DTF_D0sPi_M, LOG_D0_IPCHI2_OWNPV) ," ( DTF_D0sPi_M > 2015 ) && ( DTF_D0sPi_M < 2020 ) ")
#Introduce fit variables
## Johnson parameters
J_mu = RooRealVar("J_mu","J_mu", 2011, 2000, 2020)
J_sigma = RooRealVar("J_sigma","J_sigma", 0.045, 0.01, 0.1)
J_delta = RooRealVar("J_delta","J_delta", 0., -1, 1)
J_gamma = RooRealVar("J_gamma","J_gamma", 0., -1, 1)
## Gaussian parameters
G1_mu = RooRealVar("G1_mu","G1_mu", 2010, 2008, 2012)
G1_sigma = RooRealVar("G1_sigma","G1_sigma", 1.0, 0.01, 5)
G2_mu = RooRealVar("G2_mu","G2_mu", 2010, 2008, 2012)
G2_sigma = RooRealVar("G2_sigma","G2_sigma", 0.4, 0.01, 5)
G3_mu = RooRealVar("G3_mu","G3_mu", 2010, 2008, 2012)
G3_sigma = RooRealVar("G3_sigma","G3_sigma", 0.2, 0.01, 5)
## Signal yields ratios
fJ = RooRealVar("fJ","fJ", 0.5, 0., 1)
fG1 = RooRealVar("fG1","fG1", 0.5, 0., 1)
fG2 = RooRealVar("fG2","fG2", 0.5, 0., 1)
##Background parameters
B_b = RooRealVar("B_b","B_b", 1.09, 0.9, 1.5)
B_c = RooRealVar("B_c","B_c", 0.0837, 0.01, 0.2)
##Total yield
N_S = RooRealVar("N_S","N_S", 0.6*dataset.numEntries(), 0, 1.1*dataset.numEntries())
N_B = RooRealVar("N_B","N_B", 0.3*dataset.numEntries(), 0, 1.1*dataset.numEntries())
#Define shapes
s_Johnson = ROOT.Johnson("s_Johnson", "s_Johnson", DTF_D0sPi_M, J_mu, J_sigma, J_delta, J_gamma)
s_Gauss1 = ROOT.RooGaussian("s_Gauss1","s_Gauss1", DTF_D0sPi_M, G1_mu, G1_sigma)
s_Gauss2 = ROOT.RooGaussian("s_Gauss2","s_Gauss2", DTF_D0sPi_M, G2_mu, G2_sigma)
s_Gauss3 = ROOT.RooGaussian("s_Gauss3","s_Gauss3", DTF_D0sPi_M, G3_mu, G3_sigma)
s_Background = ROOT.Background("s_Background", "s_Background", DTF_D0sPi_M, B_b, B_c)
s_Signal = RooAddPdf("s_Signal", "s_Signal", RooArgList(s_Gauss1, s_Gauss2, s_Gauss3), RooArgList(fG1, fG2), True)
s_Total = RooAddPdf("s_Total", "s_Total", RooArgList(s_Signal, s_Background), RooArgList(N_S, N_B))
dataset_binned = RooDataHist("dataset_binned","Binned data", RooArgSet(DTF_D0sPi_M), dataset)
#Fit shapes
fit_hists = s_Total.fitTo(dataset_binned,RooFit.SumW2Error(True),RooFit.Save())
if not silent:
ipframe_1 = DTF_D0sPi_M.frame(RooFit.Title("Fit example"))
dataset_binned.plotOn(ipframe_1)
s_Total.plotOn(ipframe_1, RooFit.Components("s_Signal"), RooFit.LineColor(2),RooFit.LineWidth(4))
s_Total.plotOn(ipframe_1, RooFit.Components("s_Johnson"), RooFit.LineColor(5),RooFit.LineWidth(2), RooFit.LineStyle(3))
s_Total.plotOn(ipframe_1, RooFit.Components("s_Gauss1"), RooFit.LineColor(6),RooFit.LineWidth(2), RooFit.LineStyle(3))
s_Total.plotOn(ipframe_1, RooFit.Components("s_Gauss2"), RooFit.LineColor(7),RooFit.LineWidth(2), RooFit.LineStyle(3))
s_Total.plotOn(ipframe_1, RooFit.Components("s_Gauss3"), RooFit.LineColor(8),RooFit.LineWidth(2), RooFit.LineStyle(3))
s_Total.plotOn(ipframe_1, RooFit.Components("s_Background"), RooFit.LineColor(4),RooFit.LineWidth(4))
s_Total.plotOn(ipframe_1, RooFit.LineColor(1), RooFit.LineWidth(4))
DTF_D0sPi_M.setRange("Background_region", 2015, 2020)
DTF_D0sPi_M.setRange("Signal_region", 2002, 2015)
Bckg_int = s_Background.createIntegral(RooArgSet(DTF_D0sPi_M), RooArgSet(DTF_D0sPi_M), "Background_region")
Sig_int = s_Background.createIntegral(RooArgSet(DTF_D0sPi_M), RooArgSet(DTF_D0sPi_M), "Signal_region")
w = RooRealVar("w","w",-1,1)
w.setVal(1)
dataset_sig.addColumn(w, False)
w.setVal(-float(Sig_int.getVal())/float(Bckg_int.getVal()))
dataset_bckg.addColumn(w, False)
dataset_all = RooDataSet("dataset_all", "dataset_all",dataset_bckg, RooArgSet(DTF_D0sPi_M, LOG_D0_IPCHI2_OWNPV, w), "1>0", "w")
dataset_all.append(dataset_sig)
if not silent:
ipframe_2 = LOG_D0_IPCHI2_OWNPV.frame(RooFit.Title("IPChi2 distribution"))
dataset_bckg.plotOn(ipframe_2, RooFit.LineColor(4), RooFit.MarkerColor(4))
dataset_all.plotOn(ipframe_2, RooFit.LineColor(3), RooFit.MarkerColor(3))
dataset_sig.plotOn(ipframe_2, RooFit.LineColor(2), RooFit.MarkerColor(2))
c1 = TCanvas("c1","c1",900,900)
ipframe_1.Draw()
c1.SaveAs("plots/Subtraction_Control/Bin_"+str(bin)+"_frame1.pdf")
c1.SaveAs("plots/Subtraction_Control/Bin_"+str(bin)+"_frame1_C.C")
c2 = TCanvas("c2","c2",900,900)
ipframe_2.Draw()
c2.SaveAs("plots/Subtraction_Control/Bin_"+str(bin)+"_frame2.pdf")
c2.SaveAs("plots/Subtraction_Control/Bin_"+str(bin)+"_frame2_C.C")
ipframe_1.SaveAs("plots/Subtraction_Control/Bin_"+str(bin)+"_ipframe1.C")
ipframe_2.SaveAs("plots/Subtraction_Control/Bin_"+str(bin)+"_ipframe2.C")
return dataset_all
# In[9]:
alldata.numEntries()
# In[10]:
alldata.numEntries()
c = TCanvas("canvas","canvas",1200,800)
massFrame = mass.frame()
alldata.plotOn(massFrame)
massFrame.Draw()
c.SaveAs("plots/testmass.png")
massKKFrame = masskk.frame(Range(phimin,phimax))
alldata.plotOn(massKKFrame,RooLinkedList())
massKKFrame.Draw()
c.SaveAs("plots/testmasskk.png")
# In[11]:
#b0dataNonPromptMass = b0dataNonPrompt.reduce(SelectVars(RooArgSet(mass)))
simPdf.addPdf(model3S,'b3')
fitregion = x.setRange('fitregion',10.35,10.8)
fitregion_1S = x.setRange('fitregion_1S',10.35,10.8)
fitregion_2S = x.setRange('fitregion_2S',10.35,10.8)
fitregion_3S = x.setRange('fitregion_3S',10.35,10.8)
#simPdf.fitTo(combData,RooFit.Save(), RooFit.Range('fitregion'),RooFit.SplitRange())
simPdf.fitTo(combData,RooFit.Save())
#plotting
frame1 = x.frame()
combData.plotOn(frame1,
RooFit.Cut("sample==sample::b1"),
RooFit.MarkerSize(0.7))
simPdf.plotOn(frame1, RooFit.Slice(sample,'b1'),
RooFit.ProjWData(RooArgSet(sample),combData),
RooFit.LineWidth(2))
c1 = TCanvas('c1','c1')
frame1.Draw()
frame2 = x.frame()
combData.plotOn(frame2,
RooFit.Cut("sample==sample::b2"),
RooFit.MarkerSize(0.7))
simPdf.plotOn(frame2, RooFit.Slice(sample,'b2'),
RooFit.ProjWData(RooArgSet(sample),combData),
RooFit.LineWidth(2))
if (getattr(treeIn,"categories")==1 or getattr(treeIn,"categories")==3) and getattr(treeIn,"mZZ")> rrv_mass_lvj.getMin() and getattr(treeIn,"mZZ")<rrv_mass_lvj.getMax() and tmp_jet_mass>=65 and tmp_jet_mass<=105 and options.category == "HP" :
isGoodEvent = 1 ;
if (getattr(treeIn,"categories")==0 or getattr(treeIn,"categories")==2) and getattr(treeIn,"mZZ")> rrv_mass_lvj.getMin() and getattr(treeIn,"mZZ")<rrv_mass_lvj.getMax() and tmp_jet_mass>=65 and tmp_jet_mass<=130 and options.category == "LP" :
isGoodEvent = 1 ;
if isGoodEvent == 1:
### weigh MC events
tmp_event_weight = getattr(treeIn,"weight")*luminosity;
rrv_mass_lvj.setVal(getattr(treeIn,"mZZ"));
rdataset4fit_mlvj.add(RooArgSet(rrv_mass_lvj),tmp_event_weight/tmp_scale_to_lumi);
### signal over convolution plot
mplot_sig = rrv_mass_lvj.frame(RooFit.Title(""),RooFit.Bins(rrv_mass_lvj.getBins()));
rdataset4fit_mlvj.plotOn(mplot_sig,RooFit.Name("data_invisible"),RooFit.MarkerSize(1.5),RooFit.DataError(RooAbsData.SumW2), RooFit.XErrorSize(0));
doubleCB_sig = ROOT.RooDoubleCrystalBall("DoubleCB_BulkG_WW_mlvj","DoubleCB_BulkG_WW_mlvj",rrv_mass_lvj,rrv_mean_CB,rrv_total_sigma_CB,rrv_alpha1_CB,rrv_n1_CB,rrv_alpha2_CB,rrv_n2_CB);
bw_sig = RooBreitWigner("bw_"+options.channel,"bw_"+options.channel,rrv_mass_lvj,rrv_total_mean_CB,rrv_width_BW);
original_bin = rrv_mass_lvj.getBins();
rrv_mass_lvj.setBins(1000,"cache");
model_pdf_sig = RooFFTConvPdf("sig_xww_%s_%s"%(options.channel,options.category),"sigxww_%s_%s"%(options.channel,options.category),rrv_mass_lvj,bw_sig,doubleCB_sig);
model_pdf_sig.setBufferFraction(1.0);
rrv_mass_lvj.setBins(int(original_bin));
model_pdf_sig.plotOn(mplot_sig,RooFit.Name("total_MC"),RooFit.Normalization((rrv_number_signal.getVal()/tmp_scale_to_lumi)/(6.25*rdataset4fit_mlvj.sumEntries())),RooFit.DrawOption("L"), RooFit.LineColor(kBlue), RooFit.VLines(),RooFit.LineWidth(2),RooFit.LineStyle(1));
mplot_pull = get_pull(rrv_mass_lvj,mplot_sig);
mplot_sig.GetYaxis().SetRangeUser(0.,mplot_sig.GetMaximum()*1.2);
tau_result.setVal(tau.getVal())
tau_result.setError(tau.getError())
result_data.add(RooArgSet(tau_result))
tau.setVal(tau_true.getVal())
tau.setError(0.1)
tau_pull = RooPullVar("tau_pull", "tau_pull", tau_result._target_(), tau_true._target_())
tau_pull = result_data.addColumn(tau_pull)
tau_pull = w.put(tau_pull)
mean = RealVar('mean', Value = 0, MinMax = (-4, 4))
sigma = RealVar('sigma', Value = 1, MinMax = (0.1, 5))
from ROOT import RooGaussian
gauss = Pdf(Name = 'gauss', Type = RooGaussian, Parameters = (tau_pull, mean, sigma))
result = gauss.fitTo(result_data, **fitOpts)
from ROOT import TPostScript
ps = TPostScript("test.eps", 113)
ps.FontEmbed()
from ROOT import TCanvas
canvas = TCanvas('canvas', 'canvas', 600, 400)
frame = tau_pull.frame(Range = (-5, 5))
frame.GetXaxis().SetTitle('#tau pull')
frame.GetYaxis().SetTitle('M Experiments')
result_data.plotOn(frame)
gauss.plotOn(frame)
frame.Draw()
canvas.Update()
ps.Close()
def alpha(channel):
nElec = channel.count('e')
nMuon = channel.count('m')
nLept = nElec + nMuon
nBtag = channel.count('b')
# Channel-dependent settings
# Background function. Semi-working options are: EXP, EXP2, EXPN, EXPTAIL
if nLept == 0:
treeName = 'SR'
signName = 'XZh'
colorVjet = sample['DYJetsToNuNu']['linecolor']
triName = "HLT_PFMET"
leptCut = "0==0"
topVeto = selection["TopVetocut"]
massVar = "X_cmass"
binFact = 1
#fitFunc = "EXP"
#fitFunc = "EXP2"
#fitFunc = "EXPN"
#fitFunc = "EXPTAIL"
fitFunc = "EXPN" if nBtag < 2 else "EXP"
fitAltFunc = "EXPTAIL" if nBtag < 2 else "EXPTAIL"
fitFuncVjet = "ERFEXP" if nBtag < 2 else "ERFEXP"
fitFuncVV = "EXPGAUS"
fitFuncTop = "GAUS2"
elif nLept == 1:
treeName = 'WCR'
signName = 'XWh'
colorVjet = sample['WJetsToLNu']['linecolor']
triName = "HLT_Ele" if nElec > 0 else "HLT_Mu"
leptCut = "isWtoEN" if nElec > 0 else "isWtoMN"
topVeto = selection["TopVetocut"]
massVar = "X_mass"
binFact = 2
if nElec > 0:
fitFunc = "EXP" if nBtag < 2 else "EXP"
fitAltFunc = "EXPTAIL" if nBtag < 2 else "EXPTAIL"
else:
fitFunc = "EXPTAIL" if nBtag < 2 else "EXP"
fitAltFunc = "EXPN" if nBtag < 2 else "EXPTAIL"
fitFuncVjet = "ERFEXP" if nBtag < 2 else "ERFEXP"
fitFuncVV = "EXPGAUS"
fitFuncTop = "GAUS3" if nBtag < 2 else "GAUS2"
else:
treeName = 'XZh'
signName = 'XZh'
colorVjet = sample['DYJetsToLL']['linecolor']
triName = "HLT_Ele" if nElec > 0 else "HLT_Mu"
leptCut = "isZtoEE" if nElec > 0 else "isZtoMM"
topVeto = "0==0"
massVar = "X_mass"
binFact = 5
if nElec > 0:
fitFunc = "EXP" if nBtag < 2 else "EXP"
fitAltFunc = "POW" if nBtag < 2 else "POW"
else:
fitFunc = "EXP" if nBtag < 2 else "EXP"
fitAltFunc = "POW" if nBtag < 2 else "POW"
fitFuncVjet = "ERFEXP" if nBtag < 2 else "EXP"
fitFuncVV = "EXPGAUS2"
fitFuncTop = "GAUS"
btagCut = selection["2Btag"] if nBtag == 2 else selection["1Btag"]
print "--- Channel", channel, "---"
print " number of electrons:", nElec, " muons:", nMuon, " b-tags:", nBtag
print " read tree:", treeName, "and trigger:", triName
if ALTERNATIVE: print " using ALTERNATIVE fit functions"
print "-"*11*2
# Silent RooFit
RooMsgService.instance().setGlobalKillBelow(RooFit.FATAL)
#*******************************************************#
# #
# Variables and selections #
# #
#*******************************************************#
# Define all the variables from the trees that will be used in the cuts and fits
# this steps actually perform a "projection" of the entire tree on the variables in thei ranges, so be careful once setting the limits
X_mass = RooRealVar( massVar, "m_{X}" if nLept > 0 else "m_{T}^{X}", XBINMIN, XBINMAX, "GeV")
J_mass = RooRealVar( "fatjet1_prunedMassCorr", "corrected pruned mass", HBINMIN, HBINMAX, "GeV")
CSV1 = RooRealVar( "fatjet1_CSVR1", "", -1.e99, 1.e4 )
CSV2 = RooRealVar( "fatjet1_CSVR2", "", -1.e99, 1.e4 )
nBtag = RooRealVar( "fatjet1_nBtag", "", 0., 4 )
CSVTop = RooRealVar( "bjet1_CSVR", "", -1.e99, 1.e4 )
isZtoEE = RooRealVar("isZtoEE", "", 0., 2 )
isZtoMM = RooRealVar("isZtoMM", "", 0., 2 )
isWtoEN = RooRealVar("isWtoEN", "", 0., 2 )
isWtoMN = RooRealVar("isWtoMN", "", 0., 2 )
weight = RooRealVar( "eventWeightLumi", "", -1.e9, 1. )
# 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, CSV1, CSV2, nBtag, CSVTop)
variables.add(RooArgSet(isZtoEE, isZtoMM, isWtoEN, isWtoMN, weight))
# Define the ranges in fatJetMass - these will be used to define SB and SR
J_mass.setRange("LSBrange", LOWMIN, LOWMAX)
J_mass.setRange("HSBrange", HIGMIN, HIGMAX)
J_mass.setRange("VRrange", LOWMAX, SIGMIN)
J_mass.setRange("SRrange", SIGMIN, SIGMAX)
J_mass.setBins(54)
# Define the selection for the various categories (base + SR / LSBcut / HSBcut )
baseCut = leptCut + " && " + btagCut + "&&" + topVeto
massCut = massVar + ">%d" % XBINMIN
baseCut += " && " + massCut
# Cuts
SRcut = baseCut + " && %s>%d && %s<%d" % (J_mass.GetName(), SIGMIN, J_mass.GetName(), SIGMAX)
LSBcut = baseCut + " && %s>%d && %s<%d" % (J_mass.GetName(), LOWMIN, J_mass.GetName(), LOWMAX)
HSBcut = baseCut + " && %s>%d && %s<%d" % (J_mass.GetName(), HIGMIN, J_mass.GetName(), HIGMAX)
SBcut = baseCut + " && ((%s>%d && %s<%d) || (%s>%d && %s<%d))" % (J_mass.GetName(), LOWMIN, J_mass.GetName(), LOWMAX, J_mass.GetName(), HIGMIN, J_mass.GetName(), HIGMAX)
VRcut = baseCut + " && %s>%d && %s<%d" % (J_mass.GetName(), LOWMAX, J_mass.GetName(), SIGMIN)
# Binning
binsJmass = RooBinning(HBINMIN, HBINMAX)
binsJmass.addUniform(HBINS, HBINMIN, HBINMAX)
binsXmass = RooBinning(XBINMIN, XBINMAX)
binsXmass.addUniform(binFact*XBINS, XBINMIN, XBINMAX)
#*******************************************************#
# #
# Input files #
# #
#*******************************************************#
# Import the files using TChains (separately for the bkg "classes" that we want to describe: here DY and VV+ST+TT)
treeData = TChain(treeName)
treeMC = TChain(treeName)
treeVjet = TChain(treeName)
treeVV = TChain(treeName)
treeTop = TChain(treeName)
# treeSign = {}
# nevtSign = {}
# Read data
print "read data start"
pd = getPrimaryDataset(triName)
if len(pd)==0: raw_input("Warning: Primary Dataset not recognized, continue?")
for i, s in enumerate(pd): treeData.Add(NTUPLEDIR + s + ".root")
# Read V+jets backgrounds
print "read V+jet start"
for i, s in enumerate(["WJetsToLNu_HT", "DYJetsToNuNu_HT", "DYJetsToLL_HT"]):
for j, ss in enumerate(sample[s]['files']): treeVjet.Add(NTUPLEDIR + ss + ".root")
# Read VV backgrounds
print "read VV start"
for i, s in enumerate(["VV"]):
for j, ss in enumerate(sample[s]['files']): treeVV.Add(NTUPLEDIR + ss + ".root")
# Read Top backgrounds
print "read Top start"
for i, s in enumerate(["ST", "TTbar"]):
for j, ss in enumerate(sample[s]['files']): treeTop.Add(NTUPLEDIR + ss + ".root")
# Sum all background MC
treeMC.Add(treeVjet)
treeMC.Add(treeVV)
treeMC.Add(treeTop)
# print "prepare SB dataset"
# create a dataset to host data in sideband (using this dataset we are automatically blind in the SR!)
# setDataSB = RooDataSet("setDataSB", "setDataSB", variables, RooFit.Cut(SBcut), RooFit.WeightVar(weight), RooFit.Import(treeData))
# setDataLSB = RooDataSet("setDataLSB", "setDataLSB", variables, RooFit.Import(setDataSB), RooFit.Cut(LSBcut), RooFit.WeightVar(weight))
# setDataHSB = RooDataSet("setDataHSB", "setDataHSB", variables, RooFit.Import(setDataSB), RooFit.Cut(HSBcut), RooFit.WeightVar(weight))
# print "prepare SR dataset"
# Observed data (WARNING, BLIND!)
# setDataSR = RooDataSet("setDataSR", "setDataSR", variables, RooFit.Cut(SRcut), RooFit.WeightVar(weight), RooFit.Import(treeData))
# setDataVR = RooDataSet("setDataVR", "setDataVR", variables, RooFit.Cut(VRcut), RooFit.WeightVar(weight), RooFit.Import(treeData)) # Observed in the VV mass, just for plotting purposes
print "prepare MC dataset"
# same for the bkg datasets from MC, where we just apply the base selections (not blind)
setVjet = RooDataSet("setVjet", "setVjet", variables, RooFit.Cut(baseCut), RooFit.WeightVar(weight), RooFit.Import(treeVjet))
setVjetSB = RooDataSet("setVjetSB", "setVjetSB", variables, RooFit.Import(setVjet), RooFit.Cut(SBcut), RooFit.WeightVar(weight))
setVjetSR = RooDataSet("setVjetSR", "setVjetSR", variables, RooFit.Import(setVjet), RooFit.Cut(SRcut), RooFit.WeightVar(weight))
print "finish Vjet dataset"
# setVV = RooDataSet("setVV", "setVV", variables, RooFit.Cut(baseCut), RooFit.WeightVar(weight), RooFit.Import(treeVV))
# setVVSB = RooDataSet("setVVSB", "setVVSB", variables, RooFit.Import(setVV), RooFit.Cut(SBcut), RooFit.WeightVar(weight))
# setVVSR = RooDataSet("setVVSR", "setVVSR", variables, RooFit.Import(setVV), RooFit.Cut(SRcut), RooFit.WeightVar(weight))
# print "finish VV dataset"
# setTop = RooDataSet("setTop", "setTop", variables, RooFit.Cut(baseCut), RooFit.WeightVar(weight), RooFit.Import(treeTop))
# setTopSB = RooDataSet("setTopSB", "setTopSB", variables, RooFit.Import(setTop), RooFit.Cut(SBcut), RooFit.WeightVar(weight))
# setTopSR = RooDataSet("setTopSR", "setTopSR", variables, RooFit.Import(setTop), RooFit.Cut(SRcut), RooFit.WeightVar(weight))
# print "finish Top dataset"
# print " Data events SB: %.2f" % setDataSB.sumEntries()
print " V+jets entries: %.2f" % setVjet.sumEntries()
# print " VV, VH entries: %.2f" % setVV.sumEntries()
# print " Top,ST entries: %.2f" % setTop.sumEntries()
# the relative normalization of the varius bkg is taken from MC by counting all the events in the full fatJetMass range
#coef = RooRealVar("coef", "coef", setVV.sumEntries()/setVjet.sumEntries(),0.,1.)
# coef_VV_Vjet = RooRealVar("coef2_1", "coef2_1", setVV.sumEntries()/setVjet.sumEntries(), 0., 1.)
# coef_Top_VVVjet = RooRealVar("coef3_21", "coef3_21", setTop.sumEntries()/(setVjet.sumEntries()+setVV.sumEntries()),0.,1.);
# coef_VV_Vjet.setConstant(True)
# coef_Top_VVVjet.setConstant(True)
# Define entries
entryVjet = RooRealVar("entryVjets", "V+jets normalization", setVjet.sumEntries(), 0., 1.e6)
# entryVV = RooRealVar("entryVV", "VV normalization", setVV.sumEntries(), 0., 1.e6)
# entryTop = RooRealVar("entryTop", "Top normalization", setTop.sumEntries(), 0., 1.e6)
# entrySB = RooRealVar("entrySB", "Data SB normalization", setDataSB.sumEntries(SBcut), 0., 1.e6)
# entrySB.setError(math.sqrt(entrySB.getVal()))
# entryLSB = RooRealVar("entryLSB", "Data LSB normalization", setDataSB.sumEntries(LSBcut), 0., 1.e6)
# entryLSB.setError(math.sqrt(entryLSB.getVal()))
# entryHSB = RooRealVar("entryHSB", "Data HSB normalization", setDataSB.sumEntries(HSBcut), 0., 1.e6)
# entryHSB.setError(math.sqrt(entryHSB.getVal()))
#*******************************************************#
# #
# NORMALIZATION #
# #
#*******************************************************#
# set reasonable ranges for J_mass and X_mass
# these are used in the fit in order to avoid ROOFIT to look in regions very far away from where we are fitting
J_mass.setRange("h_reasonable_range", LOWMIN, HIGMAX)
X_mass.setRange("X_reasonable_range", XBINMIN, XBINMAX)
# Set RooArgSets once for all, see https://root.cern.ch/phpBB3/viewtopic.php?t=11758
jetMassArg = RooArgSet(J_mass)
##############################
# #
# Yu-hsiang test region #
# #
##############################
# test it in the channel "XZhnnb"
print "the channel is", channel
if channel == "XZhnnb":
# -------------------------------------------------------------------
# draw the setVjet
Jmass_frame = J_mass.frame(RooFit.Title("test frame"))
setVjet.plotOn(Jmass_frame)
# -------------------------------------------------------------------
# use a PDF to fit the dataset
print "fitFuncVjet is", fitFuncVjet
constVjet_value_initial = -0.020
offsetVjet_value_initial = 30.
widthVjet_value_initial = 100.
constVjet_test = RooRealVar("constVjet_test", "slope of the exp", constVjet_value_initial , -1., 0.)
offsetVjet_test = RooRealVar("offsetVjet_test", "offset of the erf", offsetVjet_value_initial, -50., 200.)
widthVjet_test = RooRealVar("widthVjet_test", "width of the erf", widthVjet_value_initial, 1., 200.)
modelVjet_test = RooErfExpPdf("modelVjet_test", "error function for V+jets mass", J_mass, constVjet_test, offsetVjet_test, widthVjet_test)
# constVjet_test.Print()
# offsetVjet_test.Print()
# widthVjet_test.Print()
# constVjet_test.setConstant(True)
# offsetVjet_test.setConstant(True)
# widthVjet_test.setConstant(True)
frVjet_test = modelVjet_test.fitTo(setVjet, RooFit.SumW2Error(True), RooFit.Range("h_reasonable_range"), RooFit.Strategy(2), RooFit.Minimizer("Minuit2"), RooFit.Save(1), RooFit.PrintLevel(1 if VERBOSE else -1))
# constVjet_test.Print()
# offsetVjet_test.Print()
# widthVjet_test.Print()
constVjet_value_fit_MC = constVjet_test.getVal()
offsetVjet_value_fit_MC = offsetVjet_test.getVal()
widthVjet_value_fit_MC = widthVjet_test.getVal()
print "constVjet_value_fit_MC:", constVjet_value_fit_MC, "offsetVjet_value_fit_MC:",offsetVjet_value_fit_MC,"widthVjet_value_fit_MC:",widthVjet_value_fit_MC
modelVjet_test.plotOn(Jmass_frame,RooFit.LineColor(4))
# -------------------------------------------------------------------
# use the shape of fit to generate the psudo-data
Entries_pseudo_data = setVjet.sumEntries()
# Entries_pseudo_data = 502
# Entries_pseudo_data = 5021
pseudo_data = modelVjet_test.generate(RooArgSet(J_mass),Entries_pseudo_data )
# pseudo_data = modelVjet_test.generate(RooArgSet(J_mass),setVjet.sumEntries())
# pseudo_data = modelVjet_test.generate(RooArgSet(J_mass),502 )
# pseudo_data = modelVjet_test.generate(RooArgSet(J_mass),5021 )
# pseudo_data.Print("v")
Jmass_frame2 = J_mass.frame(RooFit.Title("test frame2"))
pseudo_data.plotOn(Jmass_frame2)
modelVjet_test.plotOn(Jmass_frame2,RooFit.LineColor(4))
# -------------------------------------------------------------------
# make another dataset that remove the signal region
pseudo_data_SB = RooDataSet("pseudo_data_SB", "pseudo_data_SB", RooArgSet(J_mass), RooFit.Import(pseudo_data), RooFit.Cut("fatjet1_prunedMassCorr<65 || fatjet1_prunedMassCorr>135") )
pseudo_data_SB.plotOn(Jmass_frame2,RooFit.LineColor(2))
# -------------------------------------------------------------------
# use another PDF to fit the pseudo-data in SB only
constVjet_test2 = RooRealVar("constVjet_test2", "slope of the exp", constVjet_value_fit_MC , -1., 0.)
offsetVjet_test2 = RooRealVar("offsetVjet_test2", "offset of the erf", offsetVjet_value_fit_MC , -50., 200.)
widthVjet_test2 = RooRealVar("widthVjet_test2", "width of the erf", widthVjet_value_fit_MC , 1., 200.)
modelVjet_test2 = RooErfExpPdf("modelVjet_test2", "error function for V+jets mass", J_mass, constVjet_test2, offsetVjet_test2, widthVjet_test2)
frVjet_test2 = modelVjet_test2.fitTo(pseudo_data_SB, RooFit.SumW2Error(True), RooFit.Range("LSBrange,HSBrange"), RooFit.Strategy(2), RooFit.Minimizer("Minuit2"), RooFit.Save(1), RooFit.PrintLevel(1 if VERBOSE else -1))
# frVjet_test2 = modelVjet_test2.fitTo(pseudo_data_SB, RooFit.SumW2Error(True), RooFit.Range("h_reasonable_range"), RooFit.Strategy(2), RooFit.Minimizer("Minuit2"), RooFit.Save(1), RooFit.PrintLevel(1 if VERBOSE else -1))
constVjet_value_fit_pseudo_data_SB = constVjet_test2.getVal()
offsetVjet_value_fit_pseudo_data_SB = offsetVjet_test2.getVal()
widthVjet_value_fit_pseudo_data_SB = widthVjet_test2.getVal()
print "constVjet_value_fit_pseudo_data_SB:", constVjet_value_fit_pseudo_data_SB, "offsetVjet_value_fit_pseudo_data_SB:",offsetVjet_value_fit_pseudo_data_SB,"widthVjet_value_fit_pseudo_data_SB:",widthVjet_value_fit_pseudo_data_SB
Jmass_frame3 = J_mass.frame(RooFit.Title("test frame3, fit the pseudo-data in SB only"))
pseudo_data_SB.plotOn(Jmass_frame3)
modelVjet_test2.plotOn(Jmass_frame3,RooFit.LineColor(4),RooFit.Range("h_reasonable_range"))
# -------------------------------------------------------------------
# calculate the Gen_value, the Fit_value and the Bias= ( Fit_value - Gen_value)/Gen_value
iGen_value = modelVjet_test.createIntegral(RooArgSet(J_mass), RooFit.Range("VRrange,SRrange"))
print "iGen_value:", iGen_value.getVal()
iFit_value = modelVjet_test2.createIntegral(RooArgSet(J_mass), RooFit.Range("VRrange,SRrange"))
print "iFit_value:", iFit_value.getVal()
Bias_value = ( iFit_value.getVal() - iGen_value.getVal() ) / iGen_value.getVal()
print "Bias_value of VR+SR:", Bias_value
# --------------
iGen_value = modelVjet_test.createIntegral(RooArgSet(J_mass), RooFit.Range("SRrange"))
print "iGen_value:", iGen_value.getVal()
iFit_value = modelVjet_test2.createIntegral(RooArgSet(J_mass), RooFit.Range("SRrange"))
print "iFit_value:", iFit_value.getVal()
Bias_value = ( iFit_value.getVal() - iGen_value.getVal() ) / iGen_value.getVal()
print "Bias_value of SR:", Bias_value
# iGen_value = modelVjet_test.createIntegral(RooArgSet(J_mass),RooFit.NormSet(RooArgSet(J_mass)), RooFit.Range("VRrange,SRrange"))
# print "iGen_value:", iGen_value.getVal()
# iGen_value = modelVjet_test.createIntegral(RooArgSet(J_mass),RooFit.NormSet(RooArgSet(J_mass)), RooFit.Range("VRrange,SRrange"))
# print "iGen_value:", iGen_value.getVal()
# iGen_value = modelVjet_test.createIntegral(RooArgSet(J_mass), RooFit.Range("h_reasonable_range"))
# print "iGen_value:", iGen_value.getVal()
# iGen_value = modelVjet_test.createIntegral(RooArgSet(J_mass))
# print "iGen_value:", iGen_value.getVal()
# -------------------------------------------------------------------
# repeat thousand times to see bias distribution
h_Bias = TH1D("h_Bias","h_Bias",80,-1,1);
Jmass_frame4 = J_mass.frame(RooFit.Title("test frame4"))
times_max = 50000
constVjet_test.setConstant(True)
offsetVjet_test.setConstant(True)
widthVjet_test.setConstant(True)
constVjet_test3 = RooRealVar("constVjet_test3", "slope of the exp", constVjet_value_fit_MC , -1., 0.)
offsetVjet_test3 = RooRealVar("offsetVjet_test3", "offset of the erf", offsetVjet_value_fit_MC , -50., 200.)
widthVjet_test3 = RooRealVar("widthVjet_test3", "width of the erf", widthVjet_value_fit_MC , 1., 200.)
for times in range(0,times_max):
# inside loop
# print "times:", times
if times % 10 == 0 :
print "Processing times:", times+1 ,"of", times_max
# generate pseudo-data
# n_1_prime = gRandom->Poisson(n_1);
Entries_pseudo_data_fluc = gRandom.Poisson( Entries_pseudo_data )
# print "Entries_pseudo_data:", Entries_pseudo_data
# print "Entries_pseudo_data_fluc:", Entries_pseudo_data_fluc
# pseudo_data2 = modelVjet_test.generate(RooArgSet(J_mass),Entries_pseudo_data )
pseudo_data2 = modelVjet_test.generate(RooArgSet(J_mass),Entries_pseudo_data_fluc )
# pseudo_data2.plotOn(Jmass_frame3,RooFit.LineColor(4),RooFit.Range("h_reasonable_range"))
# take out VR+SR
pseudo_data_SB2 = RooDataSet("pseudo_data_SB2", "pseudo_data_SB2", RooArgSet(J_mass), RooFit.Import(pseudo_data2), RooFit.Cut("fatjet1_prunedMassCorr<65 || fatjet1_prunedMassCorr>135") )
# use other PDF to fit
# print "constVjet_value_fit_MC:",constVjet_value_fit_MC
constVjet_test3.setVal(constVjet_value_fit_MC)
offsetVjet_test3.setVal(offsetVjet_value_fit_MC)
widthVjet_test3.setVal(widthVjet_value_fit_MC)
modelVjet_test3 = RooErfExpPdf("modelVjet_test3", "error function for V+jets mass", J_mass, constVjet_test3, offsetVjet_test3, widthVjet_test3)
frVjet_test3 = modelVjet_test3.fitTo(pseudo_data_SB2, RooFit.SumW2Error(True), RooFit.Range("LSBrange,HSBrange"), RooFit.Strategy(2), RooFit.Minimizer("Minuit2"), RooFit.Save(1), RooFit.PrintLevel(1 if VERBOSE else -1))
# calculate the bias
iGen_value2 = modelVjet_test.createIntegral(jetMassArg,RooFit.NormSet(jetMassArg), RooFit.Range("SRrange"))
# print "iGen_value2:", iGen_value2.getVal()
iFit_value2 = modelVjet_test3.createIntegral(jetMassArg,RooFit.NormSet(jetMassArg), RooFit.Range("SRrange"))
# print "iFit_value2:", iFit_value2.getVal()
Bias_value2 = ( iFit_value2.getVal() - iGen_value2.getVal() ) / iGen_value2.getVal()
# print "Bias_value2 of VR+SR:", Bias_value2
h_Bias.Fill(Bias_value2)
# -------------------------------------------------------------------
# plot and save
Save_Dir = "/afs/cern.ch/user/y/yuchang/www/jacopo_plotsAlpha/yu_hsiang_bias_study"
c_test = TCanvas("test","test draw",800,600)
c_test.cd()
Jmass_frame.Draw()
c_test.SaveAs(Save_Dir+"/"+"VJet_MC_fit_get_shape.pdf")
c_test2 = TCanvas("test2","test draw 2",800,600)
c_test2.cd()
Jmass_frame2.Draw()
c_test2.SaveAs(Save_Dir+"/"+"use_shape_to_generate_pseudo_data.pdf")
# c_test2.SaveAs(Save_Dir+"/"+"use_shape_to_generate_pseudo_data_test.pdf")
c_test3 = TCanvas("test3","test draw 3",800,600)
c_test3.cd()
Jmass_frame3.Draw()
c_test3.SaveAs(Save_Dir+"/"+"fit_pseudo_data_in_SB_only.pdf")
c_test4 = TCanvas("test4","test draw 4",800,600)
c_test4.cd()
h_Bias.Draw()
c_test4.SaveAs(Save_Dir+"/"+"h_Bias.pdf")
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
space.factory('Gaussian::gPDF_MC_{0}2(lbtkMass,mu_{0} ,sigma_MC_{0}2[0.05,0.0001,5.])'.format(fitLabel))
space.factory('SUM::gPDF_MC_{0}(frac_{0}1[0.70,0.6,1.0]*gPDF_MC_{0}1,gPDF_MC_{0}2)'.format(fitLabel))
myPDF=space.pdf('gPDF_MC_{0}'.format(fitLabel))
fitRes=myPDF.fitTo(dataset,RooFit.Range(fitLabel),RooFit.Minos(True),RooFit.Save(True))
space.var('mu_{0}'.format(fitLabel)).setConstant(True)
space.var('sigma_MC_{0}1'.format(fitLabel)).setConstant(True)
space.var('sigma_MC_{0}2'.format(fitLabel)).setConstant(True)
space.var('frac_{0}1'.format(fitLabel)).setConstant(True)
myFrame=mass.frame(RooFit.Title(fitLabel),RooFit.Range(fitLabel))
if setAxis: myFrame=mass.frame(xaxisMin,xaxisMax)
dataset.plotOn(myFrame)
myPDF.plotOn(myFrame,RooFit.LineColor(2))
space.pdf('gPDF_MC_{0}'.format(fitLabel)).plotOn(myFrame,RooFit.LineColor(43),RooFit.LineWidth(1))
myFrame.Draw()
figDir.cd()
myFrame.Write(fitLabel)
canv.SaveAs(outFig)
fitDir.cd()
fitRes.Write(fitLabel)
# fit lbtkMass in Lb MC end }}}
# fit lbtkMass in antiLb MC {{{
if fitToLbMass_AntiLbTkMC:
fitLabel='lbtkDist_antilbtkMC'
if args[0].startswith('MC'):
from ROOT import TFile
sig_file = TFile(input_data['sig_cache'])
sig_data = sig_file.Get(input_data['sig_dataset'])
prompt_file = TFile(input_data['prompt_cache'])
prompt_data = prompt_file.Get(input_data['prompt_dataset'])
else:
sig_file = TFile("/project/bfys/jleerdam/data/Bs2Jpsiphi/Reco14/fitNTuple_peakBkg_2011_2012_Reco14_TOS_HLT2B_20140415.root")
sig_tree = sig_file.Get("DecayTree")
period = 'p' + args[0][:4]
sig_data = RooDataSet('sig_data', 'sig_data', RooArgSet(momentum, weight, runPeriod),
RooFit.Import(sig_tree), RooFit.WeightVar(weight),
RooFit.Cut('runPeriod == runPeriod::%s' % period))
from ROOT import TFile
prompt_file = TFile(input_data['cache'])
prompt_data = prompt_file.Get(input_data['dataset'])
from ROOT import TCanvas
canvas = TCanvas('momentum_canvas', 'momentum_canvas', 600, 400)
canvas.SetLogy()
frame = momentum.frame(Range = (0, 500000))
from ROOT import kBlue, kGreen
sig_data.plotOn(frame, Rescale = (1 / sig_data.sumEntries()), MarkerColor = kBlue)
prompt_data.plotOn(frame, Rescale = (1 / prompt_data.sumEntries()), MarkerColor = kGreen)
frame.GetXaxis().SetTitle("P_{B} [MeV]")
frame.GetYaxis().SetTitle("scaled candidates")
frame.GetYaxis().SetRangeUser(0.00005, 0.1)
frame.Draw()
can = ut.box_canvas()
gPad.SetRightMargin(0.02)
gPad.SetTopMargin(0.04)
gPad.SetBottomMargin(0.09)
gPad.SetLeftMargin(0.1)
frame = z.frame(rf.Bins(nbins), rf.Title(""))
frame.SetTitle("")
frame.GetXaxis().SetTitleOffset(1.2)
frame.GetYaxis().SetTitleOffset(1.4)
if binned == True:
dataH.plotOn(frame, rf.Name("data"))
else:
data.plotOn(frame, rf.Name("data"))
model.plotOn(frame, rf.Name("g0"), rf.Components("g0"), rf.LineColor(col0))
model.plotOn(frame, rf.Name("gL"), rf.Components("gL"),
rf.LineColor(colLR))
model.plotOn(frame, rf.Name("gR"), rf.Components("gR"),
rf.LineColor(colLR))
model.plotOn(frame, rf.Name("Model"), rf.LineColor(colM))
frame.SetXTitle("ZDC vertex along #it{z} (cm)")
frame.SetYTitle("Events / {0:.1f} cm".format(vbin))
print "chi2/ndf:", frame.chiSquare("Model", "data", 9)
frame.Draw()
