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 rooFit202():
print ">>> setup model component: gaussian signals and Chebychev polynomial background..."
x = RooRealVar("x", "x", 0, 10)
mean = RooRealVar("mean", "mean of gaussian", 5)
sigma1 = RooRealVar("sigma1", "width of gaussian", 0.5)
sigma2 = RooRealVar("sigma2", "width of gaussian", 1.0)
sig1 = RooGaussian("sig1", "Signal component 1", x, mean, sigma1)
sig2 = RooGaussian("sig2", "Signal component 2", x, mean, sigma2)
a0 = RooRealVar("a0", "a0", 0.5, 0., 1.)
a1 = RooRealVar("a1", "a1", -0.2, 0., 1.)
bkg = RooChebychev("bkg", "Background", x, RooArgList(a0, a1))
# Sum the signal components into a composite signal p.d.f.
sig1frac = RooRealVar("sig1frac", "fraction of component 1 in signal", 0.8,
0., 1.)
sig = RooAddPdf("sig", "Signal", RooArgList(sig1, sig2),
RooArgList(sig1frac))
print ">>>\n>>> METHOD 1"
print ">>> construct extended composite model..."
# Sum the composite signal and background into an extended pdf nsig*sig+nbkg*bkg
nsig = RooRealVar("nsig", "number of signal events", 500, 0., 10000)
nbkg = RooRealVar("nbkg", "number of background events", 500, 0, 10000)
model = RooAddPdf("model", "(g1+g2)+a", RooArgList(bkg, sig),
RooArgList(nbkg, nsig))
print ">>> sample, fit and plot extended model...\n"
# Generate a data sample of expected number events in x from model
# nsig + nbkg = model.expectedEvents()
# NOTE: since the model predicts a specific number events, one can
# omit the requested number of events to be generated
# Introduce Poisson fluctuation with Extended(kTRUE)
data = model.generate(RooArgSet(x), Extended(kTRUE)) # RooDataSet
# Fit model to data, extended ML term automatically included
# NOTE: Composite extended pdfs can only be successfully fit if the extended likelihood
# term -log(Poisson(Nobs,Nexp)) is included in the minimization because they have
# one extra degree of freedom in their parameterization that is constrained by
# this extended term. If a pdf is capable of calculating an extended term (i.e.
# any extended RooAddPdf), the extended term is AUTOMATICALLY included in the
# likelihood calculation. Override this behaviour with Extended():
# Extended(kTRUE) ADD extended likelihood term
# Extended(kFALSE) DO NOT ADD extended likelihood
#model.fitTo(data,Extended(kTRUE))
model.fitTo(data)
print "\n>>> plot data, model and model components..."
# Plot data and PDF overlaid, use expected number of events for pdf projection
# normalization, rather than observed number of events, data.numEntries()
frame1 = x.frame(Title("extended ML fit example")) # RooPlot
data.plotOn(frame1, Binning(30), Name("data"))
model.plotOn(frame1, Normalization(1.0, RooAbsReal.RelativeExpected),
Name("model"))
# Overlay the background components of model
# NOTE: By default, the pdf is normalized to event count of the last dataset added
# to the plot frame. Use "RelativeExpected" to normalize to the expected
# event count of the pdf instead
argset1 = RooArgSet(bkg)
argset2 = RooArgSet(sig1)
argset3 = RooArgSet(sig2)
argset4 = RooArgSet(bkg, sig2)
model.plotOn(frame1, Components(argset1), LineStyle(kDashed),
LineColor(kBlue),
Normalization(1.0, RooAbsReal.RelativeExpected), Name("bkg"))
#model.plotOn(frame1,Components(argset1),LineStyle(kDashed),LineColor(kBlue), Name("bkg2"))
model.plotOn(frame1, Components(argset2), LineStyle(kDotted),
LineColor(kMagenta),
Normalization(1.0, RooAbsReal.RelativeExpected), Name("sig1"))
model.plotOn(frame1, Components(argset3), LineStyle(kDotted),
LineColor(kPink),
Normalization(1.0, RooAbsReal.RelativeExpected), Name("sig2"))
model.plotOn(frame1, Components(argset4), LineStyle(kDashed),
LineColor(kAzure - 4),
Normalization(1.0, RooAbsReal.RelativeExpected),
Name("bkgsig2"))
print "\n>>> structure of composite pdf:"
model.Print("t") # "tree" mode
print "\n>>> parameters:"
params = model.getVariables() # RooArgSet
params.Print("v")
params.Print()
print "\n>>> params.find(\"...\").getVal():"
print ">>> sigma1 = %.2f" % params.find("sigma1").getVal()
print ">>> sigma2 = %.2f" % params.find("sigma2").getVal()
print ">>> nsig = %6.2f, sig1frac = %5.2f" % (
params.find("nsig").getVal(), params.find("sig1frac").getVal())
print ">>> nbkg = %6.2f" % params.find("nbkg").getVal()
print ">>>\n>>> components:"
comps = model.getComponents() # RooArgSet
sig = comps.find("sig") # RooAbsArg
sigVars = sig.getVariables() # RooArgSet
sigVars.Print()
print ">>>\n>>> METHOD 2"
print ">>> construct extended components first..."
# Associated nsig/nbkg as expected number of events with sig/bkg
nsig = RooRealVar("nsig", "number of signal events", 500, 0., 10000)
nbkg = RooRealVar("nbkg", "number of background events", 500, 0, 10000)
esig = RooExtendPdf("esig", "extended signal pdf", sig, nsig)
ebkg = RooExtendPdf("ebkg", "extended background pdf", bkg, nbkg)
print ">>> sum extended components without coefficients..."
# Construct sum of two extended p.d.f. (no coefficients required)
model2 = RooAddPdf("model2", "(g1+g2)+a", RooArgList(ebkg, esig))
# METHOD 2 is functionally completely equivalent to METHOD 1.
# Its advantage is that the yield parameter is associated to the shape pdf
# directly, while in METHOD 1 the association is made after constructing
# a RooAddPdf. Also, class RooExtendPdf offers extra functionality to
# interpret event counts in a different range.
print ">>> plot model..."
model2.plotOn(frame1, LineStyle(kDashed), LineColor(kRed),
Normalization(1.0, RooAbsReal.RelativeExpected),
Name("model2"))
print ">>> draw on canvas..."
canvas = TCanvas("canvas", "canvas", 100, 100, 800, 600)
legend = TLegend(0.2, 0.85, 0.4, 0.65)
legend.SetTextSize(0.032)
legend.SetBorderSize(0)
legend.SetFillStyle(0)
gPad.SetLeftMargin(0.14)
gPad.SetRightMargin(0.02)
frame1.GetYaxis().SetLabelOffset(0.008)
frame1.GetYaxis().SetTitleOffset(1.4)
frame1.GetYaxis().SetTitleSize(0.045)
frame1.GetXaxis().SetTitleSize(0.045)
frame1.Draw()
legend.AddEntry("data", "data", 'LEP')
legend.AddEntry("model", "composite model", 'L')
legend.AddEntry("model2", "composite model (method 2)", 'L')
legend.AddEntry("bkg", "background only", 'L')
#legend.AddEntry("bkg2", "background only (no extended norm)", 'L')
legend.AddEntry("sig1", "signal 1", 'L')
legend.AddEntry("sig2", "signal 2", 'L')
legend.AddEntry("bkgsig2", "background + signal 2", 'L')
legend.Draw()
canvas.SaveAs("rooFit202.png")
def rooFit201():
print ">>> setup model component: gaussian signals and Chebychev polynomial background..."
x = RooRealVar("x","x",0,11)
mean = RooRealVar("mean","mean of gaussians",5)
sigma1 = RooRealVar("sigma1","width of gaussians",0.5)
sigma2 = RooRealVar("sigma2","width of gaussians",1)
sig1 = RooGaussian("sig1","Signal component 1",x,mean,sigma1)
sig2 = RooGaussian("sig2","Signal component 2",x,mean,sigma2)
a0 = RooRealVar("a0","a0",0.5,0.,1.)
a1 = RooRealVar("a1","a1",-0.2,0.,1.)
bkg = RooChebychev("bkg","Background",x,RooArgList(a0,a1))
print ">>>\n>>> METHOD 1 - Two RooAddPdfs"
print ">>> add signal components..."
# Sum the signal components into a composite signal p.d.f.
sig1frac = RooRealVar("sig1frac","fraction of component 1 in signal",0.8,0.,1.)
sig = RooAddPdf("sig","Signal",RooArgList(sig1,sig2),RooArgList(sig1frac))
print ">>> add signal and background..."
# Sum the composite signal and background
bkgfrac = RooRealVar("bkgfrac","fraction of background",0.5,0.,1.)
model = RooAddPdf("model","g1+g2+a",RooArgList(bkg,sig),RooArgList(bkgfrac))
print ">>> sample, fit and plot model..."
data = model.generate(RooArgSet(x),1000) # RooDataSet
model.fitTo(data)
frame1 = x.frame(Title("Example of composite pdf=(sig1+sig2)+bkg")) # RooPlot
data.plotOn(frame1,Binning(50),Name("data"))
model.plotOn(frame1,Name("model"))
# Overlay the background component of model with a dashed line
argset1 = RooArgSet(bkg)
model.plotOn(frame1,Components(argset1),LineWidth(2),Name("bkg")) #,LineStyle(kDashed)
# Overlay the background+sig2 components of model with a dotted line
argset2 = RooArgSet(bkg,sig2)
model.plotOn(frame1,Components(argset2),LineWidth(2),LineStyle(kDashed),LineColor(kAzure-4),Name("bkgsig2")) #,LineStyle(kDotted)
print "\n>>> structure of composite pdf:"
model.Print("t") # "tree" mode
print "\n>>> parameters:"
params = model.getVariables() # RooArgSet
params.Print("v")
params.Print()
print "\n>>> params.find(\"...\").getVal():"
print ">>> sigma1 = %.2f" % params.find("sigma1").getVal()
print ">>> sigma2 = %.2f" % params.find("sigma2").getVal()
print ">>> bkgfrac = %5.2f" % params.find("bkgfrac").getVal()
print ">>> sig1frac = %5.2f" % params.find("sig1frac").getVal()
print ">>>\n>>> components:"
comps = model.getComponents() # RooArgSet
sig = comps.find("sig") # RooAbsArg
sigVars = sig.getVariables() # RooArgSet
sigVars.Print()
print ">>>\n>>> METHOD 2 - One RooAddPdf with recursive fractions"
# Construct sum of models on one go using recursive fraction interpretations
# model2 = bkg + (sig1 + sig2)
model2 = RooAddPdf("model","g1+g2+a",RooArgList(bkg,sig1,sig2),RooArgList(bkgfrac,sig1frac),kTRUE)
# NB: Each coefficient is interpreted as the fraction of the
# left-hand component of the i-th recursive sum, i.e.
# sum4 = A + ( B + ( C + D ) )
# with fraction fA, fB and fC expands to
# sum4 = fA*A + (1-fA)*(fB*B + (1-fB)*(fC*C + (1-fC)*D))
print ">>> plot recursive addition model..."
argset3 = RooArgSet(bkg,sig2)
model2.plotOn(frame1,LineColor(kRed),LineStyle(kDashDotted),LineWidth(3),Name("model2"))
model2.plotOn(frame1,Components(argset3),LineColor(kMagenta),LineStyle(kDashDotted),LineWidth(3),Name("bkgsig22"))
model2.Print("t")
print ">>> draw pdfs and fits on canvas..."
canvas = TCanvas("canvas","canvas",100,100,800,600)
legend = TLegend(0.57,0.87,0.95,0.65)
legend.SetTextSize(0.030)
legend.SetBorderSize(0)
legend.SetFillStyle(0)
gPad.SetLeftMargin(0.14); gPad.SetRightMargin(0.02)
frame1.GetYaxis().SetLabelOffset(0.008)
frame1.GetYaxis().SetTitleOffset(1.4)
frame1.GetYaxis().SetTitleSize(0.045)
frame1.GetXaxis().SetTitleSize(0.045)
frame1.Draw()
legend.AddEntry("data", "data", 'LEP')
legend.AddEntry("model", "composite model", 'L')
legend.AddEntry("model2", "composite model (method 2)", 'L')
legend.AddEntry("bkg", "background only", 'L')
legend.AddEntry("bkgsig2", "background + signal 2", 'L')
legend.AddEntry("bkgsig22","background + signal 2 (method 2)",'L')
legend.Draw()
canvas.SaveAs("rooFit201.png")
