def rooFit103():
print ">>> construct generic pdf from interpreted expression..."
# To construct a proper p.d.f, the formula expression is explicitly normalized internally
# by dividing it by a numeric integral of the expresssion over x in the range [-20,20]
x = RooRealVar("x", "x", -20, 20)
alpha = RooRealVar("alpha", "alpha", 5, 0.1, 10)
genpdf = RooGenericPdf("genpdf", "genpdf",
"(1+0.1*abs(x)+sin(sqrt(abs(x*alpha+0.1))))",
RooArgList(x, alpha))
print ">>> generate and fit toy data...\n"
data = genpdf.generate(RooArgSet(x), 10000) # RooDataSet
genpdf.fitTo(data)
frame1 = x.frame(Title("Interpreted expression pdf")) # RooPlot
data.plotOn(frame1, Binning(40))
genpdf.plotOn(frame1)
print "\n>>> construct standard pdf with formula replacing parameter..."
mean2 = RooRealVar("mean2", "mean^2", 10, 0, 200)
sigma = RooRealVar("sigma", "sigma", 3, 0.1, 10)
mean = RooFormulaVar("mean", "mean", "sqrt(mean2)", RooArgList(mean2))
gaus2 = RooGaussian("gaus2", "gaus2", x, mean, sigma)
print ">>> generate and fit toy data...\n"
gaus1 = RooGaussian("gaus1", "gaus1", x, RooConst(10), RooConst(3))
data2 = gaus1.generate(RooArgSet(x), 1000) # RooDataSet
result = gaus2.fitTo(data2, Save()) # RooFitResult
result.Print()
frame2 = x.frame(Title("Tailored Gaussian pdf")) # RooPlot
data2.plotOn(frame2, Binning(40))
gaus2.plotOn(frame2)
print "\n>>> draw pfds and fits 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("rooFit103.png")
def rooFit105():
print ">>> bind TMath::Erf C function..."
x = RooRealVar("x", "x", -3, 3)
#print ">>> type(%s) = %s" % ("TMath.Erf",type(TMath.Erf))
fa1 = TF1("fa2", "TMath::Erf(x)", 0, 10)
erf = bindFunction(fa1, x) # RooAbsReal
#erf = bindFunction("erf",TMath.Erf,RooArgList(x)) # RooAbsReal
erf.Print()
frame1 = x.frame(Title("TMath::Erf bound as RooFit function")) # RooPlot
erf.plotOn(frame1)
print ">>> bind ROOT::Math::beta_pdf C pdf..."
x2 = RooRealVar("x2", "x2", 0, 0.999)
a = RooRealVar("a", "a", 5, 0, 10)
b = RooRealVar("b", "b", 2, 0, 10)
print ">>> type(%s) = %s" % ("Math.beta_pdf", type(Math.beta_pdf))
#beta = bindPdf("beta",Math.beta_pdf,RooArgList(x2,a,b)) # RooAbsPdf
beta = bindPdf("beta", Math.beta_pdf, x2, a, b) # RooAbsPdf
beta.Print()
print ">>> generate and fit data...\n"
data = beta.generate(x2, 10000) # RooDataSet
beta.fitTo(data)
frame2 = x2.frame(Title("ROOT::Math::Beta bound as RooFit pdf")) # RooPlot
data.plotOn(frame2)
beta.plotOn(frame2)
print ">>> bind ROOT::TF1 as RooFit function..."
fa3 = TF1("fa3", "sin(x)/x", 0, 10)
x3 = RooRealVar("x3", "x3", 0.01, 20)
rfa1 = bindFunction(fa3, RooArgList(x3)) # RooAbsReal
rfa1.Print()
frame3 = x3.frame(Title("TF1 bound as RooFit function")) # RooPlot
rfa1.plotOn(frame3)
print ">>> draw functions and toy data on canvas..."
canvas = TCanvas("canvas", "canvas", 100, 100, 1800, 600)
canvas.Divide(3)
for i, frame in enumerate([frame1, frame2, frame3], 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("rooFit105.png")
def rooFit106():
print ">>> setup model..."
x = RooRealVar("x", "x", -3, 3)
mean = RooRealVar("mean", "mean of gaussian", 1, -10, 10)
sigma = RooRealVar("sigma", "width of gaussian", 1, 0.1, 10)
gauss = RooGaussian("gauss", "gauss", x, mean, sigma)
data = gauss.generate(RooArgSet(x), 10000) # RooDataSet
gauss.fitTo(data)
print ">>> plot pdf and data..."
frame = x.frame(Name("frame"), Title("RooPlot with decorations"),
Bins(40)) # RooPlot
data.plotOn(frame)
gauss.plotOn(frame)
print ">>> RooGaussian::paramOn - add box with pdf parameters..."
# https://root.cern.ch/doc/master/classRooAbsPdf.html#aa43b2556a1b419bad2b020ba9b808c1b
# Layout(Double_t xmin, Double_t xmax, Double_t ymax)
# left edge of box starts at 20% of x-axis
gauss.paramOn(frame, Layout(0.55))
print ">>> RooDataSet::statOn - add box with data statistics..."
# https://root.cern.ch/doc/master/classRooAbsData.html#a538d58020b296a1623323a84d2bb8acb
# x size of box is from 55% to 99% of x-axis range, top of box is at 80% of y-axis range)
data.statOn(frame, Layout(0.20, 0.55, 0.8))
print ">>> add text and arrow..."
text = TText(2, 100, "Signal")
text.SetTextSize(0.04)
text.SetTextColor(kRed)
frame.addObject(text)
arrow = TArrow(2, 100, -1, 50, 0.01, "|>")
arrow.SetLineColor(kRed)
arrow.SetFillColor(kRed)
arrow.SetLineWidth(3)
frame.addObject(arrow)
print ">>> persist frame with all decorations in ROOT file..."
file = TFile("rooFit106.root", "RECREATE")
frame.Write()
file.Close()
# To read back and plot frame with all decorations in clean root session do
# [0] TFile f("rooFit106.root")
# [1] xframe->Draw()
print ">>> draw functions and toy data on canvas..."
canvas = TCanvas("canvas", "canvas", 100, 100, 800, 600)
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("rooFit106.png")
def rooFit203():
print ">>> setup model..."
x = RooRealVar("x", "x", -10, 10)
mean = RooRealVar("mean", "mean of gaussian", 0, -10, 10)
gauss = RooGaussian("gauss", "gaussian PDF", x, mean, RooConst(1))
# Construct px = 1 (flat in x)
px = RooPolynomial("px", "px", x)
# Construct model = f*gx + (1-f)px
f = RooRealVar("f", "f", 0., 1.)
model = RooAddPdf("model", "model", RooArgList(gauss, px), RooArgList(f))
data = model.generate(RooArgSet(x), 10000) # RooDataSet
print ">>> fit to full data range..."
result_full = model.fitTo(data, Save(kTRUE)) # RooFitResult
print "\n>>> fit \"signal\" range..."
# Define "signal" range in x as [-3,3]
x.setRange("signal", -3, 3)
result_sig = model.fitTo(data, Save(kTRUE),
Range("signal")) # RooFitResult
print "\n>>> plot and print results..."
# Make plot frame in x and add data and fitted model
frame1 = x.frame(Title("Fitting a sub range")) # RooPlot
data.plotOn(frame1, Name("data"))
model.plotOn(frame1, Range("Full"), LineColor(kBlue),
Name("model")) # Add shape in full ranged dashed
model.plotOn(frame1, LineStyle(kDashed), LineColor(kRed),
Name("model2")) # By default only fitted range is shown
print "\n>>> result of fit on all data:"
result_full.Print()
print "\n>>> result of fit in in signal region (note increased error on signal fraction):"
result_sig.Print()
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", "fit (full range)", 'L')
legend.AddEntry("model2", "fit (signal range)", 'L')
legend.Draw()
canvas.SaveAs("rooFit203.png")
def rooFit107():
print ">>> setup model..."
x = RooRealVar("x","x",-3,3)
mean = RooRealVar("mean","mean of gaussian",1,-10,10)
sigma = RooRealVar("sigma","width of gaussian",1,0.1,10)
gauss = RooGaussian("gauss","gauss",x,mean,sigma)
data = gauss.generate(RooArgSet(x),1000) # RooDataSet
gauss.fitTo(data)
print ">>> plot pdf and data..."
frame1 = x.frame(Name("frame1"),Title("Red Curve / SumW2 Histo errors"),Bins(20)) # RooPlot
frame2 = x.frame(Name("frame2"),Title("Dashed Curve / No XError bars"),Bins(20)) # RooPlot
frame3 = x.frame(Name("frame3"),Title("Filled Curve / Blue Histo"),Bins(20)) # RooPlot
frame4 = x.frame(Name("frame4"),Title("Partial Range / Filled Bar chart"),Bins(20)) # RooPlot
print ">>> data plotting styles..."
data.plotOn(frame1,DataError(RooAbsData.SumW2))
data.plotOn(frame2,XErrorSize(0)) # Remove horizontal error bars
data.plotOn(frame3,MarkerColor(kBlue),LineColor(kBlue)) # Blue markers and error bors
data.plotOn(frame4,DrawOption("B"),DataError(RooAbsData.None),
XErrorSize(0),FillColor(kGray)) # Filled bar chart
print ">>> data plotting styles..."
gauss.plotOn(frame1,LineColor(kRed))
gauss.plotOn(frame2,LineStyle(kDashed)) # Change line style to dashed
gauss.plotOn(frame3,DrawOption("F"),FillColor(kOrange),MoveToBack()) # Filled shapes in green color
gauss.plotOn(frame4,Range(-8,3),LineColor(kMagenta))
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("rooFit107.png")
def makePlot(mds, ds, pdf):
""" Mass fit plot """
frame = mds.frame()
ds.plotOn(frame, DataError(RooAbsData.SumW2), MarkerSize(1))
pdf.plotOn(frame, LineWidth(2))
# pull histogram
pullHist = frame.pullHist()
pullFrame = mds.frame(Title(''))
pullFrame.addPlotable(pullHist, 'P')
pullFrame.GetYaxis().SetRangeUser(-5, 5)
canvas = TCanvas('m(Ds)', 'm(Ds)', 600, 700)
canvas.cd()
pad1 = TPad('pad1', 'pad1', .01, .20, .99, .99)
pad2 = TPad('pad2', 'pad2', .01, .01, .99, .20)
pad1.Draw()
pad2.Draw()
pad1.cd()
pad1.SetLeftMargin(0.15)
pad1.SetFillColor(0)
frame.GetXaxis().SetTitleSize(0.05)
frame.GetXaxis().SetTitleOffset(0.85)
frame.GetXaxis().SetLabelSize(0.04)
frame.GetYaxis().SetTitleOffset(1.6)
frame.Draw()
pad2.cd()
pad2.SetLeftMargin(0.15)
pad2.SetFillColor(0)
pullFrame.SetMarkerSize(0.05)
pullFrame.Draw()
mdsRange = fitRange()['mDs']
lineUp = TLine(mdsRange[0], 3, mdsRange[1], 3)
lineUp.SetLineColor(kBlue)
lineUp.SetLineStyle(2)
lineUp.Draw()
lineCe = TLine(mdsRange[0], 0, mdsRange[1], 0)
lineCe.SetLineColor(kBlue)
lineCe.SetLineStyle(1)
lineCe.SetLineWidth(2)
lineCe.Draw()
lineD0 = TLine(mdsRange[0], -3, mdsRange[0], -3)
lineD0.SetLineColor(kBlue)
lineD0.SetLineStyle(2)
lineD0.Draw()
canvas.Update()
def rooFit208():
print ">>> setup component pdfs..."
t = RooRealVar("t", "t", -10, 30)
ml = RooRealVar("ml", "mean landau", 5., -20, 20)
sl = RooRealVar("sl", "sigma landau", 1, 0.1, 10)
landau = RooLandau("lx", "lx", t, ml, sl)
mg = RooRealVar("mg", "mg", 0)
sg = RooRealVar("sg", "sg", 2, 0.1, 10)
gauss = RooGaussian("gauss", "gauss", t, mg, sg)
print ">>> construct convolution pdf..."
# Set #bins to be used for FFT sampling to 10000
t.setBins(10000, "cache")
# Construct landau (x) gauss
convolution = RooFFTConvPdf("lxg", "landau (X) gauss", t, landau, gauss)
print ">>> sample, fit and plot convoluted pdf..."
data = convolution.generate(RooArgSet(t), 10000) # RooDataSet
convolution.fitTo(data)
print "\n>>> plot everything..."
frame1 = t.frame(Title("landau #otimes gauss convolution")) # RooPlot
data.plotOn(frame1, Binning(50), Name("data"))
convolution.plotOn(frame1, Name("lxg"))
gauss.plotOn(frame1, LineStyle(kDashed), LineColor(kRed), Name("gauss"))
landau.plotOn(frame1, LineStyle(kDashed), LineColor(kGreen),
Name("landau"))
print "\n>>> draw pfds and fits on canvas..."
canvas = TCanvas("canvas", "canvas", 100, 100, 800, 600)
legend = TLegend(0.6, 0.8, 0.8, 0.6)
legend.SetTextSize(0.032)
legend.SetBorderSize(0)
legend.SetFillStyle(0)
gPad.SetLeftMargin(0.15)
gPad.SetRightMargin(0.02)
frame1.GetYaxis().SetLabelOffset(0.008)
frame1.GetYaxis().SetTitleOffset(1.5)
frame1.GetYaxis().SetTitleSize(0.045)
frame1.GetXaxis().SetTitleSize(0.045)
frame1.Draw()
legend.AddEntry("data", "data", 'LEP')
legend.AddEntry("lxg", "convolution", 'L')
legend.AddEntry("landau", "landau", 'L')
legend.AddEntry("gauss", "gauss", 'L')
legend.Draw()
canvas.SaveAs("rooFit208.png")
def rooFit101():
print ">>> build gaussian pdf..."
x = RooRealVar("x", "x", -10, 10)
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)
print ">>> plot pdf..."
frame1 = x.frame(Title("Gaussian pdf")) # RooPlot
#xframe.SetTitle("Gaussian pdf")
gauss.plotOn(frame1)
print ">>> change parameter value and plot..."
sigma.setVal(3)
gauss.plotOn(frame1, LineColor(kRed))
print ">>> generate events..."
data = gauss.generate(RooArgSet(x), 10000) # RooDataSet
frame2 = x.frame()
data.plotOn(frame2, Binning(40))
gauss.plotOn(frame2)
print ">>> fit gaussian...\n"
gauss.fitTo(data)
mean.Print()
sigma.Print()
print "\n>>> draw pdfs and fits 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("rooFit101.png")
def rooFit503():
print ">>> open and workspace file, list its contents and get workspace object..."
file = TFile("rooFit502_workspace.root")
file.ls()
workspace = file.Get("workspace") # RooWorkspace
print "\n>>> retrieve pdf, data from workspace..."
x = workspace.var("x") # RooRealVar
model = workspace.pdf("model") # RooAbsPdf
data = workspace.data("modelData")
print ">>> print model tree:..."
model.Print("t")
print "\n>>> fit model to data..."
model.fitTo(data)
frame1 = x.frame(Title("Model and data read from workspace")) # RooPlot
print ">>> plot everything..."
data.plotOn(frame1, Name("data"), Binning(40))
model.plotOn(frame1, Name("model"))
model.plotOn(frame1, Components("bkg"), LineStyle(kDashed), Name("bkg"))
model.plotOn(frame1, Components("bkg,sig2"), LineStyle(kDotted),
Name("bkgsig2"))
print "\n>>> draw on canvas..."
canvas = TCanvas("canvas", "canvas", 100, 100, 800, 600)
legend = TLegend(0.2, 0.8, 0.4, 0.6)
legend.SetTextSize(0.032)
legend.SetBorderSize(0)
legend.SetFillStyle(0)
gPad.SetLeftMargin(0.15)
gPad.SetRightMargin(0.02)
frame1.GetYaxis().SetLabelOffset(0.008)
frame1.GetYaxis().SetTitleOffset(1.5)
frame1.GetYaxis().SetTitleSize(0.045)
frame1.GetXaxis().SetTitleSize(0.045)
frame1.Draw()
legend.AddEntry("data", "data", 'LEP')
legend.AddEntry("model", "model", 'L')
legend.AddEntry("bkg", "background only", 'L')
legend.AddEntry("bkgsig2", "background + signal 2", 'L')
legend.Draw()
canvas.SaveAs("rooFit503.png")
def rooFit111():
print ">>> setup model..."
x = RooRealVar("x", "x", -10, 10)
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)
print ">>> create and plot derivatives wrt x..."
dgdx = gauss.derivative(x, 1) # RooAbsReal
d2gdx2 = gauss.derivative(x, 2) # RooAbsReal
d3gdx3 = gauss.derivative(x, 3) # RooAbsReal
print ">>> plot gaussian and its first two derivatives..."
frame1 = x.frame(Title("d^{n}(Gauss)/dx^{n}")) # RooPlot
norm1 = 10
gauss.plotOn(frame1, LineColor(kBlue), Name(gauss.GetName()),
Normalization(norm1))
dgdx.plotOn(frame1, LineColor(kMagenta), Name("dgdx"))
d2gdx2.plotOn(frame1, LineColor(kRed), Name("d2gdx2"))
d3gdx3.plotOn(frame1, LineColor(kOrange), Name("d3gdx3"))
print ">>> create and plot derivatives wrt sigma..."
dgds = gauss.derivative(sigma, 1) # RooAbsReal
d2gds2 = gauss.derivative(sigma, 2) # RooAbsReal
d3gds3 = gauss.derivative(sigma, 3) # RooAbsReal
print ">>> plot gaussian and its first two derivatives..."
frame2 = sigma.frame(Title("d^{n}(Gauss)/d(sigma)^{n}"),
Range(0., 2.)) # RooPlot
(norm2, norm21, norm22) = (8000, 15, 5)
gauss.plotOn(frame2, LineColor(kBlue), Name(gauss.GetName()),
Normalization(norm2))
dgds.plotOn(frame2, LineColor(kMagenta), Name("dgds"),
Normalization(norm21))
d2gds2.plotOn(frame2, LineColor(kRed), Name("d2gds2"),
Normalization(norm22))
d3gds3.plotOn(frame2, LineColor(kOrange), Name("d3gds3"))
print ">>> draw pdfs and fits on canvas..."
canvas = TCanvas("canvas", "canvas", 100, 100, 1400, 600)
legend1 = TLegend(0.22, 0.85, 0.4, 0.65)
legend2 = TLegend(0.60, 0.85, 0.8, 0.65)
for legend in [legend1, legend2]:
legend.SetTextSize(0.032)
legend.SetBorderSize(0)
legend.SetFillStyle(0)
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()
legend1.AddEntry(gauss.GetName(), "gaussian G (#times%s)" % norm1, 'L')
legend1.AddEntry("dgdx", "d(G)/dx", 'L')
legend1.AddEntry("d2gdx2", "d^{2}(G)/dx^{2}", 'L')
legend1.AddEntry("d3gdx3", "d^{3}(G)/dx^{3}", 'L')
legend1.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()
legend2.AddEntry(gauss.GetName(), "gaussian G (#times%s)" % norm2, 'L')
legend2.AddEntry("dgds", "d(G)/ds (#times%s)" % norm21, 'L')
legend2.AddEntry("d2gds2", "d^{2}(G)/ds^{2} (#times%s)" % norm22, 'L')
legend2.AddEntry("d3gds3", "d^{3}(G)/ds^{3}", 'L')
legend2.Draw()
canvas.SaveAs("rooFit111.png")
def rooFit206():
print ">>> setup model signal components: gaussians..."
x = RooRealVar("x", "x", 0, 10)
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)
sig1frac = RooRealVar("sig1frac", "fraction of component 1 in signal", 0.8,
0., 1.)
sig = RooAddPdf("sig", "Signal", RooArgList(sig1, sig2),
RooArgList(sig1frac))
print ">>> setup model background components: Chebychev polynomial plus exponential..."
a0 = RooRealVar("a0", "a0", 0.5, 0., 1.)
a1 = RooRealVar("a1", "a1", -0.2, 0., 1.)
bkg1 = RooChebychev("bkg1", "Background 1", x, RooArgList(a0, a1))
alpha = RooRealVar("alpha", "alpha", -1)
bkg2 = RooExponential("bkg2", "Background 2", x, alpha)
bkg1frac = RooRealVar("bkg1frac", "fraction of component 1 in background",
0.2, 0., 1.)
bkg = RooAddPdf("bkg", "Signal", RooArgList(bkg1, bkg2),
RooArgList(bkg1frac))
print ">>> sum signal and background component..."
bkgfrac = RooRealVar("bkgfrac", "fraction of background", 0.5, 0., 1.)
model = RooAddPdf("model", "g1+g2+a", RooArgList(bkg, sig),
RooArgList(bkgfrac))
print ">>> composite tree in ASCII:"
model.Print("t")
print "\n>>> write to txt file"
model.printCompactTree("", "rooFit206_asciitree.txt")
print ">>> draw composite tree graphically..."
# Print GraphViz DOT file with representation of tree
model.graphVizTree("rooFit206_model.dot")
# Make graphic output file with one of the GraphViz tools
# (freely available from www.graphviz.org)
#
# 'Top-to-bottom graph'
# unix> dot -Tgif -o rooFit206_model_dot.gif rooFit206_model.dot
#
# 'Spring-model graph'
# unix> fdp -Tgif -o rooFit206_model_fdp.gif rooFit206_model.dot
print ">>> plot everything..."
data = model.generate(RooArgSet(x), 1000) # RooDataSet
frame1 = x.frame(
Title("Component plotting of pdf=(sig1+sig2)+(bkg1+bkg2)")) # RooPlot
data.plotOn(frame1, Name("data"), Binning(40))
model.plotOn(frame1, Name("model"))
argset1 = RooArgSet(bkg)
argset2 = RooArgSet(bkg2)
argset3 = RooArgSet(bkg, sig2)
model.plotOn(frame1, Components(argset1), LineColor(kRed), Name("bkg"))
model.plotOn(frame1, Components(argset2), LineStyle(kDashed),
LineColor(kRed), Name("bkg2"))
model.plotOn(frame1, Components(argset3), LineStyle(kDotted),
Name("bkgsig2"))
print "\n>>> draw pfds and fits on canvas..."
canvas = TCanvas("canvas", "canvas", 100, 100, 800, 600)
legend = TLegend(0.22, 0.85, 0.4, 0.65)
legend.SetTextSize(0.032)
legend.SetBorderSize(0)
legend.SetFillStyle(0)
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()
legend.AddEntry("data", "data", 'LEP')
legend.AddEntry("model", "model", 'L')
legend.AddEntry("bkg", "bkg", 'L')
legend.AddEntry("bkg2", "bkg2", 'L')
legend.AddEntry("bkgsig2", "bkg,sig2", 'L')
legend.Draw()
canvas.SaveAs("rooFit206.png")
nfits = nfits + 1
#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)
def rooFit109():
print ">>> setup model..."
x = RooRealVar("x", "x", -10, 10)
sigma = RooRealVar("sigma", "sigma", 3, 0.1, 10)
mean = RooRealVar("mean", "mean", 0, -10, 10)
gauss = RooGaussian("gauss", "gauss", x, RooConst(0),
sigma) # RooConst(0) gives segfaults
data = gauss.generate(RooArgSet(x), 100000) # RooDataSet
#sigma = 3.15 # overwrites RooRealVar with a float
sigma.setVal(3.15)
print ">>> plot data and slightly distorted model..."
frame1 = x.frame(Title("Data with distorted Gaussian pdf"),
Bins(40)) # RooPlot
data.plotOn(frame1, DataError(RooAbsData.SumW2))
gauss.plotOn(frame1)
print ">>> calculate chi^2..."
# Show the chi^2 of the curve w.r.t. the histogram
# If multiple curves or datasets live in the frame you can specify
# the name of the relevant curve and/or dataset in chiSquare()
print ">>> chi^2 = %.2f" % frame1.chiSquare()
print ">>> construct histograms with the residuals and pull of the data wrt the curve"
hresid = frame1.residHist() # RooHist
hpull = frame1.pullHist() # RooHist
frame2 = x.frame(Title("Residual Distribution")) # RooPlot
frame2.addPlotable(hresid, "P")
frame3 = x.frame(Title("Pull Distribution")) # RooPlot
frame3.addPlotable(hpull, "P")
print ">>> draw functions and toy data on canvas..."
canvas = TCanvas("canvas", "canvas", 100, 100, 2000, 400)
canvas.Divide(3)
for i, frame in enumerate([frame1, frame2, frame3], 1):
canvas.cd(i)
gPad.SetLeftMargin(0.14)
gPad.SetRightMargin(0.04)
frame.GetYaxis().SetTitleOffset(1.5)
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("rooFit109.png")
canvas.Close()
# ratio/pull/residual plot
print ">>> draw with pull plot..."
canvas = TCanvas("canvas", "canvas", 100, 100, 1000, 1000)
canvas.Divide(2)
canvas.cd(1)
gPad.SetPad("pad1", "pad1", 0, 0.33, 1, 1, 0, -1, 0)
gPad.SetTopMargin(0.10)
gPad.SetBottomMargin(0.03)
gPad.SetLeftMargin(0.14)
gPad.SetRightMargin(0.04)
gPad.SetBorderMode(0)
gStyle.SetTitleFontSize(0.062)
frame1.GetYaxis().SetTitle("Events / %.3g GeV" % frame1.getFitRangeBinW())
frame1.GetYaxis().SetTitleSize(0.059)
frame1.GetYaxis().SetTitleOffset(1.21)
#frame1.GetYaxis().SetLabelOffset(0.010)
frame1.GetXaxis().SetLabelSize(0)
frame1.GetYaxis().SetLabelSize(0.045)
frame1.Draw()
canvas.cd(2)
gPad.SetPad("pad2", "pad2", 0, 0, 1, 0.33, 0, -1, 0)
gPad.SetTopMargin(0.01)
gPad.SetBottomMargin(0.30)
gPad.SetLeftMargin(0.14)
gPad.SetRightMargin(0.04)
gPad.SetBorderMode(0)
gPad.SetGridy(kTRUE)
line1 = TLine(frame3.GetXaxis().GetXmin(), 0,
frame3.GetXaxis().GetXmax(), 0)
line2 = TLine(frame3.GetXaxis().GetXmin(), 0,
frame3.GetXaxis().GetXmax(), 0)
line1.SetLineColor(0) # white to clear dotted grid lines
line2.SetLineColor(12) # dark grey
line2.SetLineStyle(2)
frame3.SetTitle("")
frame3.GetYaxis().SetTitle("pull")
frame3.GetXaxis().SetTitle("#Deltam^{2}_{ll} [GeV]")
frame3.GetXaxis().SetTitleSize(0.13)
frame3.GetYaxis().SetTitleSize(0.12)
frame3.GetXaxis().SetTitleOffset(1.0)
frame3.GetYaxis().SetTitleOffset(0.58)
frame3.GetXaxis().SetLabelSize(0.10)
frame3.GetYaxis().SetLabelSize(0.10)
frame3.GetXaxis().SetLabelOffset(0.02)
frame3.GetYaxis().SetLabelOffset(0.01)
frame3.GetYaxis().SetRangeUser(-5, 5)
frame3.GetYaxis().CenterTitle(True)
frame3.GetYaxis().SetNdivisions(505)
frame3.Draw("")
line1.Draw("SAME")
line2.Draw("SAME")
frame3.Draw("SAME")
canvas.SaveAs("rooFit109_ratiolike.png")
canvas.Close()
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.)
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")
mean.setConstant(True)
gamma.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)
rPhifit = tot.fitTo(splotData, Range(phimin, phimax), RooFit.NumCPU(args.ncpu),
RooFit.Verbose(False))
nfits = nfits + 1
c = TCanvas("canvas", "canvas", 1200, 800)
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))
tot.paramOn(phiFrame, RooFit.Layout(0.57, 0.99, 0.65))
phiFrame.Draw()
sidesigma = np.sqrt(gamma.getValV()**2 + sigma.getValV()**2)
plotmax = 1.5 * float(nentries / binning)
lowside = -3. * sidesigma + mean.getValV()
#mean.setConstant(True)
#gamma.setConstant(False)
#rPhifit = tot.fitTo(splotData,Range(psimin,psimax),RooFit.NumCPU(20),RooFit.Verbose(False))
#nfits = nfits + 1
mean.setConstant(False)
sigma.setConstant(False)
rPhifit = tot.fitTo(splotData, Range(psimin, psimax), RooFit.NumCPU(20),
RooFit.Verbose(False))
nfits = nfits + 1
c = TCanvas("canvas", "canvas", 1200, 800)
phiFrame = psiPrimeMass.frame(Range(psimin, psimax),
Normalization((nSig.getValV() + nBkg.getValV())),
Title("#mu#mu#pi#pi candidates - #psi(2S) fit "))
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)
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 rooFit205():
print ">>> setup model signal components: gaussians..."
x = RooRealVar("x", "x", 0, 10)
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)
sig1frac = RooRealVar("sig1frac", "fraction of component 1 in signal", 0.8,
0., 1.)
sig = RooAddPdf("sig", "Signal", RooArgList(sig1, sig2),
RooArgList(sig1frac))
print ">>> setup model background components: Chebychev polynomial plus exponential..."
a0 = RooRealVar("a0", "a0", 0.5, 0., 1.)
a1 = RooRealVar("a1", "a1", -0.2, 0., 1.)
bkg1 = RooChebychev("bkg1", "Background 1", x, RooArgList(a0, a1))
alpha = RooRealVar("alpha", "alpha", -1)
bkg2 = RooExponential("bkg2", "Background 2", x, alpha)
bkg1frac = RooRealVar("bkg1frac", "fraction of component 1 in background",
0.2, 0., 1.)
bkg = RooAddPdf("bkg", "Signal", RooArgList(bkg1, bkg2),
RooArgList(bkg1frac))
print ">>> sum signal and background component..."
bkgfrac = RooRealVar("bkgfrac", "fraction of background", 0.5, 0., 1.)
model = RooAddPdf("model", "g1+g2+a", RooArgList(bkg, sig),
RooArgList(bkgfrac))
print ">>> setup basic plot with data and full pdf..."
data = model.generate(RooArgSet(x), 1000) # RooDataSet
frame1 = x.frame(
Title("Component plotting of pdf=(sig1+sig2)+(bkg1+bkg2)")) # RooPlot
data.plotOn(frame1, Name("data"))
model.plotOn(frame1, Name("model"))
print ">>> clone frame for reuse..."
frame2 = frame1.Clone("frame2") # RooPlot
frame2.SetTitle("Get components with regular expressions")
print ">>> make omponent by object reference..."
# Plot multiple background components specified by object reference
# Note that specified components may occur at any level in object tree
# (e.g bkg is component of 'model' and 'sig2' is component 'sig')
argset1 = RooArgSet(bkg)
argset2 = RooArgSet(bkg2)
argset3 = RooArgSet(bkg, sig2)
model.plotOn(frame1, Components(argset1), LineColor(kRed), Name("bkgs1"))
model.plotOn(frame1, Components(argset2), LineStyle(kDashed),
LineColor(kRed), Name("bkg2"))
model.plotOn(frame1, Components(argset3), LineStyle(kDotted),
Name("bkgssig21"))
print "\n>>> make component by name / regular expressions..."
model.plotOn(frame2, Components("bkg"), LineColor(kAzure - 4),
Name("bkgs2")) # by name
model.plotOn(frame2, Components("bkg1,sig2"), LineColor(kAzure - 4),
LineStyle(kDotted), Name("bkg1sig22")) # by name
model.plotOn(frame2, Components("sig*"), LineColor(kAzure - 4),
LineStyle(kDashed), Name("sigs2")) # with regexp (wildcard *)
model.plotOn(frame2, Components("bkg1,sig*"), LineColor(kYellow),
LineStyle(kDashed),
Name("bkg1sigs2")) # with regexp (,) #Invisible()
print "\n>>> draw pfds and fits on canvas..."
canvas = TCanvas("canvas", "canvas", 100, 100, 1400, 600)
legend1 = TLegend(0.22, 0.85, 0.4, 0.65)
legend2 = TLegend(0.22, 0.85, 0.4, 0.65)
for legend in [legend1, legend2]:
legend.SetTextSize(0.032)
legend.SetBorderSize(0)
legend.SetFillStyle(0)
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()
legend1.AddEntry("data", "data", 'LEP')
legend1.AddEntry("model", "model", 'L')
legend1.AddEntry("bkgs1", "bkg", 'L')
legend1.AddEntry("bkg2", "bkg2", 'L')
legend1.AddEntry("bkgssig21", "bkg,sig2", 'L')
legend1.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()
legend2.AddEntry("data", "data", 'LEP')
legend2.AddEntry("model", "model", 'L')
legend2.AddEntry("bkgs2", "\"bkg\"", 'L')
legend2.AddEntry("bkg1sig22", "\"bkg1,sig2\"", 'L')
legend2.AddEntry("sigs2", "\"sig*\"", 'L')
legend2.AddEntry("bkg1sigs2", "\"bkg1,sig*\"", 'L')
legend2.Draw()
canvas.SaveAs("rooFit205.png")
def rooFit108():
print ">>> setup model - a B decay with mixing..."
dt = RooRealVar("dt","dt",-20,20)
dm = RooRealVar("dm","dm",0.472)
tau = RooRealVar("tau","tau",1.547)
w = RooRealVar("w","mistag rate",0.1)
dw = RooRealVar("dw","delta mistag rate",0.)
# Build categories - possible values states
# https://root.cern/doc/v610/classRooCategory.html
mixState = RooCategory("mixState","B0/B0bar mixing state")
mixState.defineType("mixed",-1)
mixState.defineType("unmixed",1)
tagFlav = RooCategory("tagFlav","Flavour of the tagged B0")
tagFlav.defineType("B0",1)
tagFlav.defineType("B0bar",-1)
# Build a gaussian resolution model
dterr = RooRealVar("dterr","dterr",0.1,1.0)
bias1 = RooRealVar("bias1","bias1",0)
sigma1 = RooRealVar("sigma1","sigma1",0.1)
gm1 = RooGaussModel("gm1","gauss model 1",dt,bias1,sigma1)
# Construct Bdecay (x) gauss
# https://root.cern/doc/v610/classRooBMixDecay.html
bmix = RooBMixDecay("bmix","decay",dt,mixState,tagFlav,tau,dm,w,dw,gm1,RooBMixDecay.DoubleSided)
print ">>> sample data from data..."
data = bmix.generate(RooArgSet(dt,mixState,tagFlav),2000) # RooDataSet
print ">>> show dt distribution with custom binning..."
# Make plot of dt distribution of data in range (-15,15) with fine binning for dt>0
# and coarse binning for dt<0
tbins = RooBinning(-15,15) # Create binning object with range (-15,15)
tbins.addUniform(60,-15,0) # Add 60 bins with uniform spacing in range (-15,0)
tbins.addUniform(15,0,15) # Add 15 bins with uniform spacing in range (0,15)
dtframe = dt.frame(Range(-15,15),Title("dt distribution with custom binning")) # RooPlot
data.plotOn(dtframe,Binning(tbins))
bmix.plotOn(dtframe)
# NB: Note that bin density for each bin is adjusted to that of default frame
# binning as shown in Y axis label (100 bins --> Events/0.4*Xaxis-dim) so that
# all bins represent a consistent density distribution
print ">>> plot mixstate asymmetry with custom binning..."
# Make plot of dt distribution of data asymmetry in 'mixState' with variable binning
abins = RooBinning(-10,10) # Create binning object with range (-10,10)
abins.addBoundary(0) # Add boundaries at 0
abins.addBoundaryPair(1) # Add boundaries at (-1,1)
abins.addBoundaryPair(2) # Add boundaries at (-2,2)
abins.addBoundaryPair(3) # Add boundaries at (-3,3)
abins.addBoundaryPair(4) # Add boundaries at (-4,4)
abins.addBoundaryPair(6) # Add boundaries at (-6,6)
aframe = dt.frame(Range(-10,10),Title("MixState asymmetry distribution with custom binning")) # RooPlot
# Plot mixState asymmetry of data with specified customg binning
data.plotOn(aframe,Asymmetry(mixState),Binning(abins))
# Plot corresponding property of pdf
bmix.plotOn(aframe,Asymmetry(mixState))
# Adjust vertical range of plot to sensible values for an asymmetry
aframe.SetMinimum(-1.1)
aframe.SetMaximum( 1.1)
# NB: For asymmetry distributions no density corrects are needed (and are thus not applied)
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)
dtframe.GetYaxis().SetLabelOffset(0.008)
dtframe.GetYaxis().SetTitleOffset(1.6)
dtframe.GetYaxis().SetTitleSize(0.045)
dtframe.GetXaxis().SetTitleSize(0.045)
dtframe.Draw()
canvas.cd(2)
gPad.SetLeftMargin(0.15); gPad.SetRightMargin(0.02)
aframe.GetYaxis().SetLabelOffset(0.008)
aframe.GetYaxis().SetTitleOffset(1.6)
aframe.GetYaxis().SetTitleSize(0.045)
aframe.GetXaxis().SetTitleSize(0.045)
aframe.Draw()
canvas.SaveAs("rooFit108.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")
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 rooFit204():
print ">>> setup model signal components: gaussians..."
x = RooRealVar("x","x",0,10)
mean = RooRealVar("mean","mean of gaussian",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)
sig1frac = RooRealVar("sig1frac","fraction of component 1 in signal",0.8,0.,1.)
sig = RooAddPdf("sig","Signal",RooArgList(sig1,sig2),RooArgList(sig1frac))
print ">>> setup model background components: Chebychev polynomial..."
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 ">>> construct extended components with specified range..."
# Define signal range in which events counts are to be defined
x.setRange("signalRange",5,6)
# Associated nsig/nbkg as expected number of events with sig/bkg _in_the_range_ "signalRange"
nsig = RooRealVar("nsig","number of signal events in signalRange", 500,0.,10000)
nbkg = RooRealVar("nbkg","number of background events in signalRange",500,0,10000)
esig = RooExtendPdf("esig","extended signal pdf", sig,nsig,"signalRange")
ebkg = RooExtendPdf("ebkg","extended background pdf",bkg,nbkg,"signalRange")
print ">>> sum extended components..."
# Construct sum of two extended p.d.f. (no coefficients required)
model = RooAddPdf("model","(g1+g2)+a",RooArgList(ebkg,esig))
print ">>> sample data, fit model..."
data = model.generate(RooArgSet(x),1000) # RooDataSet
result = model.fitTo(data,Extended(kTRUE),Save()) # RooFitResult
print "\n>>> fit result:"
result.Print()
print "\n>>> plot everything..."
frame1 = x.frame(Title("Fitting a sub range")) # RooPlot
data.plotOn(frame1,Binning(50),Name("data"))
model.plotOn(frame1,LineColor(kBlue),Name("model"))
argset1 = RooArgSet(bkg)
model.plotOn(frame1,Components(argset1),LineStyle(kDashed),LineColor(kBlue),Name("bkg"),Normalization(1.0,RooAbsReal.RelativeExpected))
#model.plotOn(frame1,Components(argset1),LineStyle(kDashed),LineColor(kRed),Name("bkg2"))
print ">>> draw on canvas..."
canvas = TCanvas("canvas","canvas",100,100,800,600)
legend = TLegend(0.2,0.85,0.4,0.7)
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", "fit", 'L')
legend.AddEntry("bkg", "background only",'L')
#legend.AddEntry("bkg2", "background only (no extended norm)",'L')
legend.Draw()
canvas.SaveAs("rooFit204.png")
def rooFit104():
print ">>> RooClassFactory::makePdf - write class (\"MyPdfV1\") skeleton code..."
# Write skeleton p.d.f class with variable x,a,b
# To use this class,
# - Edit the file MyPdfV1.cxx and implement the evaluate() method in terms of x,a and b
# - Compile and link class with '.x MyPdfV1.cxx+'
RooClassFactory.makePdf("MyPdfV1", "x,A,B")
print ">>> write class (\"MyPdfV2\") with added initial value expression..."
# Write skeleton pdf class with variable x,a,b and given formula expression
# To use this class,
# - Compile and link class with '.x MyPdfV2.cxx+'
RooClassFactory.makePdf("MyPdfV2", "x,A,B", "", "A*fabs(x)+pow(x-B,2)")
print ">>> write class (\"MyPdfV3\") with added analytical integral expression..."
# Write skeleton p.d.f class with variable x,a,b, given formula expression _and_
# given expression for analytical integral over x
# To use this class,
# - Compile and link class with '.x MyPdfV3.cxx+'
RooClassFactory.makePdf(
"MyPdfV3", "x,A,B", "", "A*fabs(x)+pow(x-B,2)", kTRUE, kFALSE,
"x:(A/2)*(pow(x.max(rangeName),2)+pow(x.min(rangeName),2))+(1./3)*(pow(x.max(rangeName)-B,3)-pow(x.min(rangeName)-B,3))"
)
print ">>> compile and load \"MyPdfV3\" class..."
gROOT.ProcessLineSync(".x MyPdfV3.cxx+")
#MyPdfV3 = gROOT.LoadClass("MyPdfV3.cxx+") # TClass
#print ">>> type(%s) = %s" % ("MyPdfV3",type(MyPdfV3))
from ROOT import MyPdfV3
print ">>> generate and fit data with \"MyPdfV3\" class...\n"
a = RooRealVar("a", "a", 1)
b = RooRealVar("b", "b", 2, -10, 10)
x = RooRealVar("x", "x", -10, 10)
pdf = MyPdfV3("pdf", "pdf", x, a, b)
frame1 = x.frame(Title("Compiled class MyPdfV3")) # RooPlot
data = pdf.generate(RooArgSet(x), 1000) # RooDataSet
pdf.fitTo(data)
data.plotOn(frame1, Binning(40))
pdf.plotOn(frame1)
print "\n>>> RooClassFactory::makePdfInstance - generate a pdf instance directly..."
# The RooClassFactory::makePdfInstance() function performs code writing, compiling, linking
# and object instantiation in one go and can serve as a straight replacement of RooGenericPdf
y = RooRealVar("y", "y", -20, 20)
alpha = RooRealVar("alpha", "alpha", 5, 0.1, 10)
genpdf = RooClassFactory.makePdfInstance(
"GenPdf", "(1+0.1*fabs(y)+sin(sqrt(fabs(y*alpha+0.1))))",
RooArgList(y, alpha)) # RooAbsPdf
print ">>> generate and fit data with pdf instance...\n"
data2 = genpdf.generate(RooArgSet(y), 10000) # RooDataSet
genpdf.fitTo(data2)
frame2 = y.frame(Title("Compiled version of pdf of rf103")) # RooPlot
data2.plotOn(frame2, Binning(50))
genpdf.plotOn(frame2)
print "\n>>> draw pdfs and fits 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("rooFit104.png")
if args.prompt:
theData = theData.reduce("xL < 1.5")
if args.ptcuts is not None:
theData = theData.reduce("trigp_pT > " + str(args.ptcuts))
theData = theData.reduce("trign_pT > " + str(args.ptcuts))
cuts += "P_t_" + str(args.ptcuts) + "_"
#### #### Plotting variables
#### TrakTrak Data
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 . . .")
def rooFit110():
print ">>> setup model..."
x = RooRealVar("x","x",-10,10)
mean = RooConst(-2)
sigma = RooConst(3)
gauss = RooGaussian("gauss","gauss",x,mean,sigma)
frame1 = x.frame(Title("Gaussian pdf")) # RooPlot
gauss.plotOn(frame1)
print ">>> retrieve raw & normalized values of RooFit pdfs...\n>>>"
# Return 'raw' unnormalized value of gauss
print ">>> raw value: gauss.getVal( ) = %.3f" % gauss.getVal() + " depends on x range"
print ">>> normalization: gauss.getVal(x) = %.3f" % gauss.getVal(RooArgSet(x))
print ">>> normalization: gauss.getNorm(x) = %.3f" % gauss.getNorm(RooArgSet(x))
print ">>> 1/(sigma*sqrt(2*pi)) = %.3f" % (1/(sigma.getValV()*sqrt(2*pi)))
# Create object representing integral over gauss
# which is used to calculate gauss_Norm[x] == gauss / gauss_Int[x]
igauss = gauss.createIntegral(RooArgSet(x)) # RooAbsReal
print ">>> integral: igauss.getVal( ) = %.3f" % igauss.getVal()
print ">>> igauss.getVal(x) = %.3f" % igauss.getVal(RooArgSet(x))
print ">>> 1/igauss.getVal(x) = %.3f" % (1/igauss.getVal(RooArgSet(x)))
print ">>> gauss.getVal()/igauss.getVal(x) = %.3f" % (gauss.getVal()/igauss.getVal(RooArgSet(x)))
# maximum
print ">>> maximum: gauss.getMaxVal(x) = %.3f" % gauss.getMaxVal(RooArgSet(x))
print ">>> gauss.getMax(0) = %.3f" % gauss.maxVal(0)
print ">>> gauss.getMax(1) = %.3f" % gauss.maxVal(1)
print ">>> gauss.asTF(x).GetMaximumX() = %.3f" % gauss.asTF(RooArgList(x)).GetMaximumX()
print ">>> gauss.asTF(x).GetMaximum() = %.3f" % gauss.asTF(RooArgList(x)).GetMaximum()
xmaxVal = gauss.asTF(RooArgList(x)).GetMaximumX()
xmax = RooRealVar("x_max","x_max",xmaxVal)
x.setVal(xmaxVal)
print ">>> gauss.getVal( ) = %.3f" % gauss.getVal()
print ">>> gauss.getVal(xmax) = %.3f" % gauss.getVal(RooArgSet(x))
print ">>> gauss.getVal(xmax) = %.3f" % gauss.getVal(RooArgSet(xmax))
print ">>> plot"
frame2 = x.frame(Title("integral of Gaussian pdf")) # RooPlot
line1 = RooLinearVar("line1","line1",x,RooConst(0),RooConst(gauss.getVal()))
line2 = RooLinearVar("line2","line2",x,RooConst(0),RooConst(gauss.getVal(RooArgSet(x))))
igauss.plotOn(frame2,LineColor(kBlue), Name(igauss.GetName()))
line1.plotOn( frame2,LineColor(kRed), Name(line1.GetName()))
line2.plotOn( frame2,LineColor(kGreen),Name(line2.GetName()))
print ">>>\n>>> integrate normalized pdf over subrange..."
# Define a range named "signal" in x from -5,5
x.setRange("signal",-5,5)
# Create an integral of gauss_Norm[x] over x in range "signal"
# This is the fraction of of p.d.f. gauss_Norm[x] which is in
# the range named "signal"
igauss_sig = gauss.createIntegral(RooArgSet(x),NormSet(RooArgSet(x)),Range("signal")) # RooAbsReal
print ">>> gauss_Int[x|signal]_Norm[x] = %.2f" % igauss_sig.getVal()
print ">>> construct cumulative distribution function from pdf..."
# Create the cumulative distribution function of
# gauss, i.e. calculate Int[-10,x] gauss(x') dx'
gauss_cdf = gauss.createCdf(RooArgSet(x)) # RooAbsReal
frame3 = x.frame(Title("cdf of Gaussian pdf")) # RooPlot
gauss_cdf.plotOn(frame3)
print ">>> draw functions on canvas..."
canvas = TCanvas("canvas","canvas",100,100,2000,400)
legend = TLegend(0.6,0.6,0.8,0.42)
legend.SetTextSize(0.032)
legend.SetBorderSize(0)
legend.SetFillStyle(0)
canvas.Divide(3)
for i, frame in enumerate([frame1,frame2,frame3],1):
canvas.cd(i)
gPad.SetLeftMargin(0.14); gPad.SetRightMargin(0.04)
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()
if i is 2:
legend.AddEntry(line1.GetName(), " gauss.getVal( )",'L')
legend.AddEntry(line2.GetName(), " gauss.getVal(x)",'L')
legend.AddEntry(igauss.GetName(),"igauss.getVal( )",'L')
legend.Draw()
canvas.SaveAs("rooFit110.png")