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")
B_1 = RooRealVar ( "B_{1}" , "B_1 " , 0.3 , -20 , 100 )
B_2 = RooRealVar ( "B_{2}" , "B_2" , 0.3 , -20 , 100 )
B_3 = RooRealVar ( "B_{3}" , "B_3" , 0.3 , -20 , 100 )
B_4 = RooRealVar ( "B_{4}" , "B_4" , 0.3 , -20 , 100 )
bkg = RooChebychev("pdfB" , "pdfB" , masskk , RooArgList(aset))
tot = RooAddPdf("tot","g+cheb",RooArgList(signal,bkg),RooArgList(nSig,nBkg))
nfits = 0
mean.setConstant(True)
gamma.setConstant(True)
sigma_1.setConstant(True)
sigma_2.setConstant(True)
rPhifit = tot.fitTo(splotData,Range(phimin,phimax),RooFit.NumCPU(args.ncpu),RooFit.Verbose(False))
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)
B_1 = RooRealVar("B_{1}", "B_1 ", 0.3, -20, 100)
B_2 = RooRealVar("B_{2}", "B_2", 0.3, -20, 100)
B_3 = RooRealVar("B_{3}", "B_3", 0.3, -20, 100)
B_4 = RooRealVar("B_{4}", "B_4", 0.3, -20, 100)
bkg = RooChebychev("pdfB", "pdfB", masskk, RooArgList(aset))
tot = RooAddPdf("tot", "g+cheb", RooArgList(signal, bkg),
RooArgList(nSig, nBkg))
nfits = 0
mean.setConstant(True)
gamma.setConstant(True)
rPhifit = tot.fitTo(splotData, Range(phimin, phimax), RooFit.NumCPU(args.ncpu),
RooFit.Verbose(False))
nfits = nfits + 1
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
B_1 = RooRealVar("B_{1}", "B_1 ", 0.3, -20, 100)
B_2 = RooRealVar("B_{2}", "B_2", 0.3, -20, 100)
B_3 = RooRealVar("B_{3}", "B_3", 0.3, -20, 100)
B_4 = RooRealVar("B_{4}", "B_4", 0.3, -20, 100)
bkg = RooChebychev("pdfB", "pdfB", psiPrimeMass, RooArgList(aset))
tot = RooAddPdf("tot", "g+cheb", RooArgList(signal, bkg),
RooArgList(nSig, nBkg))
nfits = 0
mean.setConstant(True)
gamma.setConstant(True)
rPhifit = tot.fitTo(splotData, Range(psimin, psimax), RooFit.NumCPU(20),
RooFit.Verbose(False))
nfits = nfits + 1
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)
gamma.setConstant(False)
rPhifit = tot.fitTo(splotData, Range(psimin, psimax), RooFit.NumCPU(20),
RooFit.Verbose(False))
nfits = nfits + 1
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")
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)))
# In[12]:
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.)
sigma2 = RooRealVar("sigma2", "width of gaussian2", 0.004, 0.004, 0.01)
pdfS1 = RooGaussian("pdfS1", "gaus", mass, mean, sigma1)
pdfS2 = RooGaussian("pdfS2", "gaus", mass, mean, sigma2)
# pdfB = RooExponential("pdfB" , "pdfB" , mbs , B_c)
pdfB = RooBernstein("pdfB", "pdfB", mass, RooArgList(B_c, B_b))
alist1 = RooArgList(pdfS1, pdfS2, pdfB)
alist2 = RooArgList(S1, S2, B)
tot = RooAddPdf("model", "model", alist1, alist2)
# In[ ]:
#xDataPromptKK = xdataPrompt.reduce(SelectVars(RooArgSet(masskk)))
mean.setConstant(True)
rPhifit = tot.fitTo(xdataPrompt, Range(massmin, massmax), RooFit.NumCPU(8))
mean.setConstant(False)
rPhifit = tot.fitTo(xdataPrompt, Range(massmin, massmax), RooFit.NumCPU(8))
# In[ ]:
c = TCanvas("canvas", "canvas", 1200, 800)
phiFrame = masskk.frame(Range(massmin, massmax))
xdataPrompt.plotOn(phiFrame)
tot.plotOn(phiFrame)
phiFrame.Draw()
c.SaveAs("phiMassSPlotPhi.png")
c.SaveAs("phiMassSPlotPhi.root")
c.Clear()
B_1 = RooRealVar("B_1", "B_1 ", 0.3, -20, 100)
B_2 = RooRealVar("B_2", "B_2", 0.3, -20, 100)
B_3 = RooRealVar("B_3", "B_3", 0.3, -20, 100)
B_4 = RooRealVar("B_4", "B_4", 0.3, -20, 100)
bkg = RooBernstein("pdfB", "pdfB", masskk, RooArgList(B_1, B_2, B_3, B_4))
tot = RooAddPdf("tot", "g+cheb", RooArgList(signal, bkg),
RooArgList(nSig, nBkg))
# In[ ]:
#xDataPromptKK = xdataPrompt.reduce(SelectVars(RooArgSet(masskk)))
mean.setConstant(True)
gamma.setConstant(True)
rPhifit = tot.fitTo(xdataPrompt, Range(phimin, phimax), RooFit.NumCPU(4))
#rPhifit = tot.fitTo(xdataPrompt,Range(phimin,phimax),RooFit.NumCPU(4))
mean.setConstant(False)
gamma.setConstant(False)
rPhifit = tot.fitTo(xdataPrompt, Range(phimin, phimax), RooFit.NumCPU(4))
# In[ ]:
c = TCanvas("canvas", "canvas", 1200, 800)
phiFrame = masskk.frame(Range(phimin, phimax),
Normalization((nSig.getValV() + nBkg.getValV())))
xdataPrompt.plotOn(phiFrame)
tot.plotOn(phiFrame)
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")
# kkfit = kkTot.fitTo(traKFitData,Range(fitphimin+0.005,fitphimax-0.005),RooFit.PrintLevel(-1), RooFit.NumCPU(7),RooFit.Save())
# nfit +=1
if not debugging:
kkMean.setConstant(ROOT.kFALSE)
# kkfit = kkTot.fitTo(traKFitData,Range(fitphimin+0.005,fitphimax-0.005),RooFit.PrintLevel(-1), RooFit.NumCPU(7),RooFit.Save())
# nfit +=1
kkGamma.setConstant(ROOT.kFALSE)
# kkfit = kkTot.fitTo(traKFitData,Range(fitphimin+0.005,fitphimax-0.005),RooFit.PrintLevel(-1), RooFit.NumCPU(7),RooFit.Save())
# nfit +=1
kkfit = kkTot.fitTo(traKFitData,
Range(fitphimin + 0.005, fitphimax - 0.005),
RooFit.NumCPU(7), RooFit.Save())
nfit += 1
sigmaside_kk = math.sqrt(kkGamma.getValV()**2 + kkSigma.getValV()**2)
kkFrame = tt_mass.frame(Range(fitphimin + 0.005, fitphimax - 0.005))
leftlowside_kk = -6. * sigmaside_kk + kkMean.getValV()
leftupside_kk = -4. * sigmaside_kk + kkMean.getValV()
rightlowside_kk = +4. * sigmaside_kk + kkMean.getValV()
rightupside_kk = +6. * sigmaside_kk + kkMean.getValV()
signallow = -3. * sigmaside_kk + kkMean.getValV()
signalup = +3. * sigmaside_kk + kkMean.getValV()
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")
#xb->setRange("alt","x_coarse_bin1,x_coarse_bin3,x_coarse_bin5,x_coarse_bin7,x_coarse_bin9") ;
b0dataNonPrompt = ((
alldata.reduce('xHlt!=8')).reduce('xM>5.2')).reduce("xL>3.0")
# In[ ]:
b0dataNonPromptMass = b0dataNonPrompt.reduce(SelectVars(RooArgSet(mass)))
b0dataNonPrompt.numEntries()
# In[ ]:
c = TCanvas("canvas", "canvas", 1200, 800)
mass.setRange("fitRange", massmin, massmax)
mass.setBins(200)
massFrame = mass.frame(Range(massmin, massmax))
b0dataNonPrompt.plotOn(massFrame)
massFrame.Draw()
c.SaveAs("testmass.png")
# In[13]:
phimean = 1.019
sigma = RooRealVar("sigma", "width of gaussian", 0.0013)
gamma = RooRealVar("gamma", "gamma of bw", 0.004253, 0.001, 0.01)
mean = RooRealVar("mean", "mean of gaussian", phimean, phimean - 0.005,
phimean + 0.005)
nSig = RooRealVar("nSig", "nSig", 1E6, 0., 5.0E6)
nBkg = RooRealVar("nBkg", "nBkg", 5E5, 0., 5.0E6)
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")
a3.setConstant(kTRUE)
a4.setConstant(kTRUE)
a5.setConstant(kTRUE)
# a6.setConstant(kTRUE)
kkMean.setConstant(kFALSE)
# kkfit = kkTot.fitTo(traKFitData,Range(fitphimin+0.005,fitphimax-0.005),RooFit.PrintLevel(-1), RooFit.NumCPU(7),RooFit.Save())
# nfit +=1
kkGamma.setConstant(kFALSE)
# kkfit = kkTot.fitTo(traKFitData,Range(fitphimin+0.005,fitphimax-0.005),RooFit.PrintLevel(-1), RooFit.NumCPU(7),RooFit.Save())
# nfit +=1
#kkfit = kkTot.fitTo(traKFitData,Range(fitphimin+0.005,fitphimax-0.005), RooFit.NumCPU(7),RooFit.Save())
kkfit = kkTot.fitTo(traKFitData, Range(fitphimin, fitphimax),
RooFit.PrintLevel(-1), RooFit.NumCPU(numcpus),
RooFit.Save())
nfit += 1
sigmaside_kk = math.sqrt(kkGamma.getValV()**2 + kkSigma.getValV()**2)
sigmaside_kk = kkGamma.getValV()
if debugging:
sigmaside_kk = 0.001
leftlowside = -sidehigh * sigmaside_kk + kkMean.getValV()
leftupside = -sidelow * sigmaside_kk + kkMean.getValV()
rightlowside = +sidelow * sigmaside_kk + kkMean.getValV()
rightupside = +sidehigh * sigmaside_kk + kkMean.getValV()
signallow = -signalside * sigmaside_kk + kkMean.getValV()
B_1 = RooRealVar("B_{1}", "B_1 ", 0.3, -20, 100)
B_2 = RooRealVar("B_{2}", "B_2", 0.3, -20, 100)
B_3 = RooRealVar("B_{3}", "B_3", 0.3, -20, 100)
B_4 = RooRealVar("B_{4}", "B_4", 0.3, -20, 100)
bkg = RooChebychev("pdfB", "pdfB", mass_ref_c_kkk, RooArgList(aset))
tot = RooAddPdf("tot", "g+cheb", RooArgList(signal, bkg),
RooArgList(nSig, nBkg))
nfits = 0
mean.setConstant(True)
gamma.setConstant(True)
rPhifit = tot.fitTo(splotData, Range(bumin, bumax), RooFit.NumCPU(8),
RooFit.Verbose(False))
nfits = nfits + 1
mean.setConstant(True)
gamma.setConstant(False)
rPhifit = tot.fitTo(splotData, Range(bumin, bumax), RooFit.NumCPU(8),
RooFit.Verbose(False))
nfits = nfits + 1
mean.setConstant(False)
gamma.setConstant(False)
rPhifit = tot.fitTo(splotData, Range(bumin, bumax), RooFit.NumCPU(8),
RooFit.Verbose(False))
nfits = nfits + 1