# tauk = Inverse('tauk', 'tauk', kgamma)
## acceptance
spline_knots = [0.5, 1.0, 1.5, 2.0, 3.0, 12.0]
spline_coeffs = [
0.03902e-01, 7.32741e-01, 9.98736e-01, 1.16514e+00, 1.25167e+00,
1.28624e+00
]
assert (len(spline_knots) == len(spline_coeffs))
# knot binning
mode = "Bs2DsPi"
knotbinning = RooBinning(time.getMin(), time.getMax(),
'{}_knotbinning'.format(mode))
for v in spline_knots:
knotbinning.addBoundary(v)
knotbinning.removeBoundary(time.getMin())
knotbinning.removeBoundary(time.getMax())
oldbinning, lo, hi = time.getBinning(), time.getMin(), time.getMax()
time.setBinning(knotbinning, '{}_knotbinning'.format(mode))
time.setBinning(oldbinning)
time.setRange(lo, hi)
del knotbinning, oldbinning, lo, hi
# knot coefficients
coefflist = RooArgList()
for i, v in enumerate(spline_coeffs):
coefflist.add(const(v))
i = len(spline_coeffs)
coefflist.add(one)
spline_knots.append(time.getMax())
one = const(1.)
zero = const(0.)
tau = Inverse('tau', 'tau', gamma)
# tauk = Inverse('tauk', 'tauk', kgamma)
## acceptance
spline_knots = [ 0.5, 1.0, 1.5, 2.0, 3.0, 12.0 ]
spline_coeffs = [ 5.03902e-01, 7.32741e-01, 9.98736e-01,
1.16514e+00, 1.25167e+00, 1.28624e+00 ]
assert(len(spline_knots) == len(spline_coeffs))
# knot binning
knotbinning = RooBinning(time.getMin(), time.getMax(),
'{}_knotbinning'.format(mode))
for v in spline_knots:
knotbinning.addBoundary(v)
knotbinning.removeBoundary(time.getMin())
knotbinning.removeBoundary(time.getMax())
oldbinning, lo, hi = time.getBinning(), time.getMin(), time.getMax()
time.setBinning(knotbinning, '{}_knotbinning'.format(mode))
time.setBinning(oldbinning)
time.setRange(lo, hi)
del knotbinning, oldbinning, lo, hi
# knot coefficients
coefflist = RooArgList()
for i, v in enumerate(spline_coeffs):
coefflist.add(const(v))
i = len(spline_coeffs)
coefflist.add(one)
spline_knots.append(time.getMax())
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")