コード例 #1
0
# 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())
コード例 #2
0
ファイル: plot-kfactor-MC.py プロジェクト: suvayu/B2DXFitters
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())
コード例 #3
0
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")