def test_johnsonSU():
logger.info("Test JohnsonSU-Distribution")
model = Models.Fit1D(signal=Models.JohnsonSU_pdf('JSU',
xvar=mass,
xi=signal_gauss.mean),
background=Models.Bkg_pdf('BkgJSU',
xvar=mass,
power=0),
S=S,
B=B)
signal = model.signal
model.S.setVal(5000)
model.B.setVal(500)
with rooSilent():
result, f = model.fitTo(dataset0)
result, f = model.fitTo(dataset0)
signal.lambd.release()
signal.delta.release()
result, f = model.fitTo(dataset0)
model.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info("Johnson-SU function\n%s" % result.table(prefix="# "))
models.add(model)
def test_johnsonSU () :
logger.info("Test JohnsonSU-Distribution")
model = Models.Fit1D (
signal = Models.JohnsonSU_pdf( 'JSU' ,
xvar = mass ,
xi = signal_gauss.mean ) ,
background = Models.Bkg_pdf ('BkgJSU', xvar = mass , power = 0 ))
signal = model.signal
model.S.setVal(5000)
model.B.setVal( 500)
with rooSilent() :
result,f = model.fitTo ( dataset0 )
result,f = model.fitTo ( dataset0 )
signal.lambd .release()
signal.delta.release()
result,f = model.fitTo ( dataset0 )
if 0 != result.status() or 3 != result.covQual() :
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' % ( result.status() , result.covQual() ) )
print(result)
else :
logger.info ( 'Signal & Background are: %-28s & %-28s ' % ( result ( 'S' )[0] , result( 'B' )[0] ) )
logger.info ( 'Xi is: %-28s ' % result ( signal.xi )[0] )
logger.info ( 'Lambda is: %-28s ' % result ( signal.lambd )[0] )
logger.info ( 'Delta is: %-28s ' % result ( signal.delta )[0] )
logger.info ( 'Gamma is: %-28s ' % result ( signal.gamma )[0] )
models.add ( model )
def draw_jsu():
im = ROOT.RooRealVar('im', 'im', 0., 1.)
model_jsu = md.JohnsonSU_pdf('J_SU',
im,
xi=0.5,
lambd=.1,
delta=5.,
gamma=0.5)
model_jsu.draw_options['total_fit_options'] = ROOT.RooFit.LineColor(
ROOT.kBlue), ROOT.RooFit.LineWidth(5)
model_jsu.draw()
def fit_jsu():
im = ROOT.RooRealVar('im', 'im', 2.42, 2.52)
pk = ROOT.RooRealVar('pk', 'peak', 2.47, 2.46, 2.48)
gam = ROOT.RooRealVar('gamma', 'gamma', 1., 0., 1.5)
delt = ROOT.RooRealVar('delta', 'delta', 0., 0., 20.0)
sig_ap = Models.JohnsonSU_pdf('sig_ap', xvar=im, xi = pk, lambd = None, \
delta = delt, gamma=gam)
bkg0 = Models.Bkg_pdf('bkg0', xvar=im, power=1.)
model = Models.Fit1D(signal=sig_ap, background=bkg0)
rfile = ROOT.TFile("../datasets/test_xic_100invpb.root", "READ")
ds = rfile["da_lc"]
ds = ds.reduce("im > 2.42 && im < 2.52")
dh = (ds.reduce(ROOT.RooArgSet(im), "im>0")).binnedClone()
with timing():
r, w = model.fitTo(dh)
r, w = model.fitTo(ds, draw=True, nbins=200, ncpu=4)
r.draw()