def test_bukin():
logger.info(
'Test Bukin_pdf: Bukin function: skew gaussian core + exponenial/gaussian tails'
)
model_bukin = Models.Fit1D(signal=Models.Bukin_pdf(
name='Bukin',
xvar=mass,
xi=0,
rhoL=0,
rhoR=0,
mean=signal_gauss.mean,
sigma=signal_gauss.sigma),
background=Models.Bkg_pdf('BkgBK',
xvar=mass,
power=0),
S=S,
B=B)
model_bukin.signal.mean.fix(m.value())
model_bukin.signal.sigma.fix(m.error())
model_bukin.S.setVal(5000)
model_bukin.B.setVal(500)
with rooSilent():
result, frame = model_bukin.fitTo(dataset0)
model_bukin.signal.xi.release()
result, frame = model_bukin.fitTo(dataset0)
model_bukin.signal.rhoL.release()
result, frame = model_bukin.fitTo(dataset0)
model_bukin.signal.rhoR.release()
result, frame = model_bukin.fitTo(dataset0)
model_bukin.signal.mean.release()
model_bukin.signal.sigma.release()
result, frame = model_bukin.fitTo(dataset0)
model_bukin.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('Bukin function\n%s' % result.table(prefix="# "))
models.add(model_bukin)
def test_bukin():
logger.info(
'Test Bukin_pdf: Bukin function: skew gaussian core + exponenial/gaussian tails'
)
model_bukin = Models.Fit1D(signal=Models.Bukin_pdf(
name='Bukin',
mass=mass,
xi=0,
rhol=0,
rhor=0,
mean=signal_gauss.mean,
sigma=signal_gauss.sigma),
background=Models.Bkg_pdf('BkgBK',
mass=mass,
power=0))
model_bukin.signal.mean.fix(m.value())
model_bukin.signal.sigma.fix(m.error())
model_bukin.s.setVal(5000)
model_bukin.b.setVal(500)
with rooSilent():
result, frame = model_bukin.fitTo(dataset0)
model_bukin.signal.xi.release()
result, frame = model_bukin.fitTo(dataset0)
model_bukin.signal.rhol.release()
result, frame = model_bukin.fitTo(dataset0)
model_bukin.signal.rhor.release()
result, frame = model_bukin.fitTo(dataset0)
model_bukin.signal.mean.release()
model_bukin.signal.sigma.release()
result, frame = model_bukin.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('Mean & Sigma are: %-28s & %-28s ' %
(result('mean_Gauss')[0], result('sigma_Gauss')[0]))
models.add(model_bukin)
from Functions import *
#-------------------------------------------------------------------------------
rfile = ROOT.TFile("test_file.root", "READ")
ds = rfile["ds"]
#-------------------------------------------------------------------------------
im = ROOT.RooRealVar('im', 'im', 2.240, 2.330)
pk = ROOT.RooRealVar('pk', 'peak ', 2.2875, 2.286, 2.289)
sg = ROOT.RooRealVar('sg', 'sigma', 0.0045, 0.003, 0.010)
rl = ROOT.RooRealVar('rl', 'rhoL', 0.0000, 0.000, 2.000)
rr = ROOT.RooRealVar('rr', 'rhoR', 0.0000, 0.000, 2.000)
xi = ROOT.RooRealVar('xi', 'xi', 0.0000, -0.010, 0.010)
#-------------------------------------------------------------------------------
sig_bu = Models.Bukin_pdf('sig_bu',
xvar=im,
mean=pk,
sigma=sg,
xi=xi,
rhoL=rl,
rhoR=rr)
bkg0 = Models.Bkg_pdf('bkg0', xvar=im, power=0)
model = Models.Fit1D(signal=sig_bu, background=bkg0)
#-------------------------------------------------------------------------------
dh = (ds.reduce(ROOT.RooArgSet(im), "im>0")).binnedClone()
with timing():
r, w = model.fitTo(dh)
r, w = model.fitTo(ds, draw=True, nbins=100, ncpu=4)
#-------------------------------------------------------------------------------
print(r)
#-------------------------------------------------------------------------------
h = w.pullHist()
draw_param(r,