def test_apolonios2():
logger.info(
'Test Apolonios2_pdf: modified Gaussian with exponential tails')
model_apolonios2 = Models.Fit1D(signal=Models.Apolonios2_pdf(
name='AP2',
mass=mass,
mean=signal_gauss.mean,
sigma=signal_gauss.sigma,
asymmetry=0),
background=Models.Bkg_pdf('BkgAPO2',
mass=mass,
power=0))
model_apolonios2.signal.mean.fix(m.value())
model_apolonios2.s.setVal(5000)
model_apolonios2.b.setVal(500)
with rooSilent():
result, frame = model_apolonios2.fitTo(dataset0)
model_apolonios2.signal.asym.release()
result, frame = model_apolonios2.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_apolonios2)
#-------------------------------------------------------------------------------
import ostap.fitting.models as Models
from ostap.utils.timing import timing
from ostap.histos.histos import h1_axis
from Functions import *
#-------------------------------------------------------------------------------
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 )
am = ROOT.RooRealVar ('am' , 'asymmetry' , 0.0000 , -0.300 , 0.300 )
bt = ROOT.RooRealVar ('bt' , 'beta' , 1.0000 , 0.500 , 9.999 )
#-------------------------------------------------------------------------------
sig_ap = Models.Apolonios2_pdf( 'sig_ap', xvar=im, mean=pk, sigma=sg, asymmetry=am, beta=bt)
bkg0 = Models.Bkg_pdf ( 'bkg0' , xvar = im , power = 0 )
#
#-------------------------------------------------------------------------------
#
if __name__=="__main__":
#-------------------------------------------------------------------------------
model = Models.Fit1D ( signal = sig_ap , background = bkg0 )
#-------------------------------------------------------------------------------
rfile = ROOT.TFile("test_file.root","READ")
ds = rfile["ds"]
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()