def test_2gauss():
logger.info('Test DoubleGauss_pdf: Double Gaussian')
signal_2gauss = Models.DoubleGauss_pdf(name='Gau2',
mean=signal_gauss.mean,
sigma=signal_gauss.sigma,
xvar=mass,
fraction=0.9,
scale=1.2)
model_2gauss = Models.Fit1D(signal=signal_2gauss,
background=Models.Bkg_pdf('Bkg22G',
xvar=mass,
power=0),
S=S,
B=B)
model_2gauss.B.setVal(500)
model_2gauss.S.setVal(6000)
with rooSilent():
result, frame = model_2gauss.fitTo(dataset0)
signal_2gauss.fraction.release()
result, frame = model_2gauss.fitTo(dataset0)
model_2gauss.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('double Gaussian function\n%s' % result.table(prefix="# "))
models.add(model_2gauss)
def test_2gauss():
logger.info('Test DoubleGauss_pdf: Double Gaussian')
signal_2gauss = Models.DoubleGauss_pdf(name='Gau2',
mean=signal_gauss.mean,
sigma=signal_gauss.sigma,
xvar=mass,
fraction=0.9,
scale=1.2)
model_2gauss = Models.Fit1D(signal=signal_2gauss,
background=Models.Bkg_pdf('Bkg22G',
xvar=mass,
power=0))
model_2gauss.B.setVal(500)
model_2gauss.S.setVal(6000)
with rooSilent():
result, frame = model_2gauss.fitTo(dataset0)
signal_2gauss.fraction.release()
result, frame = model_2gauss.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_2gauss)