def test_logistic():
logger.info("Test LOGISTIC: Logistic distribution")
model = Models.Fit1D(signal=Models.Logistic_pdf('LOGI',
xvar=mass,
mean=signal_gauss.mean),
background=Models.Bkg_pdf('BkgLOGI',
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.mean.release()
signal.sigma.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("Logistic distribution\n%s" % result.table(prefix="# "))
models.add(model)
def test_logistic():
logger.info("Test LOGISTIC: Logistic distribution")
model = Models.Fit1D(signal=Models.Logistic_pdf('LOGI',
mass=mass,
mean=signal_gauss.mean),
background=Models.Bkg_pdf('BkgLOGI',
mass=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.mean.release()
signal.sigma.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('Mean is: %-28s ' % result(signal.mean)[0])
logger.info('Sigma is: %-28s ' %
result(signal.sigma)[0])
models.add(model)