def test_sinhasinh():
logger.info("Test SinhAsinh-Distribution")
model = Models.Fit1D(signal=Models.SinhAsinh_pdf('SASH',
mass=mass,
mean=signal_gauss.mean),
background=Models.Bkg_pdf('BkgSAS',
mass=mass,
power=0))
signal = model.signal
model.s.setVal(5000)
model.b.setVal(500)
signal.mu.setVal(3.10)
signal.sigma.setVal(0.015)
signal.epsilon.setVal(0.021)
signal.delta.setVal(1.0)
with rooSilent():
result, f = model.fitTo(dataset0)
result, f = model.fitTo(dataset0)
signal.delta.release()
result, f = model.fitTo(dataset0)
signal.epsilon.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('Mu is: %-28s ' % result(signal.mu)[0])
logger.info('Sigma is: %-28s ' %
result(signal.sigma)[0])
logger.info('Epsilon is: %-28s ' %
result(signal.epsilon)[0])
logger.info('delta is: %-28s ' %
result(signal.delta)[0])
models.add(model)
def test_sinhasinh():
logger.info("Test SinhAsinh-Distribution")
model = Models.Fit1D(signal=Models.SinhAsinh_pdf('SASH',
xvar=mass,
mean=signal_gauss.mean),
background=Models.Bkg_pdf('BkgSAS',
xvar=mass,
power=0),
S=S,
B=B)
signal = model.signal
model.S.setVal(5000)
model.B.setVal(500)
signal.mu.setVal(3.10)
signal.sigma.setVal(0.015)
signal.epsilon.setVal(0.021)
signal.delta.setVal(1.0)
with rooSilent():
result, f = model.fitTo(dataset0)
result, f = model.fitTo(dataset0)
signal.delta.release()
result, f = model.fitTo(dataset0)
signal.epsilon.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("SinhAsinh function\n%s" % result.table(prefix="# "))
models.add(model)