def test_rasingcosine():
logger.info("Test RaisingCosine")
model = Models.Fit1D(signal=Models.RaisingCosine_pdf(
'RC', xvar=mass, mean=signal_gauss.mean),
background=1,
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.scale.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("Raising cosine function\n%s" % result.table(prefix="# "))
models.add(model)
def test_rasingcosine():
logger.info("Test RaisingCosine")
model = Models.Fit1D(signal=Models.RaisingCosine_pdf(
'RC', xvar=mass, mean=signal_gauss.mean),
background=1)
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.scale.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('Scale is: %-28s ' %
result(signal.scale)[0])
models.add(model)