def test_gengauss_v1():
logger.info('Test GenGaussV1_pdf: Generalized Gaussian V1')
model_gauss_gv1 = Models.Fit1D(signal=Models.GenGaussV1_pdf(
name='Gv1', xvar=mass, mean=signal_gauss.mean),
background=Models.Bkg_pdf('BkgGGV1',
xvar=mass,
power=0),
S=S,
B=B)
model_gauss_gv1.signal.beta.fix(2)
model_gauss_gv1.signal.mean.fix(m.value())
model_gauss_gv1.S.setVal(5000)
model_gauss_gv1.B.setVal(500)
with rooSilent():
result, frame = model_gauss_gv1.fitTo(dataset0)
model_gauss_gv1.signal.alpha.release()
result, frame = model_gauss_gv1.fitTo(dataset0)
model_gauss_gv1.signal.mean.release()
result, frame = model_gauss_gv1.fitTo(dataset0)
model_gauss_gv1.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('generalized Gaussian(v1) function\n%s' %
result.table(prefix="# "))
models.add(model_gauss_gv1)
def test_gengauss_v1():
logger.info('Test GenGaussV1_pdf: Generalized Gaussian V1')
model_gauss_gv1 = Models.Fit1D(signal=Models.GenGaussV1_pdf(
name='Gv1', mass=mass, mean=signal_gauss.mean),
background=Models.Bkg_pdf('BkgGGV1',
mass=mass,
power=0))
model_gauss_gv1.signal.beta.fix(2)
model_gauss_gv1.signal.mean.fix(m.value())
model_gauss_gv1.s.setVal(5000)
model_gauss_gv1.b.setVal(500)
with rooSilent():
result, frame = model_gauss_gv1.fitTo(dataset0)
model_gauss_gv1.signal.alpha.release()
result, frame = model_gauss_gv1.fitTo(dataset0)
model_gauss_gv1.signal.mean.release()
result, frame = model_gauss_gv1.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]))
models.add(model_gauss_gv1)