def test_skewgauss():
logger.info('Test SkewGauss_pdf: Skew Gaussian function')
model_gauss_skew = Models.Fit1D(signal=Models.SkewGauss_pdf(
name='GSk', xvar=mass, mean=signal_gauss.mean),
background=Models.Bkg_pdf('BkgSkG',
xvar=mass,
power=0),
S=S,
B=B)
model_gauss_skew.signal.alpha.fix(0)
model_gauss_skew.S.setVal(5000)
model_gauss_skew.B.setVal(500)
with rooSilent():
result, frame = model_gauss_skew.fitTo(dataset0)
result, frame = model_gauss_skew.fitTo(dataset0)
model_gauss_skew.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('skew Gaussian function\n%s' % result.table(prefix="# "))
models.add(model_gauss_skew)
def test_skewgauss():
logger.info('Test SkewGauss_pdf: Skew Gaussian function')
model_gauss_skew = Models.Fit1D(signal=Models.SkewGauss_pdf(
name='GSk', xvar=mass, mean=signal_gauss.mean),
background=Models.Bkg_pdf('BkgSkG',
xvar=mass,
power=0))
model_gauss_skew.signal.alpha.fix(0)
model_gauss_skew.S.setVal(5000)
model_gauss_skew.B.setVal(500)
with rooSilent():
result, frame = model_gauss_skew.fitTo(dataset0)
result, frame = model_gauss_skew.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_skew)