def test_qgauss():
logger.info('Test QGaussian_pdf: q-Gaussian')
model_qgauss = Models.Fit1D(signal=Models.QGaussian_pdf(
name='qG',
xvar=mass,
q=(1, 0.7, 1.2),
mean=signal_gauss.mean,
scale=signal_gauss.sigma),
background=Models.Bkg_pdf('BkgQG',
xvar=mass,
power=0),
S=S,
B=B)
s = model_qgauss.signal
s.scale = 0.015
with rooSilent():
result, frame = model_qgauss.fitTo(dataset0)
model_qgauss.signal.scale.release()
result, frame = model_qgauss.fitTo(dataset0)
model_qgauss.signal.q.release()
result, frame = model_qgauss.fitTo(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('Q-Gaussian function\n%s' % result.table(prefix="# "))
models.add(model_qgauss)
def test_qgauss():
logger.info('Test QGaussian_pdf: q-Gaussian')
model_qgauss = Models.Fit1D(signal=Models.QGaussian_pdf(
name='qG',
xvar=mass,
q=(1, 0.7, 1.2),
mean=signal_gauss.mean,
scale=signal_gauss.sigma),
background=Models.Bkg_pdf('BkgQG',
xvar=mass,
power=0))
s = model_qgauss.signal
s.scale = 0.015
with rooSilent():
result, frame = model_qgauss.fitTo(dataset0)
model_qgauss.signal.scale.release()
result, frame = model_qgauss.fitTo(dataset0)
model_qgauss.signal.q.release()
result, frame = model_qgauss.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('Scale & Q are: %-28s & %-28s ' %
(result(s.scale)[0], result(s.q)[0]))
models.add(model_qgauss)