for i in xrange(0, events):
v1 = random.expovariate(1 / 3.0)
if not 0 < v1 < 10: continue
x.setVal(v1)
dataset4.add(varset4)
v2 = 10 - v1
x.setVal(v2)
dataset5.add(varset4)
import Ostap.FitModels as Models
positive = Models.PolyPos_pdf('PP', mass4, power=3)
increasing = Models.Monothonic_pdf('PI', mass4, power=3, increasing=True)
decreasing = Models.Monothonic_pdf('PD', mass4, power=3, increasing=False)
inc_convex = Models.Convex_pdf('PIX',
mass4,
power=3,
increasing=True,
convex=True)
dec_convex = Models.Convex_pdf('PDX',
mass4,
power=3,
increasing=False,
convex=True)
convex = Models.ConvexOnly_pdf('PX', mass4, power=3, convex=True)
concave = Models.ConvexOnly_pdf('PX', mass4, power=3, convex=False)
with timing('Positive 5'), rooSilent():
result, f = m_p4.fitTo(dataset2)
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:
for phi in m_p4.phis:
print "\tPoly6: phi= %s " % phi.ve()
models.append(m_p4)
# =============================================================================
logger.info("Test monothonic Poly(4)-Distribution")
# =============================================================================
m_d4 = Models.Monothonic_pdf('D4', x, power=4, increasing=False)
with rooSilent():
result, f = m_d4.fitTo(dataset2)
result, f = m_d4.fitTo(dataset2)
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:
for phi in m_d4.phis:
print "\tPoly4: phi= %s " % phi.ve()
models.append(m_d4)
