## random.seed(10)
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)
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:
print "\tPoly2e2 alpha= %s " % result(m_p2e2.alpha.GetName())[0]
print "\tPoly2e2 delta= %s " % result(m_p2e2.delta.GetName())[0]
for phi in m_p2e2.phis:
print "\tPoly2e2: phi= %s " % phi.ve()
models.append(m_p2e2)
# =============================================================================
logger.info("Test Poly(6)-Distribution")
# =============================================================================
m_p6 = Models.PolyPos_pdf('P6', x, power=6)
with rooSilent():
result, f = m_p6.fitTo(dataset2)
result, f = m_p6.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_p6.phis:
print "\tPoly6: phi= %s " % phi.ve()
models.append(m_p6)
result, f = m_gamma.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:
print "\tGamma: k = %s " % result(m_gamma.k.GetName())[0]
print "\tGamma: theta= %s " % result(m_gamma.theta.GetName())[0]
models.append(m_gamma)
# =============================================================================
logger.info("Test Poly(4)-Distribution")
# =============================================================================
m_p4 = Models.PolyPos_pdf('P4', x, power=4)
with rooSilent():
result, f = m_p4.fitTo(dataset2)
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)
