How to use the smelli.ckm function in smelli
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smelli / smelli / smelli / test_ckm.py View on Github 
def test_sm(self):
scheme = ckm.CKMSchemeRmuBtaunuBxlnuDeltaM()
scheme.ckm_covariance()
Vus, Vcb, Vub, delta = scheme.ckm_np(None)
self.assertAlmostEqual(Vus, 0.225, delta=0.001)
self.assertAlmostEqual(Vcb, 0.041, delta=0.0015)
self.assertAlmostEqual(Vub, 0.004, delta=0.0005)
self.assertAlmostEqual(delta, 1.15, delta=0.2)def test_ckm_np(self):
scheme = ckm.CKMSchemeRmuBtaunuBxlnuDeltaM()
w = Wilson({'lq3_1123': 0.04e-5,
'lq3_3313': 0.008e-5,
'lq3_2212': 0.5e-7,
'qq1_2323': 1e-12,
}, 91.1876, 'SMEFT', 'Warsaw')
Vus, Vcb, Vub, delta = scheme.ckm_np(w, iterate=10)
w.set_option('parameters', {'Vus': Vus, 'Vcb': Vcb, 'Vub': Vub, 'gamma': delta})
_Vus, _Vcb, _Vub, _delta = scheme._ckm_np(w, Vus=Vus, Vcb=Vcb, Vub=Vub, delta=delta)
self.assertAlmostEqual(_Vus, Vus, places=2)
self.assertAlmostEqual(_Vcb, Vcb, places=2)
self.assertAlmostEqual(_Vub, Vub, places=2)
self.assertAlmostEqual(_delta, delta, places=2)smelli
A Python package providing a global likelihood function in the space of dimension-6 Wilson coefficients of the Standard Model Effective Field Theory (SMEFT)
