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def test_init(self):
gl = smelli.GlobalLikelihood()
# with fix_ckm
gl_fixckm = smelli.GlobalLikelihood(fix_ckm=True)
self.assertEqual(gl.par_dict_default['Vcb'], par['Vcb'])
VcbSM = gl.par_dict_sm['Vcb']
VubSM = gl.par_dict_sm['Vub']
VusSM = gl.par_dict_sm['Vus']
deltaSM = gl.par_dict_sm['delta']
self.assertAlmostEqual(par['Vcb'], VcbSM, delta=0.0002)
self.assertAlmostEqual(par['Vub'], VubSM, delta=0.0005)
self.assertAlmostEqual(par['Vus'], VusSM, delta=0.0006)
pre = -4 * par['GF'] / sqrt(2)
# Vcb
w = Wilson({'lq3_1123': 0.5 * pre * VcbSM * (-0.5)}, 91.1876, 'SMEFT', 'Warsaw')
pp = gl.parameter_point(w)
self.assertAlmostEqual(pp.par_dict_np['Vcb'] / VcbSM, 1.5, delta=0.03)
# with fix_ckm
def test_init(self):
gl = smelli.GlobalLikelihood()
# with fix_ckm
gl_fixckm = smelli.GlobalLikelihood(fix_ckm=True)
self.assertEqual(gl.par_dict_default['Vcb'], par['Vcb'])
VcbSM = gl.par_dict_sm['Vcb']
VubSM = gl.par_dict_sm['Vub']
VusSM = gl.par_dict_sm['Vus']
deltaSM = gl.par_dict_sm['delta']
self.assertAlmostEqual(par['Vcb'], VcbSM, delta=0.0002)
self.assertAlmostEqual(par['Vub'], VubSM, delta=0.0005)
self.assertAlmostEqual(par['Vus'], VusSM, delta=0.0006)
pre = -4 * par['GF'] / sqrt(2)
# Vcb
w = Wilson({'lq3_1123': 0.5 * pre * VcbSM * (-0.5)}, 91.1876, 'SMEFT', 'Warsaw')
pp = gl.parameter_point(w)
self.assertAlmostEqual(pp.par_dict_np['Vcb'] / VcbSM, 1.5, delta=0.03)
# with fix_ckm
pp = gl_fixckm.parameter_point(w)
self.assertEqual(pp.par_dict_np['Vcb'] / par['Vcb'], 1)
def test_fast_likelihoods(self):
scheme = ckm.CKMSchemeRmuBtaunuBxlnuDeltaM()
ckm_central = scheme.ckm_np()
gl = smelli.GlobalLikelihood()
for fl in gl.fast_likelihoods.values():
par = fl.par_obj
self.assertAlmostEqual(par.get_central('Vus'), ckm_central[0], delta=0.00001)
self.assertAlmostEqual(par.get_central('Vcb'), ckm_central[1], delta=0.00001)
self.assertAlmostEqual(par.get_central('Vub'), ckm_central[2], delta=0.00001)
self.assertAlmostEqual(par.get_central('delta'), ckm_central[3], delta=0.0001)
parser.add_argument('-n', type=int, default=5000,
help='Number of evaluations (default 5000)')
parser.add_argument('-t', type=int, default=1,
help='Number of threads (default 1)')
parser.add_argument('-f', action='store_true',
help='Force recomputation (default false)')
parser.add_argument('-s', type=str, default=DEFAULT_ckm_scheme,
help="Name of CKM scheme (default {})".format(
DEFAULT_ckm_scheme))
parser.add_argument('--fix_ckm', action='store_true',
help='Fix CKM values to their SM values (default false)')
args = parser.parse_args()
from smelli import GlobalLikelihood
gl = GlobalLikelihood(ckm_scheme=args.s, fix_ckm=args.fix_ckm)
logging.info("Computing covariances with N={} and {} threads".format(args.n, args.t))
gl.make_measurement(N=args.n, threads=args.t, force=args.f)
gl.save_sm_covariances('.')