How to use smelli - 10 common examples
To help you get started, we’ve selected a few smelli examples, based on popular ways it is used in public projects.
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smelli / smelli / smelli / test_ckm.py View on Github 
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_ckmdef 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)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)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)def _check_flavio_version():
version_installed = flavio.__version__.split('.')
version_required = __flavio__version__.split('.')
if version_installed[:2] == version_required[:2]:
return True
else:
return Falseparser.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('.')def _get_yaml_path(self, name):
"""Return a path for the YAML file specified by `name`.
If a YAML file with that name is found in the package's data
directory, that is used. Otherwise, `name` is assumed to be a path.
Raises `FileNotFoundError` if path does not exists.
"""
path = get_datapath('smelli', 'data/yaml/' + name)
if os.path.exists(path):
return path
path = get_datapath('smelli', 'data/yaml/' + name + '.yaml')
if os.path.exists(path):
return path
if os.path.exists(name):
return name
if os.path.exists(name + '.yaml'):
return name + '.yaml'
else:
raise FileNotFoundError("Likelihood YAML file '{}' was not found".format(name))def _get_yaml_path(self, name):
"""Return a path for the YAML file specified by `name`.
If a YAML file with that name is found in the package's data
directory, that is used. Otherwise, `name` is assumed to be a path.
Raises `FileNotFoundError` if path does not exists.
"""
path = get_datapath('smelli', 'data/yaml/' + name)
if os.path.exists(path):
return path
path = get_datapath('smelli', 'data/yaml/' + name + '.yaml')
if os.path.exists(path):
return path
if os.path.exists(name):
return name
if os.path.exists(name + '.yaml'):
return name + '.yaml'
else:
raise FileNotFoundError("Likelihood YAML file '{}' was not found".format(name))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)
