How to use the smelli.ckm.get_ckm_schemes function in smelli

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github smelli / smelli / smelli / data / yaml / View on Github external
        "values": {
          "distribution": "multivariate_normal",
          "central_value": list(val),
          "standard_deviation": err.tolist(),
          "correlation": corr.tolist()

    with open(file_path, 'w') as f:
        yaml.dump(dat, f)

for name, ckm_scheme in get_ckm_schemes().items():
    file_path = '{}_{}.yaml'.format(FILE,name)
github smelli / smelli / smelli / View on Github external
elements in the SM. If set to True, the CKM elements are fixed to
          their SM values, which can lead to inconsistent results, but also
          to a significant speedup in specific cases.
        - ckm_scheme: A string with the name of the class defining the CKM
        self.eft = eft
        self.basis = basis or self._default_bases[self.eft]
        par_dict = par_dict or {}  # initialize empty if not given
        # take missing parameters from flavio defaults
        self.par_dict_default = flavio.default_parameters.get_central_all()
        self._par_dict_sm = None
        self.fix_ckm = fix_ckm
            self._ckm_scheme = get_ckm_schemes()[ckm_scheme]
            self._ckm_scheme_name = ckm_scheme
            raise ValueError("CKM scheme '{}' is not defined.".format(ckm_scheme))
        self.likelihoods = {}
        self.fast_likelihoods = {}
        self._custom_likelihoods_dict = custom_likelihoods or {}
        self.custom_likelihoods = {}
        self._Nexp = Nexp
        if exp_cov_folder is not None:
        self._sm_cov_loaded = False
            if sm_cov_folder is None:
                self.load_sm_covariances(get_datapath('smelli', 'data/cache'))


A Python package providing a global likelihood function in the space of dimension-6 Wilson coefficients of the Standard Model Effective Field Theory (SMEFT)

Latest version published 5 months ago

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