(elementary_effects: np.ndarray, num_vars: int,
                                 num_resamples: int, conf_level: float,
                                 groups: np.ndarray,
                                 unique_group_names: List)
return Si
Computes the statistical parameters related to Morris method.

    Parameters
    ----------
    elementary_effects: np.ndarray
        Morris elementary effects.
    num_vars: int
        Number of problem's variables
    num_resamples: int
        Number of resamples
    conf_level: float
        Confidence level
    groups: np.ndarray
        Array defining the distribution of groups
    unique_group_names: List
        Names of the groups

    Returns
    -------
    Si: ResultDict
        Morris statistical parameters.
Computes the statistical parameters related to Morris method.
Computes the statistical parameters related to Morris method .
def _compute_statistical_outputs(elementary_effects: np.ndarray, num_vars: int,
                                 num_resamples: int, conf_level: float,
                                 groups: np.ndarray,
                                 unique_group_names: List) -> ResultDict:
    """ Computes the statistical parameters related to Morris method.

    Parameters
    ----------
    elementary_effects: np.ndarray
        Morris elementary effects.
    num_vars: int
        Number of problem's variables
    num_resamples: int
        Number of resamples
    conf_level: float
        Confidence level
    groups: np.ndarray
        Array defining the distribution of groups
    unique_group_names: List
        Names of the groups

    Returns
    -------
    Si: ResultDict
        Morris statistical parameters.
    """

    Si = ResultDict((k, [None] * num_vars) for k in ['names', 'mu', 'mu_star',
                                                     'sigma', 'mu_star_conf'])

    mu = np.average(elementary_effects, 1)
    mu_star = np.average(np.abs(elementary_effects), 1)
    sigma = np.std(elementary_effects, axis=1, ddof=1)
    mu_star_conf = _compute_mu_star_confidence(elementary_effects, num_vars,
                                               num_resamples, conf_level)

    Si['names'] = unique_group_names
    Si['mu'] = _compute_grouped_sigma(mu, groups)
    Si['mu_star'] = _compute_grouped_metric(mu_star, groups)
    Si['sigma'] = _compute_grouped_sigma(sigma, groups)
    Si['mu_star_conf'] = _compute_grouped_metric(mu_star_conf, groups)

    return Si
def _compute_statistical_outputs ( elementary_effects : np . ndarray , num_vars : int , num_resamples : int , conf_level : float , groups : np . ndarray , unique_group_names : List ) -> ResultDict : Si = ResultDict ( ( k , [ None ] * num_vars ) for k in [ 'names' , 'mu' , 'mu_star' , 'sigma' , 'mu_star_conf' ] ) mu = np . average ( elementary_effects , 1 ) mu_star = np . average ( np . abs ( elementary_effects ) , 1 ) sigma = np . std ( elementary_effects , axis = 1 , ddof = 1 ) mu_star_conf = _compute_mu_star_confidence ( elementary_effects , num_vars , num_resamples , conf_level ) Si [ 'names' ] = unique_group_names Si [ 'mu' ] = _compute_grouped_sigma ( mu , groups ) Si [ 'mu_star' ] = _compute_grouped_metric ( mu_star , groups ) Si [ 'sigma' ] = _compute_grouped_sigma ( sigma , groups ) Si [ 'mu_star_conf' ] = _compute_grouped_metric ( mu_star_conf , groups ) return Si