Method input_multi_data

pyfair/model/model.py:284–328

Input data for multiple items that roll up into an aggregate As of now, this is only used for Secondary Loss when calculating mutliple secondary loss line items (e.g. 'Reputation' has a probability of A and a loss of B; 'Morale' has a probability of C and a loss of D

(self, target, kwargs_dict)

        return self

    def input_multi_data(self, target, kwargs_dict):
        """Input data for multiple items that roll up into an aggregate

        As of now, this is only used for Secondary Loss when calculating
        mutliple secondary loss line items (e.g. 'Reputation' has a
        probability of A and a loss of B; 'Morale' has a probability
        of C and a loss of D, etc.).

        Parameters
        ----------
        target : str
            The name of the node for which the arguments are directed
        kwargs_dict : dict
            The arguments used to generate a distribution for the node

        Returns
        -------
        pyfair.model.FairModel
            A reference to this object of type FairModel

        Examples
        --------
        >>> model = pyfair.FairModel(name="Insider Threat")
        >>> model1.input_multi_data('Secondary Loss', {
        ...     'Reputational': {
        ...         'Secondary Loss Event Frequency': {'constant': 4000},
        ...         'Secondary Loss Event Magnitude': {'low': 10, 'mode': 20, 'high': 100},
        ...     },
        ...     'Legal': {
        ...         'Secondary Loss Event Frequency': {'constant': 2000},
        ...         'Secondary Loss Event Magnitude': {'low': 10, 'mode': 20, 'high': 100},        
        ...     }
        ... })

        """
        # Generate our data
        data = self._data_input.generate_multi(target, self._n_simulations, kwargs_dict)
        # Multitargets are prefixed with 'multi_'
        mod_target = target.lstrip('multi_')
        # Supplied then calculated is a nasty workaround to propegate down then change.
        self._tree.update_status(mod_target, 'Supplied')
        self._tree.update_status(mod_target, 'Calculated')
        # Update model table with data
        self._model_table[mod_target] = data
        return self

    def bulk_import_data(self, param_dictionary):
        """Takes multiple inputs via nested dictionaries.