(self, input_sample, num_samples, num_params,
                       maximum_combo, num_groups=None)
return output
Picks the trajectories from the input

        Parameters
        ----------
        input_sample : numpy.ndarray
        num_samples : int
        num_params : int
        maximum_combo : list
        num_groups : int
Picks the trajectories from the input
Picks the trajectories from the input
def compile_output(self, input_sample, num_samples, num_params,
                       maximum_combo, num_groups=None):
        """Picks the trajectories from the input

        Parameters
        ----------
        input_sample : numpy.ndarray
        num_samples : int
        num_params : int
        maximum_combo : list
        num_groups : int

        """

        if num_groups is None:
            num_groups = num_params

        self.check_input_sample(input_sample, num_groups, num_samples)

        index_list = self._make_index_list(num_samples, num_params, num_groups)

        output = np.zeros(
            (np.size(maximum_combo) * (num_groups + 1), num_params))
        for counter, combo in enumerate(maximum_combo):
            output[index_list[counter]] = np.array(
                input_sample[index_list[combo]])
        return output
def compile_output ( self , input_sample , num_samples , num_params , maximum_combo , num_groups = None ) : if num_groups is None : num_groups = num_params self . check_input_sample ( input_sample , num_groups , num_samples ) index_list = self . _make_index_list ( num_samples , num_params , num_groups ) output = np . zeros ( ( np . size ( maximum_combo ) * ( num_groups + 1 ) , num_params ) ) for counter , combo in enumerate ( maximum_combo ) : output [ index_list [ counter ] ] = np . array ( input_sample [ index_list [ combo ] ] ) return output