private: set the omitted_predictions attribute

def __load_omitted_predictions(self):
        """private: set the omitted_predictions attribute
        """
        # if there are no base predictions
        if self.predictions is None:
            raise Exception("ErrVar.__load_omitted_predictions(): " +
                            "no 'included' predictions is None")
        if self.omitted_predictions_arg is None and \
                        self.omitted_par_arg is None:
            raise Exception("ErrVar.__load_omitted_predictions: " +
                            "both omitted args are None")
        # try to set omitted_predictions by
        # extracting from existing predictions
        if self.omitted_predictions_arg is None and \
                        self.omitted_par_arg is not None:
            # check to see if omitted par names are in each predictions
            found = True
            missing_par,missing_pred = None, None
            for par_name in self.omitted_jco.col_names:
                for prediction in self.predictions_iter:
                    if par_name not in prediction.row_names:
                        found = False
                        missing_par = par_name
                        missing_pred = prediction.col_names[0]
                        break
            if found:
                opreds = []
                # need to access the attribute directly,
                # not a view of attribute
                opred_mat = self._LinearAnalysis__predictions.extract(
                            row_names=self.omitted_jco.col_names)
                opreds = [opred_mat.get(col_names=name) for name in self.forecast_names]

                #for prediction in self._LinearAnalysis__predictions:
                #    opred = prediction.extract(self.omitted_jco.col_names)
                #    opreds.append(opred)
                self.__omitted_predictions = opreds
            else:
                raise Exception("ErrVar.__load_omitted_predictions(): " +
                                " omitted parameter " + str(missing_par) +\
                                " not found in prediction vector " +
                                str(missing_pred))
        elif self.omitted_parcov_arg is not None:
            raise NotImplementedError()