def _fetch_items(self, store_load, iterable, args, kwargs):
        """ Method used by f_store/load/remove_items to find corresponding items in the tree.


        :param store_load:

            String constant specifying if we want to store, load or remove.
            The corresponding constants are defined at the top of this module.

        :param iterable:

            Iterable over items to look for in the tree. Can be strings specifying names,
            can be the item instances themselves or already correctly formatted tuples.

        :param args: Additional arguments passed to the storage service

        :param kwargs:

            Additional keyword arguments passed to the storage service.
            Two optional keyword arguments are popped and used by this method.

            only_empties:

                Can be in kwargs if only empty parameters and results should be considered.

            non_empties:

                Can be in kwargs if only non-empty parameters and results should be considered.


        :return:

            A list containing formatted tuples.
            These tuples can be handled by the storage service, they have
            the following format: (msg, item_to_store_load_or_remove, args, kwargs)

        """
        only_empties = kwargs.pop('only_empties', False)

        non_empties = kwargs.pop('non_empties', False)

        item_list = []
        # Iterate through the iterable and apply the appropriate fetching method via try and error.
        for iter_item in iterable:

            try:
                item_tuple = self._fetch_from_string(store_load, iter_item, args, kwargs)
            except TypeError:
                try:
                    item_tuple = self._fetch_from_node(store_load, iter_item, args, kwargs)
                except AttributeError:
                    item_tuple = self._fetch_from_tuple(store_load, iter_item, args, kwargs)

            item = item_tuple[1]
            msg = item_tuple[0]

            if item.v_is_leaf:
                if only_empties and not item.f_is_empty():
                    continue
                if non_empties and item.f_is_empty():
                    continue

            # # Explored Parameters cannot be deleted, this would break the underlying hdf5 file
            # # structure
            # if (msg == pypetconstants.DELETE and
            #             item.v_full_name in self._root_instance._explored_parameters and
            #             len(self._root_instance._explored_parameters) == 1):
            #     raise TypeError('You cannot the last explored parameter of a trajectory stored '
            #                     'into an hdf5 file.')

            item_list.append(item_tuple)

        return item_list
def _fetch_items(self, store_load, iterable, args, kwargs):
        """ Method used by f_store/load/remove_items to find corresponding items in the tree.


        :param store_load:

            String constant specifying if we want to store, load or remove.
            The corresponding constants are defined at the top of this module.

        :param iterable:

            Iterable over items to look for in the tree. Can be strings specifying names,
            can be the item instances themselves or already correctly formatted tuples.

        :param args: Additional arguments passed to the storage service

        :param kwargs:

            Additional keyword arguments passed to the storage service.
            Two optional keyword arguments are popped and used by this method.

            only_empties:

                Can be in kwargs if only empty parameters and results should be considered.

            non_empties:

                Can be in kwargs if only non-empty parameters and results should be considered.


        :return:

            A list containing formatted tuples.
            These tuples can be handled by the storage service, they have
            the following format: (msg, item_to_store_load_or_remove, args, kwargs)

        """
        only_empties = kwargs.pop('only_empties', False)

        non_empties = kwargs.pop('non_empties', False)

        item_list = []
        # Iterate through the iterable and apply the appropriate fetching method via try and error.
        for iter_item in iterable:

            try:
                item_tuple = self._fetch_from_string(store_load, iter_item, args, kwargs)
            except TypeError:
                try:
                    item_tuple = self._fetch_from_node(store_load, iter_item, args, kwargs)
                except AttributeError:
                    item_tuple = self._fetch_from_tuple(store_load, iter_item, args, kwargs)

            item = item_tuple[1]
            msg = item_tuple[0]

            if item.v_is_leaf:
                if only_empties and not item.f_is_empty():
                    continue
                if non_empties and item.f_is_empty():
                    continue

            # # Explored Parameters cannot be deleted, this would break the underlying hdf5 file
            # # structure
            # if (msg == pypetconstants.DELETE and
            #             item.v_full_name in self._root_instance._explored_parameters and
            #             len(self._root_instance._explored_parameters) == 1):
            #     raise TypeError('You cannot the last explored parameter of a trajectory stored '
            #                     'into an hdf5 file.')

            item_list.append(item_tuple)

        return item_list
def _fetch_items ( self , store_load , iterable , args , kwargs ) : only_empties = kwargs . pop ( 'only_empties' , False ) non_empties = kwargs . pop ( 'non_empties' , False ) item_list = [ ] # Iterate through the iterable and apply the appropriate fetching method via try and error. for iter_item in iterable : try : item_tuple = self . _fetch_from_string ( store_load , iter_item , args , kwargs ) except TypeError : try : item_tuple = self . _fetch_from_node ( store_load , iter_item , args , kwargs ) except AttributeError : item_tuple = self . _fetch_from_tuple ( store_load , iter_item , args , kwargs ) item = item_tuple [ 1 ] msg = item_tuple [ 0 ] if item . v_is_leaf : if only_empties and not item . f_is_empty ( ) : continue if non_empties and item . f_is_empty ( ) : continue # # Explored Parameters cannot be deleted, this would break the underlying hdf5 file # # structure # if (msg == pypetconstants.DELETE and #             item.v_full_name in self._root_instance._explored_parameters and #             len(self._root_instance._explored_parameters) == 1): #     raise TypeError('You cannot the last explored parameter of a trajectory stored ' #                     'into an hdf5 file.') item_list . append ( item_tuple ) return item_list
Method used by f_store/load/remove_items to find corresponding items in the tree.


        :param store_load:

            String constant specifying if we want to store, load or remove.
            The corresponding constants are defined at the top of this module.

        :param iterable:

            Iterable over items to look for in the tree. Can be strings specifying names,
            can be the item instances themselves or already correctly formatted tuples.

        :param args: Additional arguments passed to the storage service

        :param kwargs:

            Additional keyword arguments passed to the storage service.
            Two optional keyword arguments are popped and used by this method.

            only_empties:

                Can be in kwargs if only empty parameters and results should be considered.

            non_empties:

                Can be in kwargs if only non-empty parameters and results should be considered.


        :return:

            A list containing formatted tuples.
            These tuples can be handled by the storage service, they have
            the following format: (msg, item_to_store_load_or_remove, args, kwargs)
Method used by f_store / load / remove_items to find corresponding items in the tree .