pypet/storageservice.py
def _tree_load_nodes_dfs(self, parent_traj_node, load_data, with_links, recursive,
                         max_depth, current_depth, trajectory, as_new, hdf5_group):
        """Loads a node from hdf5 file and if desired recursively everything below

        :param parent_traj_node: The parent node whose child should be loaded
        :param load_data: How to load the data
        :param with_links: If links should be loaded
        :param recursive: Whether loading recursively below hdf5_group
        :param max_depth: Maximum depth
        :param current_depth: Current depth
        :param trajectory: The trajectory object
        :param as_new: If trajectory is loaded as new
        :param hdf5_group: The hdf5 group containing the child to be loaded

        """
        if max_depth is None:
            max_depth = float('inf')

        loading_list = [(parent_traj_node, current_depth, hdf5_group)]

        while loading_list:
            parent_traj_node, current_depth, hdf5_group = loading_list.pop()

            if isinstance(hdf5_group, pt.link.SoftLink):
                if with_links:
                    # We end up here when auto-loading a soft link
                    self._tree_load_link(parent_traj_node, load_data=load_data, traj=trajectory,
                                         as_new=as_new, hdf5_soft_link=hdf5_group)
                continue


            name = hdf5_group._v_name
            is_leaf = self._all_get_from_attrs(hdf5_group, HDF5StorageService.LEAF)
            in_trajectory = name in parent_traj_node._children

            if is_leaf:
                # In case we have a leaf node, we need to check if we have to create a new
                # parameter or result

                if in_trajectory:
                    instance = parent_traj_node._children[name]
                # Otherwise we need to create a new instance
                else:
                    instance = self._tree_create_leaf(name, trajectory, hdf5_group)

                    # Add the instance to the trajectory tree
                    parent_traj_node._add_leaf_from_storage(args=(instance,), kwargs={})

                self._prm_load_parameter_or_result(instance, load_data=load_data,
                                                   _hdf5_group=hdf5_group)
                if as_new:
                    instance._stored = False

            else:
                if in_trajectory:
                    traj_group = parent_traj_node._children[name]

                    if load_data == pypetconstants.OVERWRITE_DATA:
                        traj_group.v_annotations.f_empty()
                        traj_group.v_comment = ''
                else:
                    if HDF5StorageService.CLASS_NAME in hdf5_group._v_attrs:
                        class_name = self._all_get_from_attrs(hdf5_group,
                                                              HDF5StorageService.CLASS_NAME)
                        class_constructor = trajectory._create_class(class_name)
                        instance = trajectory._construct_instance(class_constructor, name)
                        args = (instance,)
                    else:
                        args = (name,)
                    # If the group does not exist create it'
                    traj_group = parent_traj_node._add_group_from_storage(args=args, kwargs={})

                # Load annotations and comment
                self._grp_load_group(traj_group, load_data=load_data, with_links=with_links,
                                     recursive=False, max_depth=max_depth,
                                     _traj=trajectory, _as_new=as_new,
                                     _hdf5_group=hdf5_group)

                if recursive and current_depth < max_depth:
                    new_depth = current_depth + 1
                    for children in (hdf5_group._v_groups, hdf5_group._v_links):
                        for new_hdf5_group_name in children:
                            new_hdf5_group = children[new_hdf5_group_name]
                            loading_list.append((traj_group, new_depth, new_hdf5_group))
def _tree_load_nodes_dfs(self, parent_traj_node, load_data, with_links, recursive,
                         max_depth, current_depth, trajectory, as_new, hdf5_group):
        """Loads a node from hdf5 file and if desired recursively everything below

        :param parent_traj_node: The parent node whose child should be loaded
        :param load_data: How to load the data
        :param with_links: If links should be loaded
        :param recursive: Whether loading recursively below hdf5_group
        :param max_depth: Maximum depth
        :param current_depth: Current depth
        :param trajectory: The trajectory object
        :param as_new: If trajectory is loaded as new
        :param hdf5_group: The hdf5 group containing the child to be loaded

        """
        if max_depth is None:
            max_depth = float('inf')

        loading_list = [(parent_traj_node, current_depth, hdf5_group)]

        while loading_list:
            parent_traj_node, current_depth, hdf5_group = loading_list.pop()

            if isinstance(hdf5_group, pt.link.SoftLink):
                if with_links:
                    # We end up here when auto-loading a soft link
                    self._tree_load_link(parent_traj_node, load_data=load_data, traj=trajectory,
                                         as_new=as_new, hdf5_soft_link=hdf5_group)
                continue


            name = hdf5_group._v_name
            is_leaf = self._all_get_from_attrs(hdf5_group, HDF5StorageService.LEAF)
            in_trajectory = name in parent_traj_node._children

            if is_leaf:
                # In case we have a leaf node, we need to check if we have to create a new
                # parameter or result

                if in_trajectory:
                    instance = parent_traj_node._children[name]
                # Otherwise we need to create a new instance
                else:
                    instance = self._tree_create_leaf(name, trajectory, hdf5_group)

                    # Add the instance to the trajectory tree
                    parent_traj_node._add_leaf_from_storage(args=(instance,), kwargs={})

                self._prm_load_parameter_or_result(instance, load_data=load_data,
                                                   _hdf5_group=hdf5_group)
                if as_new:
                    instance._stored = False

            else:
                if in_trajectory:
                    traj_group = parent_traj_node._children[name]

                    if load_data == pypetconstants.OVERWRITE_DATA:
                        traj_group.v_annotations.f_empty()
                        traj_group.v_comment = ''
                else:
                    if HDF5StorageService.CLASS_NAME in hdf5_group._v_attrs:
                        class_name = self._all_get_from_attrs(hdf5_group,
                                                              HDF5StorageService.CLASS_NAME)
                        class_constructor = trajectory._create_class(class_name)
                        instance = trajectory._construct_instance(class_constructor, name)
                        args = (instance,)
                    else:
                        args = (name,)
                    # If the group does not exist create it'
                    traj_group = parent_traj_node._add_group_from_storage(args=args, kwargs={})

                # Load annotations and comment
                self._grp_load_group(traj_group, load_data=load_data, with_links=with_links,
                                     recursive=False, max_depth=max_depth,
                                     _traj=trajectory, _as_new=as_new,
                                     _hdf5_group=hdf5_group)

                if recursive and current_depth < max_depth:
                    new_depth = current_depth + 1
                    for children in (hdf5_group._v_groups, hdf5_group._v_links):
                        for new_hdf5_group_name in children:
                            new_hdf5_group = children[new_hdf5_group_name]
                            loading_list.append((traj_group, new_depth, new_hdf5_group))
def _tree_load_nodes_dfs ( self , parent_traj_node , load_data , with_links , recursive , max_depth , current_depth , trajectory , as_new , hdf5_group ) : if max_depth is None : max_depth = float ( 'inf' ) loading_list = [ ( parent_traj_node , current_depth , hdf5_group ) ] while loading_list : parent_traj_node , current_depth , hdf5_group = loading_list . pop ( ) if isinstance ( hdf5_group , pt . link . SoftLink ) : if with_links : # We end up here when auto-loading a soft link self . _tree_load_link ( parent_traj_node , load_data = load_data , traj = trajectory , as_new = as_new , hdf5_soft_link = hdf5_group ) continue name = hdf5_group . _v_name is_leaf = self . _all_get_from_attrs ( hdf5_group , HDF5StorageService . LEAF ) in_trajectory = name in parent_traj_node . _children if is_leaf : # In case we have a leaf node, we need to check if we have to create a new # parameter or result if in_trajectory : instance = parent_traj_node . _children [ name ] # Otherwise we need to create a new instance else : instance = self . _tree_create_leaf ( name , trajectory , hdf5_group ) # Add the instance to the trajectory tree parent_traj_node . _add_leaf_from_storage ( args = ( instance , ) , kwargs = { } ) self . _prm_load_parameter_or_result ( instance , load_data = load_data , _hdf5_group = hdf5_group ) if as_new : instance . _stored = False else : if in_trajectory : traj_group = parent_traj_node . _children [ name ] if load_data == pypetconstants . OVERWRITE_DATA : traj_group . v_annotations . f_empty ( ) traj_group . v_comment = '' else : if HDF5StorageService . CLASS_NAME in hdf5_group . _v_attrs : class_name = self . _all_get_from_attrs ( hdf5_group , HDF5StorageService . CLASS_NAME ) class_constructor = trajectory . _create_class ( class_name ) instance = trajectory . _construct_instance ( class_constructor , name ) args = ( instance , ) else : args = ( name , ) # If the group does not exist create it' traj_group = parent_traj_node . _add_group_from_storage ( args = args , kwargs = { } ) # Load annotations and comment self . _grp_load_group ( traj_group , load_data = load_data , with_links = with_links , recursive = False , max_depth = max_depth , _traj = trajectory , _as_new = as_new , _hdf5_group = hdf5_group ) if recursive and current_depth < max_depth : new_depth = current_depth + 1 for children in ( hdf5_group . _v_groups , hdf5_group . _v_links ) : for new_hdf5_group_name in children : new_hdf5_group = children [ new_hdf5_group_name ] loading_list . append ( ( traj_group , new_depth , new_hdf5_group ) )
Loads a node from hdf5 file and if desired recursively everything below

        :param parent_traj_node: The parent node whose child should be loaded
        :param load_data: How to load the data
        :param with_links: If links should be loaded
        :param recursive: Whether loading recursively below hdf5_group
        :param max_depth: Maximum depth
        :param current_depth: Current depth
        :param trajectory: The trajectory object
        :param as_new: If trajectory is loaded as new
        :param hdf5_group: The hdf5 group containing the child to be loaded
Loads a node from hdf5 file and if desired recursively everything below