How to use the pypet.pypetconstants.LOAD_SKELETON function in pypet

def f_load(self,
             name=None,
             index = None,
             as_new=False,
             load_parameters=pypetconstants.LOAD_DATA,
             load_derived_parameters=pypetconstants.LOAD_SKELETON,
             load_results=pypetconstants.LOAD_SKELETON,
             load_other_data=pypetconstants.LOAD_SKELETON,
             force=False):
        """Loads a trajectory via the storage service.


        If you want to load individual results or parameters manually, you can take
        a look at :func:`~pypet.trajectory.Trajectory.f_load_items`.
        To only load subtrees check out :func:`~pypet.naturalnaming.NNGroupNode.f_load_child`.


        For `f_load` you can pass the following arguments:

        :param name:

            Name of the trajectory to be loaded. If no name or index is specified

def f_load(self,
             name=None,
             index = None,
             as_new=False,
             load_parameters=pypetconstants.LOAD_DATA,
             load_derived_parameters=pypetconstants.LOAD_SKELETON,
             load_results=pypetconstants.LOAD_SKELETON,
             load_other_data=pypetconstants.LOAD_SKELETON,
             force=False):
        """Loads a trajectory via the storage service.


        If you want to load individual results or parameters manually, you can take
        a look at :func:`~pypet.trajectory.Trajectory.f_load_items`.
        To only load subtrees check out :func:`~pypet.naturalnaming.NNGroupNode.f_load_child`.


        For `f_load` you can pass the following arguments:

        :param name:

            Name of the trajectory to be loaded. If no name or index is specified
            the current name of the trajectory is used.

def f_update_skeleton(self):
        """Loads the full skeleton from the storage service.

        This needs to be done after a successful exploration in order to update the
        trajectory tree with all results and derived parameters from the individual single runs.
        This will only add empty results and derived parameters (i.e. the skeleton)
        and load annotations.

        """
        self.f_load(self.v_name,None, False,
                    load_parameters=pypetconstants.LOAD_SKELETON,
                    load_derived_parameters=pypetconstants.LOAD_SKELETON,
                    load_results=pypetconstants.LOAD_SKELETON,
                    load_other_data=pypetconstants.LOAD_SKELETON)

def f_update_skeleton(self):
        """Loads the full skeleton from the storage service.

        This needs to be done after a successful exploration in order to update the
        trajectory tree with all results and derived parameters from the individual single runs.
        This will only add empty results and derived parameters (i.e. the skeleton)
        and load annotations.

        """
        self.f_load(self.v_name,None, False,
                    load_parameters=pypetconstants.LOAD_SKELETON,
                    load_derived_parameters=pypetconstants.LOAD_SKELETON,
                    load_results=pypetconstants.LOAD_SKELETON,
                    load_other_data=pypetconstants.LOAD_SKELETON)

def f_update_skeleton(self):
        """Loads the full skeleton from the storage service.

        This needs to be done after a successful exploration in order to update the
        trajectory tree with all results and derived parameters from the individual single runs.
        This will only add empty results and derived parameters (i.e. the skeleton)
        and load annotations.

        """
        self.f_load(self.v_name,None, False,
                    load_parameters=pypetconstants.LOAD_SKELETON,
                    load_derived_parameters=pypetconstants.LOAD_SKELETON,
                    load_results=pypetconstants.LOAD_SKELETON,
                    load_other_data=pypetconstants.LOAD_SKELETON)

def f_load(self,
             name=None,
             index = None,
             as_new=False,
             load_parameters=pypetconstants.LOAD_DATA,
             load_derived_parameters=pypetconstants.LOAD_SKELETON,
             load_results=pypetconstants.LOAD_SKELETON,
             load_other_data=pypetconstants.LOAD_SKELETON,
             force=False):
        """Loads a trajectory via the storage service.


        If you want to load individual results or parameters manually, you can take
        a look at :func:`~pypet.trajectory.Trajectory.f_load_items`.
        To only load subtrees check out :func:`~pypet.naturalnaming.NNGroupNode.f_load_child`.


        For `f_load` you can pass the following arguments:

        :param name:

            Name of the trajectory to be loaded. If no name or index is specified
            the current name of the trajectory is used.

traj.huge_matrices.f_set(monty='Always look on the bright side of life!')

# Next we can store our new string called monty to disk. Since our trajectory was already
# stored to disk once, we can make use of the functionality to store individual items:
traj.f_store_item('huge_matrices')

# Neat, hu? Ok now let's load some of it back, for educational purposes let's start with a fresh
# trajectory. Let's keep the old trajectory name in mind. The current time is added to the
# trajectory name on creation (if you do not want this, just say `add_time=False`).
# Thus, the name is not `example_09_huge_data`, but `example_09_huge_data_XXXX_XX_XX_XXhXXmXXs`:
old_traj_name = traj.v_name
del traj
traj = Trajectory(filename=filename)

# We only want to load the skeleton but not the data:
traj.f_load(name=old_traj_name, load_results=pypetconstants.LOAD_SKELETON)

# Check if we only loaded the skeleton, that means the `huge_matrices` result must be empty:
if traj.huge_matrices.f_is_empty():
    print('Told you!')
else:
    print('Unbelievable, this sucks!')

# Now let's only load `monty` and `mat1`.
# We can do this by passing the keyword argument `load_only` to the load item function:
traj.f_load_item('huge_matrices', load_only=['monty','mat1'])

# Check if this worked:
if ('monty' in traj.huge_matrices and
     'mat1' in traj.huge_matrices and
      not 'mat2' in traj.huge_matrices ):

def f_update_skeleton(self):
        """Loads the full skeleton from the storage service.

        This needs to be done after a successful exploration in order to update the
        trajectory tree with all results and derived parameters from the individual single runs.
        This will only add empty results and derived parameters (i.e. the skeleton)
        and load annotations.

        """
        self.f_load(self.v_name,None, False,
                    load_parameters=pypetconstants.LOAD_SKELETON,
                    load_derived_parameters=pypetconstants.LOAD_SKELETON,
                    load_results=pypetconstants.LOAD_SKELETON,
                    load_other_data=pypetconstants.LOAD_SKELETON)

if load_data in [pypetconstants.LOAD_DATA, pypetconstants.UPDATE_DATA]:
                self._prm_load_parameter_or_result(instance,_hdf5_group=hdf5group)
        else:

            if not name in parent_traj_node._children:
                new_traj_node = parent_traj_node._nn_interface._add_from_group_name(
                                                                            parent_traj_node, name)
                newly_created = True
            else:
                new_traj_node = parent_traj_node._children[name]
                newly_created=False

            if (load_data in [pypetconstants.LOAD_DATA, pypetconstants.LOAD_SKELETON] or
                                            newly_created):

                self._ann_load_annotations(new_traj_node,node=hdf5group)

            if recursive:
                try:
                    for new_hdf5group in hdf5group._f_iter_nodes(classname='Group'):
                        self._tree_load_recursively(traj,new_traj_node,new_hdf5group,load_data)
                except AttributeError:
                    for new_hdf5group in hdf5group._f_iterNodes(classname='Group'):
                        self._tree_load_recursively(traj,new_traj_node,new_hdf5group,load_data)