class Parameter(BaseParameter):
    """The standard container that handles access to simulation parameters.

    Parameters are simple container objects for data values. They handle single values as well as
    the so called exploration range. An array containing multiple values which are accessed
    one after the other in individual simulation runs.

    Parameter exploration is usually initiated through the trajectory see
    `:func:~pypet.trajectory.Trajectory.f_explore` and
    `:func:~pypet.trajectory.Trajectory.f_expand`.

    To access the parameter's data value one can call the :func:`~pypet.parameter.Parameter.f_get`
    method.

    Parameters support the concept of locking. Once a value of the parameter has been accessed,
    the parameter cannot be changed anymore unless it is explicitly unlocked using
    :func:`~pypet.parameter.Parameter.f_unlock`.
    Locking prevents parameters from being changed during runtime of a simulation.

    Supported data values for the parameter are

    * python natives (int, long, str, bool, float, complex),

    * numpy natives, arrays and matrices of type
      np.int8-64, np.uint8-64, np.float32-64, np.complex, np.str

    * python homogeneous non-nested tuples and lists

    Note that for larger numpy arrays it is recommended to use the
    :class:`~pypet.parameter.ArrayParameter`.


    In case you create a new parameter you can pass the following arguments:

    :param full_name: The full name of the parameter. Grouping can be achieved by using colons.

    :param data:

        A data value that is handled by the parameter. It is checked whether the parameter
        :func:`~pypet.parameter.Parameter.f_supports` the data. If not a TypeError is thrown.
        If the parameter becomes explored, the data value is kept as a default. After
        simulation the default value will be restored.

        The data can be accessed as follows:

        >>> param.f_get()
        42

        Or using
        >>> param.data
        42

        [It is not `v_data` because the data is supposed to be part of the trajectory tree
        or extension of the natural naming scheme and not considered as an
        attribute/variable of the parameter container.]

        To change the data after parameter creation one can call
        :func:`~pypet.parameter.Parameter.f_set`:

        >>> param.f_set(43)
        >>> param.f_get()
        43

    :param comment:

        A useful comment describing the parameter.
        The comment can be changed later on using the 'v_comment' variable.

        >>> param.v_comment = 'Example comment'
        >>> print param.v_comment
        'Example comment'

    :raises: TypeError: If `data` is not supported by the parameter.

    Example usage:

    >>> param = Parameter('traffic.mobiles.ncars',data=42, comment='I am a neat example')

    """

    __slots__ = ("_data", "_default", "_explored_range")

    def __init__(self, full_name, data=None, comment=""):
        super(Parameter, self).__init__(full_name, comment)
        self._data = None

        self._default = (
            None  # The default value, which is the same as _data in the beginning,
        )
        # but it is necessary to keep a reference to it to restore the original value
        # after exploration

        self._explored_range = (
            []
        )  # List that will changed later on if parameter is explored
        self._set_logger()

        if data is not None:
            self.f_set(data)

    def _restore_default(self):
        """Restores the default data that was set with the
        `:func:`~pypet.parameter.Parameter.f_set` method (or at initialisation).

        If the parameter is explored during the runtime of a simulation,
        the actual value of the parameter is changed and taken from the exploration range.
        Calling :func:`~pypet.parameter.Parameter._restore_default` sets the parameter's value
        back to it's original value.

        Example usage:

        >>> param = Parameter('supergroup1.subgroup2.', data=44, comment='Im a comment!')
        >>> param._explore([1,2,3,4])
        >>> param._set_parameter_access(2)
        >>> param.f_get()
        3
        >>> param._restore_default()
        >>> param.f_get()
        44

        """
        self._data = self._default

    @copydoc(BaseParameter.f_is_empty)
    def f_is_empty(self):
        """True if no data has been assigned to the parameter.

        Example usage:

        >>> param = Parameter('myname.is.example', comment='I am _empty!')
        >>> param.f_is_empty()
        True
        >>> param.f_set(444)
        >>> param.f_is_empty()
        False

        """
        return self._data is None

    def __len__(self):
        """Returns length of parameter.

        :return:

            0 if empty

            1 if not explored

            length of range if explored and has range

        """
        if self._data is None:
            return 0
        elif len(self._explored_range) > 0:
            return len(self._explored_range)
        else:
            return 1

    def f_has_range(self):
        """If the parameter has a range.

        Does not have to be `True` if the parameter is explored.
        The range might be removed during pickling to save memory.
        Accordingly, `v_explored` remains `True` whereas `f_has_range` is `False`.

        """
        return len(self._explored_range) > 0

    def __getstate__(self):
        """Returns the actual state of the parameter for pickling.

        If `v_full_copy` is true the exploration range is also pickled, otherwise it is omitted.

        """
        result = super(Parameter, self).__getstate__()

        # If we don't need a full copy of the Parameter (because a single process needs
        # only access to a single point in the parameter space) we can delete the rest
        if not self._full_copy:
            result["_explored_range"] = []

        return result

    # @no_prefix_getattr
    def __getattr__(self, item):
        """Allows to query for `.data` as an attribute"""
        if item == "data":
            return self.f_get()
        elif item == "default":
            return self.f_get_default()
        else:
            raise AttributeError(
                "`%s` object has no attribute `%s`." % (self.f_get_class_name(), item)
            )

    def __getitem__(self, item):
        """Equivalent to `f_get_range[item] if item is integer`

        Allows also ['data'] access which is equivalent to `f_get`

        :raises: TypeError if parameter has no range

        """
        if item == "data":
            return self.f_get()
        elif item == "default" or item == -1:
            return self.f_get_default()
        else:
            return super(Parameter, self).__getitem__(item)

    @copydoc(BaseParameter._set_parameter_access)
    def _set_parameter_access(self, idx=0):
        if idx >= len(self) and self.f_has_range():
            raise ValueError(
                "You try to access data item No. %d in the parameter range, "
                "yet there are only %d potential items." % (idx, len(self))
            )
        elif self.f_has_range():
            self._data = self._explored_range[idx]
        else:
            self._logger.warning(
                "You try to change the access to a parameter range of parameter"
                " `%s`. The parameter has no range, your setting has no"
                " effect."
            )

    def f_supports(self, data):
        """Checks if input data is supported by the parameter."""
        dtype = type(data)
        if dtype is tuple or dtype is list:

            # Parameters cannot handle empty tuples
            if len(data) == 0:
                return False

            old_type = None

            # Check if the data in the tuple is homogeneous
            for item in data:
                if not type(item) in pypetconstants.PARAMETER_SUPPORTED_DATA:
                    return False
                if not old_type is None and old_type != type(item):
                    return False
                old_type = type(item)
            return True

        elif dtype is np.ndarray or dtype is np.matrix:

            if data.size == 0:
                return False  # Parameters cannot handle empty arrays and matrices

            # Numpy has many string types that depend on the length of the string,
            # We allow all of them
            dtype = data.dtype
            if np.issubdtype(dtype, np.str):
                dtype = np.str

        return dtype in pypetconstants.PARAMETER_SUPPORTED_DATA

    def _values_of_same_type(self, val1, val2):
        """Checks if two values agree in type.

        Raises a TypeError if both values are not supported by the parameter.
        Returns false if only one of the two values is supported by the parameter.

        Example usage:

        >>>param._values_of_same_type(42,43)
        True

        >>>param._values_of_same_type(42,'43')
        False

        :raises: TypeError

        """
        if self.f_supports(val1) != self.f_supports(val2):
            return False

        if not self.f_supports(val1) and not self.f_supports(val2):
            raise TypeError(
                "I do not support the types of both inputs (`%s` and `%s`),"
                " therefore I cannot judge whether the two are of same type."
                % str(type(val1)),
                str(type(val2)),
            )

        if not type(val1) is type(val2):
            return False

        # Numpy arrays must agree in data type and shape
        if type(val1) is np.array:
            if not val1.dtype is val2.dtype:
                return False

            if not np.shape(val1) == np.shape(val2):
                return False

        # For tuples we now from earlier checks that the data is homogeneous.
        # Thus, only the type of the first item and the length must agree.
        if type(val1) is tuple:
            return (type(val1[0]) is type(val2[0])) and (len(val1) == len(val2))

        return True

    @copydoc(BaseParameter.f_set)
    def f_set(self, data):

        if self.v_locked:
            raise pex.ParameterLockedException(
                "Parameter `%s` is locked!" % self._full_name
            )

        if self.f_has_range():
            raise AttributeError(
                "Your Parameter is an explored array can no longer change values!"
            )

        if self.v_stored:
            self._logger.debug(
                "You are changing an already stored parameter. If "
                "you not explicitly overwrite the data on disk, this change "
                "might be lost and not propagated to disk."
            )

        if not self.f_supports(data):
            raise TypeError(
                "Unsupported data `%s` of type `%s`. "
                "If you passed a tuple or list, this error might also be caused "
                "by heterogeneous data within the "
                "tuple or list." % (str(data), str(type(data)))
            )

        self._data = data
        self._default = self._data

    @copydoc(BaseParameter.f_get_default)
    def f_get_default(self):
        if self.f_is_empty():
            raise TypeError(
                "Parameter `%s` is empty cannot access data" % self.v_full_name
            )

        self.f_lock()  # As soon as someone accesses an entry the parameter gets locked
        return self._default

    def f_get_range(self, copy=True):
        """Returns a python iterable containing the exploration range.

        :param copy:

            If the range should be copied before handed over to avoid tempering with data

        Example usage:

        >>> param = Parameter('groupA.groupB.myparam',data=22, comment='I am a neat example')
        >>> param._explore([42,43,43])
        >>> param.f_get_range()
        (42,43,44)

        :raises: TypeError: If parameter is not explored.

        """
        if not self.f_has_range():
            raise TypeError(
                "Your parameter `%s` is not array, so cannot return array."
                % self.v_full_name
            )
        elif copy:
            return self._explored_range[:]
        else:
            return self._explored_range

    def _explore(self, explore_iterable):
        """Explores the parameter according to the iterable.

        Raises ParameterLockedException if the parameter is locked.
        Raises TypeError if the parameter does not support the data,
        the types of the data in the iterable are not the same as the type of the default value,
        or the parameter has already an exploration range.

        Note that the parameter will iterate over the whole iterable once and store
        the individual data values into a tuple. Thus, the whole exploration range is
        explicitly stored in memory.

        :param explore_iterable: An iterable specifying the exploration range

        For example:

        >>> param._explore([3.0,2.0,1.0])
        >>> param.f_get_range()
        (3.0, 2.0, 1.0)

        :raises TypeError,ParameterLockedException

        """
        if self.v_locked:
            raise pex.ParameterLockedException(
                "Parameter `%s` is locked!" % self.v_full_name
            )

        if self.f_has_range():
            raise TypeError(
                "Your parameter `%s` is already explored, "
                "cannot _explore it further!" % self._name
            )

        if self._data is None:
            raise TypeError(
                "Your parameter `%s` has no default value, please specify one "
                "via `f_set` before exploration. " % self.v_full_name
            )

        data_list = self._data_sanity_checks(explore_iterable)

        self._explored_range = data_list
        self._explored = True
        self.f_lock()

    def _expand(self, explore_iterable):
        """Explores the parameter according to the iterable and appends to the exploration range.

        Raises ParameterLockedException if the parameter is locked.
        Raises TypeError if the parameter does not support the data,
        the types of the data in the iterable are not the same as the type of the default value,
        or the parameter did not have an array before.

        Note that the parameter will iterate over the whole iterable once and store
        the individual data values into a tuple. Thus, the whole exploration range is
        explicitly stored in memory.

        :param explore_iterable: An iterable specifying the exploration range

         For example:

         >>> param = Parameter('Im.an.example', data=33.33, comment='Wooohoo!')
         >>> param._explore([3.0,2.0,1.0])
         >>> param._expand([42.0, 43.42])
         >>> param.f_get_range()
         >>> [3.0, 2.0, 1.0, 42.0, 43.42]

        :raises TypeError, ParameterLockedException

        """
        if self.v_locked:
            raise pex.ParameterLockedException(
                "Parameter `%s` is locked!" % self.v_full_name
            )

        if not self.f_has_range():
            raise TypeError(
                "Your Parameter `%s` is not an array and can therefore "
                "not be expanded." % self._name
            )

        data_list = self._data_sanity_checks(explore_iterable)

        self._explored_range.extend(data_list)
        self.f_lock()

    def _data_sanity_checks(self, explore_iterable):
        """Checks if data values are  valid.

        Checks if the data values are supported by the parameter and if the values are of the same
        type as the default value.

        """
        data_list = []

        for val in explore_iterable:

            if not self.f_supports(val):
                raise TypeError(
                    "%s is of not supported type %s." % (repr(val), str(type(val)))
                )

            if not self._values_of_same_type(val, self._default):
                raise TypeError(
                    "Data of `%s` is not of the same type as the original entry value, "
                    "new type is %s vs old type %s."
                    % (self.v_full_name, str(type(val)), str(type(self._default)))
                )

            data_list.append(val)

        if len(data_list) == 0:
            raise ValueError("Cannot explore an empty list!")

        return data_list

    def _store(self):
        """Returns a dictionary of formatted data understood by the storage service.

        The data is put into an :class:`~pypet.parameter.ObjectTable` named 'data'.
        If the parameter is explored, the exploration range is also put into another table
        named 'explored_data'.

        :return: Dictionary containing the data and optionally the exploration range.

        """
        if self._data is not None:
            store_dict = {"data": ObjectTable(data={"data": [self._data]})}

        if self.f_has_range():
            store_dict["explored_data"] = ObjectTable(
                data={"data": self._explored_range}
            )

        self._locked = True

        return store_dict

    def _load(self, load_dict):
        """Loads the data and exploration range from the `load_dict`.

        The `load_dict` needs to be in the same format as the result of the
        :func:`~pypet.parameter.Parameter._store` method.

        """
        if self.v_locked:
            raise pex.ParameterLockedException(
                "Parameter `%s` is locked!" % self.v_full_name
            )

        if "data" in load_dict:
            self._data = load_dict["data"]["data"][0]
            self._default = self._data
        else:
            self._logger.warning(
                "Your parameter `%s` is empty, "
                "I did not find any data on disk." % self.v_full_name
            )

        if "explored_data" in load_dict:
            self._explored_range = [
                x for x in load_dict["explored_data"]["data"].tolist()
            ]
            self._explored = True

        self._locked = True

    @copydoc(BaseParameter.f_get)
    def f_get(self):
        if self.f_is_empty():
            raise TypeError(
                "Parameter `%s` is empty cannot access data" % self.v_full_name
            )

        self.f_lock()  # As soon as someone accesses an entry the parameter gets locked
        return self._data

    @copydoc(BaseParameter._shrink)
    def _shrink(self):

        if self.v_locked:
            raise pex.ParameterLockedException(
                "Parameter %s is locked!" % self.v_full_name
            )

        if not self.f_has_range():
            raise TypeError("Cannot shrink Parameter without a range.")

        if self.f_is_empty():
            raise TypeError("Cannot shrink empty Parameter.")

        del self._explored_range
        self._explored_range = []
        self._explored = False

    @copydoc(BaseParameter.f_empty)
    def f_empty(self):

        if self.v_locked:
            raise pex.ParameterLockedException(
                "Parameter %s is locked!" % self.v_full_name
            )

        if self.f_has_range():
            self._shrink()

        del self._data
        del self._default
        self._data = None
        self._default = None
        self._explored = False class PickleParameter(Parameter):
    """A parameter class that supports all picklable objects, and pickles everything!

    If you use the default HDF5 storage service, the pickle dumps are stored to disk.
    Works similar to the array parameter regarding memory management (Equality of objects
    is based on object id).

    There is no straightforward check to guarantee that data is picklable, so you have to
    take care that all data handled by the PickleParameter supports pickling.

    You can pass the pickle protocol via `protocol=2` to the constructor or change it with
    the `v_protocol` property. Default protocol is 0.
    Note that after storage to disk changing the protocol has no effect.
    If the parameter is loaded, `v_protocol` is set to the protocol used to store the data.

    """

    PROTOCOL = "__pckl_prtcl__"

    __slots__ = ("_protocol",)

    def __init__(self, full_name, data=None, comment="", protocol=2):
        super(PickleParameter, self).__init__(full_name, data, comment)
        self._protocol = None
        self.v_protocol = protocol

    @property
    def v_protocol(self):
        """The protocol used to pickle data, default is 0.

        See pickle_ documentation for the protocols.

        .. _pickle: http://docs.python.org/2/library/pickle.html

        """
        return self._protocol

    @v_protocol.setter
    def v_protocol(self, value):
        """Sets the protocol"""
        self._protocol = value

    def f_supports(self, data):
        """There is no straightforward check if an object can be pickled and this function will
        always return `True`.

        So you have to take care in advance that the item can be pickled.

        """
        return True

    @staticmethod
    def _build_name(name_id):
        """Formats names for storage

        Explored data is stored as 'xp_XXXXXXXX' where 'XXXXXXXX' is the index of the object.

        """
        return "xp_%08d" % name_id

    def _store(self):
        """Returns a dictionary for storage.

        Every element in the dictionary except for 'explored_data' is a pickle dump.

        Reusage of objects is identified over the object id, i.e. python's built-in id function.

        'explored_data' contains the references to the objects to be able to recall the
        order of objects later on.

        """
        store_dict = {}

        if self._data is not None:
            dump = pickle.dumps(self._data, protocol=self.v_protocol)
            store_dict["data"] = dump
            store_dict[PickleParameter.PROTOCOL] = self.v_protocol

        if self.f_has_range():

            store_dict["explored_data"] = ObjectTable(
                columns=["idx"], index=list(range(len(self)))
            )

            smart_dict = {}
            count = 0

            for idx, val in enumerate(self._explored_range):

                obj_id = id(val)

                if obj_id in smart_dict:
                    name_id = smart_dict[obj_id]
                    add = False
                else:
                    name_id = count
                    add = True

                name = self._build_name(name_id)
                store_dict["explored_data"]["idx"][idx] = name_id

                if add:
                    store_dict[name] = pickle.dumps(val, protocol=self.v_protocol)
                    smart_dict[obj_id] = name_id
                    count += 1

        self._locked = True

        return store_dict

    @staticmethod
    def _get_protocol(dump):
        pops = pickletools.genops(dump)
        proto = (
            2
            if next(pops)[0].proto == 2
            else int(any(op.proto for op, fst, snd in pops))
        )
        return proto

    def _load(self, load_dict):
        """Reconstructs objects from the pickle dumps in `load_dict`.

        The 'explored_data' entry in `load_dict` is used to reconstruct
        the exploration range in the correct order.

        Sets the `v_protocol` property to the protocol used to store 'data'.

        """
        if self.v_locked:
            raise pex.ParameterLockedException(
                "Parameter `%s` is locked!" % self.v_full_name
            )

        if "data" in load_dict:
            dump = load_dict["data"]
            self._data = pickle.loads(dump)
        else:
            self._logger.warning(
                "Your parameter `%s` is empty, "
                "I did not find any data on disk." % self.v_full_name
            )

        try:
            self.v_protocol = load_dict[PickleParameter.PROTOCOL]
        except KeyError:
            # For backwards compatibility
            self.v_protocol = PickleParameter._get_protocol(dump)

        if "explored_data" in load_dict:
            explore_table = load_dict["explored_data"]

            name_col = explore_table["idx"]

            explore_list = []
            for name_id in name_col:
                arrayname = self._build_name(name_id)
                loaded = pickle.loads(load_dict[arrayname])
                explore_list.append(loaded)

            self._explored_range = explore_list
            self._explored = True

        self._default = self._data
        self._locked = True class ArrayParameter(Parameter):
    """Similar to the :class:`~pypet.parameter.Parameter`, but recommended for
    large numpy arrays and python tuples.

    The array parameter is a bit smarter in memory management than the parameter.
    If a numpy array is used several times within an exploration, only one numpy array is stored by
    the default HDF5 storage service. For each individual run references to the corresponding
    numpy array are stored.

    Since the ArrayParameter inherits from :class:`~pypet.parameter.Parameter` it also
    supports all other native python types.

    """

    __slots__ = ()

    IDENTIFIER = "__rr__"
    """Identifier to mark stored data as an array"""

    def _store(self):
        """Creates a storage dictionary for the storage service.

        If the data is not a numpy array, a numpy matrix, or a tuple, the
        :func:`~pypet.parameter.Parmater._store` method of the parent class is called.

        Otherwise the array is put into the dictionary with the key 'data__rr__'.

        Each array of the exploration range is stored as a separate entry named
        'xa__rr__XXXXXXXX' where 'XXXXXXXX' is the index of the array. Note if an array
        is used more than once in an exploration range (for example, due to cartesian product
        exploration), the array is stored only once.
        Moreover, an :class:`~pypet.parameter.ObjectTable` containing the references
        is stored under the name 'explored_data__rr__' in order to recall
        the order of the arrays later on.

        """
        if type(self._data) not in (np.ndarray, tuple, np.matrix, list):
            return super(ArrayParameter, self)._store()
        else:
            store_dict = {"data" + ArrayParameter.IDENTIFIER: self._data}

            if self.f_has_range():
                # Supports smart storage by hashable arrays
                # Keys are the hashable arrays or tuples and values are the indices
                smart_dict = {}

                store_dict["explored_data" + ArrayParameter.IDENTIFIER] = ObjectTable(
                    columns=["idx"], index=list(range(len(self)))
                )

                count = 0
                for idx, elem in enumerate(self._explored_range):

                    # First we need to distinguish between tuples and array and extract a
                    # hashable part of the array
                    if isinstance(elem, np.ndarray):
                        # You cannot hash numpy arrays themselves, but if they are read only
                        # you can hash array.data
                        hash_elem = HashArray(elem)
                    elif isinstance(elem, list):
                        hash_elem = tuple(elem)
                    else:
                        hash_elem = elem

                    # Check if we have used the array before,
                    # i.e. element can be found in the dictionary
                    if hash_elem in smart_dict:
                        name_idx = smart_dict[hash_elem]
                        add = False
                    else:
                        name_idx = count
                        add = True

                    name = self._build_name(name_idx)
                    # Store the reference to the array
                    store_dict["explored_data" + ArrayParameter.IDENTIFIER]["idx"][
                        idx
                    ] = name_idx

                    # Only if the array was not encountered before,
                    # store the array and remember the index
                    if add:
                        store_dict[name] = elem
                        smart_dict[hash_elem] = name_idx
                        count += 1

            self._locked = True

            return store_dict

    @staticmethod
    def _build_name(name_idx):
        """Formats a name for storage

        :return:

            'xa__rr__XXXXXXXX' where 'XXXXXXXX' is the index of the array

        """
        return "explored%s.set_%05d.xa_%08d" % (
            ArrayParameter.IDENTIFIER,
            name_idx // 1000,
            name_idx,
        )

    def _load(self, load_dict):
        """Reconstructs the data and exploration array.

        Checks if it can find the array identifier in the `load_dict`, i.e. '__rr__'.
        If not calls :class:`~pypet.parameter.Parameter._load` of the parent class.

        If the parameter is explored, the exploration range of arrays is reconstructed
        as it was stored in :func:`~pypet.parameter.ArrayParameter._store`.

        """
        if self.v_locked:
            raise pex.ParameterLockedException(
                "Parameter `%s` is locked!" % self.v_full_name
            )

        try:
            self._data = load_dict["data" + ArrayParameter.IDENTIFIER]

            if "explored_data" + ArrayParameter.IDENTIFIER in load_dict:
                explore_table = load_dict["explored_data" + ArrayParameter.IDENTIFIER]

                idx = explore_table["idx"]

                explore_list = []

                # Recall the arrays in the order stored in the ObjectTable 'explored_data__rr__'
                for name_idx in idx:
                    arrayname = self._build_name(name_idx)
                    explore_list.append(load_dict[arrayname])

                self._explored_range = [x for x in explore_list]
                self._explored = True

        except KeyError:
            super(ArrayParameter, self)._load(load_dict)

        self._default = self._data
        self._locked = True

    def _values_of_same_type(self, val1, val2):
        """Checks if two values agree in type.

        The array parameter is less restrictive than the parameter. If both values
        are arrays, matrices or tuples, they are considered to be of same type
        regardless of their size and values they contain.

        """
        if (type(val1) in (np.ndarray, tuple, np.matrix)) and (
            type(val2) is type(val1)
        ):
            return True
        else:
            return super(ArrayParameter, self)._values_of_same_type(val1, val2)

    def f_supports(self, data):
        """Checks if input data is supported by the parameter."""
        dtype = type(data)
        if dtype is tuple or dtype is list and len(data) == 0:
            return True  #  ArrayParameter does support empty tuples
        elif dtype is np.ndarray and data.size == 0 and data.ndim == 1:
            return True  #  ArrayParameter supports empty numpy arrays
        else:
            return super(ArrayParameter, self).f_supports(data) class SparseParameter(ArrayParameter):
    """Parameter that handles Scipy csr, csc, bsr and dia sparse matrices.

    Sparse Parameter inherits from :class:`~pypet.parameter.ArrayParameter` and supports
    arrays and native python data as well.

    Uses similar memory management as its parent class.

    """

    IDENTIFIER = "__spsp__"
    """Identifier to mark stored data as a sparse matrix"""

    __slots__ = ()

    def _values_of_same_type(self, val1, val2):
        """Checks if two values agree in type.

        The sparse parameter is less restrictive than the parameter. If both values
        are sparse matrices they are considered to be of same type
        regardless of their size and values they contain.

        """
        if self._is_supported_matrix(val1) and self._is_supported_matrix(val2):
            return True
        else:
            return super(SparseParameter, self)._values_of_same_type(val1, val2)

    def _equal_values(self, val1, val2):
        """Matrices are equal if they serialize to the same value."""
        if self._is_supported_matrix(val1):
            if self._is_supported_matrix(val2):

                serial_string1 = self._serialize_matrix(val1)
                serial_string2 = self._serialize_matrix(val2)

                return serial_string1 == serial_string2
            else:
                return False
        else:
            return super(SparseParameter, self)._equal_values(val1, val2)

    @staticmethod
    def _is_supported_matrix(data):
        """Checks if a data is csr, csc, bsr, or dia Scipy sparse matrix"""
        return (
            spsp.isspmatrix_csc(data)
            or spsp.isspmatrix_csr(data)
            or spsp.isspmatrix_bsr(data)
            or spsp.isspmatrix_dia(data)
        )

    def f_supports(self, data):
        """Sparse matrices support Scipy csr, csc, bsr and dia matrices and everything their parent
        class the :class:`~pypet.parameter.ArrayParameter` supports.

        """
        if self._is_supported_matrix(data):
            return True
        else:
            return super(SparseParameter, self).f_supports(data)

    @staticmethod
    def _serialize_matrix(matrix):
        """Extracts data from a sparse matrix to make it serializable in a hex string.

        :return: Serialization string for npz in hexadecimal

        """

        f = BytesIO()
        spsp.save_npz(f, matrix)
        result = f.getvalue().hex()
        return result

    def _store(self):
        """Creates a storage dictionary for the storage service.

        If the data is not a supported sparse matrix, the
        :func:`~pypet.parameter.ArrayParmater._store` method of the parent class is called.

        Otherwise the matrix is split into parts with
        :func:`~pypet.parameter.SparseParameter._serialize_matrix` and these are named
        'data__spsp__'.

        The exploration range is handled similar as in the parent class. Yet, the matrices
        are split into the relevant parts and each part is stored as
        'xspm__spsp__XXXX` where 'XXXX' refers to the sparse matrix index.

        The :class:`~pypet.parameter.ObjectTable` `explored_data__spsp__` stores the order
        of the matrices .

        """
        if not self._is_supported_matrix(self._data):
            return super(SparseParameter, self)._store()
        else:
            store_dict = {}
            serial_string = self._serialize_matrix(self._data)
            store_dict["data%s" % SparseParameter.IDENTIFIER] = serial_string

            if self.f_has_range():
                # # Supports smart storage by hashing
                smart_dict = {}

                store_dict["explored_data" + SparseParameter.IDENTIFIER] = ObjectTable(
                    columns=["idx"], index=list(range(len(self)))
                )

                count = 0
                for idx, elem in enumerate(self._explored_range):

                    serial_string = self._serialize_matrix(elem)

                    # Use the hash_tuple as a key for the smart_dict
                    if serial_string in smart_dict:
                        name_idx = smart_dict[serial_string]
                        add = False
                    else:
                        name_idx = count
                        add = True

                    store_dict["explored_data" + SparseParameter.IDENTIFIER]["idx"][
                        idx
                    ] = name_idx

                    if add:

                        store_dict[
                            "xspm%s%08d" % (SparseParameter.IDENTIFIER, name_idx)
                        ] = serial_string

                        smart_dict[serial_string] = name_idx
                        count += 1

            self._locked = True

            return store_dict

    @staticmethod
    def _reconstruct_matrix(serial_string):
        """Reconstructs a matrix from a hex string"""
        from_hex = bytes.fromhex(serial_string)
        f = BytesIO(from_hex)
        matrix = spsp.load_npz(f)
        return matrix

    def _load(self, load_dict):
        """Reconstructs the data and exploration array

        Checks if it can find the array identifier in the `load_dict`, i.e. '__spsp__'.
        If not, calls :class:`~pypet.parameter.ArrayParameter._load` of the parent class.

        If the parameter is explored, the exploration range of matrices is reconstructed
        as it was stored in :func:`~pypet.parameter.SparseParameter._store`.

        """
        if self.v_locked:
            raise pex.ParameterLockedException(
                "Parameter `%s` is locked!" % self.v_full_name
            )

        try:
            serial_string = load_dict["data%s" % SparseParameter.IDENTIFIER]
            self._data = self._reconstruct_matrix(serial_string)

            if "explored_data" + SparseParameter.IDENTIFIER in load_dict:
                explore_table = load_dict["explored_data" + SparseParameter.IDENTIFIER]
                idx_col = explore_table["idx"]
                explore_list = []
                for irun, name_idx in enumerate(idx_col):
                    serial_string = load_dict[
                        "xspm%s%08d" % (SparseParameter.IDENTIFIER, name_idx)
                    ]
                    matrix = self._reconstruct_matrix(serial_string)
                    explore_list.append(matrix)

                self._explored_range = explore_list
                self._explored = True

        except KeyError as e:
            super(SparseParameter, self)._load(load_dict)

        self._default = self._data
        self._locked = True class ObjectTable(DataFrame):
    """Wrapper class for pandas_ DataFrames.

    It creates data frames with `dtype=object`.

    Data stored into an object table preserves its original type when stored to disk.
    For instance, a python int is not automatically converted to a numpy 64 bit integer (np.int64).

    The object table serves as a data structure to hand data to a storage service.

    Example Usage:

    >>> ObjectTable(data={'characters':['Luke', 'Han', 'Spock'], 'Random_Values' :[42,43,44] })

    Creates the following table:

        ======  ==============  ===========
        Index   Random_Values   characters
        ======  ==============  ===========
        0       42              Luke
        1       43              Han
        2       44              Spock
        ======  ==============  ===========

    .. _pandas: http://pandas.pydata.org/

    """

    def __init__(self, data=None, index=None, columns=None, copy=False):
        super(ObjectTable, self).__init__(
            data=data, index=index, columns=columns, dtype=object, copy=copy
        ) class Result(BaseResult):
    """Light Container that stores basic python and numpy data.

    Note that no sanity checks on individual data is made (only on outer data structure)
    and you have to take care, that your data is understood by the storage service.
    It is assumed that results tend to be large and therefore sanity checks would be too expensive.

    Data that can safely be stored into a Result are:

        *   python natives (int, long, str, bool, float, complex),

        *
            numpy natives, arrays and matrices of type
            np.int8-64, np.uint8-64, np.float32-64, np.complex, np.str

        *
            python lists and tuples of the previous types
            (python natives + numpy natives and arrays)
            Lists and tuples are not allowed to be nested and must be
            homogeneous, i.e. only contain data of one particular type.
            Only integers, or only floats, etc.

        *
            python dictionaries of the previous types (not nested!), data can be
            heterogeneous, keys must be strings. For example, one key-value pair
            of string and int and one key-value pair of string and float, and so
            on.

        *   pandas DataFrames_

        *   :class:`~pypet.parameter.ObjectTable`

    .. _DataFrames: http://pandas.pydata.org/pandas-docs/dev/dsintro.html#dataframe

    Note that containers should NOT be empty (like empty dicts or lists) at the time
    they are saved to disk. The standard HDF5 storage service cannot store empty containers!
    The Result emits a warning if you hand over an empty container.

    Data is set on initialisation or with :func:`~pypet.parameter.Result.f_set`

    Example usage:

    >>> res = Result('supergroup.subgroup.myresult', comment='I am a neat example!' \
        [1000,2000], {'a':'b','c':333}, hitchhiker='Arthur Dent')


    In case you create a new result you can pass the following arguments:

    :param fullanme: The fullname of the result, grouping can be achieved by colons,

    :param comment:

        A useful comment describing the result.
        The comment can later on be changed using the `v_comment` variable

        >>> param.v_comment
        'I am a neat example!'

    :param args:

        Data that is handled by the result.
        The first positional argument is stored with the name of the result.
        Following arguments are stored with `name_X` where `X` is the position
        of the argument.

    :param kwargs:

        Data that is handled by the result, it is kept by the result under the names
        specified by the keys of kwargs.

        >>> res.f_get(0)
        [1000,2000]
        >>> res.f_get(1)
        {'a':'b','c':'d'}
        >>> res.f_get('myresult')
        [1000,2000]
        >>> res.f_get('hitchhiker')
        'ArthurDent'
        >>> res.f_get('myresult','hitchhiker')
        ([1000,2000], 'ArthurDent')

        Can be changed or more can be added via :func:`~pypet.parameter.Result.f_set`

        >>> result.f_set('Uno',x='y')
        >>> result.f_get(0)
        'Uno'
        >>> result.f_get('x')
        'y'


        Alternative method to put and retrieve data from the result container is via
        ``__getattr__`` and ``__setattr__``:

        >>> res.ford = 'prefect'
        >>> res.ford
        'prefect'

    :raises: TypeError:

        If the data format in args or kwargs is not known to the result. Checks type of
        outer data structure, i.e. checks if you have a list or dictionary.
        But it does not check on individual values within dicts or lists.

    """

    __slots__ = ("_data_",)

    SUPPORTED_DATA = set(
        (np.ndarray, ObjectTable, DataFrame, Series, dict, tuple, list, np.matrix)
        + pypetconstants.PARAMETER_SUPPORTED_DATA
    )

    def __init__(self, full_name, *args, **kwargs):
        comment = kwargs.pop("comment", "")
        super(Result, self).__init__(full_name, comment)
        self._data_ = None
        self._set_logger()
        self.f_set(*args, **kwargs)

    @property
    def _data(self):
        """To avoid the overhead of producing an empty dictionary"""
        if self._data_ is None:
            self._data_ = {}
        return self._data_

    def __dir__(self):
        """Adds all data to auto-completion"""
        result = super(Result, self).__dir__()
        if self._data_ is not None:
            result.extend(self._data.keys())
        return result

    def f_translate_key(self, key):
        """Translates integer indices into the appropriate names"""
        if isinstance(key, int):
            if key == 0:
                key = self.v_name
            else:
                key = self.v_name + "_%d" % key
        return key

    def __contains__(self, key):
        key = self.f_translate_key(key)
        return key in self._data

    def f_val_to_str(self):
        """Summarizes data handled by the result as a string.

        Calls `__repr__` on all handled data. Data is NOT ordered.

        Truncates the string if it is longer than
        :const:`pypetconstants.HDF5_STRCOL_MAX_VALUE_LENGTH`

        :return: string

        """

        resstrlist = []
        strlen = 0

        for key in self._data:
            val = self._data[key]
            resstr = "%s=%s, " % (key, repr(val))
            resstrlist.append(resstr)

            strlen += len(resstr)
            if strlen > pypetconstants.HDF5_STRCOL_MAX_VALUE_LENGTH:
                break

        return_string = "".join(resstrlist)
        if len(return_string) > pypetconstants.HDF5_STRCOL_MAX_VALUE_LENGTH:
            return_string = (
                return_string[0 : pypetconstants.HDF5_STRCOL_MAX_VALUE_LENGTH - 3]
                + "..."
            )
        else:
            return_string = return_string[0:-2]  # Delete the last `, `

        return return_string

    def __repr__(self):
        return "<%s>" % self.__str__()

    def __str__(self):
        """String representation of the result.

        Output format is '<class_name> name (`comment`): value_string'

        The `value_string` is obtained from :func:`~pypet.parameter.Result.f_val_to_str`.

        If the comment is the empty string, the comment is omitted.

        """
        datastr = self.f_val_to_str()
        return_string = "%s %s" % (self.f_get_class_name(), self.v_full_name)
        if self.v_comment:
            return_string += " (`%s`)" % self.v_comment
        if datastr:
            return_string += ": " + datastr

        return return_string

    def f_to_dict(self, copy=True):
        """Returns all handled data as a dictionary.

        :param copy:

            Whether the original dictionary or a shallow copy is returned.

        :return: Data dictionary

        """
        if copy:
            return self._data.copy()
        else:
            return self._data

    def f_is_empty(self):
        """True if no data has been put into the result.

        Also True if all data has been erased via :func:`~pypet.parameter.Result.f_empty`.

        """
        return len(self._data) == 0

    @copydoc(BaseResult.f_empty)
    def f_empty(self):
        self._data_ = None

    def f_set(self, *args, **kwargs):
        """Method to put data into the result.

        :param args:

            The first positional argument is stored with the name of the result.
            Following arguments are stored with `name_X` where `X` is the position
            of the argument.

        :param kwargs: Arguments are stored with the key as name.

        :raises: TypeError if outer data structure is not understood.

        Example usage:

        >>> res = Result('supergroup.subgroup.myresult', comment='I am a neat example!')
        >>> res.f_set(333,42.0, mystring='String!')
        >>> res.f_get('myresult')
        333
        >>> res.f_get('myresult_1')
        42.0
        >>> res.f_get(1)
        42.0
        >>> res.f_get('mystring')
        'String!'

        """
        if args and self.v_name is None:
            raise AttributeError(
                "Cannot set positional value because I do not have a name!"
            )
        for idx, arg in enumerate(args):
            valstr = self.f_translate_key(idx)
            self.f_set_single(valstr, arg)

        for key, arg in kwargs.items():
            self.f_set_single(key, arg)

    def __getitem__(self, name):
        """ Equivalent to calling `f_get`"""
        return self.f_get(name)

    def __setitem__(self, key, value):
        """Almost equivalent to calling __setattr__.

        Treats integer values as `f_get`.

        """
        key = self.f_translate_key(key)

        setattr(self, key, value)

    def __iter__(self):
        """Equivalent to iterating over the keys of the data dictionary."""
        return self._data.__iter__()

    def f_get(self, *args):
        """Returns items handled by the result.

         If only a single name is given, a single data item is returned. If several names are
         given, a list is returned. For integer inputs the result returns `resultname_X`.

         If the result contains only a single entry you can call `f_get()` without arguments.
         If you call `f_get()` and the result contains more than one element a ValueError is
         thrown.

         If the requested item(s) cannot be found an AttributeError is thrown.

        :param args: strings-names or integers

        :return: Single data item or tuple of data

        Example:

        >>> res = Result('supergroup.subgroup.myresult', comment='I am a neat example!' \
        [1000,2000], {'a':'b','c':333}, hitchhiker='Arthur Dent')
        >>> res.f_get('hitchhiker')
        'Arthur Dent'
        >>> res.f_get(0)
        [1000,2000]
        >>> res.f_get('hitchhiker', 'myresult')
        ('Arthur Dent', [1000,2000])

        """

        if len(args) == 0:
            if len(self._data) == 1:
                return list(self._data.values())[0]
            elif len(self._data) > 1:
                raise ValueError(
                    "Your result `%s` contains more than one entry: "
                    "`%s` Please use >>f_get<< with one of these."
                    % (self.v_full_name, str(list(self._data.keys())))
                )
            else:
                raise AttributeError(
                    "Your result `%s` is empty, cannot access data." % self.v_full_name
                )

        result_list = []
        for name in args:
            name = self.f_translate_key(name)
            if not name in self._data:
                if name == "data" and len(self._data) == 1:
                    return self._data[list(self._data.keys())[0]]
                else:
                    raise AttributeError(
                        "`%s` is not part of your result `%s`."
                        % (name, self.v_full_name)
                    )

            result_list.append(self._data[name])

        if len(args) == 1:
            return result_list[0]
        else:
            return result_list

    def f_set_single(self, name, item):
        """Sets a single data item of the result.

        Raises TypeError if the type of the outer data structure is not understood.
        Note that the type check is shallow. For example, if the data item is a list,
        the individual list elements are NOT checked whether their types are appropriate.

        :param name: The name of the data item

        :param item: The data item

        :raises: TypeError

        Example usage:

        >>> res.f_set_single('answer', 42)
        >>> res.f_get('answer')
        42

        """

        if self.v_stored:
            self._logger.debug(
                "You are changing an already stored result. If "
                "you not explicitly overwrite the data on disk, this change "
                "might be lost and not propagated to disk."
            )

        if self._supports(item):

            # self._check_if_empty(item, name) # No longer needed

            if name in self._data:
                self._logger.debug(
                    "Replacing `%s` in result `%s`." % (name, self.v_full_name)
                )

            self._data[name] = item
        else:
            raise TypeError(
                "Your result `%s` of type `%s` is not supported."
                % (name, str(type(item)))
            )

    def _supports(self, item):
        """Checks if outer data structure is supported."""
        return type(item) in Result.SUPPORTED_DATA

    def f_supports_fast_access(self):
        """Whether or not the result supports fast access.

        A result supports fast access if it contains exactly one item with the name of the result.

        """
        return len(self._data) == 1 and self.v_name in self._data

    def _store(self):
        """Returns a storage dictionary understood by the storage service.

        Simply returns a shallow copy of its own data dictionary.

        """
        store_dict = {}
        store_dict.update(self._data)
        return store_dict

    def _load(self, load_dict):
        """Loads data from load_dict"""
        self._data_ = load_dict

    def f_remove(self, *args):
        """Removes `*args` from the result"""
        for arg in args:
            arg = self.f_translate_key(arg)
            if arg in self._data:
                del self._data[arg]
            else:
                raise AttributeError(
                    "Your result `%s` does not contain %s." % (self.name_, arg)
                )

    def __delitem__(self, key):
        """ Deletes an item, see also __delattr__"""
        self.f_remove(key)

    def __delattr__(self, item):
        """Deletes an item from the result.

        If the item has been stored to disk before with a storage service, this storage is not
        deleted!

        :param item: Name of item to delete

        :raises: AttributeError if the item does not exist

        Example usage:

        >>> res = Result('Iam.an.example', comment='And a neat one, indeed!', fortytwo=42)
        >>> 'fortytwo' in res
        True
        >>> del res.fortytwo
        >>> 'fortytwo' in res
        False

        """
        if item[0] == "_":
            super(Result, self).__delattr__(item)
        else:
            self.f_remove(item)

    def __setattr__(self, key, value):
        if key[0] == "_":
            # We set a private attribute
            super(Result, self).__setattr__(key, value)
        elif hasattr(self.__class__, key):
            # Work around for python properties
            python_property = getattr(self.__class__, key)
            if python_property.fset is None:
                raise AttributeError("%s is read only!" % key)
            else:
                python_property.fset(self, value)
        else:
            self.f_set_single(key, value)

    # @no_prefix_getattr
    def __getattr__(self, name):
        return self.f_get(name) class SparseResult(Result):
    """Handles Scipy sparse matrices.

    Supported Formats are csr, csc, bsr, and dia.

    Subclasses the standard result and can also handle all data supported by
    :class:`~pypet.parameter.Result`.

    """

    IDENTIFIER = SparseParameter.IDENTIFIER
    """Identifier string to label sparse matrix data"""

    __slots__ = ()

    @copydoc(Result.f_set_single)
    def f_set_single(self, name, item):
        if SparseResult.IDENTIFIER in name:
            raise AttributeError(
                "Your result name contains the identifier for sparse matrices,"
                " please do not use %s in your result names." % SparseResult.IDENTIFIER
            )
        else:
            super(SparseResult, self).f_set_single(name, item)

    def _supports(self, item):
        """Supports everything of parent class and csr, csc, bsr, and dia sparse matrices."""
        if SparseParameter._is_supported_matrix(item):
            return True
        else:
            return super(SparseResult, self)._supports(item)

    def _store(self):
        """Returns a storage dictionary understood by the storage service.

        Sparse matrices are extracted similar to the :class:`~pypet.parameter.SparseParameter` and
        marked with the identifier `__spsp__`.

        """
        store_dict = {}
        for key in self._data:
            val = self._data[key]
            if SparseParameter._is_supported_matrix(val):
                serial_string = SparseParameter._serialize_matrix(val)
                store_dict["%s%s" % (key, SparseParameter.IDENTIFIER)] = serial_string
            else:
                store_dict[key] = val

        return store_dict

    def _load(self, load_dict):
        """Loads data from `load_dict`

        Reconstruction of sparse matrices similar to the :class:`~pypet.parameter.SparseParameter`.

        """
        for key in list(load_dict.keys()):
            # We delete keys over time:
            if key in load_dict:
                if SparseResult.IDENTIFIER in key:
                    new_key = key.split(SparseResult.IDENTIFIER)[0]
                    matrix = SparseParameter._reconstruct_matrix(load_dict[key])
                    self._data[new_key] = matrix
                else:
                    self._data[key] = load_dict[key] class PickleResult(Result):
    """Result that digest everything and simply pickles it!

    Note that it is not checked whether data can be pickled, so take care that it works!

    You can pass the pickle protocol via `protocol=2` to the constructor or change it with
    the `v_protocol` property. Default protocol is 0.

    Note that after storage to disk changing the protocol has no effect.
    If the parameter is loaded, `v_protocol` is set to a protocol used to
    store an item. Note that items are reconstructed from a dictionary and the protocol
    is taken from the first one found in the dictionary. This is a rather arbitrary choice.
    Yet, the underlying assumption is that all items were pickled with the same protocol,
    which is the general case.

    """

    PROTOCOL = PickleParameter.PROTOCOL

    __slots__ = ("_protocol",)

    def __init__(self, full_name, *args, **kwargs):
        self._protocol = None
        protocol = kwargs.pop("protocol", 0)
        self.v_protocol = protocol

        super(PickleResult, self).__init__(full_name, *args, **kwargs)

    @property
    def v_protocol(self):
        """The protocol used to pickle data, default is 0.

        See pickle_ documentation for the protocols.

        .. _pickle: http://docs.python.org/2/library/pickle.html

        """
        return self._protocol

    @v_protocol.setter
    def v_protocol(self, value):
        """Sets the protocol"""
        self._protocol = value

    def f_set_single(self, name, item):
        """Adds a single data item to the pickle result.

        Note that it is NOT checked if the item can be pickled!

        """

        if self.v_stored:
            self._logger.debug(
                "You are changing an already stored result. If "
                "you not explicitly overwrite the data on disk, this change "
                "might be lost and not propagated to disk."
            )

        if name == PickleResult.PROTOCOL:
            raise AttributeError(
                "You cannot name an entry `%s`" % PickleResult.PROTOCOL
            )

        self._data[name] = item

    def _store(self):
        """Returns a dictionary containing pickle dumps"""
        store_dict = {}
        for key, val in self._data.items():
            store_dict[key] = pickle.dumps(val, protocol=self.v_protocol)
        store_dict[PickleResult.PROTOCOL] = self.v_protocol
        return store_dict

    def _load(self, load_dict):
        """Reconstructs all items from the pickle dumps in `load_dict`.

        Sets the `v_protocol` property to the protocol of the first reconstructed item.

        """
        try:
            self.v_protocol = load_dict.pop(PickleParameter.PROTOCOL)
        except KeyError:
            # For backwards compatibility
            dump = next(load_dict.values())
            self.v_protocol = PickleParameter._get_protocol(dump)
        for key in load_dict:
            val = load_dict[key]
            self._data[key] = pickle.loads(val) class BaseParameter(NNLeafNode):
    """Abstract class that specifies the methods for a trajectory parameter.

    Parameters are simple container objects for data values. They handle single values as well as
    ranges of potential values. These range arrays contain multiple values which are accessed
    one after the other in individual simulation runs.

    Parameter exploration is usually initiated through the trajectory see
    :func:`~pypet.trajectory.Trajectory.f_explore` and
    :func:`~pypet.trajectory.Trajectory.f_expand`.

    To access the parameter's data value one can call the
    :func:`~pypet.parameter.BaseParameter.f_get` method.

    Parameters support the concept of locking. Once a value of the parameter has been accessed,
    the parameter cannot be changed anymore unless it is explicitly unlocked using
    :func:`~pypet.parameter.BaseParameter.f_unlock`.
    This prevents parameters from being changed during runtime of a simulation.

    If multiprocessing is desired the parameter must be picklable!

    :param full_name:

        The full name of the parameter in the trajectory tree, groupings are
        separated by a colon: `fullname = 'supergroup.subgroup.paramname'`

    :param comment:

        A useful comment describing the parameter:
        `comment = 'Some useful text, dude!'`

    """

    __slots__ = ("_locked", "_full_copy", "_explored")

    def __init__(self, full_name, comment=""):
        super(BaseParameter, self).__init__(full_name, comment, is_parameter=True)

        self._locked = False

        # Whether to keep the full range array when pickled or not
        self._full_copy = False
        self._explored = False  # If explored or not

    def f_supports(self, data):
        """Checks whether the data is supported by the parameter."""
        return type(data) in pypetconstants.PARAMETER_SUPPORTED_DATA

    @property
    def v_locked(self):
        """Whether or not the parameter is locked and prevents further modification"""
        return self._locked

    def f_supports_fast_access(self):
        """Checks if parameter supports fast access.

        A parameter supports fast access if it is NOT empty!

        """
        return not self.f_is_empty()

    @property
    def v_explored(self):
        """Whether parameter is explored.

        Does not necessarily have to be similar to
        :func:`~pypet.parameter.BaseParameter.f_has_range` since the range can be
        deleted on pickling and the parameter remains explored.

        """
        return self._explored

    @property
    def v_full_copy(self):
        """Whether or not the full parameter including the range or only the current
        data is copied during pickling.

        If you run your simulations in multiprocessing mode, the whole trajectory and all
        parameters need to be pickled and are sent to the individual processes.
        Each process than runs an individual point in the parameter space.
        As a consequence, you do not need the full ranges during these calculations.
        Thus, if the full copy mode is set to `False` the parameter is pickled without
        the range array and you can save memory.

        If you want to access the full range during individual runs, you need to set
        `v_full_copy` to `True`.

        It is recommended NOT to do that in order to save memory and also do obey the
        philosophy that individual simulation runs are independent.

        Example usage:

        >>> import pickle
        >>> param = Parameter('examples.fullcopy', data=333, comment='I show you how the copy mode works!')
        >>> param._explore([1,2,3,4])
        >>> dump=pickle.dumps(param)
        >>> newparam = pickle.loads(dump)
        >>> newparam.f_get_range()
        TypeError

        >>> param.v_full_copy=True
        >>> dump = pickle.dumps(param)
        >>> newparam=pickle.loads(dump)
        >>> newparam.f_get_range()
        (1,2,3,4)

        """
        return self._full_copy

    @v_full_copy.setter
    def v_full_copy(self, val):
        """Sets the full copy mode"""
        val = bool(val)
        self._full_copy = val

    def f_has_range(self):
        """Returns true if the parameter contains a range array.

        Not necessarily equal to `v_explored` if the range is removed on
        pickling due to `v_full_copy=False`.

        ABSTRACT: Needs to be defined in subclass

        """
        raise NotImplementedError("Should have implemented this.")

    def _restore_default(self):
        """Restores original data if changed due to exploration.

        If a Parameter is explored, the actual data is changed over the course of different
        simulations. This method restores the original data assigned before exploration.

        ABSTRACT: Needs to be defined in subclass

        """
        raise NotImplementedError("Should have implemented this.")

    def f_get_range_length(self):
        """Returns the length of the parameter range.

        Raises TypeError if the parameter has no range.

        Does not need to be implemented if the parameter supports
        ``__len__`` appropriately.

        """
        if not self.f_has_range():
            raise TypeError("Not applicable, parameter does not have a range")
        elif hasattr(self, "__len__"):
            return len(self)
        else:
            raise NotImplementedError("Should have implemented this.")

    def f_val_to_str(self):
        """String summary of the value handled by the parameter.

        Note that representing the parameter as a string accesses its value,
        but for simpler debugging, this does not lock the parameter or counts as usage!

        Calls `__repr__` of the contained value.

        """
        old_locked = self._locked
        try:
            return repr(self.f_get())
        except Exception:
            return "No Evaluation possible (yet)!"
        finally:
            self._locked = old_locked

    def _equal_values(self, val1, val2):
        """Checks if the parameter considers two values as equal.

        This is important for the trajectory in case of merging. In case you want to delete
        duplicate parameter points, the trajectory needs to know when two parameters
        are equal. Since equality is not always implemented by values handled by
        parameters in the same way, the parameters need to judge whether their values are equal.

        The straightforward example here is a numpy array.
        Checking for equality of two numpy arrays yields
        a third numpy array containing truth values of a piecewise comparison.
        Accordingly, the parameter could judge two numpy arrays equal if ALL of the numpy
        array elements are equal.

        In this BaseParameter class values are considered to be equal if they obey
        the function :func:`~pypet.utils.comparisons.nested_equal`.
        You might consider implementing a different equality comparison in your subclass.

        :raises: TypeError: If both values are not supported by the parameter.

        """
        if self.f_supports(val1) != self.f_supports(val2):
            return False

        if not self.f_supports(val1) and not self.f_supports(val2):
            raise TypeError(
                "I do not support the types of both inputs (`%s` and `%s`), "
                "therefore I cannot judge whether "
                "the two are equal." % (str(type(val1)), str(type(val2)))
            )

        if not self._values_of_same_type(val1, val2):
            return False

        return comparisons.nested_equal(val1, val2)

    def _values_of_same_type(self, val1, val2):
        """Checks if two values agree in type.

        For example, two 32 bit integers would be of same type, but not a string and an integer,
        nor a 64 bit and a 32 bit integer.

        This is important for exploration. You are only allowed to explore data that
        is of the same type as the default value.

        One could always come up with a trivial solution of `type(val1) is type(val2)`.
        But sometimes your parameter does want even more strict equality or
        less type equality.

        For example, the :class:`~pypet.parameter.Parameter` has a stricter sense of
        type equality regarding numpy arrays. In order to have two numpy arrays of the same type,
        they must also agree in shape. However, the :class:`~pypet.parameter.ArrayParameter`,
        considers all numpy arrays as of being of same type regardless of their shape.

        Moreover, the :class:`~pypet.parameter.SparseParameter` considers all supported
        sparse matrices (csc, csr, bsr, dia) as being of the same type. You can make
        explorations using all these four types at once.

        The difference in how strict types are treated arises from the way parameter data
        is stored to disk and how the parameters hand over their data to the storage service
        (see :func:`pypet.parameter.BaseParameter._store`).

        The :class:`~pypet.parameter.Parameter` puts all it's data in an
        :class:`~pypet.parameter.ObjectTable` which
        has strict constraints on the column sizes. This means that numpy array columns only
        accept numpy arrays with a particular size. In contrast, the array and sparse
        parameter hand over their data as individual items which yield individual entries
        in the hdf5 node. In order to see what I mean simply run an experiment with all 3
        parameters, explore all of them, and take a look at the resulting hdf5 file!

        However, this BaseParameter class implements the straightforward version of
        `type(val1) is type(val2)` to consider data to be of the same type.

        :raises: TypeError: if both values are not supported by the parameter.

        """
        if self.f_supports(val1) != self.f_supports(val2):
            return False

        if not self.f_supports(val1) and not self.f_supports(val2):
            raise TypeError(
                "I do not support the types of both inputs (`%s` and `%s`),"
                " therefore I cannot judge whether the two are of same type."
                % str(type(val1)),
                str(type(val2)),
            )

        return type(val1) is type(val2)

    def __repr__(self):
        return "<%s>" % self.__str__()

    def __str__(self):
        """String representation of the Parameter

        Output format is:`<class_name> full_name (len:X, `comment`): value`.
        If comment is the empty string, the comment is omitted.
        If the parameter is not explored the length is omitted.

        """
        if self.f_has_range():
            lenstr = "len:%d" % self.f_get_range_length()
        else:
            lenstr = ""

        if self.v_comment:
            commentstr = "`%s`" % self.v_comment
        else:
            commentstr = ""

        if commentstr or lenstr:
            if commentstr and lenstr:
                combined_str = "%s, %s" % (lenstr, commentstr)
            elif commentstr:
                combined_str = commentstr
            elif lenstr:
                combined_str = lenstr
            else:
                raise RuntimeError("You shall not pass!")

            infostr = " (%s)" % combined_str

        else:
            infostr = ""

        return_string = "%s %s%s" % (self.f_get_class_name(), self.v_full_name, infostr)

        if not self.f_is_empty():
            return_string += ": " + self.f_val_to_str()

        return return_string

    def f_unlock(self):
        """Unlocks the locked parameter.

        Please use it very carefully, or best do not use this function at all.
        There should better be no reason to unlock a locked parameter!
        The only exception I can think of is to unlock a large derived parameter
        after usage to subsequently call :func:`~pypet.parameter.BaseParameter.f_empty`
        to clear memory.

        """
        self._locked = False

    def f_lock(self):
        """Locks the parameter and forbids further manipulation.

        Changing the data value or exploration range of the parameter are no longer allowed.

        """
        self._locked = True

    def f_set(self, data):
        """Sets a data value for a parameter.

        Example usage:

        >>> param = Parameter('groupA.groupB.myparam', comment='I am a neat example')
        >>> param.f_set(44.0)
        >>> param.f_get()
        44.0

        :raises:

                ParameterLockedException: If parameter is locked

                TypeError: If the type of the data value is not supported by the parameter

        ABSTRACT: Needs to be defined in subclass

        """
        raise NotImplementedError("Should have implemented this.")

    def __getitem__(self, item):
        """Equivalent to `f_get_range()[idx]`

        :raises: TypeError if parameter has no range

        """
        return self.f_get_range().__getitem__(item)

    def f_get(self):
        """Returns the current data value of the parameter and locks the parameter.

        :raises: TypeError if the parameter is empty

        Example usage:

        >>> param = Parameter('groupA.groupB.myparam', comment='I am a neat example')
        >>> param.f_set(44.0)
        >>> param.f_get()
        44.0:

        ABSTRACT: Needs to be defined in subclass

        """
        raise NotImplementedError("Should have implemented this.")

    def f_get_default(self):
        """Returns the default value of the parameter and locks it."""
        raise NotImplementedError("Should have implemented this.")

    def f_get_range(self, copy=True):
        """Returns an iterable to iterate over the values of the exploration range.

        Note that the returned values should be either a copy of the exploration range
        unless explicetly requested otherwise.

        :param copy:

            If range should be copied to avoid tempering with data.

        :return: Iterable

        :raises: TypeError if the parameter is not explored

        Example usage:

        >>> param = Parameter('groupA.groupB.myparam',data=22, comment='I am a neat example')
        >>> param._explore([42,43,43])
        >>> param.f_get_range()
        (42,43,44)

        ABSTRACT: Needs to be defined in subclass

        """
        raise NotImplementedError("Should have implemented this.")

    def _explore(self, iterable):
        """The method to explore a parameter and create a range of entries.

        :param iterable: An iterable specifying the exploration range

             For example:

             >>> param = Parameter('groupA.groupB.myparam',data=22.33,\
              comment='I am a neat example')
             >>> param._explore([3.0,2.0,1.0])

        :raises:

            ParameterLockedException: If the parameter is locked

            TypeError: If the parameter is already explored

        ABSTRACT: Needs to be defined in subclass

        """
        raise NotImplementedError("Should have implemented this.")

    def _expand(self, iterable):
        """Similar to :func:`~pypet.parameter.BaseParameter._explore` but appends to
        the exploration range.

        :param iterable: An iterable specifying the exploration range.

        :raises:

            ParameterLockedException: If the parameter is locked

            TypeError: If the parameter did not have a range before

        Example usage:

        >>> param = Parameter('groupA.groupB.myparam', data=3.13, comment='I am a neat example')
        >>> param._explore([3.0,2.0,1.0])
        >>> param._expand([42.0,43.0])
        >>> param.f_get_range()
        (3.0,2.0,1.0,42.0,43.0)

        ABSTRACT: Needs to be defined in subclass

        """
        raise NotImplementedError("Should have implemented this.")

    def _set_parameter_access(self, idx=0):
        """Sets the current value according to the `idx` in the exploration range.

        Prepares the parameter for further usage, and tells it which point in the parameter
        space should be accessed by calls to :func:`~pypet.parameter.BaseParameter.f_get`.

        :param idx: The index within the exploration range.

            If the parameter has no range, the single data value is considered
            regardless of the value of `idx`.
            Raises ValueError if the parameter is explored and `idx>=len(param)`.

        :raises: ValueError:

            If the parameter has a range and `idx` is larger or equal to the
            length of the parameter.

        Example usage:

        >>> param = Parameter('groupA.groupB.myparam',data=22.33, comment='I am a neat example')
        >>> param._explore([42.0,43.0,44.0])
        >>> param._set_parameter_access(idx=1)
        >>> param.f_get()
        43.0

        ABSTRACT: Needs to be defined in subclass

        """
        raise NotImplementedError("Should have implemented this.")

    def f_get_class_name(self):
        """ Returns the name of the class i.e. `return self.__class__.__name__`"""
        return self.__class__.__name__

    def f_is_empty(self):
        """True if no data has been assigned to the parameter.

        Example usage:

        >>> param = Parameter('myname.is.example', comment='I am _empty!')
        >>> param.f_is_empty()
        True
        >>> param.f_set(444)
        >>> param.f_is_empty()
        False

        """
        raise NotImplementedError("Implement this!")

    def _shrink(self):
        """If a parameter is explored, i.e. it has a range, the whole exploration range is deleted.

        Note that this function does not erase data from disk. So if the parameter has
        been stored with a service to disk and is shrunk, it can be restored by loading from disk.

        :raises:

            ParameterLockedException: If the parameter is locked

            TypeError: If the parameter has no range


        ABSTRACT: Needs to be defined in subclass

        """
        raise NotImplementedError("Should have implemented this.")

    def f_empty(self):
        """Erases all data in the parameter.

        Does not erase data from disk. So if the parameter has
        been stored with a service to disk and is emptied,
        it can be restored by loading from disk.

        :raises: ParameterLockedException: If the parameter is locked.

        ABSTRACT: Needs to be defined in subclass

        """
        raise NotImplementedError("Should have implemented this.") class Trajectory(DerivedParameterGroup, ResultGroup, ParameterGroup, ConfigGroup):
    """The trajectory manages results and parameters.

    The trajectory provides all functionality to define how the parameter space
    of your simulation should be explored. During single runs based on a particular
    parameter point, the functionality fo the trajectory is reduced.

    You can add four types of data to the trajectory:

    * Config:

        These are special parameters specifying modalities of how to run your simulations.
        Changing a config parameter should NOT have any influence on the results you
        obtain from your simulations.

        They specify runtime environment parameters like how many CPUs you use for
        multiprocessing etc.

        In fact, if you use the default runtime environment of this project, the environment
        will add some config parameters to your trajectory.

        The method to add more config is :func:`~pypet.naturalnaming.ConfigGroup.f_add_config`

        Config parameters are put into the subtree `traj.config` (with `traj` being your
        trajectory instance).

    * Parameters:

        These are your primary ammunition in numerical simulations. They specify
        how your simulation works. They can only be added before the actual
        running of the simulation exploring the parameter space. They can be
        added via :func:`~pypet.naturalnaming.ParameterGroup.f_add_parameter`
        and be explored using :func:`~pypet.trajectory.Trajectory.f_explore`.
        Or to expand an existing trajectory use :func:`~pypet.trajectory.Trajectory.f_expand`.

        Your parameters should encompass all values that completely define your simulation,
        I recommend also storing random number generator seeds as parameters to
        guarantee that a simulation can be repeated exactly the way it was run the first time.

        Parameters are put into the subtree `traj.parameters`.

    * Derived Parameters:

        They are not much different from parameters except that they can be added anytime.

        Conceptually this encompasses stuff that is intermediately
        computed from the original parameters. For instance, as your original
        parameters you have a random number seed and some other parameters.
        From these you compute a connection matrix for a neural network.
        This connection matrix could be stored as a derived parameter.

        Derived parameters are added via
        :func:`~pypet.naturalnaming.DerivedParameterGroup.f_add_derived_parameter`.

        Derived parameters are put into the subtree `traj.derived_parameters`.
        They are further sorted into  `traj.derived_parameters.runs.run_XXXXXXXX` if they were
        added during a single run. `XXXXXXXX` is replaced by the index of the corresponding run,
        for example `run_00000001`.

    * Results:

        Result are added via the :func:`~pypet.naturalnaming.ResultGroup.f_add_result`.
        They are kept under the subtree `traj.results` and are further sorted into
        `traj.results.runs.run_XXXXXXXX` if they are added during a single run.

    There are several ways to access the parameters and results, to learn about these, fast access,
    and natural naming see :ref:`more-on-access`.


    In case you create a new trajectory you can pass the following arguments:

    :param name:

        Name of the trajectory, if `add_time=True` the current time is added as a string
        to the parameter name.

    :param add_time:

        Boolean whether to add the current time in human readable format to the trajectory name.

    :param comment:

        A useful comment describing the trajectory.

    :param dynamic_imports:

        If you've written a custom parameter that needs to be loaded dynamically during runtime,
        this needs to be specified here as a list of classes or strings naming classes
        and there module paths. For example:
        `dynamic_imports = ['pypet.parameter.PickleParameter',MyCustomParameter]`

        If you only have a single class to import, you do not need the list brackets:
        `dynamic_imports = 'pypet.parameter.PickleParameter'`

    :param wildcard_functions:

        Dictionary of wildcards like `$` and corresponding functions that are called upon
        finding such a wildcard.

    :param storage_service:

        Pass a storage service used by the Trajectory. Alternatively, pass a constructor
        and other ``**kwargs`` are passed onto the constructor.

    :param kwargs:

        Other arguments passed to the storage service constructor

    :raises:

        ValueError: If the name of the trajectory contains invalid characters or
                    not all additional keyword arguments are used.

        TypeError: If the dynamically imported classes are not classes or strings.


    Example usage:

    >>> traj = Trajectory('ExampleTrajectory', dynamic_imports=['Some.custom.class'],\
    comment = 'I am a neat example!', storage_service=HDF5StorageService, \
    filename='experiment.hdf5', file_title='Experiments')

    """

    @kwargs_api_change('dynamically_imported_classes', 'dynamic_imports')
    def __init__(self, name='my_trajectory', add_time=False, comment='',
                 dynamic_imports=None, wildcard_functions=None,
                 storage_service=None, **kwargs):

        copy_traj = kwargs.pop('_copy_traj', False)  # True if trajectory is copied

        # This is true during the actual runs:
        self._is_run = False

        super(Trajectory, self).__init__()

        # Helper variable: During a multiprocessing single run, the trajectory is usually
        # pickled without all the parameter exploration ranges and all run information
        # As a consequence, __len__ would return 1, so we need to store the length in this
        # helper variable to return the correct length during single runs.
        self._length = 1

        if not copy_traj:
            self._set_logger()

            init_time = time.time()
            formatted_time = format_time(init_time)
            self._timestamp = init_time
            self._time = formatted_time

            if add_time:
                self._name = name + '_' + str(formatted_time)
            else:
                self._name = name

        self._version = VERSION
        self._python = '.'.join([str(x) for x in sys.version_info[0:3]])

        self._parameters = {}  # Contains all parameters
        self._derived_parameters = {}  # Contains all derived parameters
        self._results = {}  # Contains all results
        self._explored_parameters = {}  # Contains all explored parameters, even when they are not
        # loaded it still contains all keys!
        self._config = {}  # Contains all config parameters
        self._all_groups = {}  # Contains ALL groups regardless in which subtree they are
        self._run_parent_groups = {}  # Contains all groups which are parents of run groups
        self._new_nodes = OrderedDict()  # Contains all elements added during a
        # particular single run with full names as keys and a (parent, child) pair as value
        self._new_links = OrderedDict()  # Contains all new added links during a single run
        self._other_leaves = {}  # Contains ALL other user added leaves
        self._linked_by = {}  # Contains all nodes that are linked to

        self._changed_default_parameters = {}  # Needed for paremeter presetting

        self._single_run_ids = {}  # A bidrectional dictionary conataining the mapping between
        # a run name and the run index (e.g. `1 <-> 'run_00000001'`), in both directions

        self._run_information = {}  # Nested Dictionary with run names as keys and
        # dictionaries as values. The inner dictionaries contain meta information about the runs
        # like time of creation, whether they have been completed and so on.
        # Check function 'f_get_run_information' for a description

        self._updated_run_information = set() # Set of updated run information which
        # needs to be updated in case the trajectory is stored.

        self._nn_interface = NaturalNamingInterface(root_instance=self)
        self._fast_access = True
        self._shortcuts = True
        self._iter_recursive = False
        self._max_depth = None
        self._auto_load = False
        self._with_links = True

        self._environment_hexsha = None
        self._environment_name = None

        # Index of a trajectory is -1, if the trajectory should behave like a single run
        # and blind out other single run results, this can be changed via 'v_crun'.
        self._idx = -1
        self._crun = None

        self._standard_parameter = Parameter
        self._standard_result = Result
        self._standard_leaf = Result

        self._auto_run_prepend = True
        self._no_clobber = False

        self._run_started = False  # For manually using a trajectory
        self._run_by_environment = False  # To disable manual running of experiment

        self._full_copy = False

        if not copy_traj:
            self._dynamic_imports = ['pypet.parameter.PickleParameter']

            if not dynamic_imports is None:
                self.f_add_to_dynamic_imports(dynamic_imports)

            faulty_names = self._nn_interface._check_names([name])

            if '.' in name:
                faulty_names += ' colons >>.<< are not allowed in trajectory names,'

            if faulty_names:
                raise ValueError('Your Trajectory %s f_contains the following not admissible names: '
                                 '%s please choose other names.'
                                 % (name, faulty_names))

            internal_wildcard_functions = {('$', 'crun'): make_run_name,
                                           ('$set', 'crunset'): make_set_name}
            if wildcard_functions:
                internal_wildcard_functions.update(wildcard_functions)

            self._wildcard_functions = {}
            self._wildcard_cache = {}
            self._wildcard_keys = {}
            self._reversed_wildcards = {} # Only needed for merging

            self.f_add_wildcard_functions(internal_wildcard_functions)

            # Per Definition the length is set to be 1. Even with an empty trajectory
            # in principle you could make a single run
            self._add_run_info(0)
            self._test_run_addition(1)

            self._comment = ''
            self.v_comment = comment

            self._storage_service, unused_kwargs = storage_factory(storage_service=storage_service,
                                                                   trajectory=self, **kwargs)
            if len(unused_kwargs) > 0:
                raise ValueError('The following keyword arguments were not used: `%s`' %
                                 str(unused_kwargs))

    def f_get_wildcards(self):
        """Returns a list of all defined wildcards"""
        return list(self._wildcard_keys.keys())

    def f_is_wildcard(self, wildcard):
        """Checks if a given `wildcard` is really a wildcard."""
        return wildcard in self._wildcard_keys

    def f_add_wildcard_functions(self, func_dict):
        """#TODO"""
        for wildcards, function in func_dict.items():
            if not isinstance(wildcards, tuple):
                wildcards = (wildcards,)
            for wildcard in wildcards:
                if wildcard in self._wildcard_keys:
                    raise ValueError('Your wildcard `%s` is used twice1' % wildcard)
                self._wildcard_keys[wildcard] = wildcards
            self._wildcard_functions[wildcards] = function
            self._logger.debug('Added wildcard function `%s`.' % str(wildcards))

    def f_wildcard(self, wildcard='$', run_idx=None):
        """#TODO"""
        if run_idx is None:
            run_idx = self.v_idx
        wildcards = self._wildcard_keys[wildcard]
        try:
            return self._wildcard_cache[(wildcards, run_idx)]
        except KeyError:
            translation = self._wildcard_functions[wildcards](run_idx)
            self._wildcard_cache[(wildcards, run_idx)] = translation
            return translation

    def __getstate__(self):
        result = super(Trajectory, self).__getstate__()

        # Do not copy run information in case `v_full_copy` is `False`.
        if not self.v_full_copy:
            if self.v_crun is not None:
                idx = self.v_idx
            else:
                idx = 0
            runname = self._single_run_ids[idx]
            result['_run_information'] = {runname: self._run_information[runname]}
            result['_single_run_ids'] = {idx: runname, runname: idx}
            result['_updated_run_information'] = set()

        result['_wildcard_cache'] = {}
        return result

    def __str__(self):
        if self.v_comment:
            commentstring = '`%s`, ' % self.v_comment
        else:
            commentstring = ''

        info_string = '(%slen:%d)'  % (commentstring, len(self))
        children_string = self._get_children_representation()

        if self._is_run:
            runstr = ' (during single run)'
        else:
            runstr = ''

        return '%s%s %s %s: %s' % (self.f_get_class_name(), runstr, self.v_name, info_string,
                                 children_string)

    @property
    def v_version(self):
        """The version of *pypet* that was used to create the trajectory"""
        return self._version

    @property
    def v_python(self):
        """The version of python as a string that was used to create the trajectory"""
        return self._python

    @property
    def v_comment(self):
        """Should be a nice descriptive comment"""
        return self._comment

    @v_comment.setter
    @not_in_run
    def v_comment(self, comment):
        """Sets the comment"""
        comment = str(comment)
        self._comment = comment

    @property
    def v_storage_service(self):
        """The service that can store the trajectory to disk or wherever.

        Default is None or if a filename was provided on construction
        the :class:`~pypet.storageservice.HDF5StorageService`.

        """
        return self._storage_service

    @v_storage_service.setter
    def v_storage_service(self, service):
        """Sets the storage service"""
        self._storage_service = service

    @property
    def v_is_run(self):
        """ ``True`` mak if trajectory is used during a single run initiated by an environment.

        Accordingly, the functionality of the trajectory is reduced.

        """
        return self._is_run

    @property
    def v_idx(self):
        """Index if you want to access the trajectory as during a single run.

        You can turn the trajectory to behave as if during the execution of your runs if you set
        `v_idx` to a particular index. Note that only integer values are appropriate here,
        not names of runs.

        Alternatively instead of directly setting `v_idx` you can call
        :func:`~pypet.trajectory.Trajectory.f_set_crun:`.

        Set to `-1` to make the trajectory turn everything back to default.

        """
        return self._idx

    @v_idx.setter
    @not_in_run
    def v_idx(self, idx):
        """Changes the index to make the trajectory behave as a single run"""
        self.f_set_crun(idx)

    @property
    def v_crun_(self):
        """"
        Similar to ``v_crun`` but returns ``'run_ALL'`` if ``v_crun`` is ``None``.
        """
        return self.v_crun if self.v_crun is not None else self.f_wildcard('$', -1)

    @property
    def v_crun(self):
        """Run name if you want to access the trajectory as a single run.

        You can turn the trajectory to behave as during a single run if you set
        `v_crun` to a particular run name. Note that only string values are appropriate here,
        not indices. Check the `v_idx` property if you want to provide an index.

        Alternatively instead of directly setting `v_crun` you can call
        :func:`~pypet.trajectory.Trajectory.f_set_crun:`.

        Set to `None` to make the trajectory to turn everything back to default.

        """
        return self._crun

    @v_crun.setter
    @not_in_run
    def v_crun(self, run_name):
        """Changes the run name to make the trajectory behave as during a single run"""
        self.f_set_crun(run_name)

    @property
    def v_full_copy(self):
        """Whether trajectory is copied fully during pickling or only the current
        parameter space point.

        Note if the trajectory is copied as a whole, also during a single run
        you can access the full parameter space.

        Changing `v_full_copy` will also change `v_full_copy` of all explored parameters!

        """
        return self._full_copy

    @v_full_copy.setter
    @not_in_run
    def v_full_copy(self, val):
        """ Sets full copy mode of trajectory and (!) ALL explored parameters!"""
        self._full_copy = bool(val)
        for param in self._explored_parameters.values():
            if param is not None:
                param.v_full_copy = bool(val)

    @property
    def v_with_links(self):
        """Whether links should be considered in case using natural naming
        or squared bracket indexing"""
        return self._with_links

    @v_with_links.setter
    def v_with_links(self, val):
        """Sets the usage of links"""
        self._with_links = bool(val)

    def f_set_properties(self, **kwargs):
        """Sets properties like ``v_fast_access``.

        For example: ``traj.f_set_properties(v_fast_access=True, v_auto_load=False)``

        """
        for name in kwargs:
            val = kwargs[name]
            if not name.startswith('v_'):
                name = 'v_' + name
            if not name in self._nn_interface._not_admissible_names:
                raise AttributeError('Cannot set property `%s` does not exist.' % name)
            else:
                setattr(self, name, val)

    @not_in_run
    def f_add_to_dynamic_imports(self, dynamic_imports):
        """Adds classes or paths to classes to the trajectory to create custom parameters.

        :param dynamic_imports:

            If you've written custom parameter that needs to be loaded dynamically during runtime,
            this needs to be specified here as a list of classes or strings naming classes
            and there module paths. For example:
            `dynamic_imports = ['pypet.parameter.PickleParameter',MyCustomParameter]`

            If you only have a single class to import, you do not need the list brackets:
            `dynamic_imports = 'pypet.parameter.PickleParameter'`

        """

        if not isinstance(dynamic_imports, (list, tuple)):
            dynamic_imports = [dynamic_imports]

        for item in dynamic_imports:
            if not (isinstance(item, str) or inspect.isclass(item)):
                raise TypeError('Your dynamic import `%s` is neither a class nor a string.' %
                                str(item))

        self._dynamic_imports.extend(dynamic_imports)

    @not_in_run
    def f_set_crun(self, name_or_idx):
        """Can make the trajectory behave as during a particular single run.

        It allows easier data analysis.

         Has the following effects:

        *
            `v_idx` and `v_crun` are set to the appropriate index and run name

        *
            All explored parameters are set to the corresponding value in the exploration
            ranges, i.e. when you call :func:`~pypet.parameter.Parameter.f_get` (or fast access)
            on them you will get in return the value at the corresponding `v_idx` position
            in the exploration range.

        *
            If you perform a search in the trajectory tree, the trajectory will
            only search the run subtree under *results* and *derived_parameters* with the
            corresponding index.
            For instance, if you use `f_set_crun('run_00000007')` or `f_set_crun(7)`
            and search for `traj.results.z` this will search for `z` only in the subtree
            `traj.results.run_00000007`. Yet, you can still explicitly name other subtrees,
            i.e. `traj.results.run_00000004.z` will still work.

        """
        if (name_or_idx is None or name_or_idx == self.f_wildcard('$', -1) or
                    name_or_idx == -1):
            self.f_restore_default()
        else:
            if isinstance(name_or_idx, str):
                self._idx = self.f_idx_to_run(name_or_idx)
                self._crun = name_or_idx
            else:
                self._crun = self.f_idx_to_run(name_or_idx)
                self._idx = name_or_idx

            self._set_explored_parameters_to_idx(self.v_idx)

    @not_in_run
    def f_iter_runs(self, start=0, stop=None, step=1, yields='name'):
        """Makes the trajectory iterate over all runs.

        :param start: Start index of run

        :param stop: Stop index, leave ``None`` for length of trajectory

        :param step: Stepsize

        :param yields:

            What should be yielded: ``'name'`` of run, ``idx`` of run
            or ``'self'`` to simply return the trajectory container.

            You can also pick ``'copy'`` to get **shallow** copies (ie the tree is copied but
            no leave nodes except explored ones.) of your trajectory,
            might lead to some of overhead.

        Note that after a full iteration, the trajectory is set back to normal.

        Thus, the following code snippet

        ::

            for run_name in traj.f_iter_runs():

                 # Do some stuff here...


        is equivalent to

        ::

            for run_name in traj.f_get_run_names(sort=True):
                traj.f_set_crun(run_name)

                # Do some stuff here...

            traj.f_set_crun(None)


        :return:

            Iterator over runs. The iterator itself will return the run names but modify
            the trajectory in each iteration and set it back do normal in the end.


        """
        if stop is None:
            stop =len(self)
        elif stop > len(self):
            raise ValueError('Stop cannot be larger than the trajectory lenght.')
        yields = yields.lower()
        if yields == 'name':
            yield_func = lambda x: self.f_idx_to_run(x)
        elif yields == 'idx':
            yield_func = lambda x: x
        elif yields == 'self':
            yield_func = lambda x: self
        elif yields == 'copy':
            yield_func = lambda x: self.__copy__()
        else:
            raise ValueError('Please choose yields among: `name`, `idx`, or `self`.')
        for idx in range(start, stop, step):
            self.f_set_crun(idx)
            yield yield_func(idx)

        self.f_set_crun(None)

    @not_in_run
    def f_shrink(self, force=False):
        """ Shrinks the trajectory and removes all exploration ranges from the parameters.
        Only possible if the trajectory has not been stored to disk before or was loaded as new.

        :param force:

            Usually you cannot shrink the trajectory if it has been stored to disk,
            because there's no guarantee that it is actually shrunk if there
            still exist explored parameters on disk. In case you are certain that
            you did not store explored parameters to disk set or you deleted all
            of them from disk set `force=True`.

        :raises: TypeError if the trajectory was stored before.

        """
        if self._stored and not force:
            raise TypeError('Your trajectory is already stored to disk or database, shrinking is '
                            'not allowed.')

        for param in self._explored_parameters.values():
            param.f_unlock()
            try:
                param._shrink()
            except Exception as exc:
                self._logger.error('Could not shrink `%s` because of:`%s`' %
                                   (param.v_full_name, repr(exc)))

        # If we shrink, we do not have any explored parameters left and we can erase all
        # run information, and the length of the trajectory is 1 again.
        self._explored_parameters = {}
        self._run_information = {}
        self._single_run_ids = {}
        self._add_run_info(0)
        self._test_run_addition(1)

    def _preset(self, name, args, kwargs):
        """Generic preset function, marks a parameter or config for presetting."""
        if self.f_contains(name, shortcuts=False):
            raise ValueError('Parameter `%s` is already part of your trajectory, use the normal'
                             'accessing routine to change config.' % name)
        else:
            self._changed_default_parameters[name] = (args, kwargs)

    @not_in_run
    def f_preset_config(self, config_name, *args, **kwargs):
        """Similar to func:`~pypet.trajectory.Trajectory.f_preset_parameter`"""

        if not config_name.startswith('config.'):
            config_name = 'config.' + config_name

        self._preset(config_name, args, kwargs)

    @not_in_run
    def f_preset_parameter(self, param_name, *args, **kwargs):
        """Presets parameter value before a parameter is added.

        Can be called before parameters are added to the Trajectory in order to change the
        values that are stored into the parameter on creation.

        After creation of a parameter, the instance of the parameter is called
        with `param.f_set(*args,**kwargs)` with `*args`, and `**kwargs` provided by the user
        with `f_preset_parameter`.

        Before an experiment is carried out it is checked if all parameters that were
        marked were also preset.

        :param param_name:

            The full name (!) of the parameter that is to be changed after its creation.

        :param args:

            Arguments that will be used for changing the parameter's data

        :param kwargs:

            Keyword arguments that will be used for changing the parameter's data

        Example:

        >>> traj.f_preset_parameter('groupA.param1', data=44)
        >>> traj.f_add_parameter('groupA.param1', data=11)
        >>> traj.parameters.groupA.param1
        44

        """

        if not param_name.startswith('parameters.'):
            param_name = 'parameters.' + param_name

        self._preset(param_name, args, kwargs)

    def _prepare_experiment(self):
        """Called by the environment to make some initial configurations before performing the
        individual runs.

        Checks if all parameters marked for presetting were preset. If not raises a
        DefaultReplacementError.

        Locks all parameters.

        Removal of potential results of previous runs in case the trajectory was expanded to avoid
        mixing up undesired shortcuts in natural naming.

        """
        if len(self._changed_default_parameters):
            raise pex.PresettingError(
                'The following parameters were supposed to replace a '
                'default value, but it was never tried to '
                'add default values with these names: %s' %
                str(self._changed_default_parameters))

        self.f_lock_parameters()
        self.f_lock_derived_parameters()

    @not_in_run
    @kwargs_api_change('backwards_search')
    @kwargs_api_change('where')
    def f_get_from_runs(self, name, include_default_run=True, use_indices=False,
                        fast_access=False, with_links = True,
                        shortcuts=True, max_depth=None, auto_load=False):
        """Searches for all occurrences of `name` in each run.

        Generates an ordered dictionary with the run names or indices as keys and
        found items as values.

        Example:

        >>> traj.f_get_from_runs(self, 'deep.universal_answer', use_indices=True, fast_access=True)
        OrderedDict([(0, 42), (1, 42), (2, 'fortytwo), (3, 43)])


        :param name:

            String description of the item(s) to find.
            Cannot be full names but the part of the names that are below
            a `run_XXXXXXXXX` group.

        :param include_default_run:

            If results found under ``run_ALL`` should be accounted for every run or simply be
            ignored.

        :param use_indices:

            If `True` the keys of the resulting dictionary are the run indices
            (e.g. 0,1,2,3), otherwise the keys are run names (e.g. `run_00000000`,
            `run_000000001`)

        :param fast_access:

            Whether to return parameter or result instances or the values handled by these.

        :param with_links:

            If links should be considered

        :param shortcuts:

            If shortcuts are allowed and the trajectory can *hop* over nodes in the
            path.

        :param max_depth:

            Maximum depth (relative to start node) how search should progress in tree.
            `None` means no depth limit.  Only relevant if `shortcuts` are allowed.

        :param auto_load:

            If data should be loaded from the storage service if it cannot be found in the
            current trajectory tree. Auto-loading will load group and leaf nodes currently
            not in memory and it will load data into empty leaves. Be aware that auto-loading
            does not work with shortcuts.

        :return:

            Ordered dictionary with run names or indices as keys and found items as values.
            Will only include runs where an item was actually found.

        """
        result_dict = OrderedDict()
        old_crun = self.v_crun

        try:

            if len(self._run_parent_groups) > 0:
                for run_name in self.f_iter_runs():
                    # Iterate over all runs
                    value = None
                    already_found = False
                    for run_parent_group in self._run_parent_groups.values():
                        if run_name not in run_parent_group._children:
                            continue

                        try:
                            value = run_parent_group.f_get(run_name + '.' + name,
                                                           fast_access=False,
                                                           with_links=with_links,
                                                           shortcuts=shortcuts,
                                                           max_depth=max_depth,
                                                           auto_load=auto_load)
                            if already_found:
                                raise pex.NotUniqueNodeError('`%s` has been found several times '
                                                             'in one run.' % name)
                            else:
                                already_found = True

                        except (AttributeError, pex.DataNotInStorageError):
                            pass

                    if value is None and include_default_run:
                        for run_parent_group in self._run_parent_groups.values():
                            try:
                                value = run_parent_group.f_get(self.f_wildcard('$', -1) +
                                                               '.' + name,
                                                               fast_access=False,
                                                               with_links=with_links,
                                                               shortcuts=shortcuts,
                                                               max_depth=max_depth,
                                                               auto_load=auto_load)
                                if already_found:
                                    raise pex.NotUniqueNodeError('`%s` has been found several '
                                                                 'times in one run.' % name)
                                else:
                                    already_found = True
                            except (AttributeError, pex.DataNotInStorageError):
                                pass

                    if value is not None:

                        if value.v_is_leaf:
                            value = self._nn_interface._apply_fast_access(value, fast_access)

                        if use_indices:
                            key = self.f_idx_to_run(run_name)
                        else:
                            key = run_name

                        result_dict[key] = value

            return result_dict
        finally:
            self.v_crun = old_crun

    def __len__(self):
        """Length of trajectory, minimum length is 1"""
        return self._length

    @not_in_run
    def f_is_completed(self, name_or_id=None):
        """Whether or not a given run is completed.

        If no run is specified it is checked whether all runs were completed.

        :param name_or_id: Nam or id of a run to check

        :return: True or False

        """
        return self._is_completed(name_or_id)

    def _is_completed(self, name_or_id=None):
        """Private function such that it can still be called by the environment during
        a single run"""

        if name_or_id is None:
            return all(
                (runinfo['completed'] for runinfo in self._run_information.values()))
        else:
            return self.f_get_run_information(name_or_id, copy=False)['completed']

    @not_in_run
    def f_expand(self, build_dict, fail_safe=True):
        """Similar to :func:`~pypet.trajectory.Trajectory.f_explore`, but can be used to enlarge
        already completed trajectories.

        Please ensure before usage, that all explored parameters are loaded!

        :param build_dict:

            Dictionary containing the expansion

        :param fail_safe:

            If old ranges should be **deep-copied** in order to allow to restore
            the original exploration if something fails during expansion.
            Set to `False` if deep-copying your parameter ranges causes errors.

        :raises:

            TypeError: If not all explored parameters are enlarged

            AttributeError: If keys of dictionary cannot be found in the trajectory

            NotUniqueNodeError:

                If dictionary keys do not unambiguously map to single parameters

            ValueError: If not all explored parameter ranges are of the same length

        """
        if len(self._explored_parameters) == 0:
            self._logger.info('Your trajectory has not been explored, yet. '
                              'I will call `f_explore` instead.')
            return self.f_explore(build_dict)

        enlarge_set = set([self.f_get(key).v_full_name
                           for key in build_dict.keys()])

        # Check if all explored parameters will be enlarged, otherwise
        # We cannot enlarge the trajectory
        if not set(self._explored_parameters.keys()) == enlarge_set:
            raise TypeError('You have to enlarge dimensions you have explored before! Currently'
                            ' explored parameters are not the ones you specified in your building'
                            ' dictionary, i.e. %s != %s' %
                            (str(set(self._explored_parameters.keys())),
                             str(set(build_dict.keys()))))
        if any(x is None for x in self._explored_parameters.values()):
            raise TypeError('At least one of your explored parameters is not fully loaded, '
                            'please load it.')

        old_ranges = None
        if fail_safe:
            old_ranges = {}
            for param_name in self._explored_parameters:
                old_ranges[param_name] = self._explored_parameters[param_name].f_get_range()
            try:
                old_ranges = cp.deepcopy(old_ranges)
            except Exception:
                self._logger.error('Cannot deepcopy old parameter ranges, if '
                                     'something fails during `f_expand` I cannot revert the '
                                     'trajectory to old settings.')
                old_ranges = None

        try:
            count = 0
            length = None
            for key, builditerable in build_dict.items():
                act_param = self.f_get(key)

                act_param.f_unlock()
                act_param._expand(builditerable)

                name = act_param.v_full_name

                self._explored_parameters[name] = act_param

                # Compare the length of two consecutive parameters in the `build_dict`
                if count == 0:
                    length = act_param.f_get_range_length()
                elif not length == act_param.f_get_range_length():
                    raise ValueError('The parameters to explore have not the same size!')
                count += 1

            original_length = len(self)
            for irun in range(original_length, length):
                self._add_run_info(irun)
            self._test_run_addition(length)

            # We need to update the explored parameters in case they were stored:
            self._remove_exploration()

        except Exception:
            if old_ranges is not None:
                # Try to restore the original parameter exploration
                for param_name in old_ranges:
                    param_range = old_ranges[param_name]
                    param = self._explored_parameters[param_name]
                    param.f_unlock()
                    try:
                        param._shrink()
                    except Exception as exc:
                        self._logger.error('Could not shrink parameter `%s` '
                                           'because of:`%s`' % (param_name, repr(exc)))
                    param._explore(param_range)
                    param._explored = True
            raise

    def _test_run_addition(self, length):
        """Tests if the user's run naming works and every index produces a new run name"""
        if not len(self) == length:
            raise RuntimeError('Your trajectory has a wrong length. Your naming function '
                               'does not work!')
        dummy = self.f_wildcard('$', -1)
        if dummy in self._run_information:
            raise RuntimeError('Your renaming function does not return an appropriate value for '
                               '`-1`. `%s` is actual an already given run name.' % dummy)

    def _remove_exploration(self):
        """ Called if trajectory is expanded, deletes all explored parameters from disk """
        for param in self._explored_parameters.values():
            if param._stored:
                try:
                    self.f_delete_item(param)
                except Exception:
                    self._logger.exception('Could not delete expanded parameter `%s` '
                                           'from disk.' % param.v_full_name)

    def __copy__(self):
        """Returns a shallow copy"""
        return self.f_copy(copy_leaves=True,
                           with_links=True)

    def f_copy(self, copy_leaves=True,
                     with_links=True):
        """Returns a *shallow* copy of a trajectory.

        :param copy_leaves:

            If leaves should be **shallow** copied or simply referred to by both trees.
            **Shallow** copying is established using the copy module.

            Accepts the setting ``'explored'`` to only copy explored parameters.
            Note that ``v_full_copy`` determines how these will be copied.

        :param with_links: If links should be ignored or followed and copied as well

        :return: A shallow copy

        """
        new_traj = Trajectory(_copy_traj=True)

        new_traj._length = self._length
        new_traj._name = self._name

        new_traj._timestamp = self._timestamp
        new_traj._time = self._time

        new_traj._single_run_ids = self._single_run_ids
        new_traj._run_information = self._run_information
        new_traj._updated_run_information = self._updated_run_information

        new_traj._fast_access = self._fast_access
        new_traj._shortcuts = self._shortcuts
        new_traj._iter_recursive = self._iter_recursive
        new_traj._max_depth = self._max_depth
        new_traj._auto_load = self._auto_load
        new_traj._with_links = self._with_links

        new_traj._environment_hexsha = self._environment_hexsha
        new_traj._environment_name = self._environment_name

        # Index of a trajectory is -1, if the trajectory should behave like a single run
        # and blind out other single run results, this can be changed via 'v_crun'.
        new_traj._idx = self._idx
        new_traj._crun = self._crun

        new_traj._standard_parameter = self._standard_parameter
        new_traj._standard_result = self._standard_result
        new_traj._standard_leaf = self._standard_leaf

        new_traj._auto_run_prepend = self._auto_run_prepend
        new_traj._no_clobber = self._no_clobber

        new_traj._run_started = self._run_started # For manually using a trajectory
        new_traj._run_by_environment = self._run_by_environment  # To disable manual running of experiment

        new_traj._full_copy = self._full_copy

        new_traj._dynamic_imports = self._dynamic_imports

        new_traj._wildcard_functions = self._wildcard_functions
        new_traj._wildcard_keys = self._wildcard_keys
        new_traj._reversed_wildcards = self._reversed_wildcards
        new_traj._wildcard_cache = self._wildcard_cache

        new_traj._comment = self._comment

        new_traj._stored = self._stored

        new_traj._storage_service = self._storage_service

        new_traj._is_run = self._is_run

        new_traj._copy_from(self, copy_leaves=copy_leaves,
                                   with_links=with_links)

        # Copy references to new nodes and leaves
        for my_dict, new_dict in ((self._new_nodes, new_traj._new_nodes),
                                 (self._new_links, new_traj._new_links)):
            for key in my_dict:
                value = my_dict[key]
                parent, child = value
                if parent is self:
                    new_parent = new_traj
                else:
                    new_parent = new_traj.f_get(parent.v_full_name,
                                            shortcuts=False,
                                            with_links=False,
                                            auto_load=False)
                new_child = new_parent._children[key[1]]
                new_dict[key] = (new_parent, new_child)

        return new_traj

    def _copy_from(self, node,
                          copy_leaves=True,
                          overwrite=False,
                          with_links=True):
        """Pass a ``node`` to insert the full tree to the trajectory.

        Considers all links in the given node!
        Ignored nodes already found in the current trajectory.

        :param node: The node to insert

        :param copy_leaves:

            If leaves should be **shallow** copied or simply referred to by both trees.
            **Shallow** copying is established using the copy module.

            Accepts the setting ``'explored'`` to only copy explored parameters.
            Note that ``v_full_copy`` determines how these will be copied.

        :param overwrite:

            If existing elemenst should be overwritten. Requries ``__getstate__`` and
            ``__setstate__`` being implemented in the leaves.

        :param with_links: If links should be ignored or followed and copied as well

        :return: The corresponding (new) node in the tree.

        """

        def _copy_skeleton(node_in, node_out):
            """Copies the skeleton of from `node_out` to `node_in`"""
            new_annotations = node_out.v_annotations
            node_in._annotations = new_annotations
            node_in.v_comment = node_out.v_comment

        def _add_leaf(leaf):
            """Adds a leaf to the trajectory"""
            leaf_full_name = leaf.v_full_name
            try:
                found_leaf = self.f_get(leaf_full_name,
                                        with_links=False,
                                        shortcuts=False,
                                        auto_load=False)
                if overwrite:
                    found_leaf.__setstate__(leaf.__getstate__())
                return found_leaf
            except AttributeError:
                pass
            if copy_leaves is True or (copy_leaves == 'explored' and
                                           leaf.v_is_parameter and leaf.v_explored):
                new_leaf = self.f_add_leaf(cp.copy(leaf))
            else:
                new_leaf = self.f_add_leaf(leaf)
            if new_leaf.v_is_parameter and new_leaf.v_explored:
                self._explored_parameters[new_leaf.v_full_name] = new_leaf
            return new_leaf

        def _add_group(group):
            """Adds a new group to the trajectory"""
            group_full_name = group.v_full_name
            try:
                found_group = self.f_get(group_full_name,
                                             with_links=False,
                                             shortcuts=False,
                                             auto_load=False)
                if overwrite:
                    _copy_skeleton(found_group, group)
                return found_group
            except AttributeError:
                pass
            new_group = self.f_add_group(group_full_name)
            _copy_skeleton(new_group, group)
            return new_group

        is_run = self._is_run
        self._is_run = False  # So that we can copy Config Groups and Config Data
        try:
            if node.v_is_leaf:
                return _add_leaf(node)
            elif node.v_is_group:
                other_root = node.v_root
                if other_root is self:
                    raise RuntimeError('You cannot copy a given tree to itself!')
                result = _add_group(node)
                nodes_iterator = node.f_iter_nodes(recursive=True, with_links=with_links)
                has_links = []
                if node._links:
                    has_links.append(node)
                for child in nodes_iterator:
                    if child.v_is_leaf:
                        _add_leaf(child)
                    else:
                        _add_group(child)
                        if child._links:
                            has_links.append(child)

                if with_links:
                    for current in has_links:
                        mine = self.f_get(current.v_full_name, with_links=False,
                                          shortcuts=False, auto_load=False)
                        my_link_set = set(mine._links.keys())
                        other_link_set = set(current._links.keys())
                        new_links = other_link_set - my_link_set
                        for link in new_links:
                            where_full_name = current._links[link].v_full_name
                            mine.f_add_link(link, where_full_name)

                return result
            else:
                raise RuntimeError('You shall not pass!')
        except Exception:
            self._is_run = is_run

    @not_in_run
    def f_explore(self, build_dict):
        """Prepares the trajectory to explore the parameter space.


        To explore the parameter space you need to provide a dictionary with the names of the
        parameters to explore as keys and iterables specifying the exploration ranges as values.

        All iterables need to have the same length otherwise a ValueError is raised.
        A ValueError is also raised if the names from the dictionary map to groups or results
        and not parameters.

        If your trajectory is already explored but not stored yet and your parameters are
        not locked you can add new explored parameters to the current ones if their
        iterables match the current length of the trajectory.

        Raises an AttributeError if the names from the dictionary are not found at all in
        the trajectory and NotUniqueNodeError if the keys not unambiguously map
        to single parameters.

        Raises a TypeError if the trajectory has been stored already, please use
        :func:`~pypet.trajectory.Trajectory.f_expand` then instead.

        Example usage:

        >>> traj.f_explore({'groupA.param1' : [1,2,3,4,5], 'groupA.param2':['a','b','c','d','e']})

        Could also be called consecutively:

        >>> traj.f_explore({'groupA.param1' : [1,2,3,4,5]})
        >>> traj.f_explore({'groupA.param2':['a','b','c','d','e']})

        NOTE:

        Since parameters are very conservative regarding the data they accept
        (see :ref:`type_conservation`), you sometimes won't be able to use Numpy arrays
        for exploration as iterables.

        For instance, the following code snippet won't work:

        ::

            import numpy a np
            from pypet.trajectory import Trajectory
            traj = Trajectory()
            traj.f_add_parameter('my_float_parameter', 42.4,
                                 comment='My value is a standard python float')

            traj.f_explore( { 'my_float_parameter': np.arange(42.0, 44.876, 0.23) } )


        This will result in a `TypeError` because your exploration iterable
        `np.arange(42.0, 44.876, 0.23)` contains `numpy.float64` values
        whereas you parameter is supposed to use standard python floats.

        Yet, you can use Numpys `tolist()` function to overcome this problem:

        ::

            traj.f_explore( { 'my_float_parameter': np.arange(42.0, 44.876, 0.23).tolist() } )


        Or you could specify your parameter directly as a numpy float:

        ::

            traj.f_add_parameter('my_float_parameter', np.float64(42.4),
                                   comment='My value is a numpy 64 bit float')



        """
        for run_idx in range(len(self)):
            if self.f_is_completed(run_idx):
                raise TypeError('You cannot explore a trajectory which has been explored before, '
                                'please use `f_expand` instead.')

        added_explored_parameters = []
        try:
            length = len(self)
            for key, builditerable in build_dict.items():
                act_param = self.f_get(key)
                if not act_param.v_is_leaf or not act_param.v_is_parameter:
                    raise ValueError('%s is not an appropriate search string for a parameter.' % key)

                act_param.f_unlock()

                act_param._explore(builditerable)
                added_explored_parameters.append(act_param)

                full_name = act_param.v_full_name
                self._explored_parameters[full_name] = act_param

                act_param._explored = True

                # Compare the length of two consecutive parameters in the `build_dict`
                if len(self._explored_parameters) == 1:
                    length = act_param.f_get_range_length()
                elif not length == act_param.f_get_range_length():
                    raise ValueError('The parameters to explore have not the same size!')

            for irun in range(length):
                self._add_run_info(irun)
            self._test_run_addition(length)

        except Exception:
            # Remove the added parameters again
            for param in added_explored_parameters:
                param.f_unlock()
                param._shrink()
                param._explored = False
                full_name = param.v_full_name
                del self._explored_parameters[full_name]
            if len(self._explored_parameters) == 0:
                self.f_shrink(force=True)
            raise

    def _update_run_information(self, run_information_dict):
        """Overwrites the run information of a particular run"""
        idx = run_information_dict['idx']
        name = run_information_dict['name']
        self._run_information[name] = run_information_dict
        self._updated_run_information.add(idx)

    def _add_run_info(self, idx, name='', timestamp=42.0, finish_timestamp=1.337,
                      runtime='forever and ever', time='>>Maybe time`s gone on strike',
                      completed=0, parameter_summary='Not yet my friend!',
                      short_environment_hexsha='N/A'):
        """Adds a new run to the `_run_information` dict."""

        if idx in self._single_run_ids:
            # Delete old entries, they might be replaced by a new name
            old_name = self._single_run_ids[idx]
            del self._single_run_ids[old_name]
            del self._single_run_ids[idx]
            del self._run_information[old_name]

        if name == '':
            name = self.f_wildcard('$', idx)
        # The `_single_run_ids` dict is bidirectional and maps indices to run names and vice versa
        self._single_run_ids[name] = idx
        self._single_run_ids[idx] = name

        info_dict = {'idx': idx,
                     'timestamp': timestamp,
                     'finish_timestamp': finish_timestamp,
                     'runtime': runtime,
                     'time': time,
                     'completed': completed,
                     'name': name,
                     'parameter_summary': parameter_summary,
                     'short_environment_hexsha': short_environment_hexsha}

        self._run_information[name] = info_dict
        self._length = len(self._run_information)

    @not_in_run
    def f_lock_parameters(self):
        """Locks all non-empty parameters"""
        for par in self._parameters.values():
            if not par.f_is_empty():
                par.f_lock()

    @not_in_run
    def f_lock_derived_parameters(self):
        """Locks all non-empty derived parameters"""
        for par in self._derived_parameters.values():
            if not par.f_is_empty():
                par.f_lock()

    def _finalize(self, store_meta_data=True):
        """Final rollback initiated by the environment

        Restores the trajectory as root of the tree, and stores meta data to disk.
        This updates the trajectory's information about single runs, i.e. if they've been
        completed, when they were started, etc.

        """
        self._is_run = False
        self.f_set_crun(None)
        if store_meta_data:
            self.f_store(only_init=True)

    @not_in_run
    def f_load_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, as_new=False, load_parameters=pypetconstants.LOAD_SKELETON,
                    load_derived_parameters=pypetconstants.LOAD_SKELETON,
                    load_results=pypetconstants.LOAD_SKELETON,
                    load_other_data=pypetconstants.LOAD_SKELETON,
                    with_run_information=False)

    @not_in_run
    @kwargs_api_change('load_all', 'load_data')
    @kwargs_api_change('dynamically_imported_classes', 'dynamic_imports')
    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,
               recursive=True,
               load_data=None,
               max_depth=None,
               force=False,
               dynamic_imports=None,
               with_run_information=True,
               with_meta_data=True,
               storage_service=None, **kwargs):
        """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.

        :param index:

            If you don't specify a name you can specify an integer index instead.
            The corresponding trajectory in the hdf5 file at the index
            position is loaded (counting starts with 0). Negative indices are also allowed
            counting in reverse order. For instance, `-1` refers to the last trajectory in
            the file, `-2` to the second last, and so on.

        :param as_new:

            Whether you want to rerun the experiments. So the trajectory is loaded only
            with parameters. The current trajectory name is kept in this case, which should be
            different from the trajectory name specified in the input parameter `name`.
            If you load `as_new=True` all parameters are unlocked.
            If you load `as_new=False` the current trajectory is replaced by the one on disk,
            i.e. name, timestamp, formatted time etc. are all taken from disk.

        :param load_parameters: How parameters and config items are loaded

        :param load_derived_parameters: How derived parameters are loaded

        :param load_results: How results are loaded

            You can specify how to load the parameters, derived parameters and results
            as follows:

                * :const:`pypet.pypetconstants.LOAD_NOTHING`: (0)

                    Nothing is loaded.

                * :const:`pypet.pypetconstants.LOAD_SKELETON`: (1)

                    The skeleton is loaded including annotations (See :ref:`more-on-annotations`).
                    This means that only empty
                    *parameter* and *result* objects will
                    be created  and you can manually load the data into them afterwards.
                    Note that :class:`pypet.annotations.Annotations` do not count as data and they
                    will be loaded because they are assumed to be small.

                * :const:`pypet.pypetconstants.LOAD_DATA`: (2)

                    The whole data is loaded. Note in case you have non-empty leaves
                    already in RAM, these are left untouched.

                * :const:`pypet.pypetconstants.OVERWRITE_DATA`: (3)

                    As before, but non-empty nodes are emptied and reloaded.

                Note that in all cases except :const:`pypet.pypetconstants.LOAD_NOTHING`,
                annotations will be reloaded if the corresponding instance
                is created or the annotations of an existing instance were emptied before.

        :param recursive:

            If data should be loaded recursively. If set to `None`, this is equivalent to
            set all data loading to `:const:`pypet.pypetconstants.LOAD_NOTHING`.

        :param load_data:

            As the above, per default set to `None`. If not `None` the setting of `load_data`
            will overwrite the settings of `load_parameters`, `load_derived_parameters`,
            `load_results`, and `load_other_data`. This is more or less or shortcut if all
            types should be loaded the same.

        :param max_depth:

            Maximum depth to load nodes (inclusive).

        :param force:

            *pypet* will refuse to load trajectories that have been created using *pypet* with a
            different version number or a different python version.
            To force the load of a trajectory from a previous version
            simply set ``force = True``. Note that it is not checked if other versions of packages
            differ from previous experiments, i.e. numpy, scipy, etc. But you can check
            for this manually. The versions of other packages can be found under
            ``'config.environment.name_of_environment.versions.package_name'``.

        :param dynamic_imports:

            If you've written a custom parameter that needs to be loaded dynamically
            during runtime, this needs to be specified here as a list of classes or
            strings naming classes and there module paths. For example:
            `dynamic_imports = ['pypet.parameter.PickleParameter',MyCustomParameter]`

            If you only have a single class to import, you do not need the list brackets:
            `dynamic_imports = 'pypet.parameter.PickleParameter'`

            The classes passed here are added for good and will be kept by the trajectory.
            Please add your dynamically imported classes only once.

        :param with_run_information:

            If information about the individual runs should be loaded. If you have many
            runs, like 1,000,000 or more you can spare time by setting
            `with_run_information=False`.
            Note that `f_get_run_information` and `f_idx_to_run` do not work in such a case.
            Moreover, setting `v_idx` does not work either. If you load the trajectory
            without this information, be careful, this is not recommended.

        :param wiht_meta_data:

            If meta data should be loaded.

        :param storage_service:

            Pass a storage service used by the trajectory. Alternatively pass a constructor
            and other ``**kwargs`` are passed onto the constructor. Leave `None` in combination
            with using no other kwargs, if you don't want to change the service
            the trajectory is currently using.

        :param kwargs:

            Other arguments passed to the storage service constructor. Don't pass any
            other kwargs and ``storage_service=None``,
            if you don't want to change the current service.

        """
        # Do some argument validity checks first
        if name is None and index is None:
            name = self.v_name

        if as_new:
            load_parameters = pypetconstants.LOAD_DATA
            load_derived_parameters = pypetconstants.LOAD_NOTHING
            load_results = pypetconstants.LOAD_NOTHING
            load_other_data = pypetconstants.LOAD_NOTHING

        unused_kwargs = set(kwargs.keys())
        if self.v_storage_service is None or storage_service is not None or len(kwargs) > 0:
            self._storage_service, unused_kwargs = storage_factory(storage_service=storage_service,
                                                                   trajectory=self, **kwargs)
        if len(unused_kwargs) > 0:
            raise ValueError('The following keyword arguments were not used: `%s`' %
                             str(unused_kwargs))

        if dynamic_imports is not None:
            self.f_add_to_dynamic_imports(dynamic_imports)

        if load_data is not None:
            load_parameters = load_data
            load_derived_parameters = load_data
            load_results = load_data
            load_other_data = load_data

        self._storage_service.load(pypetconstants.TRAJECTORY, self, trajectory_name=name,
                                   trajectory_index=index,
                                   as_new=as_new, load_parameters=load_parameters,
                                   load_derived_parameters=load_derived_parameters,
                                   load_results=load_results,
                                   load_other_data=load_other_data,
                                   recursive=recursive,
                                   max_depth=max_depth,
                                   with_run_information=with_run_information,
                                   with_meta_data=with_meta_data,
                                   force=force)

        # If a trajectory is newly loaded, all parameters are unlocked.
        if as_new:
            for param in self._parameters.values():
                param.f_unlock()

    def _check_if_both_have_same_parameters(self, other_trajectory,
                                            ignore_data, consecutive_merge):
        """ Checks if two trajectories live in the same space and can be merged. """

        if not isinstance(other_trajectory, Trajectory):
            raise TypeError('Can only merge trajectories, the other trajectory'
                            ' is of type `%s`.' % str(type(other_trajectory)))

        if self._stored and not consecutive_merge:
            self.f_load_skeleton()
        if other_trajectory._stored:
            other_trajectory.f_load_skeleton()

        # Check wildcard set
        other_wildcard_set = set(x[1] for x in other_trajectory._wildcard_functions.keys())
        wildcard_set = set(x[1] for x in self._wildcard_functions.keys())
        diff = wildcard_set.symmetric_difference(other_wildcard_set)
        if diff:
            raise TypeError('The wildcard sets are not matching. `%s` != `%s`' %
                            (str(wildcard_set), str(other_wildcard_set)))

        # Load all parameters of the current and the other trajectory
        if self._stored:
            # To suppress warnings if nothing needs to be loaded
            with self._nn_interface._disable_logging:
                self.f_load_items(self._parameters.keys(), only_empties=True)
        if other_trajectory._stored:
            with self._nn_interface._disable_logging:
                other_trajectory.f_load_items(other_trajectory._parameters.keys(),
                                              only_empties=True)

        self.f_restore_default()
        other_trajectory.f_restore_default()

        allmyparams = self._parameters.copy()
        allotherparams = other_trajectory._parameters.copy()

        # If not ignored, add also the trajectory derived parameters to check for merging
        if 'derived_parameters' in self:
            my_traj_dpars = self._derived_parameters
            if self._stored:
                with self._nn_interface._disable_logging:
                    self.f_load_items(my_traj_dpars.keys(), only_empties=True)
            allmyparams.update(my_traj_dpars)
            other_traj_dpars = other_trajectory._derived_parameters
            if other_trajectory._stored:
                with self._nn_interface._disable_logging:
                    other_trajectory.f_load_items(other_traj_dpars.keys(), only_empties=True)
            allotherparams.update(other_traj_dpars)

        # Check if the trajectories have the same parameters:
        my_keyset = set(allmyparams.keys())
        other_keyset = set(allotherparams.keys())
        diff = my_keyset.symmetric_difference(other_keyset) - ignore_data
        run_dummys = (self.f_wildcard('$', -1), other_trajectory.f_wildcard('$', -1))
        if diff:
            run_difference_can_be_resolved = True
            for full_name in diff:
                split_name = full_name.split('.')
                if not any(x in self._run_information or
                                x in other_trajectory._run_information or
                                    x in run_dummys
                                        for x in split_name):
                    run_difference_can_be_resolved = False
                    break
                elif full_name in allotherparams:
                    del allotherparams[full_name]
            if not run_difference_can_be_resolved:
                raise TypeError('Cannot merge trajectories, '
                                'they do not live in the same space,the '
                                'set of parameters `%s` is only '
                                'found in one trajectory.' % str(diff))

        # Check if corresponding parameters in both trajectories are of the same type
        for key, other_param in allotherparams.items():
            if key in ignore_data:
                continue
            my_param = self.f_get(key)
            split_key = key.split('.')
            if any(x in self._run_information or
                        x in other_trajectory._run_information
                            for x in split_key):
                pass
            else:
                if not my_param._values_of_same_type(my_param.f_get(), other_param.f_get()):
                    raise TypeError('Cannot merge trajectories, values of parameters `%s` are not '
                                    'of the same type. Types are %s (current) and %s (other).' %
                                    (key, str(type(my_param.f_get())),
                                     str(type(other_param.f_get()))))

    @not_in_run
    def f_backup(self, **kwargs):
        """Backs up the trajectory with the given storage service.

        Arguments of ``kwargs`` are directly passed to the storage service,
        for the HDF5StorageService you can provide the following argument:

        :param backup_filename:

            Name of file where to store the backup.

            In case you use the standard HDF5 storage service and `backup_filename=None`,
            the file will be chosen automatically.
            The backup file will be in the same folder as your hdf5 file and
            named 'backup_XXXXX.hdf5' where 'XXXXX' is the name of your current trajectory.

        """
        self._storage_service.store(pypetconstants.BACKUP, self, trajectory_name=self.v_name,
                                    **kwargs)

    def _make_reversed_wildcards(self, old_length=-1):
        """Creates a full mapping from all wildcard translations to the corresponding wildcards"""
        if len(self._reversed_wildcards) > 0:
            # We already created reversed wildcards, so we don't need to do all of them
            # again
            start = old_length
        else:
            start = -1
        for wildcards, func in self._wildcard_functions.items():
            for irun in range(start, len(self)):
                translated_name = func(irun)
                if not translated_name in self._reversed_wildcards:
                    self._reversed_wildcards[translated_name] = ([], wildcards)
                self._reversed_wildcards[translated_name][0].append(irun)

    @not_in_run
    def f_merge_many(self, other_trajectories,
                ignore_data=(),
                move_data=False,
                delete_other_trajectory=False,
                keep_info=True,
                keep_other_trajectory_info=True,
                merge_config=True,
                backup=True):
        """Can be used to merge several `other_trajectories` into your current one.

        IMPORTANT `backup=True` only backs up the current trajectory not any of
        the `other_trajectories`. If you need a backup of these, do it manually.

        Parameters as for :func:`~pypet.trajectory.Trajectory.f_merge`.

        """
        other_length = len(other_trajectories)
        self._logger.info('Merging %d trajectories into the current one.' % other_length)
        self.f_load_skeleton()

        if backup:
            self.f_backup()

        for idx, other in enumerate(other_trajectories):
            self.f_merge(other, ignore_data=ignore_data,
                         move_data=move_data,
                         delete_other_trajectory=delete_other_trajectory,
                         keep_info=keep_info,
                         keep_other_trajectory_info=keep_other_trajectory_info,
                         merge_config=merge_config,
                         backup=False,
                         consecutive_merge=True)
            self._logger.log(21,'Merged %d out of %d' % (idx + 1, other_length))
        self._logger.info('Storing data to disk')
        self._reversed_wildcards = {}
        self.f_store()
        self._logger.info('Finished final storage')

    @not_in_run
    @kwargs_api_change('backup_filename', 'backup')
    @kwargs_api_change('move_nodes', 'move_data')
    @kwargs_api_change('ignore_trajectory_derived_parameters')
    @kwargs_api_change('ignore_trajectory_results')
    def f_merge(self, other_trajectory, trial_parameter=None, remove_duplicates=False,
                ignore_data=(),
                backup=True,
                move_data=False,
                delete_other_trajectory=False,
                keep_info=True,
                keep_other_trajectory_info=True,
                merge_config=True,
                consecutive_merge=False,
                slow_merge=False):
        """Merges another trajectory into the current trajectory.

        Both trajectories must live in the same space. This means both need to have the same
        parameters with similar types of values.

        Note that links are also merged. There are exceptions: Links found under
        a generic run group called `run_ALL` or links linking to a node under such a
        group are NOT merged and simply skipped, because there is no straightforward
        way to resolve the link.

        :param other_trajectory: Other trajectory instance to merge into the current one.

        :param trial_parameter:

            If you have a particular parameter that specifies only the trial
            number, i.e. an integer parameter running form 0 to T1 and
            0 to T2, the parameter is modified such that after merging it will
            cover the range 0 to T1+T2+1. T1 is the number of individual trials in the current
            trajectory and T2 number of trials in the other trajectory.

        :param remove_duplicates:

            Whether you want to remove duplicate parameter points.
            Requires N1 * N2 (quadratic complexity in single runs).
            A ValueError is raised if no runs would be merged.

        :param ignore_data:

            List of full names of data that should be ignored and not merged.

        :param backup:

            If ``True``, backs up both trajectories into files chosen automatically
            by the storage services. If you want to customize your backup use
            the `f_backup` function instead.

        :param move_data:

           Tells the storage service to move data from one trajectory to the other
           instead of copying it.

           If you use the HDF5 storage service and both trajectories are
           stored in the same file, merging is performed fast directly within
           the file. You can choose if you want to copy nodes ('move_nodes=False`)
           from the other trajectory to the current one, or if you want to move them.
           Accordingly, the stored data is no longer accessible in the other trajectory.

        :param delete_other_trajectory:

            If you want to delete the other trajectory after merging.

        :param keep_info:

            If `True`, information about the merge is added to the trajectory `config` tree under
            `config.merge`.

        :param merge_config:

            Whether or not to merge all config parameters under `.config.git`,
            `.config.environment`, and `.config.merge` of the other trajectory
            into the current one.

        :param keep_other_trajectory_info:

            Whether to keep information like length, name, etc. of the other trajectory
            in case you want to keep all the information. Setting of `keep_other_trajectory_info`
            is irrelevant in case `keep_info=False`.

        :param consecutive_merge:
            Can be set to `True` if you are about to merge several trajectories into the current
            one within a loop to avoid quadratic complexity.
            But remember to store your trajectory manually after
            all merges. Also make sure that all parameters and derived parameters are available
            in your current trajectory and load them before the consecutive merging.
            Also avoid specifying a `trial_parameter` and set `backup=False`
            to avoid quadratic complexity in case of consecutive merges.

        :param slow_merge:
            Enforces a slow merging. This means all data is loaded one after the other to
            memory and stored to disk. Otherwise it is tried to directly copy the data
            from one file into another without explicitly loading the data.

        If you cannot directly merge trajectories within one HDF5 file, a slow merging process
        is used. Results are loaded, stored, and emptied again one after the other. Might take
        some time!

        Annotations of parameters and derived parameters under `.derived_parameters.trajectory`
        are NOT merged. If you wish to extract the annotations of these parameters you have to
        do that manually before merging. Note that annotations of results and derived parameters
        of single runs are copied, so you don't have to worry about these.

        """
        if consecutive_merge and trial_parameter is not None:
            self._logger.warning('If you do a consecutive merge and specify a trial parameter, '
                                 'your merging will still suffer from quadratic time complexity!')
        if consecutive_merge and backup:
            self._logger.warning('If you do a consecutive merge and backup, '
                                 'your merging will still suffer from quadratic time complexity!')

        # Keep the timestamp of the merge
        timestamp = time.time()
        original_ignore_data = set(ignore_data)
        ignore_data = original_ignore_data.copy()

        old_len = len(self)

        # Check if trajectories can be merged
        self._check_if_both_have_same_parameters(other_trajectory, ignore_data, consecutive_merge)

        # Create a full mapping set for renaming
        self._make_reversed_wildcards(old_length=old_len)
        other_trajectory._make_reversed_wildcards()

        # BACKUP if merge is possible
        if backup:
            other_trajectory.f_backup()
            self.f_backup()

        # Merge parameters and keep track which runs where used and which parameters need
        # to be updated
        self._logger.info('Merging the parameters')
        used_runs, changed_parameters = self._merge_parameters(
            other_trajectory,
            remove_duplicates,
            trial_parameter,
            ignore_data)

        ignore_data.update(set(changed_parameters))

        if len(used_runs) == 0:
            raise ValueError('Your merge discards all runs of the other trajectory, maybe you '
                             'try to merge a trajectory with itself?')

        # Dictionary containing the mappings between run names in the other trajectory
        # and their new names in the current trajectory
        rename_dict = {}

        self._logger.info('Merging run information')
        allowed_translations = set([translation for translation, pair in
                                    other_trajectory._reversed_wildcards.items() if
                                        any(x in used_runs for x in pair[0])])
        self._merge_single_runs(other_trajectory, used_runs)

        self._logger.info('Merging derived parameters')
        self._merge_derived_parameters(other_trajectory=other_trajectory,
                                         used_runs=used_runs,
                                         rename_dict=rename_dict,
                                         allowed_translations=allowed_translations,
                                         ignore_data=ignore_data)

        # Keep track of all trajectory results that should be merged and put
        # information into `rename_dict`
        self._merge_results(other_trajectory=other_trajectory,
                             used_runs=used_runs,
                             rename_dict=rename_dict,
                             allowed_translations=allowed_translations,
                             ignore_data=ignore_data)



        # The storage service needs to prepare the file for merging.
        # This includes updating meta information and already storing the merged parameters
        self._logger.info('Start copying results and single run derived parameters')
        self._logger.info('Updating Trajectory information and changed parameters in storage')
        self._storage_service.store(pypetconstants.PREPARE_MERGE, self,
                                    trajectory_name=self.v_name,
                                    changed_parameters=changed_parameters,
                                    old_length=old_len)

        if not slow_merge:
            try:
                # Merge the single run derived parameters and all results
                # within the same hdf5 file based on `renamed_dict`
                try:
                    # try to get the other file
                    other_filename = other_trajectory.v_storage_service.filename
                except AttributeError:
                    self._logger.warning('Could not determine the filename of the other '
                                         'trajectory, I will assume it`s in the same file.')
                    other_filename = None
                self._storage_service.store(pypetconstants.MERGE, 'FAST MERGE', trajectory_name=self.v_name,
                                            other_trajectory_name=other_trajectory.v_name,
                                            rename_dict=rename_dict, move_nodes=move_data,
                                            delete_trajectory=delete_other_trajectory,
                                            other_filename=other_filename)

            except pex.NoSuchServiceError as exc:
                # If the storage service does not support merge we end up here
                self._logger.exception('My storage service does not support merging of trajectories, '
                                     'I will use the f_load mechanism of the other trajectory and '
                                     'store the results slowly item by item. '
                                     'Note that thereby the other '
                                     'trajectory will be altered (in RAM).')
                slow_merge = True

            except ValueError as exc:
                # If both trajectories are stored in separate files we end up here
                self._logger.exception('Could not perfom fast merging. '
                                     'I will use the `f_load` method of the other trajectory and '
                                     'store the results slowly item by item. '
                                     'Note that thereby the other '
                                     'trajectory will be altered (in RAM).')
                slow_merge = True

        if slow_merge:
            self._merge_slowly(other_trajectory, rename_dict)

        # We will merge the git commits and other config data
        if merge_config:
            self._merge_config(other_trajectory)

        # Finally merging links
        self._merge_links(other_trajectory=other_trajectory,
                         used_runs=used_runs,
                         allowed_translations=allowed_translations,
                         ignore_data=original_ignore_data)

        # Add meta information about the merge to the current trajectory
        self._logger.info('Adding merge information')
        formatted_time = datetime.datetime.fromtimestamp(timestamp).strftime('%Y_%m_%d_%Hh%Mm%Ss')
        hexsha = hashlib.sha1((self.v_name +
                                             str(self.v_timestamp) +
                                             other_trajectory.v_name +
                                             str(other_trajectory.v_timestamp) +
                                             VERSION).encode('utf-8')).hexdigest()

        short_hexsha = hexsha[0:7]

        if keep_info:
            merge_name = 'merge_%s_%s' % (short_hexsha, formatted_time)

            config_name = 'merge.%s.merged_runs' % merge_name
            self.f_add_config(config_name, len(used_runs),
                              comment='Number of merged runs')

            config_name = 'merge.%s.timestamp' % merge_name
            self.f_add_config(config_name, timestamp,
                              comment='Timestamp of merge')

            config_name = 'merge.%s.hexsha' % merge_name
            self.f_add_config(config_name, hexsha,
                              comment='SHA-1 identifier of the merge')

            config_name = 'merge.%s.remove_duplicates' % merge_name
            self.f_add_config(config_name, remove_duplicates,
                              comment='Option to remove duplicate entries')

            if original_ignore_data:
                config_name = 'merge.%s.ignore_data' % merge_name
                self.f_add_config(config_name, tuple(original_ignore_data),
                                  comment='Data to ignore during merge')

            config_name = 'merge.%s.length_before_merge' % merge_name
            self.f_add_config(config_name, len(self),
                              comment='Length of trajectory before merge')

            self.config.merge.v_comment = 'Settings and information of the different merges'

            if self.v_version != VERSION:
                config_name = 'merge.%s.version' % merge_name
                self.f_add_config(config_name, self.v_version,
                                  comment='Pypet version if it differs from the version'
                                          ' of the trajectory')

            if trial_parameter is not None:
                config_name = 'merge.%s.trial_parameter' % merge_name
                self.f_add_config(config_name, len(other_trajectory),
                                  comment='Name of trial parameter')

            if keep_other_trajectory_info:

                if other_trajectory.v_version != self.v_version:
                    config_name = 'merge.%s.other_trajectory.version' % merge_name
                    self.f_add_config(config_name, other_trajectory.v_version,
                                      comment='The version of pypet you used to manage the other'
                                              ' trajectory. Only added if other trajectory\'s'
                                              ' version differs from current trajectory version.')

                config_name = 'merge.%s.other_trajectory.name' % merge_name
                self.f_add_config(config_name, other_trajectory.v_name,
                                  comment='Name of other trajectory merged into the current one')

                config_name = 'merge.%s.other_trajectory.timestamp' % merge_name
                self.f_add_config(config_name, other_trajectory.v_timestamp,
                                  comment='Timestamp of creation of other trajectory '
                                          'merged into the current one')

                config_name = 'merge.%s.other_trajectory.length' % merge_name
                self.f_add_config(config_name, len(other_trajectory),
                                  comment='Length of other trajectory')

                if other_trajectory.v_comment:
                    config_name = 'merge.%s.other_trajectory.comment' % merge_name
                    self.f_add_config(config_name, other_trajectory.v_comment,
                                      comment='Comment of other trajectory')

        # Write out the merged data to disk
        if not consecutive_merge:
            self._logger.info('Writing merged data to disk')
            self.f_store(store_data=pypetconstants.STORE_DATA)
            self._reversed_wildcards = {}
        other_trajectory._reversed_wildcards = {}

        self._logger.info('Finished Merging!')

    def _merge_single_runs(self, other_trajectory, used_runs):
        """  Updates the `run_information` of the current trajectory."""
        count = len(self)  # Variable to count the increasing new run indices and create
        # new run names

        run_indices = range(len(other_trajectory))

        run_name_dict = OrderedDict()

        to_store_groups_with_annotations = []

        for idx in run_indices:
            # Iterate through all used runs and store annotated groups and mark results and
            # derived parameters for merging
            if idx in used_runs:
                # Update the run information dict of the current trajectory
                other_info_dict = other_trajectory.f_get_run_information(idx)
                time_ = other_info_dict['time']
                timestamp = other_info_dict['timestamp']
                completed = other_info_dict['completed']
                short_environment_hexsha = other_info_dict['short_environment_hexsha']
                finish_timestamp = other_info_dict['finish_timestamp']
                runtime = other_info_dict['runtime']

                new_idx = used_runs[idx]
                new_runname = self.f_wildcard('$', new_idx)

                run_name_dict[idx] = new_runname

                info_dict = dict(
                    idx=new_idx,
                    time=time_,
                    timestamp=timestamp,
                    completed=completed,
                    short_environment_hexsha=short_environment_hexsha,
                    finish_timestamp=finish_timestamp,
                    runtime=runtime)

                self._add_run_info(**info_dict)

    def _rename_full_name(self, full_name, other_trajectory, used_runs=None, new_run_idx=None):
        """Renames a full name based on the wildcards and a particular run"""
        split_name = full_name.split('.')
        for idx, name in enumerate(split_name):
            if name in other_trajectory._reversed_wildcards:
                run_indices, wildcards = other_trajectory._reversed_wildcards[name]
                if new_run_idx is None:
                    # We can safely take the first index of the index list that matches
                    run_idx = None
                    for run_jdx in run_indices:
                        if run_jdx in used_runs:
                            run_idx = used_runs[run_jdx]
                            break
                        elif run_jdx == -1:
                            run_idx = -1
                            break
                    if run_idx is None:
                        raise RuntimeError('You shall not pass!')
                else:
                    run_idx = new_run_idx
                new_name = self.f_wildcard(wildcards[0], run_idx)
                split_name[idx] = new_name
        full_name = '.'.join(split_name)
        return full_name

    def _merge_derived_parameters(self,
                                    other_trajectory,
                                    used_runs,
                                    rename_dict,
                                    allowed_translations,
                                    ignore_data):
        """ Merges derived parameters that have the `run_ALL` in a name.

        Creates a new parameter with the name of the first new run and links to this
        parameter to avoid copying in all other runs.

        """
        other_derived_parameters = other_trajectory._derived_parameters.copy()
        # get first run_idx
        new_first_run_idx = min(used_runs.values())

        run_name_dummy = other_trajectory.f_wildcard('$', -1)
        for param_name in other_derived_parameters:
            if param_name in ignore_data:
                continue
            split_name = param_name.split('.')
            if not any(x in run_name_dummy for x in split_name):
                continue
            ignore_data.add(param_name)

            param = other_derived_parameters[param_name]
            new_param_name = self._rename_full_name(param_name, other_trajectory,
                                                    used_runs=used_runs)
            if new_param_name in self:
                my_param = self.f_get(new_param_name, fast_access=False)
                if (my_param._equal_values(my_param.f_get(), param.f_get()) and
                        not (my_param.f_has_range() or param.f_has_range())):
                    continue

            first_new_param_name = self._rename_full_name(param_name,
                                                          other_trajectory,
                                                          new_run_idx=new_first_run_idx)

            rename_dict[param_name] = first_new_param_name
            comment = param.v_comment
            param_type = param.f_get_class_name()
            param_type = self._create_class(param_type)
            first_param = self.f_add_leaf(param_type,
                                          first_new_param_name,
                                          comment=comment)
            for run_idx in used_runs.values():
                if run_idx == new_first_run_idx:
                    continue
                next_name = self._rename_full_name(param_name, other_trajectory,
                                                    new_run_idx=run_idx)
                split_name = next_name.split('.')
                link_name = split_name.pop()
                location_name = '.'.join(split_name)
                if not self.f_contains(location_name, shortcuts=False):
                    the_group = self.f_add_group(location_name)
                else:
                    the_group = self.f_get(location_name)

                the_group.f_add_link(link_name, first_param)

        for param_name in other_derived_parameters:
            if param_name in ignore_data:
                continue
            split_name = param_name.split('.')
            ignore_data.add(param_name)
            if any(x in other_trajectory._reversed_wildcards and x not in allowed_translations
                        for x in split_name):
                continue
            new_name = self._rename_full_name(param_name, other_trajectory,
                                                    used_runs=used_runs)
            if self.f_contains(new_name):
                my_param = self.f_get(new_name, fast_access=False)
                param = other_derived_parameters[param_name]
                if (my_param._equal_values(my_param.f_get(), param.f_get()) and
                        not (my_param.f_has_range() or param.f_has_range())):
                    continue
                else:
                    self._logger.error('Could not merge parameter `%s`. '
                                       'I will ignore it!' % new_name)
            rename_dict[param_name] = new_name


    def _merge_links(self, other_trajectory, used_runs, allowed_translations, ignore_data):
        """ Merges all links"""
        linked_items = other_trajectory._linked_by
        run_name_dummys = set([f(-1) for f in other_trajectory._wildcard_functions.values()])
        if len(linked_items) > 0:
            self._logger.info('Merging potential links!')
            for old_linked_name in other_trajectory._linked_by:
                if old_linked_name in ignore_data:
                    continue
                split_name = old_linked_name.split('.')
                if any(x in run_name_dummys for x in split_name):
                    self._logger.warning('Ignoring all links linking to `%s` because '
                                     'I don`t know how to resolve links under `%s` nodes.' %
                                     (old_linked_name, str(run_name_dummys)))
                    continue
                old_link_dict = other_trajectory._linked_by[old_linked_name]
                split_name = old_linked_name.split('.')
                if all(x in allowed_translations for x in split_name):
                    new_linked_full_name = self._rename_full_name(old_linked_name,
                                                                  other_trajectory,
                                                                  used_runs=used_runs)
                else:
                    new_linked_full_name = old_linked_name

                for linking_node, link_set in old_link_dict.values():

                    linking_full_name = linking_node.v_full_name
                    split_name = linking_full_name .split('.')
                    if any(x in run_name_dummys for x in split_name):
                        self._logger.warning('Ignoring links under `%s` because '
                                             'I don`t know how to resolve links '
                                             'under a `%s` node.' %
                                             (linking_full_name, str(run_name_dummys)))

                    split_name = linking_full_name .split('.')
                    if any(x in allowed_translations for x in split_name):
                        new_linking_full_name = self._rename_full_name(linking_full_name,
                                                                      other_trajectory,
                                                                      used_runs=used_runs)
                    else:
                        new_linking_full_name = linking_full_name

                    for link in link_set:
                        if (linking_full_name + '.' + link) in ignore_data:
                            continue
                        if link in run_name_dummys:
                            self._logger.warning('Ignoring link `%s` under `%s` because '
                                                 'I don`t know how to resolve '
                                                 'links named as `%s`.' %
                                                 (link,
                                                  linking_full_name,
                                                  str(run_name_dummys)))
                            continue

                        try:
                            new_linked_item = self.f_get(new_linked_full_name,
                                                         shortcuts=False)
                            if self.f_contains(new_linking_full_name):
                                new_linking_item = self.f_get(new_linking_full_name,
                                                              shortcuts=False)
                            else:
                                new_linking_item =  self.f_add_group(new_linking_full_name)
                            if link in allowed_translations:
                                run_indices, wildcards = other_trajectory._reversed_wildcards[link]
                                link = self.f_wildcard(wildcards[0], used_runs[run_indices[0]])
                            if not link in new_linking_item._links:
                                new_linking_item.f_add_link(link, new_linked_item)
                            else:
                                self._logger.debug('Link `%s` exists already under `%s`.' %
                                                    (link, new_linked_item.v_full_name))
                        except (AttributeError, ValueError) as exc:
                            self._logger.error('Could not copy link `%s` under `%s` linking '
                                               'to `%s` due to `%s`' %
                                               (link, linking_full_name, old_linked_name,
                                                repr(exc)))

    def _merge_config(self, other_trajectory):
        """Merges meta data about previous merges, git commits, and environment settings
        of the other trajectory into the current one.

        """
        self._logger.info('Merging config!')

        # Merge git commit meta data
        if 'config.git' in other_trajectory:

            self._logger.info('Merging git commits!')
            git_node = other_trajectory.f_get('config.git')
            param_list = []
            for param in git_node.f_iter_leaves(with_links=False):
                if not self.f_contains(param.v_full_name, shortcuts=False):
                    param_list.append(self.f_add_config(param))

            if param_list:
                self.f_store_items(param_list)

            self._logger.info('Merging git commits successful!')

        # Merge environment meta data
        if 'config.environment' in other_trajectory:

            self._logger.info('Merging environment config!')
            env_node = other_trajectory.f_get('config.environment')
            param_list = []
            for param in env_node.f_iter_leaves(with_links=False):
                if not self.f_contains(param.v_full_name, shortcuts=False):
                    param_list.append(self.f_add_config(param))

            if param_list:
                self.f_store_items(param_list)

            self._logger.info('Merging config successful!')

        # Merge meta data of previous merges
        if 'config.merge' in other_trajectory:

            self._logger.info('Merging merge config!')
            merge_node = other_trajectory.f_get('config.merge')
            param_list = []
            for param in merge_node.f_iter_leaves(with_links=False):
                if not self.f_contains(param.v_full_name, shortcuts=False):
                    param_list.append(self.f_add_config(param))

            if param_list:
                self.f_store_items(param_list)

            self._logger.info('Merging config successful!')

    def _merge_slowly(self, other_trajectory, rename_dict):
        """Merges trajectories by loading iteratively items of the other trajectory and
        store it into the current trajectory.

        :param rename_dict:

            Dictionary containing mappings from the old result names in the `other_trajectory`
            to the new names in the current trajectory.

        """
        for other_key in rename_dict:
            new_key = rename_dict[other_key]

            other_instance = other_trajectory.f_get(other_key)

            if other_instance.f_is_empty():
                # To suppress warnings if nothing needs to be loaded
                with self._nn_interface._disable_logging:
                    other_trajectory.f_load_item(other_instance)

            if not self.f_contains(new_key):
                class_name = other_instance.f_get_class_name()
                class_ = self._create_class(class_name)
                my_instance = self.f_add_leaf(class_, new_key)
            else:
                my_instance = self.f_get(new_key, shortcuts=False)

            if not my_instance.f_is_empty():
                raise RuntimeError('Something is wrong! Your item `%s` should be empty.' % new_key)

            load_dict = other_instance._store()
            my_instance._load(load_dict)
            my_instance.f_set_annotations(**other_instance.v_annotations.f_to_dict(copy=False))
            my_instance.v_comment = other_instance.v_comment

            self.f_store_item(my_instance)

            # We do not want to blow up the RAM Memory
            if other_instance.v_is_parameter:
                other_instance.f_unlock()
                my_instance.f_unlock()
            other_instance.f_empty()
            my_instance.f_empty()

    def _merge_results(self, other_trajectory, rename_dict, used_runs, allowed_translations,
                       ignore_data):
        """Merges all results.

        :param rename_dict:

            Dictionary that is filled with the names of results in the `other_trajectory`
            as keys and the corresponding new names in the current trajectory as values.
            Note for results kept under trajectory run branch there is actually no need to
            change the names. So we will simply keep the original name.

        """

        other_results = other_trajectory._results.copy()

        for result_name in other_results:
            if result_name in ignore_data:
                continue
            split_name = result_name.split('.')
            ignore_data.add(result_name)
            if any(x in other_trajectory._reversed_wildcards and x not in allowed_translations
                        for x in split_name):
                continue
            new_name = self._rename_full_name(result_name, other_trajectory,
                                                    used_runs=used_runs)
            if self.f_contains(new_name):
                self._logger.warning('I found result `%s` already, I will ignore it.' % new_name)
                continue
            rename_dict[result_name] = new_name

    def _merge_parameters(self, other_trajectory, remove_duplicates=False,
                          trial_parameter_name=None,
                          ignore_data=()):
        """Merges parameters from the other trajectory into the current one.

        The explored parameters in the current trajectory are directly enlarged (in RAM),
        no storage service is needed here. Later on in `f_merge` the storage service
        will be requested to store the enlarge parameters to disk.

        Note explored parameters are always enlarged. Unexplored parameters might become
        new explored parameters if they differ in their default values
        in the current and the other trajectory, respectively.

        :return: A tuple with two elements:

            1.

                Dictionary of run index mappings from old trajectroy to the new one.

            2.

                List of names of parameters that were altered.

        """

        if trial_parameter_name:
            if remove_duplicates:
                self._logger.warning('You have given a trial parameter and you want to '
                                     'remove_items duplicates. There cannot be any duplicates '
                                     'when adding trials, I will not look for duplicates.')
                remove_duplicates = False

        # Dictionary containing full parameter names as keys
        # and pairs of parameters from both trajectories as values.
        # Parameters kept in this dictionary are marked for merging and will be enlarged
        # with ranges and values of corresponding parameters in the other trajectory
        params_to_change = {}

        if trial_parameter_name:
            # We want to merge a trial parameter
            # First make some sanity checks
            my_trial_parameter = self.f_get(trial_parameter_name)
            other_trial_parameter = other_trajectory.f_get(trial_parameter_name)
            if not isinstance(my_trial_parameter, BaseParameter):
                raise TypeError('Your trial_parameter `%s` does not evaluate to a real parameter'
                                ' in the trajectory' % trial_parameter_name)

            # Extract the ranges of both trial parameters
            if my_trial_parameter.f_has_range():
                my_trial_list = my_trial_parameter.f_get_range(copy=False)
            else:
                # If we only have a single trial, we need to make a range of length 1
                # This is probably a very exceptional case
                my_trial_list = [my_trial_parameter.f_get()]

            if other_trial_parameter.f_has_range():
                other_trial_list = other_trial_parameter.f_get_range(copy=False)
            else:
                other_trial_list = [other_trial_parameter.f_get()]

            # Make sanity checks if both ranges contain all numbers from 0 to T1
            # for the current trajectory and 0 to T2 for the other trajectory
            mytrialset = set(my_trial_list)
            mymaxtrial_T1 = max(mytrialset)  # maximum trial index in current trajectory aka T1

            if mytrialset != set(range(mymaxtrial_T1 + 1)):
                raise TypeError('In order to specify a trial parameter, this parameter must '
                                'contain integers from 0 to %d, but it in fact it '
                                'contains `%s`.' % (mymaxtrial_T1, str(mytrialset)))

            othertrialset = set(other_trial_list)
            othermaxtrial_T2 = max(othertrialset)  # maximum trial index in other trajectory aka T2
            if othertrialset != set(range(othermaxtrial_T2 + 1)):
                raise TypeError('In order to specify a trial parameter, this parameter must '
                                'contain integers from 0 to %d, but it infact it contains `%s` '
                                'in the other trajectory.' %
                                (othermaxtrial_T2, str(othertrialset)))

            # If the trial parameter's name was just given in parts we update it here
            # to the full name
            trial_parameter_name = my_trial_parameter.v_full_name

            # If we had the very exceptional case, that our trial parameter was not explored,
            # aka we only had 1 trial, we have to add it to the explored parameters
            if not trial_parameter_name in self._explored_parameters:
                self._explored_parameters[trial_parameter_name] = my_trial_parameter

            # We need to mark the trial parameter for merging
            params_to_change[trial_parameter_name] = (my_trial_parameter, other_trial_parameter)

        # Dictionary containing all parameters of the other trajectory, we will iterate through it
        # to spot parameters that need to be enlarge or become new explored parameters
        params_to_merge = other_trajectory._parameters.copy()

        params_to_merge.update(other_trajectory._derived_parameters)
        for ignore in ignore_data:
            if ignore in params_to_merge:
                del params_to_merge[ignore]

        run_name_dummys = set([f(-1) for f in other_trajectory._wildcard_functions.values()])
        # Iterate through all parameters of the other trajectory
        # and check which differ from the parameters of the current trajectory
        for key in params_to_merge:
            other_param = params_to_merge[key]

            # We don't need to merge anything based on wildcards
            split_key = key.split('.')
            if any(x in other_trajectory._reversed_wildcards for x in split_key):
                continue

            my_param = self.f_get(key)
            if not my_param._values_of_same_type(my_param.f_get(), other_param.f_get()):
                raise TypeError('The parameters with name `%s` are not of the same type, cannot '
                                'merge trajectory.' % key)

            # We have taken care about the trial parameter before, it is already
            # marked for merging
            if my_param.v_full_name == trial_parameter_name:
                continue

            # If a parameter was explored in one of the trajectories or two unexplored
            # parameters differ, we need to mark them for merge
            if (my_param.f_has_range() or
                    other_param.f_has_range() or
                    not my_param._equal_values(my_param.f_get(), other_param.f_get())):

                # If two unexplored parameters differ, that means they differ in every run,
                # accordingly we do not need to check for duplicate runs anymore
                params_to_change[key] = (my_param, other_param)
                if not my_param.f_has_range() and not other_param.f_has_range():
                    remove_duplicates = False

        # Check if we use all runs or remove duplicates:
        used_runs = {}
        for idx in range(len(other_trajectory)):
            used_runs[idx] = idx
        if remove_duplicates:

            # We need to compare all parameter combinations in the current trajectory
            # to all parameter combinations in the other trajectory to spot duplicate points.
            # Quadratic Complexity!
            for irun in range(len(other_trajectory)):
                for jrun in range(len(self)):
                    change = True

                    # Check all marked parameters
                    for my_param, other_param in params_to_change.values():
                        if other_param.f_has_range():
                            other_param._set_parameter_access(irun)

                        if my_param.f_has_range():
                            my_param._set_parameter_access(jrun)

                        val1 = my_param.f_get()
                        val2 = other_param.f_get()

                        # If only one parameter differs, the parameter space point differs
                        # and we can skip the rest of the parameters
                        if not my_param._equal_values(val1, val2):
                            change = False
                            break

                    # If we found one parameter space point in the current trajectory
                    # that matches the ith point in the other, we do not need the ith
                    # point. We can also skip comparing to the rest of the points in the
                    # current trajectory
                    if change:
                        del used_runs[irun]
                        break

            # Restore changed default values
            for my_param, other_param in params_to_change.values():
                other_param._restore_default()
                my_param._restore_default()


        # Merge parameters into the current trajectory
        adding_length = len(used_runs)
        starting_length = len(self)
        if adding_length == 0:
            return used_runs, []
        count = 0
        for key in sorted(used_runs.keys()):
            used_runs[key] = starting_length + count
            count += 1

        for my_param, other_param in params_to_change.values():
            fullname = my_param.v_full_name

            # We need new ranges to enlarge all parameters marked for merging
            if fullname == trial_parameter_name:
                # The trial parameter now has to cover the range 0 to T1+T2+1
                other_range = [x + mymaxtrial_T1 + 1 for x in other_trial_list]
            else:
                # In case we do not use all runs we need to filter the ranges of the
                # parameters of the other trajectory
                if other_param.f_has_range():
                    other_range = (x for jdx, x in enumerate(other_param.f_get_range(copy=False))
                                                             if jdx in used_runs)
                else:
                    other_range = (other_param.f_get() for _ in range(adding_length))

            # If a parameter in the current trajectory was marked for merging but was not
            # explored before, we need to explore it first, simply by creating the range of
            # the current trajectory's length containing only it's default value
            if not my_param.f_has_range():
                my_param.f_unlock()
                my_param._explore((my_param.f_get() for _ in  range(len(self))))

            # After determining the new range extension `other_range`,
            # expand the parameters
            my_param.f_unlock()
            my_param._expand(other_range)

            if not fullname in self._explored_parameters:
                self._explored_parameters[fullname] = my_param

        return used_runs, list(params_to_change.keys())

    @not_in_run
    def f_migrate(self, new_name=None, in_store=False,
                  new_storage_service=None, **kwargs):
        """Can be called to rename and relocate the trajectory.

        :param new_name: New name of the trajectory, None if you do not want to change the name.

        :param in_store:

            Set this to True if the trajectory has been stored with the new name at the new
            file before  and you just want to "switch back" to the location.
            If you migrate to a store used before and you do not set `in_store=True`,
            the storage service will throw a RuntimeError in case you store the Trajectory
            because it will assume that you try to store a new trajectory that accidentally has
            the very same name as another trajectory. If set to `True` and trajectory is not found
            in the file, the trajectory is simply stored to the file.

        :param new_storage_service:

            New service where you want to migrate to. Leave none if you want to keep the olde one.

        :param kwargs:

            Additional keyword arguments passed to the service.
            For instance, to change the file of the trajectory use ``filename='my_new_file.hdf5``.

        """
        if new_name is not None:
            self._name = new_name

        unused_kwargs = set(kwargs.keys())
        if new_storage_service is not None or len(kwargs) > 0:
            self._storage_service, unused_kwargs = storage_factory(
                                                    storage_service=new_storage_service,
                                                    trajectory=self, **kwargs)
        if len(unused_kwargs) > 0:
            raise ValueError('The following keyword arguments were not used: `%s`' %
                             str(unused_kwargs))

        self._stored = in_store

    @kwargs_api_change('new_filename')
    @kwargs_api_change('new_name')
    @kwargs_mutual_exclusive('only_init', 'recursive', lambda x: not x)
    def f_store(self, only_init=False, store_data=pypetconstants.STORE_DATA,
                max_depth=None):
        """ Stores the trajectory to disk and recursively all data in the tree.

        :param only_init:

            If you just want to initialise the store. If yes, only meta information about
            the trajectory is stored and none of the groups/leaves within the trajectory.
            Alternatively, you can pass `recursive=False`.

        :param store_data:

            Only considered if ``only_init=False``. Choose of the following:

                * :const:`pypet.pypetconstants.STORE_NOTHING`: (0)

                    Nothing is store.

                * :const:`pypet.pypetconstants.STORE_DATA_SKIPPING`: (1)

                    Speedy version of normal ``STORE_DATA`` will entirely skip groups
                    (but not their children) and leaves if they have been stored before.
                    No new data is added in this case.

                * :const:`pypet.pypetconstants.STORE_DATA`: (2)

                    Stores every group and leave node. If they contain data that is not yet stored
                    to disk it is added.

                * :const:`pypet.pypetconstants.OVERWRITE_DATA`: (3)

                    Stores all groups and leave nodes and will delete all data on disk
                    and overwrite it with the current data in RAM.

                    **NOT RECOMMENDED**! Overwriting data on disk fragments the HDF5 file and
                    yields badly compressed large files. Better stick to the concept
                    write once and read many!


        If you use the HDF5 Storage Service usually (STORE_DATA (2)) only novel data
        is stored to disk.
        If you have results that have been stored to disk before only new data items are added and
        already present data is NOT overwritten.

        Overwriting (OVERWRITE_DATA (3)) existing data with the HDF5 storage service
        is not recommended due to fragmentation of the HDF5 file. Better stick to the concept
        write once, but read often.

        If you want to store individual parameters or results, you might want to
        take a look at :func:`~pypet.Trajectory.f_store_items`.
        To store whole subtrees of your trajectory check out
        :func:`~pypet.naturalnaming.NNGroupNode.f_store_child`.
        Note both functions require that your trajectory was stored to disk with `f_store`
        at least once before.


        **ATTENTION**: Calling `f_store` during a single run the behavior is different.

        To avoid re-storing the full trajectory in every single run, which is redundant,
        only sub-trees of the trajectory are really stored.

        The storage serivce looks for new data that is added below groups called `run_XXXXXXXXXX`
        and stores it where `XXXXXXXXX` is the index of this run. The `only_init` parameter is
        ignored in this case. You can avoid this behavior by using the argument from below.

        :param max_depth:

            Maximum depth to store tree (inclusive). During single runs `max_depth` is also counted
            from root.


        """
        if self._is_run:
            if self._new_nodes or self._new_links:
                self._storage_service.store(pypetconstants.SINGLE_RUN, self,
                                        trajectory_name=self.v_name,
                                        recursive=not only_init,
                                        store_data=store_data,
                                        max_depth=max_depth)
        else:
            self._storage_service.store(pypetconstants.TRAJECTORY, self,
                                        trajectory_name=self.v_name,
                                        only_init=only_init,
                                        store_data=store_data,
                                        max_depth=max_depth)

            self._stored = True # We do this in case the storage service forgot to tell the
            # Trajectory that it was actually stored

    @not_in_run
    def f_is_empty(self):
        """ Whether no results nor parameters have been added yet to the trajectory
        (ignores config)."""
        return (len(self._parameters) == 0 and
                len(self._derived_parameters) == 0 and
                len(self._results) == 0 and
                len(self._other_leaves) == 0)

    @not_in_run
    def f_restore_default(self):
        """ Restores the default value in all explored parameters and sets the
        v_idx property back to -1 and v_crun to None."""
        self._idx = -1
        self._crun = None
        for param in self._explored_parameters.values():
            if param is not None:
                param._restore_default()

    def _set_explored_parameters_to_idx(self, idx):
        """ Notifies the explored parameters what current point in the parameter space
        they should represent.

        """
        for param in self._explored_parameters.values():
            if param is not None:
                param._set_parameter_access(idx)

    def _make_single_run(self):
        """ Modifies the trajectory for single runs executed by the environment """
        self._is_run = False # to be able to use f_set_crun
        self._new_nodes = OrderedDict()
        self._new_links = OrderedDict()
        self._is_run = True
        return self

    def f_get_run_names(self, sort=True):
        """ Returns a list of run names.

        ONLY useful for a single run during multiprocessing if ``v_full_copy` was set to ``True``.
        Otherwise only the current run is available.

        :param sort:

            Whether to get them sorted, will only require O(N) [and not O(N*log N)] since we
            use (sort of) bucket sort.

        """
        if sort:
            return [self.f_idx_to_run(idx) for idx in range(len(self))]
        else:
            return list(self._run_information.keys())

    def f_get_run_information(self, name_or_idx=None, copy=True):
        """ Returns a dictionary containing information about a single run.

        ONLY useful during a single run if ``v_full_copy` was set to ``True``.
        Otherwise only the current run is available.

        The information dictionaries have the following key, value pairings:

            * completed: Boolean, whether a run was completed

            * idx: Index of a run

            * timestamp: Timestamp of the run as a float

            * time: Formatted time string

            * finish_timestamp: Timestamp of the finishing of the run

            * runtime: Total runtime of the run in human readable format

            * name: Name of the run

            * parameter_summary:

                A string summary of the explored parameter settings for the particular run

            * short_environment_hexsha: The short version of the environment SHA-1 code


        If no name or idx is given then a nested dictionary with keys as run names and
        info dictionaries as values is returned.

        :param name_or_idx: str or int

        :param copy:

            Whether you want the dictionary used by the trajectory or a copy. Note if
            you want the real thing, please do not modify it, i.e. popping or adding stuff. This
            could mess up your whole trajectory.

        :return:

            A run information dictionary or a nested dictionary of information dictionaries
            with the run names as keys.

        """
        if name_or_idx is None:
            if copy:
                return cp.deepcopy(self._run_information)
            else:
                return self._run_information
        try:
            if copy:
                # Since the information dictionaries only contain immutable items
                # (float, int, str)
                # the normal copy operation is sufficient
                return self._run_information[name_or_idx].copy()
            else:
                return self._run_information[name_or_idx]
        except KeyError:
            # Maybe the user provided an idx, this would yield a key error and we
            # have to convert it to a run name
            name_or_idx = self.f_idx_to_run(name_or_idx)
            if copy:
                return self._run_information[name_or_idx].copy()
            else:
                return self._run_information[name_or_idx]

    def f_find_idx(self, name_list, predicate):
        """ Finds a single run index given a particular condition on parameters.

        ONLY useful for a single run if ``v_full_copy` was set to ``True``.
        Otherwise a TypeError is thrown.

        :param name_list:

            A list of parameter names the predicate applies to, if you have only a single
            parameter name you can omit the list brackets.

        :param predicate:

            A lambda predicate for filtering that evaluates to either ``True`` or  ``False``

        :return: A generator yielding the matching single run indices

        Example:

        >>> predicate = lambda param1, param2: param1==4 and param2 in [1.0, 2.0]
        >>> iterator = traj.f_find_idx(['groupA.param1', 'groupA.param2'], predicate)
        >>> [x for x in iterator]
        [0, 2, 17, 36]

        """
        if self._is_run and not self.v_full_copy:
            raise TypeError('You cannot use this function during a multiprocessing signle run and '
                            'not having ``v_full_copy=True``.')
        if isinstance(name_list, str):
            name_list = [name_list]

        # First create a list of iterators, each over the range of the matched parameters
        iter_list = []
        for name in name_list:
            param = self.f_get(name)
            if not param.v_is_parameter:
                raise TypeError('`%s` is not a parameter it is a %s, find idx is not applicable' %
                                (name, str(type(param))))

            if param.f_has_range():
                iter_list.append(iter(param.f_get_range(copy=False)))
            else:
                iter_list.append(itools.repeat(param.f_get(), len(self)))

        # Create a logical iterator returning `True` or `False`
        # whether the user's predicate matches the parameter data
        logic_iter = map(predicate, *iter_list)

        # Now the run indices are the the indices where `logic_iter` evaluates to `True`
        for idx, item in enumerate(logic_iter):
            if item:
                yield idx

    def f_idx_to_run(self, name_or_idx):
        """Converts an integer idx to the corresponding single run name and vice versa.

        Note during a single run ONLY useful if ``v_full_copy`` was set to True.

        :param name_or_idx: Name of a single run or an integer index

        :return: The corresponding idx or name of the single run

        Example usage:

        >>> traj.f_idx_to_run(4)
        'run_00000004'
        >>> traj.f_idx_to_run('run_00000000')
        0

        """
        return self._single_run_ids[name_or_idx]

    def f_start_run(self, run_name_or_idx=None, turn_into_run=True):
        """ Can be used to manually allow running of an experiment without using an environment.

        :param run_name_or_idx:

            Can manually set a trajectory to a particular run. If `None` the current run
            the trajectory is set to is used.

        :param turn_into_run:

            Turns the trajectory into a run, i.e. reduces functionality but makes storing
            more efficient.

        """
        if self._run_started:
            return self

        if run_name_or_idx is None:
            if self.v_idx == -1:
                raise ValueError('Cannot start run if trajectory is not set to a particular run')
        else:
            self.f_set_crun(run_name_or_idx)

        self._run_started = True

        if turn_into_run:
            self._make_single_run()

        self._set_start()

        return self

    def f_finalize_run(self, store_meta_data=True, clean_up=True):
        """ Can be called to finish a run if manually started.

        Does NOT reset the index of the run,
        i.e. ``f_restore_default`` should be called manually if desired.

        Does NOT store any data (except meta data) so you have to call
        ``f_store`` manually before to avoid data loss.

        :param store_meta_data:

            If meta data like the runtime should be stored

        :param clean_up:

            If data added during the run should be cleaned up.
            Only works if ``turn_into_run`` was set to ``True``.

        """
        if not self._run_started:
            return self

        self._set_finish()

        if clean_up and self._is_run:
            self._finalize_run()

        self._is_run = False
        self._run_started = False

        self._updated_run_information.add(self.v_idx)

        if store_meta_data:
            self.f_store(only_init=True)

        return self

    def _set_start(self):
        """ Sets the start timestamp and formatted time to the current time. """
        init_time = time.time()
        formatted_time = datetime.datetime.fromtimestamp(init_time).strftime('%Y_%m_%d_%Hh%Mm%Ss')
        run_info_dict = self._run_information[self.v_crun]
        run_info_dict['timestamp'] = init_time
        run_info_dict['time'] = formatted_time
        if self._environment_hexsha is not None:
            run_info_dict['short_environment_hexsha'] = self._environment_hexsha[0:7]

    def _summarize_explored_parameters(self):
        """Summarizes the parameter settings.

        :param run_name: Name of the single run

        :param paramlist: List of explored parameters

        :param add_table: Whether to add the overview table

        :param create_run_group:

            If a group with the particular name should be created if it does not exist.
            Might be necessary when trajectories are merged.

        """

        runsummary = ''
        for idx, expparam in enumerate(self._explored_parameters.values()):

            # Create the run summary for the `run` overview
            if idx > 0:
                runsummary += ',   '

            valstr = expparam.f_val_to_str()

            if len(valstr) >= pypetconstants.HDF5_STRCOL_MAX_COMMENT_LENGTH:
                valstr = valstr[0:pypetconstants.HDF5_STRCOL_MAX_COMMENT_LENGTH - 3]
                valstr += '...'

            if expparam.v_name in runsummary:
                param_name = expparam.v_full_name
            else:
                param_name = expparam.v_name

            runsummary = runsummary + param_name + ': ' + valstr

        return runsummary

    def _set_finish(self):
        """ Sets the finish time and computes the runtime in human readable format """

        run_info_dict = self._run_information[self.v_crun]
        timestamp_run = run_info_dict['timestamp']

        run_summary = self._summarize_explored_parameters()

        finish_timestamp_run = time.time()

        findatetime = datetime.datetime.fromtimestamp(finish_timestamp_run)
        startdatetime = datetime.datetime.fromtimestamp(timestamp_run)

        runtime_run = str(findatetime - startdatetime)

        run_info_dict['parameter_summary'] = run_summary
        run_info_dict['completed'] = 1
        run_info_dict['finish_timestamp'] = finish_timestamp_run
        run_info_dict['runtime'] = runtime_run

    def _construct_instance(self, constructor, full_name, *args, **kwargs):
        """ Creates a new node. Checks if the new node needs to know the trajectory.

        :param constructor:  The constructor to use
        :param full_name: Full name of node
        :param args: Arguments passed to constructor
        :param kwargs: Keyword arguments passed to the constructor
        :return:
        """
        if getattr(constructor, 'KNOWS_TRAJECTORY', False):
            return constructor(full_name, self, *args, **kwargs)
        else:
            return constructor(full_name, *args, **kwargs)

    def _create_class(self, class_name):
        """Dynamically creates a class.

        It is tried if the class can be created by the already given imports.
        If not the list of the dynamically loaded classes is used.

        """
        return dynamicimports.create_class(class_name, self._dynamic_imports)

    @property
    def v_shortcuts(self):
        """Whether shortcuts are allowed if accessing data via natural naming or
        squared bracket indexing."""
        return self._shortcuts

    @v_shortcuts.setter
    def v_shortcuts(self, shortcuts):
        self._shortcuts = bool(shortcuts)

    @property
    def v_max_depth(self):
        """The maximum depth the tree should be searched if shortcuts are allowed.

        Set to `None` if there should be no depth limit.
        """
        return self._max_depth

    @v_max_depth.setter
    def v_max_depth(self, max_depth):
        self._max_depth = max_depth

    @property
    def v_iter_recursive(self):
        """Whether using `__iter__` should iterate only immediate children or
        recursively all nodes."""
        return self._iter_recursive

    @v_iter_recursive.setter
    def v_iter_recursive(self, iter_recursive):
        self._iter_recursive = bool(iter_recursive)


    @property
    def v_auto_load(self):
        """Whether the trajectory should attempt to load data on the fly.
        """
        return self._auto_load

    @v_auto_load.setter
    def v_auto_load(self, auto_load):
        self._auto_load = bool(auto_load)

    @property
    def v_timestamp(self):
        """Float timestamp of creation time"""
        return self._timestamp

    @property
    def v_time(self):
        """Formatted time string of the time the trajectory or run was created."""
        return self._time

    @property
    def v_standard_parameter(self):
        """ The standard parameter used for parameter creation"""
        return self._standard_parameter

    @v_standard_parameter.setter
    def v_standard_parameter(self, parameter):
        """Sets the standard parameter"""
        self._standard_parameter = parameter

    @property
    def v_standard_result(self):
        """The standard result class used for result creation """
        return self._standard_result

    @v_standard_result.setter
    def v_standard_result(self, result):
        """Sets standard result"""
        self._standard_result = result

    @property
    def v_standard_leaf(self):
        """The standard constructor used if you add a generic leaf.

        The constructor is only used if you do not add items under the usual four subtrees
        (`parameters`, `derived_parameters`, `config`, `results`).

        """
        return self._standard_leaf

    @v_standard_leaf.setter
    def v_standard_leaf(self, leaf):
        """Sets standard result"""
        self._standard_leaf = leaf

    @property
    def v_no_clobber(self):
        """If `f_add_leaf` should not throw an error in case something is added
        that is already part of the Trajectory. If `True` no error is thrown and
        the new data is ignored."""
        return self._no_clobber

    @v_no_clobber.setter
    def v_no_clobber(self, value):
        """Sets no_clobber"""
        self._no_clobber = bool(value)

    @property
    def v_auto_run_prepend(self):
        """If during run the `runs.run_XXXXXXXX` should be prepended if it is missing.

        Is not considered for `f_add_leaf` and `f_add_group` which never prepend.

        """
        return self._auto_run_prepend

    @v_auto_run_prepend.setter
    def v_auto_run_prepend(self, value):
        """Sets fast access"""
        self._auto_run_prepend = bool(value)

    @property
    def v_fast_access(self):
        """Whether parameter instances (False) or their values (True) are returned via natural
        naming.

        Works also for results if they contain a single item with the name of the result.

        Default is True.

        """
        return self._fast_access

    @v_fast_access.setter
    def v_fast_access(self, value):
        """Sets fast access"""
        self._fast_access = bool(value)

    # @property
    # def v_fast_adding(self):
    #     """If you can add items to the trajectory simply by calling ``setattr``
    #
    #     For example, ``traj.par.a = 4`` would add a parameter named `a` with the value 4.
    #
    #     """
    #     return self._fast_adding
    #
    # @v_fast_adding.setter
    # def v_fast_adding(self, value):
    #     """Sets fast adding"""
    #     self._fast_adding = bool(value)

    @property
    def v_environment_hexsha(self):
        """If the trajectory is used with an environment this returns
        the SHA-1 code of the environment.

        """
        return self._environment_hexsha

    @property
    def v_environment_name(self):
        """If the trajectory is used with an environment this returns
        the name of the environment.

        """
        return self._environment_name

    @staticmethod
    def _return_item_dictionary(param_dict, fast_access, copy):
        """Returns a dictionary containing either all parameters, all explored parameters,
        all config, all derived parameters, or all results.

        :param param_dict: The dictionary which is about to be returned
        :param fast_access: Whether to use fast access
        :param copy: If the original dict should be returned or a shallow copy

        :return: The dictionary

        :raises: ValueError if `copy=False` and fast_access=True`

        """

        if not copy and fast_access:
            raise ValueError('You cannot access the original dictionary and use fast access at the'
                             ' same time!')
        if not fast_access:
            if copy:
                return param_dict.copy()
            else:
                return param_dict
        else:
            resdict = {}
            for key in param_dict:
                param = param_dict[key]

                val = param.f_get()
                resdict[key] = val

            return resdict

    def _finalize_run(self):
        """Called by the environment after storing to perform some rollback operations.

        All results and derived parameters created in the current run are removed.

        Important for single processing to not blow up the parent trajectory with the results
        of all runs.

        """
        self._run_information[self.v_crun]['completed'] = 1
        while len(self._new_links):
            name_pair, child_parent_pair = self._new_links.popitem(last=False)
            parent_node, _ = child_parent_pair
            _, link = name_pair
            parent_node.f_remove_child(link)

        while len(self._new_nodes):
            _, child_parent_pair = self._new_nodes.popitem(last=False)
            parent, child = child_parent_pair
            child_name = child.v_name
            parent.f_remove_child(child_name, recursive=True)

    def f_to_dict(self, fast_access=False, short_names=False, nested=False,
                  copy=True, with_links=True):
        """Returns a dictionary with pairings of (full) names as keys and instances/values.


        :param fast_access:

            If True, parameter values are returned instead of the instances.
            Works also for results if they contain a single item with the name of the result.

        :param short_names:

            If true, keys are not full names but only the names. Raises a ValueError
            if the names are not unique.

        :param nested:

            If true, a nested dictionary is returned.

        :param copy:

            If `fast_access=False` and `short_names=False` you can access the original
            data dictionary if you set `copy=False`. If you do that, please do not
            modify anything! Raises ValueError if `copy=False` and `fast_access=True`
            or `short_names=True`.

        :param with_links:

            If links should be ignored

        :return: dictionary

        :raises: ValueError

        """
        return self._nn_interface._to_dict(self, fast_access=fast_access,
                                           short_names=short_names,
                                           nested=nested,
                                           copy=copy, with_links=with_links)

    def f_get_config(self, fast_access=False, copy=True):
        """Returns a dictionary containing the full config names as keys and the config parameters
        or the config parameter data items as values.


        :param fast_access:

            Determines whether the parameter objects or their values are returned
            in the dictionary.

        :param copy:

            Whether the original dictionary or a shallow copy is returned.
            If you want the real dictionary please do not modify it at all!
            Not Copying and fast access do not work at the same time! Raises ValueError
            if fast access is true and copy false.

        :return: Dictionary containing the config data

        :raises: ValueError

        """
        return self._return_item_dictionary(self._config, fast_access, copy)

    def f_get_parameters(self, fast_access=False, copy=True):
        """ Returns a dictionary containing the full parameter names as keys and the parameters
         or the parameter data items as values.


        :param fast_access:

            Determines whether the parameter objects or their values are returned
            in the dictionary.

        :param copy:

            Whether the original dictionary or a shallow copy is returned.
            If you want the real dictionary please do not modify it at all!
            Not Copying and fast access do not work at the same time! Raises ValueError
            if fast access is true and copy false.

        :return: Dictionary containing the parameters.

        :raises: ValueError

        """
        return self._return_item_dictionary(self._parameters, fast_access, copy)


    def f_get_explored_parameters(self, fast_access=False, copy=True):
        """ Returns a dictionary containing the full parameter names as keys and the parameters
         or the parameter data items as values.

         IMPORTANT: This dictionary always contains all explored parameters as keys.
         Even when they are not loaded, in this case the value is simply `None`.
         `fast_access` only works if all explored parameters are loaded.


        :param fast_access:

            Determines whether the parameter objects or their values are returned
            in the dictionary.

        :param copy:

            Whether the original dictionary or a shallow copy is returned.
            If you want the real dictionary please do not modify it at all!
            Not Copying and fast access do not work at the same time! Raises ValueError
            if fast access is true and copy false.

        :return: Dictionary containing the parameters.

        :raises: ValueError

        """
        return self._return_item_dictionary(self._explored_parameters, fast_access, copy)

    def f_get_derived_parameters(self, fast_access=False, copy=True):
        """ Returns a dictionary containing the full parameter names as keys and the parameters
         or the parameter data items as values.


        :param fast_access:

            Determines whether the parameter objects or their values are returned
            in the dictionary.

        :param copy:

            Whether the original dictionary or a shallow copy is returned.
            If you want the real dictionary please do not modify it at all!
            Not Copying and fast access do not work at the same time! Raises ValueError
            if fast access is true and copy false.

        :return: Dictionary containing the parameters.

        :raises: ValueError

        """
        return self._return_item_dictionary(self._derived_parameters, fast_access, copy)

    def f_get_results(self, fast_access=False, copy=True):
        """ Returns a dictionary containing the full result names as keys and the corresponding
        result objects or result data items as values.


        :param fast_access:

            Determines whether the result objects or their values are returned
            in the dictionary. Works only for results if they contain a single item with
            the name of the result.

        :param copy:

            Whether the original dictionary or a shallow copy is returned.
            If you want the real dictionary please do not modify it at all!
            Not Copying and fast access do not work at the same time! Raises ValueError
            if fast access is true and copy false.

        :return: Dictionary containing the results.

        :raises: ValueError

        """
        return self._return_item_dictionary(self._results, fast_access, copy)

    def f_store_item(self, item, *args, **kwargs):
        """Stores a single item, see also :func:`~pypet.trajectory.Trajectory.f_store_items`."""
        self.f_store_items([item], *args, **kwargs)

    def f_store_items(self, iterator, *args, **kwargs):
        """Stores individual items to disk.

        This function is useful if you calculated very large results (or large derived parameters)
        during runtime and you want to write these to disk immediately and empty them afterwards
        to free some memory.

        Instead of storing individual parameters or results you can also store whole subtrees with
        :func:`~pypet.naturalnaming.NNGroupNode.f_store_child`.


        You can pass the following arguments to `f_store_items`:

        :param iterator:

            An iterable containing the parameters or results to store, either their
            names or the instances. You can also pass group instances or names here
            to store the annotations of the groups.

        :param non_empties:

            Optional keyword argument (boolean),
            if `True` will only store the subset of provided items that are not empty.
            Empty parameters or results found in `iterator` are simply ignored.

        :param args: Additional arguments passed to the storage service

        :param kwargs:

            If you use the standard hdf5 storage service, you can pass the following additional
            keyword argument:

            :param overwrite:

                List names of parts of your item that should
                be erased and overwritten by the new data in your leaf.
                You can also set `overwrite=True`
                to overwrite all parts.

                For instance:

                    >>> traj.f_add_result('mygroup.myresult', partA=42, partB=44, partC=46)
                    >>> traj.f_store()
                    >>> traj.mygroup.myresult.partA = 333
                    >>> traj.mygroup.myresult.partB = 'I am going to change to a string'
                    >>> traj.f_store_item('mygroup.myresult', overwrite=['partA', 'partB'])

                Will store `'mygroup.myresult'` to disk again and overwrite the parts
                `'partA'` and `'partB'` with the new values `333` and
                `'I am going to change to a string'`.
                The data stored as `partC` is not changed.

                Be aware that you need to specify the names of parts as they were stored
                to HDF5. Depending on how your leaf construction works, this may differ
                from the names the data might have in your leaf in the trajectory container.

                Note that massive overwriting will fragment and blow up your HDF5 file.
                Try to avoid changing data on disk whenever you can.

        :raises:

            TypeError:

                If the (parent) trajectory has never been stored to disk. In this case
                use :func:`pypet.trajectory.f_store` first.

            ValueError: If no item could be found to be stored.

        Note if you use the standard hdf5 storage service, there are no additional arguments
        or keyword arguments to pass!

        """

        if not self._stored:
            raise TypeError('Cannot store stuff for a trajectory that has never been '
                            'stored to disk. Please call traj.f_store(only_init=True) first.')

        fetched_items = self._nn_interface._fetch_items(STORE, iterator, args, kwargs)

        if fetched_items:
            self._storage_service.store(pypetconstants.LIST, fetched_items,
                                        trajectory_name=self.v_name)
        else:
            raise ValueError('Your storage was not successful, could not find a single item '
                             'to store.')

    def f_load_item(self, item, *args, **kwargs):
        """Loads a single item, see also :func:`~pypet.trajectory.Trajectory.f_load_items`"""
        self.f_load_items([item], *args, **kwargs)

    def f_load_items(self, iterator, *args, **kwargs):
        """Loads parameters and results specified in `iterator`.

        You can directly list the Parameter objects or just their names.

        If names are given the `~pypet.naturalnaming.NNGroupNode.f_get` method is applied to find the
        parameters or results in the trajectory. Accordingly, the parameters and results
        you want to load must already exist in your trajectory (in RAM), probably they
        are just empty skeletons waiting desperately to handle data.
        If they do not exist in RAM yet, but have been stored to disk before,
        you can call :func:`~pypet.trajectory.Trajectory.f_load_skeleton` in order to
        bring your trajectory tree skeleton up to date. In case of a single run you can
        use the :func:`~pypet.naturalnaming.NNGroupNode.f_load_child` method to recursively
        load a subtree without any data.
        Then you can load the data of individual results or parameters one by one.

        If want to load the whole trajectory at once or ALL results and parameters that are
        still empty take a look at :func:`~pypet.trajectory.Trajectory.f_load`.
        As mentioned before, to load subtrees of your trajectory you might want to check out
        :func:`~pypet.naturalnaming.NNGroupNode.f_load_child`.

        To load a list of parameters or results with `f_load_items` you can pass
        the following arguments:

        :param iterator: A list with parameters or results to be loaded.

        :param only_empties:

            Optional keyword argument (boolean),
            if `True` only empty parameters or results are passed
            to the storage service to get loaded. Non-empty parameters or results found in
            `iterator` are simply ignored.

        :param args: Additional arguments directly passed to the storage service

        :param kwargs:

            Additional keyword arguments directly passed to the storage service
            (except the kwarg `only_empties`)

            If you use the standard hdf5 storage service, you can pass the following additional
            keyword arguments:

            :param load_only:

                If you load a result, you can partially load it and ignore the rest of data items.
                Just specify the name of the data you want to load. You can also provide a list,
                for example `load_only='spikes'`, `load_only=['spikes','membrane_potential']`.

                Be aware that you need to specify the names of parts as they were stored
                to HDF5. Depending on how your leaf construction works, this may differ
                from the names the data might have in your leaf in the trajectory container.

                A warning is issued if data specified in `load_only` cannot be found in the
                instances specified in `iterator`.

            :param load_except:

                Analogous to the above, but everything is loaded except names or parts
                specified in `load_except`.
                You cannot use `load_only` and `load_except` at the same time. If you do
                a ValueError is thrown.

                A warning is issued if names listed in `load_except` are not part of the
                items to load.

        """

        if not self._stored:
            raise TypeError(
                'Cannot load stuff from disk for a trajectory that has never been stored.')

        fetched_items = self._nn_interface._fetch_items(LOAD, iterator, args, kwargs)
        if fetched_items:
            self._storage_service.load(pypetconstants.LIST, fetched_items,
                                       trajectory_name=self.v_name)
        else:
            self._logger.warning('Your loading was not successful, could not find a single item '
                                 'to load.')

    @kwargs_api_change('remove_empty_groups')
    def f_remove_item(self, item, recursive=False):
        """Removes a single item, see :func:`~pypet.trajectory.Trajectory.f_remove_items`"""
        self.f_remove_items([item], recursive=recursive)

    @kwargs_api_change('remove_empty_groups')
    def f_remove_items(self, iterator, recursive=False):
        """Removes parameters, results or groups from the trajectory.

        This function ONLY removes items from your current trajectory and does not delete
        data stored to disk. If you want to delete data from disk, take a look at
        :func:`~pypet.trajectory.Trajectory.f_delete_items`.

        This will also remove all links if items are linked.

        :param iterator:

            A sequence of items you want to remove. Either the instances themselves
            or strings with the names of the items.

        :param recursive:

            In case you want to remove group nodes, if the children should be removed, too.

        """

        # Will format the request in a form that is understood by the storage service
        # aka (msg, item, args, kwargs)
        fetched_items = self._nn_interface._fetch_items(REMOVE, iterator, (), {})

        if fetched_items:
            for _, item, dummy1, dummy2 in fetched_items:
                self._nn_interface._remove_node_or_leaf(item, recursive=recursive)

        else:
            self._logger.warning('Your removal was not successful, could not find a single '
                                 'item to remove.')

    def f_delete_link(self, link, remove_from_trajectory=False):
        """Deletes a single link see :func:`~pypet.trajectory.Trajectory.f_delete_links`"""
        self.f_delete_links((link,), remove_from_trajectory)

    def f_delete_links(self, iterator_of_links, remove_from_trajectory=False):
        """Deletes several links from the hard disk.

        Links can be passed as a string ``'groupA.groupB.linkA'``
        or as a tuple  containing the node from which the link should be removed and the
        name of the link ``(groupWithLink, 'linkA')``.

        """

        to_delete_links = []

        group_link_pairs = []

        for elem in iterator_of_links:
            if isinstance(elem, str):
                split_names = elem.split('.')
                parent_name = '.'.join(split_names[:-1])
                link = split_names[-1]
                parent_node = self.f_get(parent_name) if parent_name != '' else self
                link_name = parent_node.v_full_name + '.' + link if parent_name != '' else link
                to_delete_links.append((pypetconstants.DELETE_LINK, link_name))
                group_link_pairs.append((parent_node, link))
            else:
                link_name = elem[0].v_full_name + '.' + elem[1]
                to_delete_links.append((pypetconstants.DELETE_LINK, link_name))
                group_link_pairs.append(elem)
        try:
            self._storage_service.store(pypetconstants.LIST, to_delete_links,
                                        trajectory_name=self.v_name)
        except:
            self._logger.error('Could not remove `%s` from the trajectory. Maybe the'
                               ' item(s) was/were never stored to disk.' % str(to_delete_links))
            raise

        if remove_from_trajectory:
            for group, link in group_link_pairs:
                group.f_remove_link(link)

    def f_remove(self, recursive=True, predicate=None):
        """Recursively removes all children of the trajectory

        :param recursive:

            Only here for consistency with signature of parent method. Cannot be set
            to `False` because the trajectory root node cannot be removed.

        :param predicate:

            Predicate which can evaluate for each node to ``True`` in order to remove the node or
            ``False`` if the node should be kept. Leave ``None`` if you want to remove all nodes.

        """
        if not recursive:
            raise ValueError('Nice try ;-)')

        for child in list(self._children.keys()):
            self.f_remove_child(child, recursive=True, predicate=predicate)

    @kwargs_api_change('remove_empty_groups')
    def f_delete_item(self, item, *args, **kwargs):
        """Deletes a single item, see :func:`~pypet.trajectory.Trajectory.f_delete_items`"""
        self.f_delete_items([item], *args, **kwargs)

    @kwargs_api_change('remove_empty_groups')
    def f_delete_items(self, iterator, *args, **kwargs):
        """Deletes items from storage on disk.

        Per default the item is NOT removed from the trajectory.

        Links are NOT deleted on the hard disk, please delete links manually before deleting
        data!

        :param iterator:

            A sequence of items you want to remove. Either the instances themselves
            or strings with the names of the items.

        :param remove_from_trajectory:

            If items should also be removed from trajectory. Default is `False`.


        :param args: Additional arguments passed to the storage service

        :param kwargs: Additional keyword arguments passed to the storage service

            If you use the standard hdf5 storage service, you can pass the following additional
            keyword argument:

            :param delete_only:

                You can partially delete leaf nodes. Specify a list of parts of the result node
                that should be deleted like `delete_only=['mystuff','otherstuff']`.
                This wil only delete the hdf5 sub parts `mystuff` and `otherstuff` from disk.
                BE CAREFUL,
                erasing data partly happens at your own risk. Depending on how complex the
                loading process of your result node is, you might not be able to reconstruct
                any data due to partially deleting some of it.

                Be aware that you need to specify the names of parts as they were stored
                to HDF5. Depending on how your leaf construction works, this may differ
                from the names the data might have in your leaf in the trajectory container.

                If the hdf5 nodes you specified in `delete_only` cannot be found a warning
                is issued.

                Note that massive deletion will fragment your HDF5 file.
                Try to avoid changing data on disk whenever you can.

                If you want to erase a full node, simply ignore this argument or set to `None`.

            :param remove_from_item:

                If data that you want to delete from storage should also be removed from
                the items in `iterator` if they contain these. Default is `False`.

            :param recursive:

                If you want to delete a group node and it has children you need to
                set `recursive` to `True. Default is `False`.

        """

        remove_from_trajectory = kwargs.pop('remove_from_trajectory', False)
        recursive = kwargs.get('recursive', False)

        # Will format the request in a form that is understood by the storage service
        # aka (msg, item, args, kwargs)
        fetched_items = self._nn_interface._fetch_items(REMOVE, iterator, args, kwargs)

        if fetched_items:

            try:
                self._storage_service.store(pypetconstants.LIST, fetched_items,
                                            trajectory_name=self.v_name)
            except:
                self._logger.error('Could not remove `%s` from the trajectory. Maybe the'
                                   ' item(s) was/were never stored to disk.' % str(fetched_items))
                raise


            for _, item, dummy1, dummy2 in fetched_items:
                if remove_from_trajectory:
                    self._nn_interface._remove_node_or_leaf(item, recursive=recursive)
                else:
                    item._stored = False

        else:
            self._logger.warning('Your removal was not successful, could not find a single '
                                 'item to remove.')

    @not_in_run
    @copydoc(ConfigGroup.f_add_config)
    def f_add_config(self, *args, **kwargs):
        return super(Trajectory, self).f_add_config(*args, **kwargs)


    @not_in_run
    @copydoc(ConfigGroup.f_add_config_group)
    def f_add_config_group(self, *args, **kwargs):
        return super(Trajectory, self).f_add_config_group(*args, **kwargs)

    @not_in_run
    @copydoc(ParameterGroup.f_add_parameter)
    def f_add_parameter(self, *args, **kwargs):
        return super(Trajectory, self).f_add_parameter(*args, **kwargs)

    @not_in_run
    @copydoc(ParameterGroup.f_add_parameter_group)
    def f_add_parameter_group(self, *args, **kwargs):
        return super(Trajectory, self).f_add_parameter_group(*args, **kwargs)

    def __dir__(self):
        """Adds all children to auto-completion

        In case of a single run it spares all non-available functions

        """
        result = super(Trajectory, self).__dir__()
        if self._is_run:
            result = [x for x in result if not x.startswith('f_') or
                      not getattr(getattr(self, x), '_not_in_run', False)]
        if not is_debug():
            result.extend(self._children.keys())
        return result class ChainMap(object):
    """Combine multiple mappings for sequential lookup.
    """

    def __init__(self, *maps):
        self._maps = maps

    def __getitem__(self, key):
        for mapping in self._maps:
            try:
                return mapping[key]
            except KeyError:
                pass
        raise KeyError(key)

    def __len__(self):
        length = 0

        for mapping in self._maps:
            length += len(mapping)

        return length

    def __iter__(self):
        iter_list = [mapping.keys() for mapping in self._maps]
        return itools.chain(*iter_list) def cartesian_product(parameter_dict, combined_parameters=()):
    """ Generates a Cartesian product of the input parameter dictionary.

    For example:

    >>> print cartesian_product({'param1':[1,2,3], 'param2':[42.0, 52.5]})
    {'param1':[1,1,2,2,3,3],'param2': [42.0,52.5,42.0,52.5,42.0,52.5]}

    :param parameter_dict:

        Dictionary containing parameter names as keys and iterables of data to explore.

    :param combined_parameters:

        Tuple of tuples. Defines the order of the parameters and parameters that are
        linked together.
        If an inner tuple contains only a single item, you can spare the
        inner tuple brackets.


        For example:

        >>> print cartesian_product( {'param1': [42.0, 52.5], 'param2':['a', 'b'], 'param3' : [1,2,3]}, ('param3',('param1', 'param2')))
        {param3':[1,1,2,2,3,3],'param1' : [42.0,52.5,42.0,52.5,42.0,52.5], 'param2':['a','b','a','b','a','b']}

    :returns: Dictionary with cartesian product lists.

    """
    if not combined_parameters:
        combined_parameters = list(parameter_dict)
    else:
        combined_parameters = list(combined_parameters)

    for idx, item in enumerate(combined_parameters):
        if isinstance(item, str):
            combined_parameters[idx] = (item,)

    iterator_list = []
    for item_tuple in combined_parameters:
        inner_iterator_list = [parameter_dict[key] for key in item_tuple]
        zipped_iterator = zip(*inner_iterator_list)
        iterator_list.append(zipped_iterator)

    result_dict = {}
    for key in parameter_dict:
        result_dict[key] = []

    cartesian_iterator = itools.product(*iterator_list)

    for cartesian_tuple in cartesian_iterator:
        for idx, item_tuple in enumerate(combined_parameters):
            for inneridx, key in enumerate(item_tuple):
                result_dict[key].append(cartesian_tuple[idx][inneridx])

    return result_dict def run_suite(remove=None, folder=None, suite=None):
    """Runs a particular test suite or simply unittest.main.

    Takes care that all temporary data in `folder` is removed if `remove=True`.

    """
    if remove is not None:
        testParams['remove'] = remove

    testParams['user_tempdir'] = folder

    prepare_log_config()

    # Just signal if make_temp_dir works
    make_temp_dir('tmp.txt', signal=True)

    success = False
    try:
        if suite is None:
            unittest.main(verbosity=2)
        else:
            runner = unittest.TextTestRunner(verbosity=2)
            result = runner.run(suite)
            success = result.wasSuccessful()
    finally:
        remove_data()

    if not success:
        # Exit with 1 if tests were not successful
        sys.exit(1) def parse_args():
    """Parses arguments and returns a dictionary"""
    opt_list, _ = getopt.getopt(sys.argv[1:],'k',['folder=', 'suite='])
    opt_dict = {}

    for opt, arg in opt_list:
        if opt == '-k':
            opt_dict['remove'] = False
            errwrite('I will keep all files.')

        if opt == '--folder':
            opt_dict['folder'] = arg
            errwrite('I will put all data into folder `%s`.' % arg)

        if opt == '--suite':
            opt_dict['suite_no'] = arg
            errwrite('I will run suite `%s`.' % arg)

    sys.argv = [sys.argv[0]]
    return opt_dict def make_temp_dir(filename, signal=False):
    """Creates a temporary folder and returns the joined filename"""
    try:

        if ((testParams['user_tempdir'] is not None and testParams['user_tempdir'] != '') and
                        testParams['actual_tempdir'] == ''):
            testParams['actual_tempdir'] = testParams['user_tempdir']

        if not os.path.isdir(testParams['actual_tempdir']):
            os.makedirs(testParams['actual_tempdir'])

        return os.path.join(testParams['actual_tempdir'], filename)
    except OSError as exc:
        actual_tempdir = os.path.join(tempfile.gettempdir(), testParams['tempdir'])

        if signal:
            errwrite('I used `tempfile.gettempdir()` to create the temporary folder '
                             '`%s`.' % actual_tempdir)
        testParams['actual_tempdir'] = actual_tempdir
        if not os.path.isdir(testParams['actual_tempdir']):
            try:
                os.makedirs(testParams['actual_tempdir'])
            except Exception:
                pass # race condition

        return os.path.join(actual_tempdir, filename)
    except:
        get_root_logger().error('Could not create a directory.')
        raise class TrajectoryComparator(unittest.TestCase):

    def are_results_in_order(self, results):
        self.assertGreater(len(results), 0)
        sorted_res = sorted(results, key=lambda key: key[0])
        self.assertEqual(results, sorted_res)

    def clear_handlers(self):
        """Deletes all handlers and closes all log-files"""
        root = logging.getLogger()
        for logger in list(root.manager.loggerDict.values()) + [root]:
            if hasattr(logger, 'handlers'):
                handlers = logger.handlers
                for handler in handlers:
                    if hasattr(handler, 'flush'):
                        handler.flush()
                    if hasattr(handler, 'close'):
                        handler.close()
                logger.handlers = []
        sys.stdout = sys.__stdout__
        sys.stderr = sys.__stderr__

    def tearDown(self):
        if hasattr(self, 'env') and hasattr(self.env, 'f_disable_logging'):
            self.env.f_disable_logging()
        self.clear_handlers()
        remove_data()

    def compare_trajectories(self,traj1,traj2):

        trajlength = len(traj1)

        if traj1.f_get_run_information(0)['completed']:

            # We need this fix in case the trajectory was never run
            # thus it does not contain any run groups but still has a length
            if 'results.runs' in traj1:
                rungroups = traj1.results.runs.f_children()
            else:
                rungroups = 1

            self.assertEqual(trajlength, rungroups, 'len of traj1 is %d, rungroups %d' % (trajlength, rungroups))

        old_items = to_dict_wo_config(traj1)
        new_items = to_dict_wo_config(traj2)

        self.assertEqual(len(traj1),len(traj2), 'Length unequal %d != %d.' % (len(traj1), len(traj2)))

        self.assertEqual(len(old_items),len(new_items))
        for key,item in new_items.items():
            old_item = old_items[key]

            if isinstance(item, BaseParameter):
                self.assertTrue(parameters_equal(item,old_item),
                                'For key %s: %s not equal to %s' % (key,str(old_item),str(item)))
            elif isinstance(item,BaseResult):
                self.assertTrue(results_equal(item, old_item),
                                'For key %s: %s not equal to %s' % (key, str(old_item),str(item)))
            else:
                raise RuntimeError('You shall not pass')


            self.assertTrue(str(item.v_annotations)==str(old_item.v_annotations),'%s != %s' %
                        (item.v_annotations.f_ann_to_str(),old_item.v_annotations.f_ann_to_str()))

        # Check the annotations
        for node in traj1.f_iter_nodes(recursive=True):

            if node.v_run_branch == traj1.f_wildcard('$', 0) or node.v_run_branch == 'trajectory':
                if node.v_comment != '' and node.v_full_name in traj2:
                    second_comment = traj2.f_get(node.v_full_name).v_comment
                    self.assertEqual(node.v_comment, second_comment, '%s != %s, for %s' %
                                                (node.v_comment, second_comment, node.v_full_name))

            if not node.v_annotations.f_is_empty():
                second_anns = traj2.f_get(node.v_full_name).v_annotations
                self.assertTrue(str(node.v_annotations) == str(second_anns))
import numpy as np
import sys
import unittest
import pickle
import scipy.sparse as spsp
import pypet.pypetexceptions as pex
import warnings
import pandas as pd
import pypet.utils.comparisons as comp
import pypet.pypetconstants as pypetconstants
from pypet.parameter import Parameter, PickleParameter, ArrayParameter,\
    SparseParameter, ObjectTable, Result, SparseResult, PickleResult, BaseParameter
from pypet.trajectory import Trajectory
from pypet.utils.helpful_classes import ChainMap
from pypet.utils.explore import cartesian_product
from pypet.tests.testutils.ioutils import run_suite, parse_args, make_temp_dir
from pypet.tests.testutils.data import TrajectoryComparator
            self.assertTrue(res.f_is_empty())


    def test_string_representation(self):
        self.results['kkk']=self.Constructor('test.res.kkk', answer=42)
        self.results['rrr']=self.Constructor('test.res.rrr')
        for res in self.results.values():
            resstrlist = []
            strlen = 0
            for key in res._data:
                val = res._data[key]
                resstr = '%s=%s, ' % (key, repr(val))
                resstrlist.append(resstr)

                strlen += len(resstr)
                if strlen > pypetconstants.HDF5_STRCOL_MAX_VALUE_LENGTH:
                    break

            return_string = "".join(resstrlist)
            if len(return_string) > pypetconstants.HDF5_STRCOL_MAX_VALUE_LENGTH:
                return_string =\
                    return_string[0:pypetconstants.HDF5_STRCOL_MAX_VALUE_LENGTH - 3] + '...'
            else:
                return_string = return_string[0:-2] # Delete the last `, `


            valstr = res.f_val_to_str()
            self.assertTrue(return_string==valstr)
            self.assertTrue(len(valstr)<=pypetconstants.HDF5_STRCOL_MAX_VALUE_LENGTH)


    def test_meta_settings(self):
        for key, res in self.results.items():
            self.assertEqual(res.v_full_name, key)
            self.assertEqual(res.v_name, key.split('.')[-1])
            self.assertEqual(res.v_location, '.'.join(key.split('.')[0:-1]))

    def test_natural_naming(self):
        for res_name,res in self.results.items():
            for key, val1 in res.f_to_dict().items():
                val2 = getattr(res, key)
                self.assertTrue(comp.nested_equal(val1,val2))

    def test_get_item(self):
        for res_name,res in self.results.items():
            for key, val1 in res.f_to_dict().items():
                val2 = res[key]
                self.assertTrue(comp.nested_equal(val1,val2))

    def test_f_to_dict_no_copy(self):
        for res_name,res in self.results.items():
            for key, val1 in res.f_to_dict(copy=False).items():
                val2 = res[key]
                self.assertTrue(comp.nested_equal(val1,val2))


    def test_Attribute_error_for_get_item(self):
        for res in self.results.values():
            with self.assertRaises(AttributeError):
                res['IDONOTEXIST']

    def test_reject_outer_data_structure(self):
        for res in self.results.values():
            with self.assertRaises(TypeError):
                res.f_set(doesntwork=ChainMap({},{}))

    def test_the_insertion_made_implicetly_in_setUp(self):
        for key, val1 in self.data.items():
            res = self.results['test.res.kwargs']
            val2 = res[key]
            if isinstance(val1, dict):
                for innerkey in val1:
                    innerval1 = val1[innerkey]
                    innerval2 = val2[innerkey]
                    self.assertEqual(repr(innerval1), repr(innerval2),
                                     '%s != %s' % (str(val1),str(val2)))
            else:
                self.assertEqual(repr(val1),repr(val2), '%s != %s' % (str(val1),str(val2)))


    def test_pickling(self):
        for key, res in self.results.items():

            dump = pickle.dumps(res)

            newRes = pickle.loads(dump)

            self.results[key] = newRes

        self.test_the_insertion_made_implicetly_in_setUp()

        self.test_meta_settings()

    def test_storage_and_loading(self):

        for key, res in self.results.items():
            store_dict = res._store()

            constructor = res.__class__


            res = constructor('')

            res._load(store_dict)

            res._rename(key)

            self.results[key] = res


        self.test_the_insertion_made_implicetly_in_setUp()

        self.test_meta_settings()

class PickleResultTest(ResultTest):

    tags = 'unittest', 'result', 'pickle'

    def make_constructor(self):
        self.Constructor=PickleResult

        self.dynamic_imports = [PickleResult]