How to use pypet - 10 common examples

def test_run(traj, to_print):
    
    assert isinstance(traj, SingleRun)
    
    print to_print
    
    x = traj.x.value
    y = traj.f_add_derived_parameter('y')
    y.val = x**2
    
    smurf = Result('','','','')
    z = traj.f_add_result('Nada.Moo',smurf)
    
    z.val = y()+1
    
    print 'Dat wars'

self._timestamp = init_time
            self._time = formatted_time

        # In case the user provided a git repository path, a git commit is performed
        # and the environment's hexsha is taken from the commit if the commit was triggered by
        # this particular environment, otherwise a new one is generated
        if self._git_repository is not None:
            new_commit, self._hexsha = make_git_commit(self, self._git_repository,
                                                       self._git_message, self._git_fail)
            # Identifier hexsha
        else:
            new_commit = False

        if not new_commit:
            # Otherwise we need to create a novel hexsha
            self._hexsha = hashlib.sha1(compat.tobytes(self.v_trajectory.v_name +
                                                       str(self.v_trajectory.v_timestamp) +
                                                       str(self.v_timestamp) +
                                                           VERSION)).hexdigest()

        # Create the name of the environment
        short_hexsha = self._hexsha[0:7]
        name = 'environment'
        self._name = name + '_' + str(short_hexsha) + '_' + self._time  # Name of environment

        # The trajectory should know the hexsha of the current environment.
        # Thus, for all runs, one can identify by which environment they were run.
        self._traj._environment_hexsha = self._hexsha
        self._traj._environment_name = self._name

        self._logging_manager.trajectory = self._traj
        self._logging_manager.remove_null_handler()

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

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

        """
        for key in compat.listkeys(load_dict):
            # We delete keys over time:
            if key in load_dict:
                if SparseResult.IDENTIFIER in key:
                    new_key = key.split(SparseResult.IDENTIFIER)[0]

                    is_dia = load_dict.pop(new_key + SparseResult.IDENTIFIER + 'is_dia')

                    name_list = SparseParameter._get_name_list(is_dia)
                    rename_list = ['%s%s%s' % (new_key, SparseResult.IDENTIFIER, name)
                                   for name in name_list]

                    data_list = [load_dict.pop(name) for name in rename_list]
                    matrix = SparseParameter._reconstruct_matrix(data_list)
                    self._data[new_key] = matrix
                else:
                    self._data[key] = load_dict[key]

item = args[0]
            try:
                name = item.v_full_name
                instance = item

                create_new = False
            except AttributeError:
                pass

        # If the item is not an instance yet, check if args[0] is a class and args[1] is
        # a string describing the new name of the instance.
        # If args[0] is not a class it is assumed to be the name of the new instance.
        if create_new:
            if len(args) > 0 and inspect.isclass(args[0]):
                constructor = args.pop(0)
            if len(args) > 0 and isinstance(args[0], compat.base_type):
                name = args.pop(0)
            elif 'name' in kwargs:
                name = kwargs.pop('name')
            elif 'full_name' in kwargs:
                name = kwargs.pop('full_name')
            else:
                raise ValueError('Could not determine a name of the new item you want to add. '
                                 'Either pass the name as positional argument or as a keyword '
                                 'argument `name`.')

        if check_naming:
            split_names = name.split('.')
            for idx, name in enumerate(split_names):
                translated_shortcut = self._translate_shortcut(name)
                if translated_shortcut:
                    translated_shortcut = self._replace_wildcard(translated_shortcut)

else:
        commentstr = ''

    if user_message:
        user_message += ' -- '

    message = '%sTrajectory: `%s`, Time: `%s`, %s' % \
              (user_message, traj.v_name, traj.v_time, commentstr)

    # Detect changes:
    diff = index.diff(None)

    if diff:
        if git_fail:
            # User requested fail instead of a new commit
            raise pex.GitDiffError('Found not committed changes!')
        # Make the commit
        repo.git.add('-u')
        commit = index.commit(message)
        new_commit = True

    else:
        # Take old commit
        commit = repo.commit(None)
        new_commit = False

    # Add the commit info to the trajectory
    add_commit_variables(traj, commit)

    return new_commit, commit.hexsha

new_key = key.split(SparseResult.IDENTIFIER)[0]

                    is_dia = load_dict.pop(new_key + SparseResult.IDENTIFIER + 'is_dia')

                    name_list = SparseParameter._get_name_list(is_dia)
                    rename_list = ['%s%s%s' % (new_key, SparseResult.IDENTIFIER, name)
                                   for name in name_list]

                    data_list = [load_dict.pop(name) for name in rename_list]
                    matrix = SparseParameter._reconstruct_matrix(data_list)
                    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

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

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

        """
        for key in compat.listkeys(load_dict):
            # We delete keys over time:
            if key in load_dict:
                if SparseResult.IDENTIFIER in key:
                    new_key = key.split(SparseResult.IDENTIFIER)[0]

                    is_dia = load_dict.pop(new_key + SparseResult.IDENTIFIER + 'is_dia')

                    name_list = SparseParameter._get_name_list(is_dia)
                    rename_list = ['%s%s%s' % (new_key, SparseResult.IDENTIFIER, name)
                                   for name in name_list]

                    data_list = [load_dict.pop(name) for name in rename_list]
                    matrix = SparseParameter._reconstruct_matrix(data_list)
                    self._data[new_key] = matrix
                else:
                    self._data[key] = load_dict[key]

"""
        for key in compat.listkeys(load_dict):
            # We delete keys over time:
            if key in load_dict:
                if SparseResult.IDENTIFIER in key:
                    new_key = key.split(SparseResult.IDENTIFIER)[0]

                    is_dia = load_dict.pop(new_key + SparseResult.IDENTIFIER + 'is_dia')

                    name_list = SparseParameter._get_name_list(is_dia)
                    rename_list = ['%s%s%s' % (new_key, SparseResult.IDENTIFIER, name)
                                   for name in name_list]

                    data_list = [load_dict.pop(name) for name in rename_list]
                    matrix = SparseParameter._reconstruct_matrix(data_list)
                    self._data[new_key] = matrix
                else:
                    self._data[key] = load_dict[key]

self._args = continue_dict['args']
        # Keyword arguments to the user's job function
        self._kwargs = continue_dict['kwargs']
        # Postproc Function
        self._postproc = continue_dict['postproc']
        # Postprog args
        self._postproc_args = continue_dict['postproc_args']
        # Postproc Kwargs
        self._postproc_kwargs = continue_dict['postproc_kwargs']

        old_start_timestamp = continue_dict['start_timestamp']

        # Unpack the trajectory
        self._traj.v_full_copy = continue_dict['full_copy']
        # Load meta data
        self._traj.f_load(load_parameters=pypetconstants.LOAD_NOTHING,
                          load_derived_parameters=pypetconstants.LOAD_NOTHING,
                          load_results=pypetconstants.LOAD_NOTHING,
                          load_other_data=pypetconstants.LOAD_NOTHING)

        # Now we have to reconstruct previous results
        result_list = []
        full_filename_list = []
        for filename in os.listdir(self._continue_path):
            _, ext = os.path.splitext(filename)

            if ext != '.rcnt':
                continue

            full_filename = os.path.join(self._continue_path, filename)
            cnt_file = open(full_filename, 'rb')
            result_list.append(dill.load(cnt_file))

' %d. They should be equally long!'  %
                                               (full_name,array_length,len(traj)))

                class_constructor = traj._create_class(class_name)
                instance = class_constructor(name,comment=comment)

                parent_traj_node._nn_interface._add_from_leaf_instance(parent_traj_node,instance)

                if array_length:
                    traj._explored_parameters[instance.v_full_name]=instance

                self._ann_load_annotations(instance,node=hdf5group)



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

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

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

                self._ann_load_annotations(new_traj_node,node=hdf5group)