

def run(self, runfunc, *args, **kwargs):
    ' Runs the experiments and explores the parameter space.\n\n        :param runfunc: The task or job to do\n\n        :param args: Additional arguments (not the ones in the trajectory) passed to `runfunc`\n\n        :param kwargs:\n\n            Additional keyword arguments (not the ones in the trajectory) passed to `runfunc`\n\n        :return:\n\n            List of the individual results returned by `runfunc`.\n\n            Returns a LIST OF TUPLES, where first entry is the run idx and second entry\n            is the actual result. They are always ordered according to the run index.\n\n            Does not contain results stored in the trajectory!\n            In order to access these simply interact with the trajectory object,\n            potentially after calling`~pypet.trajectory.Trajectory.f_update_skeleton`\n            and loading all results at once with :func:`~pypet.trajectory.f_load`\n            or loading manually with :func:`~pypet.trajectory.f_load_items`.\n\n            If you use multiprocessing without a pool the results returned by\n            `runfunc` still need to be pickled.\n\n        '
    pipeline = (lambda traj: ((runfunc, args, kwargs), (self._postproc, self._postproc_args, self._postproc_kwargs)))
    self._user_pipeline = False
    self._map_arguments = False
    return self._execute_runs(self)

dataset/ETHPy150Open SmokinCaterpillar/pypet/pypet/environment.py Environment.run/VarMisuse@68/473 `pipeline`->`self`