def __init__(self, trajectory=None): """ Create a simulation trajectory object Parameters ---------- trajectory : Trajectory if specified, make a deep copy of specified trajectory """ # Initialize list. list.__init__(self) StorableObject.__init__(self) self.path_probability = None # For future uses if trajectory is not None: # Try to make a copy out of whatever container we were provided if hasattr(trajectory, 'atom_indices'): self.atom_indices = trajectory.atom_indices else: self.atom_indices = None if type(trajectory) is Trajectory: self.extend(trajectory.iter_proxies()) else: self.extend(trajectory) else: self.atom_indices = None
def __init__(self, subchanges=None, samples=None, mover=None, details=None, input_samples=None): StorableObject.__init__(self) self._lazy = {} self._len = None self._collapsed = None self._results = None self._trials = None self._accepted = None self.mover = mover if subchanges is None: self.subchanges = [] else: self.subchanges = subchanges if samples is None: self.samples = [] else: self.samples = samples if input_samples is None: self.input_samples = [] else: self.input_samples = input_samples self.details = details
def __init__(self, trajectory=None): """ Create a simulation trajectory object Parameters ---------- trajectory : :obj:`Trajectory` or list of :obj:`openpathsampling.engines.BaseSnapshot` if specified, make a deep copy of specified trajectory """ # Initialize list. list.__init__(self) StorableObject.__init__(self) if trajectory is not None: if type(trajectory) is Trajectory: self.extend(trajectory.iter_proxies()) else: self.extend(trajectory)
def __init__(self, contents): StorableObject.__init__(self) frozenset.__init__(contents) self._dimensions = dict(self) self._cls = self._dimensions['class'] del self._dimensions['class']