It is common that to guarantee good statistical independence between various random generators, a singleton instance of an RNG is shared between them. So I typically have various random generator objects, which (sometimes several levels objects deep) embed an instance of RandomState. Now I have a requirement to copy a generator object (without knowing exactly what that generator object is). My solution is to use deepcopy on the top-level object. But I need to overload __deepcopy__ on the singleton RandomState object. Unfortunately, RandomState doesn't allow customization of __deepcopy__ (or anything else). And it has no __dict__. My solution is: class shared_random_state (object): def __init__ (self, rs): self.rs = rs def __getattr__ (self, attr): return getattr (self.rs, attr) def __deepcopy__ (self): return self An example usage: rs = shared_random_state (RandomState(0)) from exponential import exponential e = exponential (rs, 1) where exponential is: class exponential (object): def __init__ (self, rs, mu): self.rs = rs self.mu = mu def __call__ (self, size=None): if size == None: return self.rs.exponential (self.mu, 1)[0] else: return self.rs.exponential (self.mu, size) def __repr__ (self): return 'exp(%s)' % self.mu I wonder if anyone has any other suggestions? Personally, I would prefer if numpy provided a more direct solution to this. Either by providing for overloading RandomState deepcopy, or by making the copy behavior switchable with a flag to the constructor. -- Those who fail to understand recursion are doomed to repeat it