numpy ndarray hashability

Question

I have some problems understanding how numpy objects hashability is managed.

>>> import numpy as np
>>> class Vector(np.ndarray):
...     pass
>>> nparray = np.array([0.])
>>> vector = Vector(shape=(1,), buffer=nparray)
>>> ndarray = np.ndarray(shape=(1,), buffer=nparray)
>>> nparray
array([ 0.])
>>> ndarray
array([ 0.])
>>> vector
Vector([ 0.])
>>> '__hash__' in dir(nparray)
True
>>> '__hash__' in dir(ndarray)
True
>>> '__hash__' in dir(vector)
True
>>> hash(nparray)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unhashable type: 'numpy.ndarray'
>>> hash(ndarray)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unhashable type: 'numpy.ndarray'
>>> hash(vector)
-9223372036586049780
>>> nparray.__hash__()
269709177
>>> ndarray.__hash__()
269702147
>>> vector.__hash__()
-9223372036586049780
>>> id(nparray)
4315346832
>>> id(ndarray)
4315234352
>>> id(vector)
4299616456
>>> nparray.__hash__() == id(nparray)
False
>>> ndarray.__hash__() == id(ndarray)
False
>>> vector.__hash__() == id(vector)
False
>>> hash(vector) == vector.__hash__()
True

How come

• numpy objects define a __hash__ method but are however not hashable
• a class deriving numpy.ndarray defines __hash__ and is hashable?

Am I missing something?

I'm using Python 2.7.1 and numpy 1.6.1

Thanks for any help!

EDIT: added objects ids

EDIT2: And following deinonychusaur comment and trying to figure out if hashing is based on content, I played with numpy.nparray.dtype and have something I find quite strange:

>>> [Vector(shape=(1,), buffer=np.array([1], dtype=mytype), dtype=mytype) for mytype in ('float', 'int', 'float128')]
[Vector([ 1.]), Vector([1]), Vector([ 1.0], dtype=float128)]
>>> [id(Vector(shape=(1,), buffer=np.array([1], dtype=mytype), dtype=mytype)) for mytype in ('float', 'int', 'float128')]
[4317742576, 4317742576, 4317742576]
>>> [hash(Vector(shape=(1,), buffer=np.array([1], dtype=mytype), dtype=mytype)) for mytype in ('float', 'int', 'float128')]
[269858911, 269858911, 269858911]

I'm puzzled... is there some (type independant) caching mechanism in numpy?

Answer 1

I get the same results in Python 2.6.6 and numpy 1.3.0. According to the Python glossary, an object should be hashable if __hash__ is defined (and is not None), and either __eq__ or __cmp__ is defined. ndarray.__eq__ and ndarray.__hash__ are both defined and return something meaningful, so I don't see why hash should fail. After a quick google, I found this post on the python.scientific.devel mailing list, which states that arrays have never been intended to be hashable - so why ndarray.__hash__ is defined, I have no idea. Note that isinstance(nparray, collections.Hashable) returns True.

EDIT: Note that nparray.__hash__() returns the same as id(nparray), so this is just the default implementation. Maybe it was difficult or impossible to remove the implementation of __hash__ in earlier versions of python (the __hash__ = None technique was apparently introduced in 2.6), so they used some kind of C API magic to achieve this in a way that wouldn't propagate to subclasses, and wouldn't stop you from calling ndarray.__hash__ explicitly?

Things are different in Python 3.2.2 and the current numpy 2.0.0 from the repo. The __cmp__ method no longer exists, so hashability now requires __hash__ and __eq__ (see Python 3 glossary). In this version of numpy, ndarray.__hash__ is defined, but it is just None, so cannot be called. hash(nparray) fails andisinstance(nparray, collections.Hashable) returns False as expected. hash(vector) also fails.