On Sat, Dec 5, 2009 at 1:57 AM, David Cournapeau <courn...@gmail.com> wrote:
> On Sat, Dec 5, 2009 at 1:31 AM, Bruce Southey <bsout...@gmail.com> wrote:
>> On 12/04/2009 10:12 AM, David Cournapeau wrote:
>>> On Fri, Dec 4, 2009 at 9:23 PM, Francesc Alted<fal...@pytables.org> wrote:
>>>
>>>> A Thursday 03 December 2009 14:56:16 Dag Sverre Seljebotn escrigué:
>>>>
>>>>> Pauli Virtanen wrote:
>>>>>
>>>>>> Thu, 03 Dec 2009 14:03:13 +0100, Dag Sverre Seljebotn wrote:
>>>>>> [clip]
>>>>>>
>>>>>>
>>>>>>> Great! Are you storing the format string in the dtype types as well? (So
>>>>>>> that no release is needed and acquisitions are cheap...)
>>>>>>>
>>>>>> I regenerate it on each buffer acquisition. It's simple low-level C code,
>>>>>> and I suspect it will always be fast enough. Of course, we could *cache*
>>>>>> the result in the dtype. (If dtypes are immutable, which I don't remember
>>>>>> right now.)
>>>>>>
>>>>> We discussed this at SciPy 09 -- basically, they are not necesarrily
>>>>> immutable in implementation, but anywhere they are not that is a bug and
>>>>> no code should depend on their mutability, so we are free to assume so.
>>>>>
>>>> Mmh, the only case that I'm aware about dtype *mutability* is changing the
>>>> names of compound types:
>>>>
>>>> In [19]: t = np.dtype("i4,f4")
>>>>
>>>> In [20]: t
>>>> Out[20]: dtype([('f0', '<i4'), ('f1', '<f4')])
>>>>
>>>> In [21]: hash(t)
>>>> Out[21]: -9041335829180134223
>>>>
>>>> In [22]: t.names = ('one', 'other')
>>>>
>>>> In [23]: t
>>>> Out[23]: dtype([('one', '<i4'), ('other', '<f4')])
>>>>
>>>> In [24]: hash(t)
>>>> Out[24]: 8637734220020415106
>>>>
>>>> Perhaps this should be marked as a bug? I'm not sure about that, because
>>>> the
>>>> above seems quite useful.
>>>>
>>> Hm, that's strange - I get the same hash in both cases, but I thought
>>> I took into account names when I implemented the hashing protocol for
>>> dtype. Which version of numpy on which os are you seeing this ?
>>>
>>> David
>>> _______________________________________________
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>>>
>> Hi,
>> On the same linux 64-bit Fedora 11, I get the same hash with Python2.4
>> and numpy 1.3 but different hashes for Python2.6 and numpy 1.4.
>
> Could you check the behavior of 1.4.0 on 2.4 ? The code doing hashing
> for dtypes has not changed since 1.3.0, so normally only the python
> should have an influence.
When I say should, it should be understood as this is the only reason
why I think it could be different - the behavior should certainly not
depend on the python version.
David
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