On 12/04/2009 10:57 AM, David Cournapeau 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.
>
> David
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>
These are different with Python 2.4 and numpy 1.4. Curiously I got
different hash values with Python 2.5 and numpy 1.3. (For what it is
worth, I get the same hash values with Python 2.3 with numpy 1.1.1).
Bruce
Python 2.5.2 (r252:60911, Nov 18 2008, 09:20:42)
[GCC 4.3.2 20081105 (Red Hat 4.3.2-7)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> np.__version__
'1.3.0'
>>> t = np.dtype("i4,f4")
>>> hash(t)
-9041335829180134223
>>> t.names = ('one', 'other')
>>> hash(t)
8637734220020415106
[bsout...@starling python]$ /usr/local/bin/python2.4
Python 2.4.5 (#1, Oct 6 2008, 09:54:35)
[GCC 4.3.2 20080917 (Red Hat 4.3.2-4)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> np.__version__
'1.4.0rc1'
>>> t = np.dtype("i4,f4")
>>> hash(t)
-9041335829180134223
>>> t.names = ('one', 'other')
>>> hash(t)
8637734220020415106
Python 2.3.7 (#1, Oct 6 2008, 09:55:54)
[GCC 4.3.2 20080917 (Red Hat 4.3.2-4)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> np.__version__
'1.1.1'
>>> t = np.dtype("i4,f4")
>>> hash(t)
140552637936672
>>> t.names = ('one', 'other')
>>> hash(t)
140552637936672
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