[Numpy-discussion] numpy type mismatch

Benjamin Root ben.root@ou....
Fri Jun 10 16:43:01 CDT 2011


On Fri, Jun 10, 2011 at 3:24 PM, Charles R Harris <charlesr.harris@gmail.com
> wrote:

>
>
> On Fri, Jun 10, 2011 at 2:17 PM, Benjamin Root <ben.root@ou.edu> wrote:
>
>>
>>
>> On Fri, Jun 10, 2011 at 3:02 PM, Charles R Harris <
>> charlesr.harris@gmail.com> wrote:
>>
>>>
>>>
>>> On Fri, Jun 10, 2011 at 1:50 PM, Benjamin Root <ben.root@ou.edu> wrote:
>>>
>>>> Came across an odd error while using numpy master.  Note, my system is
>>>> 32-bits.
>>>>
>>>> >>> import numpy as np
>>>> >>> type(np.sum([1, 2, 3], dtype=np.int32)) == np.int32
>>>> False
>>>> >>> type(np.sum([1, 2, 3], dtype=np.int64)) == np.int64
>>>> True
>>>> >>> type(np.sum([1, 2, 3], dtype=np.float32)) == np.float32
>>>> True
>>>> >>> type(np.sum([1, 2, 3], dtype=np.float64)) == np.float64
>>>> True
>>>>
>>>> So, only the summation performed with a np.int32 accumulator results in
>>>> a type that doesn't match the expected type.  Now, for even more
>>>> strangeness:
>>>>
>>>> >>> type(np.sum([1, 2, 3], dtype=np.int32))
>>>> <type 'numpy.int32'>
>>>> >>> hex(id(type(np.sum([1, 2, 3], dtype=np.int32))))
>>>> '0x9599a0'
>>>> >>> hex(id(np.int32))
>>>> '0x959a80'
>>>>
>>>> So, the type from the sum() reports itself as a numpy int, but its
>>>> memory address is different from the memory address for np.int32.
>>>>
>>>>
>>> One of them is probably a long, print out the typecode, dtype.char.
>>>
>>> Chuck
>>>
>>>
>>>
>> Good intuition, but odd result...
>>
>> >>> import numpy as np
>> >>> a = np.sum([1, 2, 3], dtype=np.int32)
>> >>> b = np.int32(6)
>> >>> type(a)
>> <type 'numpy.int32'>
>> >>> type(b)
>> <type 'numpy.int32'>
>> >>> a.dtype.char
>> 'i'
>> >>> b.dtype.char
>> 'l'
>>
>> So, the standard np.int32 is getting listed as a long somehow?  To further
>> investigate:
>>
>>
> Yes, long shifts around from int32 to int64 depending on the OS. For
> instance, in 64 bit Windows it's 32 bits while in 64 bit Linux it's 64 bits.
> On 32 bit systems it is 32 bits.
>
> Chuck
>
>
Right, that makes sense.  But, the question is why does sum() put out a
result dtype that is not identical to the dtype that I requested, or even
the dtype of the input array?  Could this be an indication of a bug
somewhere?  Even if the bug is harmless (it was only noticed within the test
suite of larry), is this unexpected?

Ben Root
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://mail.scipy.org/pipermail/numpy-discussion/attachments/20110610/4967dc01/attachment.html 


More information about the NumPy-Discussion mailing list