[Numpy-discussion] numpy type mismatch
Benjamin Root
ben.root@ou....
Fri Jun 10 15:17:03 CDT 2011
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:
>>> a.dtype.itemsize
4
>>> b.dtype.itemsize
4
So, at least the sizes are right.
Ben Root
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