2011/6/10 Charles R Harris <charlesr.har...@gmail.com>
>
>
> On Fri, Jun 10, 2011 at 3:43 PM, Benjamin Root <ben.r...@ou.edu> wrote:
>
>>
>>
>> On Fri, Jun 10, 2011 at 3:24 PM, Charles R Harris <
>> charlesr.har...@gmail.com> wrote:
>>
>>>
>>>
>>> On Fri, Jun 10, 2011 at 2:17 PM, Benjamin Root <ben.r...@ou.edu> wrote:
>>>
>>>>
>>>>
>>>> On Fri, Jun 10, 2011 at 3:02 PM, Charles R Harris <
>>>> charlesr.har...@gmail.com> wrote:
>>>>
>>>>>
>>>>>
>>>>> On Fri, Jun 10, 2011 at 1:50 PM, Benjamin Root <ben.r...@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?
>>
>>
> I expect sum is using a ufunc and it acts differently on account of the
> cleanup of the ufunc casting rules. And yes, a long *is* int32 on your
> machine. On mine
>
> In [4]: dtype('q') # long long
> Out[4]: dtype('int64')
>
> In [5]: dtype('l') # long
> Out[5]: dtype('int64')
>
> The mapping from C types to numpy width types isn't 1-1. Personally, I
> think we should drop long ;) But it used to be the standard Python type in
> the C API. Mark has also pointed out the problems/confusion this ambiguity
> causes and someday we should probably think it out and fix it. But I don't
> think it is the most pressing problem.
>
> Chuck
>
>
But isn't it a bug if numpy.dtype('i') != numpy.dtype('l') on a 32 bit
computer where both are int32?
-=- Olivier
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