[Numpy-discussion] Really strange result
Bruce Southey
bsouthey@gmail....
Mon May 4 10:14:34 CDT 2009
Neal Becker wrote:
> Charles R Harris wrote:
>
>
>> On Fri, May 1, 2009 at 7:39 PM, Charles R Harris
>> <charlesr.harris@gmail.com>wrote:
>>
>>
>>> On Fri, May 1, 2009 at 7:24 PM, Neal Becker <ndbecker2@gmail.com> wrote:
>>>
>>>
>>>> Charles R Harris wrote:
>>>>
>>>>
>>>>> On Fri, May 1, 2009 at 1:02 PM, Neal Becker <ndbecker2@gmail.com>
>>>>>
>>>> wrote:
>>>>
>>>>>> In [16]: (np.linspace (0, len (x)-1, len(x)).astype
>>>>>>
>>>> (np.uint64)*2).dtype
>>>>
>>>>>> Out[16]: dtype('uint64')
>>>>>>
>>>>>> In [17]: (np.linspace (0, len (x)-1, len(x)).astype
>>>>>>
>>>> (np.uint64)*n).dtype
>>>>
>>>>>> Out[17]: dtype('float64')
>>>>>>
>>>>>> In [18]: type(n)
>>>>>> Out[18]: <type 'int'>
>>>>>>
>>>>>> Now that's just strange. What's going on?
>>>>>>
>>>>>>
>>>>>>
>>>>> The n is signed, uint64 is unsigned. So a signed type that can hold
>>>>> uint64 is needed. There ain't no such integer, so float64 is used. I
>>>>>
>>>> think
>>>>
>>>>> the logic here is a bit goofy myself since float64 doesn't have the
>>>>>
>>>> needed
>>>>
>>>>> 64 bit precision and the conversion from int kind to float kind is
>>>>> confusing. I think it would be better to raise a NotAvailable error or
>>>>> some such. Lest you think this is an isolated oddity, sometimes
>>>>> numeric arrays can be converted to object arrays.
>>>>>
>>>>> Chuck
>>>>>
>>>> I don't think that any type of integer arithmetic should ever be
>>>> automatically promoted to float.
>>>>
>>>> Besides that, what about the first example? There, I used '2' rather
>>>> than
>>>> 'n'. Is not '2' also an int?
>>>>
>>> What version of numpy are you using?
>>>
>>>
>> And what is the value of n?
>>
>>
>
>
>> Chuck
>>
>
> np.version.version
> Out[5]: '1.3.0'
> (I think the previous test was on 1.2.0 and did the same thing)
>
> (np.linspace (0, 1023,1024).astype(np.uint64)*2).dtype
> Out[2]: dtype('uint64')
>
> In [3]: n=-7
>
> In [4]: (np.linspace (0, 1023,1024).astype(np.uint64)*n).dtype
> Out[4]: dtype('float64')
>
>
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>
Hi,
//I think this behavior has been raised before. IIRC, Numpy is trying to
do the operation that is requested by converting the dtype into floats
since this is a generic solution that will avoid overflow with any ints
not just unsigned ints.
Note that you get a different result if you use subtraction than
multiplication.
>>> np.linspace (0, 1023,1024)
array([ 0.00000000e+00, 1.00000000e+00, 2.00000000e+00, ...,
1.02100000e+03, 1.02200000e+03, 1.02300000e+03])
>>> np.linspace (0, 1023,1024).astype(np.uint64)*-7
array([ -0.00000000e+00, -7.00000000e+00, -1.40000000e+01, ...,
-7.14700000e+03, -7.15400000e+03, -7.16100000e+03])
>>> np.linspace (0, 1023,1024).astype(np.uint64)-7
array([18446744073709551609, 18446744073709551610, 18446744073709551611,
..., 1014, 1015,
1016], dtype=uint64)
Bruce
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