On 4/10/12 11:43 AM, Henry Gomersall wrote:
> On 10/04/2012 17:57, Francesc Alted wrote:
>>> I'm using numexpr in the end, but this is slower than numpy.abs under linux.
>> Oh, you mean the windows version of abs(complex64) in numexpr is slower
>> than a pure numpy.abs(complex64) under linux? That
On 10/04/2012 17:57, Francesc Alted wrote:
>> I'm using numexpr in the end, but this is slower than numpy.abs under linux.
> Oh, you mean the windows version of abs(complex64) in numexpr is slower
> than a pure numpy.abs(complex64) under linux? That's weird, because
> numexpr has an independent im
On Tue, Apr 10, 2012 at 12:57 PM, Francesc Alted wrote:
> On 4/10/12 9:55 AM, Henry Gomersall wrote:
> > On 10/04/2012 16:36, Francesc Alted wrote:
> >> In [10]: timeit c = numpy.complex64(numpy.abs(numpy.complex128(b)))
> >> 100 loops, best of 3: 12.3 ms per loop
> >>
> >> In [11]: timeit c = num
On 4/10/12 9:55 AM, Henry Gomersall wrote:
> On 10/04/2012 16:36, Francesc Alted wrote:
>> In [10]: timeit c = numpy.complex64(numpy.abs(numpy.complex128(b)))
>> 100 loops, best of 3: 12.3 ms per loop
>>
>> In [11]: timeit c = numpy.abs(b)
>> 100 loops, best of 3: 8.45 ms per loop
>>
>> in your win
On 10/04/2012 16:36, Francesc Alted wrote:
> In [10]: timeit c = numpy.complex64(numpy.abs(numpy.complex128(b)))
> 100 loops, best of 3: 12.3 ms per loop
>
> In [11]: timeit c = numpy.abs(b)
> 100 loops, best of 3: 8.45 ms per loop
>
> in your windows box and see if they raise similar results?
>
No
On 4/10/12 6:44 AM, Henry Gomersall wrote:
Here is the body of a post I made on stackoverflow, but it seems to be
a non-obvious issue. I was hoping someone here might be able to shed
light on it...
On my 32-bit Windows Vista machine I notice a significant (5x)
slowdown when taking the absolut
Here is the body of a post I made on stackoverflow, but it seems to be a
non-obvious issue. I was hoping someone here might be able to shed light
on it...
On my 32-bit Windows Vista machine I notice a significant (5x) slowdown
when taking the absolute values of a fairly large |numpy.complex64|
