On Wed, Dec 1, 2010 at 6:09 PM, John Salvatier
wrote:
> On Wed, Dec 1, 2010 at 6:07 PM, Keith Goodman wrote:
>>
>> On Wed, Dec 1, 2010 at 5:53 PM, David wrote:
>>
>> > On 12/02/2010 04:47 AM, Keith Goodman wrote:
>> >> It's hard to write Cython code that can handle all dtypes and
>> >> arbitrary
On Wed, Dec 1, 2010 at 7:56 PM, David wrote:
> On 12/02/2010 12:35 PM, John Salvatier wrote:
> > Hello,
> >
> > I am writing a UFunc creation utility, and I would like to know: is
> > there a way to mimic the behavior ofPyArray_IterAllButAxis for multiple
> > arrays at a time?
>
> Is there a reas
On 12/02/2010 12:35 PM, John Salvatier wrote:
> Hello,
>
> I am writing a UFunc creation utility, and I would like to know: is
> there a way to mimic the behavior ofPyArray_IterAllButAxis for multiple
> arrays at a time?
Is there a reason why creating a separate iterator for each array is not
pos
Hello,
I am writing a UFunc creation utility, and I would like to know: is there a
way to mimic the behavior ofPyArray_IterAllButAxis for multiple arrays at a
time? I would like to be able to write UFuncs that take an axis argument and
also take multiple array arguments, for example I want to be a
On Wed, Dec 1, 2010 at 13:18, Ken Basye wrote:
> Hi Numpy folks,
> When working with floats, I prefer to have exact string
> representations in doctests and other reference-based testing; I find it
> helps a lot to avoid chasing cross-platform differences that are really
> about the string con
On 2010-12-01, at 2:18 PM, Ken Basye wrote:
> On a somewhat related note, is there a table someplace which shows
> which versions of Python are supported in each release of Numpy? I
> found an FAQ that mentioned 2.4 and 2.5, but since it didn't mention 2.6
> or 2.7 (much less 3.1), I assum
On Wed, Dec 1, 2010 at 6:07 PM, Keith Goodman wrote:
> On Wed, Dec 1, 2010 at 5:53 PM, David wrote:
>
> > On 12/02/2010 04:47 AM, Keith Goodman wrote:
> >> It's hard to write Cython code that can handle all dtypes and
> >> arbitrary number of dimensions. The former is typically dealt with
> >> u
On Wed, Dec 1, 2010 at 5:53 PM, David wrote:
> On 12/02/2010 04:47 AM, Keith Goodman wrote:
>> It's hard to write Cython code that can handle all dtypes and
>> arbitrary number of dimensions. The former is typically dealt with
>> using templates, but what do people do about the latter?
>
> The on
Hi Keith,
On 12/02/2010 04:47 AM, Keith Goodman wrote:
> It's hard to write Cython code that can handle all dtypes and
> arbitrary number of dimensions. The former is typically dealt with
> using templates, but what do people do about the latter?
The only way that I know to do that systematically
On 12/01/2010 08:47 PM, Keith Goodman wrote:
> It's hard to write Cython code that can handle all dtypes and
> arbitrary number of dimensions. The former is typically dealt with
> using templates, but what do people do about the latter?
>
What you typically do is to use the C-level iterator AP
It's hard to write Cython code that can handle all dtypes and
arbitrary number of dimensions. The former is typically dealt with
using templates, but what do people do about the latter?
I'm trying to take baby steps towards writing an apply_along_axis
function that takes as input a cython function
Hi Numpy folks,
When working with floats, I prefer to have exact string
representations in doctests and other reference-based testing; I find it
helps a lot to avoid chasing cross-platform differences that are really
about the string conversion rather than about numerical differences.
Sin
On Wed, Nov 24, 2010 at 3:16 PM, Friedrich Romstedt <
friedrichromst...@gmail.com> wrote:
> 2010/11/16 greg whittier :
> > I'd like to be able to speed up the following code.
> >
> > def replace_dead(cube, dead):
> > # cube.shape == (320, 640, 1200)
> > # dead.shape == (320, 640)
> > # cube[
@Keith Goodman
I think I figured it out. I believe something like the following will do
what you want, iterating across one axis specially, so it can apply a median
function along an axis. This code in particular is for calculating a moving
average and seems to work (though I haven't checked my ma
On Wed, Dec 1, 2010 at 7:26 AM, John Hornstein
wrote:
> Does NumPy 1.5 work with Python 2.7 or Python 3.x?
>
>
>
Yes, both. NumPy 1.5.1 fixes some small bugs and that is what you should
use.
Chuck
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Le 26/11/2010 17:48, Bruce Sherwood a écrit :
> Although this was mentioned earlier, it's worth emphasizing that if
> you need to use functions such as cosine with scalar arguments, you
> should use math.cos(), not numpy.cos(). The numpy versions of these
> functions are optimized for handling arra
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