On Mon, Jul 13, 2009 at 18:11, Ian Mallett wrote:
> Hello,
>
> I have some code that makes vertex buffer object terrain. Because the setup
> for this (a series of triangle strips) is a bit clunky, I just implemented
> the tricky parts in Python.
>
> The code works, but it's slow. How should I go
2009/7/13 Stéfan van der Walt
> Hi Darren
>
> 2009/7/13 Darren Dale :
> > I've put together a first cut at implementing __array_prepare__, which
> > appears to work, and I would like to request feedback. Here is an
> overview
> > of the approach:
>
> This is pretty neat! Do you have a quick snip
Hello,
I have some code that makes vertex buffer object terrain. Because the setup
for this (a series of triangle strips) is a bit clunky, I just implemented
the tricky parts in Python.
The code works, but it's slow. How should I go about optimizing it?
Thanks,
Ian
size = [#int_something,#in
Hi Darren
2009/7/13 Darren Dale :
> I've put together a first cut at implementing __array_prepare__, which
> appears to work, and I would like to request feedback. Here is an overview
> of the approach:
This is pretty neat! Do you have a quick snippet at hand illustrating its use?
Regards
Stéfa
I've put together a first cut at implementing __array_prepare__, which
appears to work, and I would like to request feedback. Here is an overview
of the approach:
Once the ufunc machinery has created the output arrays, it is time to offer
subclasses a chance to initialize the output arrays and det
On Sun, Jul 12, 2009 at 1:24 PM, Citi, Luca wrote:
> > That is what I thought at first, but then what is the difference between
> > array_types and scalar_types? Function signature is:
> > *find_common_type(array_types, scalar_types)*
> As I understand it, the difference is that in the following
(Sorry if this is a duplicate; I think sent this from the wrong email
the first time)
When using interpolate with a zero-rank array, I get "ValueError:
object of too small depth for desired array". The following code
reproduces this issue
>>> import numpy as np
>>> x0 = np.array(0.1)
>>>
Hi Pauli,
in my PC I have tried this and some of the regressions disappear,
maybe you can give it a try.
At the present state is compiler- and architecture-dependent,
therefore not the best choice. But it may be worth trying.
Best,
Luca
/* My additions are unindented */
/*
Wed, 08 Jul 2009 22:16:22 +, Pauli Virtanen kirjoitti:
[clip]
> On an older CPU (slower, smaller cache), the situation is slightly
> different:
>
> http://www.iki.fi/pav/tmp/athlon.png
> http://www.iki.fi/pav/tmp/athlon.txt
>
> On average, it's still an improvement in many cases. How
