On Wed, May 6, 2009 at 3:03 PM, Christopher Barker
wrote:
> Hi all,
>
> The binary for OS-X on sourceforge is called:
>
> numpy-1.3.0-py2.5-macosx10.5.dmg
>
> However, as far as I can tell, it works just fine on OS-X 10.4, and
> maybe even 10.3.9.
I have to confess I don't understand mac os x bac
Hi,
I worked on some code to detect C API mismatches both for developers
and for users:
http://github.com/cournape/numpy/tree/runtime_feature
It adds the following:
- if a numpy or ufunc function is added in the C API without the
NPY_FEATURE_VERSION to be updated, a warning is generated at buil
On May 9, 2009, at 8:17 PM, Eric Firing wrote:
> Eric Firing wrote:
>
> A part of the slowdown is what looks to me like unnecessary copying
> in _MaskedBinaryOperation.__call__. It is using getdata, which
> applies numpy.array to its input, forcing a copy. I think the copy
> is actually
Short answer to the subject: Oh yes.
Basically, MaskedArrays in its current implementation is more of a
convenience class than anything. Most of the functions manipulating
masked arrays create a lot of temporaries. When performance is needed,
I must advise you to work directly on the data and
Eric Firing wrote:
Pierre,
... I pressed "send" too soon. There are test failures with the patch I
attached to my last message. I think the basic ideas are correct, but
evidently there are wrinkles to be worked out. Maybe putmask() has to
be used instead of where() (putmask is much faster)
Eli Bressert wrote:
Hi,
I'm using masked arrays to compute large-scale standard deviation,
multiplication, gaussian, and weighted averages. At first I thought
using the masked arrays would be a great way to sidestep looping
(which it is), but it's still slower than expected. Here's a snippet
of
Eli Bressert wrote:
> Hi,
>
> I'm using masked arrays to compute large-scale standard deviation,
> multiplication, gaussian, and weighted averages. At first I thought
> using the masked arrays would be a great way to sidestep looping
> (which it is), but it's still slower than expected. Here's a s
Hi,
I'm using masked arrays to compute large-scale standard deviation,
multiplication, gaussian, and weighted averages. At first I thought
using the masked arrays would be a great way to sidestep looping
(which it is), but it's still slower than expected. Here's a snippet
of the code that I'm usin
