On Feb 16, 2008 3:21 PM, Pierre GM <[EMAIL PROTECTED]> wrote:
> > Can I safely carry around the data, mask and MaskedArray? I'm
> > considering working along the lines of the following conceptual
> > outline:
>
> That depends a lot on what calculate_results does, and whether you update the
> arrays
> Can I safely carry around the data, mask and MaskedArray? I'm
> considering working along the lines of the following conceptual
> outline:
That depends a lot on what calculate_results does, and whether you update the
arrays in place or not.
> d = numpy.array(shape, dtype)
> m = numpy.array(sh
Hi,
numpy.ctypes uses ctypes to work, it consists of some additional utility
functions.
There was a discussion on this some time ago (SWIG, ctypes, ...) with David
(C.), Gaël and others.
Why translating some code to C ? Why not using f2py ?
Matthieu
2008/2/16, dmitrey <[EMAIL PROTECTED]>:
>
>
Hello,
On Feb 16, 2008 9:14 PM, dmitrey <[EMAIL PROTECTED]> wrote:
> hi all,
> I intend to connect some C code to Python for some my purposes.
> What is the best software for the aim?
> Is it numpy.ctypes or swig or something else?
> IIRC ctypes are present in Python since v2.5, so it's ok to use
On Feb 16, 2008 12:25 PM, Pierre GM <[EMAIL PROTECTED]> wrote:
> Alexander,
> You get the gist here: process your _data and _mask separately and recombine
> them into a MaskedArray at the end. That way, you'll skip most of the
> overhead costs brought by some tests in the package (in __getitem__,
>
hi all,
I intend to connect some C code to Python for some my purposes.
What is the best software for the aim?
Is it numpy.ctypes or swig or something else?
IIRC ctypes are present in Python since v2.5, so it's ok to use just
ctypes, not numpy.ctypes, or some difference is present?
Another one qu
Alexander,
You get the gist here: process your _data and _mask separately and recombine
them into a MaskedArray at the end. That way, you'll skip most of the
overhead costs brought by some tests in the package (in __getitem__,
__setitem__...).
___
Nump
Hello:
I am starting to use record arrays and would like to
know how to keep numpy from displaying the dtype info.
For example, I can make a record array containing a
long tuple:
mydescriptor = dtype([('first', 'f4'),('second',
'f4'), ('third', [(str(x),'http://www.yahoo.com/r/hs
