Hello,
I just download the newly Python-2.5 and Numpy-1.0rc1 and all work fine
on Linux-x86 32 and 64bit platforms.
Now, I try to compile the both distributions on WindowsXP with
VisualStudio2003. No problem to compile Python-2.5, but i have some
troubles with Numpy-1.0rc1 and I didn't find
mg wrote:
Hello,
I just download the newly Python-2.5 and Numpy-1.0rc1 and all work fine
on Linux-x86 32 and 64bit platforms.
Now, I try to compile the both distributions on WindowsXP with
VisualStudio2003. No problem to compile Python-2.5, but i have some
troubles with Numpy-1.0rc1 and
Hi,
I'm installing numpy on a 2 cpus intel pentium 4 Linux box. I'm installing BLAS
and
LAPACK from sources too and I need to tune compiler flags. Here is the
question: which
are the proper flags for compiling LAPACK? Inside lapack.tgz make.inc.LINUX
says:
OPTS =
El dc 27 de 09 del 2006 a les 21:17 -0600, en/na Travis Oliphant va
escriure:
Hi all,
I'd like to release numpy 1.0rc2 on about October 5 of next week.
Then, the release of numpy 1.0 official should happen on Monday, October
17. Please try and get all fixes and improvements in before
Hi,
I noticed a behaviour which I found counter-intuitive at least when
using concatenate. I have a function which takes a numpy array of rank 2
as input, let's say foo(in):
a= N.randn((10, 2))
foo(a)
To test a ctype implementation of foo against the python version, my
test has
Hi all,
Currently, the power function returns '0' for negative powers of
integers:
In [1]: N.power(3,-2)
Out[1]: 0
(or, more confusingly)
In [1]: N.power(a,b)
Out[1]: 0
which is almost certainly not the answer you want. Two possible
solutions may be to upcast the input to float before
Emanuele,
the scipy compiler flags under
http://www.scipy.org/Installing_SciPy/BuildingGeneral
work well. However, if you happen to have to use gcc 3.2.3 (e.g. often
in Redhat Enterprise editions present), you have to turn off
optimization, otherwise lapack doesn't build properly.
The
David Cournapeau wrote:
Hi,
What are the rules concerning storage with numpy ?
The rule is that a numpy array has strides which specify how many
bytes to skip to get to the next element in the array. That's the
internal model. There are no hard and fast rules about storage order.
I've been using the new record arrays and lexsort from numpy quite a lot
lately. Very cool stuff.
Using the nightly egg for numpy from here (I believe it is up to date...):
http://code.enthought.com/enstaller/eggs/numpy-nightly-py2.4-win32.egg
I get segfaults when using lexsort on character
Travis Oliphant wrote:
mg wrote:
Hello,
I just download the newly Python-2.5 and Numpy-1.0rc1 and all work fine
on Linux-x86 32 and 64bit platforms.
Now, I try to compile the both distributions on WindowsXP with
VisualStudio2003. No problem to compile Python-2.5, but i have some
Unfortunately, no Windows-x86 or Windows-x86-64bit Numpy-1.0rc1
installer are available on SourceForge yet. So the only current solution
for us is to compile it.
Moreover, our generic C++ framework is compiled with VisualStudio on
Windows-native and we compile all additions to it with the same
Stefan van der Walt wrote:
Hi all,
Currently, the power function returns '0' for negative powers of
integers:
In [1]: N.power(3,-2)
Out[1]: 0
(or, more confusingly)
In [1]: N.power(a,b)
Out[1]: 0
which is almost certainly not the answer you want. Two possible
solutions may be to
On Sep 28, 2006, at 12:03 PM, Travis Oliphant wrote:
The other option is to improve your converter in setElements so
that it
can understand any of the array scalar integers and not just the
default
Python integer.
I think this may be the best approach.
This may be something worthwhile
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