Hi,
there are np.flipud and np.fliplr methods to flip 2d arrays on the first
and second dimension, respectively. What can I do to flip an array on an
axis which I don't know before runtime? I'd really like to see a
np.flip(arr, axis) method which lets me specify which axis to flip on.
Any ideas?
Hi,
On Wed, Jul 10, 2013 at 11:02 AM, Andreas Hilboll li...@hilboll.de wrote:
Hi,
there are np.flipud and np.fliplr methods to flip 2d arrays on the first
and second dimension, respectively. What can I do to flip an array on an
axis which I don't know before runtime? I'd really like to see a
Hi,
I am building a package that exposes some Fortran libraries through f2py.
The packages directory looks like this:
setup.py
my_pack/
|
|--__init__.py
|-- some.pyf
|--- code.f90
I thoughat that once installed, I'd get the .so and __init__.py in the same
directory
On 10.07.2013 17:06, Matthew Brett wrote:
Hi,
On Wed, Jul 10, 2013 at 11:02 AM, Andreas Hilboll li...@hilboll.de wrote:
Hi,
there are np.flipud and np.fliplr methods to flip 2d arrays on the first
and second dimension, respectively. What can I do to flip an array on an
axis which I don't
On Fri, Jul 5, 2013 at 4:20 AM, Bakhtiyor Zokhidov
bakhtiyor_zokhi...@mail.ru wrote:
Hi everybody,
I have a problem with sorting out the following function. What I expect is
that I showed as an example below.
Two problems are encountered to achieve the result:
1) The function sometimes
On Wed, Jul 10, 2013 at 12:03 PM, Andreas Hilboll li...@hilboll.de wrote:
On 10.07.2013 17:06, Matthew Brett wrote:
Hi,
On Wed, Jul 10, 2013 at 11:02 AM, Andreas Hilboll li...@hilboll.de
wrote:
Hi,
there are np.flipud and np.fliplr methods to flip 2d arrays on the first
and second
Hello NumPy,
Part of my GSoC is compatibility with SciPy's sparse matrices and NumPy's
ufuncs. Currently there is no feasible way to do this without changing
ufuncs a bit.
I've been considering a mechanism to override ufuncs based on checking the
ufuncs arguments for a __ufunc_override__
Hey Blake,
To be clear, my blog-post is just a pre-NEP and should not be perceived as
something that will transpire in NumPy anytime soon.You should take it
as a hey everyone, I think I know how to solve this problem, but I have no
time to do it, but wanted to get the word out to those who