This is the closer I got to do what you say
http://numpy-discussion.10968.n7.nabble.com/Reductions-with-nditer-working-only-with-the-last-axis-td8157.html
Converts a 3D to 2D, but only works in the last axis. Any improvement would
be welcomed.ç
2013/3/22 Neal Becker
> I frequently find I ha
42 45]
[48 51 54 57]]
nditer_fun(data, [-1, 0, 1])
[[58 58 58 58]
[58 58 58 58]
[58 58 58 58]]
Only along the last axis both functions give the same result
2012/10/1 Han Genuit :
> On Thu, Sep 27, 2012 at 6:08 PM, Sergio Pascual
> wrote:
>> Hello, I'm trying to understand h
Hello, I'm trying to understand how to work with nditer to do a
reduction, in my case converting a 3d array into a 2d array.
I followed the help here
http://docs.scipy.org/doc/numpy/reference/arrays.nditer.html and
managed to create a function that applies reduction over the last axis
of the input
Hi, I have a 3d array of bitswapped data, dimensions 2000x2000x20. I
want to apply a function over the third axis to obtain a 2000x2000
array. This is what numpy.apply_along_axis does, but I need a faster
version, so I'm writing it in C. I started by writing a generic ufunc
version of the code. It
I have written a program that makes use of NpyIter_Multi to iterate
simultaneously over a large number of 2d arrays. But I have noticed
that there is a limit of 32 in the number of simultaneous iterators
allowed. This number is NPY_MAXARGS. I typically need to iterate over
hundreds of images.
So,
You should check if you have two different compilers installed
ls /usr/bin/gcc* ?
2011/4/13 c cook :
> I have Fedora Core 3 and x86_64 architecture. But I don't understand why it
> appears to look for /usr/lib/gcc/x86_64-redhat-linux/3.4.4/
> instead of /usr/bin/gcc
> Csaba
>
> On Wed, Apr 13, 20
1, 2011 at 2:36 PM, Sergio Pascual
> wrote:
>> Hi list.
>>
>> For mi application, I would like to implement some new statistics
>> functions over numpy arrays, such as truncated mean. Ideally this new
>> function should have the same arguments
>> than numpy.
Hi list.
For mi application, I would like to implement some new statistics
functions over numpy arrays, such as truncated mean. Ideally this new
function should have the same arguments
than numpy.mean: axis, dtype and out. Is there a way of writing this
function that doesn't imply writing it in C
