On Thu, May 3, 2012 at 1:51 AM, Henry Gomersall wrote:
> Right, so this is expected behaviour then. Is this documented somewhere?
> It strikes me that this is pretty unexpected behaviour.
Imagine the way you would code this in a for-loop. You want
a = np.arange(10)
a[2:] = a[:-2]
Now you write
On Wed, 2012-05-02 at 12:58 -0700, Stéfan van der Walt wrote:
> On Wed, May 2, 2012 at 9:03 AM, Henry Gomersall
> wrote:
> > Is this some nuance of the way numpy does things? Or am I missing
> some
> > stupid bug in my code?
>
> Try playing with the parameters of the following code:
>
>
> For
On Wed, May 2, 2012 at 9:03 AM, Henry Gomersall wrote:
> Is this some nuance of the way numpy does things? Or am I missing some
> stupid bug in my code?
Try playing with the parameters of the following code:
sz = 1
N = 10
import numpy as np
x = np.arange(sz)
y = x.copy()
x[:-N] = x[N:]
np
I'm need to do some shifting of data within an array and am using the
following code:
for p in numpy.arange(array.shape[0], dtype='int64'):
for q in numpy.arange(array.shape[1]):
# A positive shift is towards zero
shift = shift_values[p, q]
if shift >= 0:
