On 25 Nov 2012, at 00:29, numpy-discussion-requ...@scipy.org wrote:
>
> Message: 3
> Date: Sat, 24 Nov 2012 23:23:36 +0100
> From: Da?id
> Subject: Re: [Numpy-discussion] numpy where function on different
> sized arrays
> To: Discussion of Numerical
On Sat, Nov 24, 2012 at 7:08 PM, David Warde-Farley <
d.warde.far...@gmail.com> wrote:
> I think that would lose information as to which value in B was at each
> position. I think you want:
>
>
(premature send, stupid Gmail...)
idx = {}
for i, x in enumerate(a):
for j, y in enumerate(x):
I think that would lose information as to which value in B was at each
position. I think you want:
On Sat, Nov 24, 2012 at 5:23 PM, Daπid wrote:
> A pure Python approach could be:
>
> for i, x in enumerate(a):
> for j, y in enumerate(x):
> if y in b:
>
A pure Python approach could be:
for i, x in enumerate(a):
for j, y in enumerate(x):
if y in b:
idx.append((i,j))
Of course, it is slow if the arrays are large, but it is very
readable, and probably very fast if cythonised.
David.
On Sat, Nov 24,
M = A[..., np.newaxis] == B
will give you a 40x60x20 boolean 3d-array where M[..., i] gives you a
boolean mask for all the occurrences of B[i] in A.
If you wanted all the (i, j) pairs for each value in B, you could do
something like
import numpy as np
from itertools import izip, groupby
from ope
