Is there a less verbose alternative to this:
for x in xrange(array.shape[0]):
for y in xrange(array.shape[1]):
do_stuff(x, y)
I came up with this:
for x, y in itertools.product(map(xrange, array.shape)):
do_stuff(x, y)
Which saves one indentation, but is still pretty ugly.
I'm hoping for something that looks like this pseudocode:
for x, y in array.indices:
do_stuff(x, y)
Does anything like that exist?
ベストアンサー1
I think you're looking for the ndenumerate.
>>> a =numpy.array([[1,2],[3,4],[5,6]])
>>> for (x,y), value in numpy.ndenumerate(a):
... print x,y
...
0 0
0 1
1 0
1 1
2 0
2 1
Regarding the performance. It is a bit slower than a list comprehension.
X = np.zeros((100, 100, 100))
%timeit list([((i,j,k), X[i,j,k]) for i in range(X.shape[0]) for j in range(X.shape[1]) for k in range(X.shape[2])])
1 loop, best of 3: 376 ms per loop
%timeit list(np.ndenumerate(X))
1 loop, best of 3: 570 ms per loop
If you are worried about the performance you could optimise a bit further by looking at the implementation of ndenumerate, which does 2 things, converting to an array and looping. If you know you have an array, you can call the .coords attribute of the flat iterator.
a = X.flat
%timeit list([(a.coords, x) for x in a.flat])
1 loop, best of 3: 305 ms per loop