I want to define an array which contains all combinations of values
[0, 0], [0, 1], [0, 2], ..., [0,N],
[1, 0], [1, 1], [1, 2], ..., [1, N],
...
[N, 0], [N, 1], [N,2], ..., [N,N].
Obviously, one could do something like this:
N = 10
array = np.array([[0,0]])
for i in range(N):
for j in range(N):
array = np.append(array, [[i,j]], axis=0)
print(array)
However, I find this "ugly". Is there a clean way to generate such an array?
You can use np.indices, if you prefer:
data=np.indices((512,512)).swapaxes(0,2).swapaxes(0,1)
data.shape
# output: (512, 512, 2)
data[5,0]
# output: array([5, 0])
data[5,25]
#output: array([5, 25])
np.transpose(data, (1, 2, 0)). I find it more readable than chaining swapaxes.Here are a few ideas you can probably use as well.
Using combinations from itertools:
from itertools import combinations
someList = [i for i in combinations(range(N), 2)]
Using product is a nice way to reduce nested for loops and condense down into list comprehensions:
someList = [[i,j] for i,j in product(range(N), range(N))]
