I want to resize a 2D array of the size (25,180) to size (50,360) with keeping all the values at the same relative place. If it was an image id should still look the same but have 4 times the pixels. So for a smaller array example of (2,3):
((1,3,6),
(4,7,8))
it should look like (4,6):
((1,1,3,3,6,6),
(1,1,3,3,6,6),
(4,4,7,7,8,8),
(4,4,7,7,8,8))
I have tried resize and reshape but none seemed to do the job for me. formulating this question i have found an easy solution using loops:
But I bet theres a clever and/or build in function for something like this right?
I have tried resize and reshape but none seemed to do the job for me. formulating this question i have found an easy solution using loops. But I bet theres a clever and/or build in function for something like this right?
ary = np.array(((1,3,6),(4,7,8)))
aarryy=np.zeros((4,6))
for i in range(len(ary)):
#print(ary[i])
for j in range(len(ary[i])):
#print(ary[i][j])
aarryy[i*2][j*2]=ary[i][j]
aarryy[i*2][j*2+1]=ary[i][j]
aarryy[i*2+1]=aarryy[i*2]
I am pretty sure there are some more elegant numpy magic (indexing is one of my weak point in numpy), and that someone will post one. But in the meantime, here is a one-liner
ary[np.arange(len(ary)*2)//2][:,np.arange(ary.shape[1]*2)//2]
The trick is that np.arange(n*2)//2 is array([0,0,1,1,...,n-1,n-1]. So I use that to index lines, and then same for columns