Numpy multidimensional advanced indexing
I have an array a with shape [3,x,y,z,n] (three 4d-images). And a second array b with shape [x,y,z] which contains the indices I want to choose from the first dimension of a (so the values of b are in the range 0 to 2).
The results I want to have would be of shape [x,y,z,n]. How can I do that in numpy?
numpy
asked Nov 23 '18 at 10:16
jasmok
288
add a comment |
I have an array a with shape [3,x,y,z,n] (three 4d-images). And a second array b with shape [x,y,z] which contains the indices I want to choose from the first dimension of a (so the values of b are in the range 0 to 2).
The results I want to have would be of shape [x,y,z,n]. How can I do that in numpy?
numpy
asked Nov 23 '18 at 10:16
jasmok
288
add a comment |
I have an array a with shape [3,x,y,z,n] (three 4d-images). And a second array b with shape [x,y,z] which contains the indices I want to choose from the first dimension of a (so the values of b are in the range 0 to 2).
The results I want to have would be of shape [x,y,z,n]. How can I do that in numpy?
numpy
asked Nov 23 '18 at 10:16
jasmok
288
I have an array a with shape [3,x,y,z,n] (three 4d-images). And a second array b with shape [x,y,z] which contains the indices I want to choose from the first dimension of a (so the values of b are in the range 0 to 2).
The results I want to have would be of shape [x,y,z,n]. How can I do that in numpy?
numpy
numpy
asked Nov 23 '18 at 10:16
jasmok
288
asked Nov 23 '18 at 10:16
jasmok
288
asked Nov 23 '18 at 10:16
jasmok
288
asked Nov 23 '18 at 10:16
jasmok
288
asked Nov 23 '18 at 10:16
jasmok
288
288
add a comment |
add a comment |
1 Answer
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Using advanced-indexing -
a[b,np.arange(x)[:,None,None],np.arange(y)[:,None],np.arange(z)]
A shorter way to express that would be -
a[tuple([b] + np.ogrid[:x,:y,:z])]
Using NumPy builtin np.take_along_axis to perform advanced-indexing by doing the dirty work under the hoods -
np.take_along_axis(a,b[None,...,None],axis=0)[0]
answered Nov 23 '18 at 10:23
Divakar
154k1483172
Great. It works as expected. Thanks!
– jasmok
Nov 23 '18 at 11:57
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Using advanced-indexing -
a[b,np.arange(x)[:,None,None],np.arange(y)[:,None],np.arange(z)]
A shorter way to express that would be -
a[tuple([b] + np.ogrid[:x,:y,:z])]
Using NumPy builtin np.take_along_axis to perform advanced-indexing by doing the dirty work under the hoods -
np.take_along_axis(a,b[None,...,None],axis=0)[0]
answered Nov 23 '18 at 10:23
Divakar
154k1483172
Great. It works as expected. Thanks!
– jasmok
Nov 23 '18 at 11:57
add a comment |
Using advanced-indexing -
a[b,np.arange(x)[:,None,None],np.arange(y)[:,None],np.arange(z)]
A shorter way to express that would be -
a[tuple([b] + np.ogrid[:x,:y,:z])]
Using NumPy builtin np.take_along_axis to perform advanced-indexing by doing the dirty work under the hoods -
np.take_along_axis(a,b[None,...,None],axis=0)[0]
answered Nov 23 '18 at 10:23
Divakar
154k1483172
Great. It works as expected. Thanks!
– jasmok
Nov 23 '18 at 11:57
add a comment |
Using advanced-indexing -
a[b,np.arange(x)[:,None,None],np.arange(y)[:,None],np.arange(z)]
A shorter way to express that would be -
a[tuple([b] + np.ogrid[:x,:y,:z])]
Using NumPy builtin np.take_along_axis to perform advanced-indexing by doing the dirty work under the hoods -
np.take_along_axis(a,b[None,...,None],axis=0)[0]
answered Nov 23 '18 at 10:23
Divakar
154k1483172
Using advanced-indexing -
a[b,np.arange(x)[:,None,None],np.arange(y)[:,None],np.arange(z)]
A shorter way to express that would be -
a[tuple([b] + np.ogrid[:x,:y,:z])]
Using NumPy builtin np.take_along_axis to perform advanced-indexing by doing the dirty work under the hoods -
np.take_along_axis(a,b[None,...,None],axis=0)[0]
answered Nov 23 '18 at 10:23
Divakar
154k1483172
edited Nov 23 '18 at 10:34
answered Nov 23 '18 at 10:23
Divakar
154k1483172
answered Nov 23 '18 at 10:23
Divakar
154k1483172
answered Nov 23 '18 at 10:23
Divakar
154k1483172
154k1483172
Great. It works as expected. Thanks!
– jasmok
Nov 23 '18 at 11:57
add a comment |
Great. It works as expected. Thanks!
– jasmok
Nov 23 '18 at 11:57
Great. It works as expected. Thanks!
– jasmok
Nov 23 '18 at 11:57
Great. It works as expected. Thanks!
– jasmok
Nov 23 '18 at 11:57
add a comment |
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