Reputation: 242
I want to split an 2D array this way:
Example.
From this 4x4 2D array:
np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]])
Create these four 2x2 2D arrays:
np.array([[1,2],[3,4]])
np.array([[5,6],[7,8]])
np.array([[9,10],[11,12]])
np.array([[13,14],[15,16]])
In a general case, from a NxN 2D array (square arrays) create 2D arrays of KxK shape, as many as possible.
Just to be more precise: to create the output array, not necessarily it will be made of all values from the row.
Example:
From a 2D 8x8 array, with values from 1 to 64, if I want to split this array in 2D 2x2 arrays, the first row from 8x8 array is a row from 1 to 8, and the first output 2D 2x2 array will be np.array([[1,2],[3,4]]), and the second output 2D 2x2 array will be np.array([[5,6],[7,8]])... It continues until the last output 2D array, that will be np.array([[61,62],[63,64]]). Look that each 2D 2x2 array was not filled with all the values from the row (CORRECT).
There is a Numpy method that do this?
Upvotes: 3
Views: 2309
Reputation: 152870
To get your desired output, you need to reshape to a 3D array and then unpack the first dimension:
>>> inp = np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]])
>>> list(inp.reshape(-1, 2, 2))
[array([[1, 2],
[3, 4]]),
array([[5, 6],
[7, 8]]),
array([[ 9, 10],
[11, 12]]),
array([[13, 14],
[15, 16]])]
You can also unpack using =
if you want to store the arrays in different variables instead of in one list of arrays:
>>> out1, out2, out3, out4 = inp.reshape(-1, 2, 2)
>>> out1
array([[1, 2],
[3, 4]])
If you're okay with a 3D array containing your 2D 2x2 arrays you don't need unpacking or the list()
call:
>>> inp.reshape(-1, 2, 2)
array([[[ 1, 2],
[ 3, 4]],
[[ 5, 6],
[ 7, 8]],
[[ 9, 10],
[11, 12]],
[[13, 14],
[15, 16]]])
The -1
is a special value for reshape
. As the documentation states:
One shape dimension can be -1. In this case, the value is inferred from the length of the array and remaining dimensions.
If you want it more general, just take the square root of the row-length and use that as argument for reshape
:
>>> inp = np.ones((8, 8)) # 8x8 array
>>> square_shape = 2
>>> inp.reshape(-1, square_shape, square_shape) # 16 2x2 arrays
>>> square_shape = 4
>>> inp.reshape(-1, square_shape, square_shape) # 4 4x4 arrays
Upvotes: 2
Reputation: 80851
You're probably looking for something like numpy.reshape
.
In your example:
numpy.array([[1,2,3,4], [5,6,7,8]]).reshape(2,4)
>>>array([[1,2], [3,4], [5,6], [7,8]])
Or, as suggested by @MSeifert, using -1 as final dimension will let numpy do the division by itself:
numpy.array([[1,2,3,4], [5,6,7,8]]).reshape(2,-1)
>>>array([[1,2], [3,4], [5,6], [7,8]])
Upvotes: 3
Reputation: 426
If you want to split it row wise, you may do np.reshape(arr,(2,2), order='C') If you want to split it column wise, you may do not.reshape(arr,(2,2), order='F')
Upvotes: 0