I have numpy arrays of shape (600,600,3), where the values are [-1.0, 1.0]. I would like to expand the array to (600,600,6), where the original values are split into the amounts above and below 0. Some examples (1,1,3) arrays, where th function foo() does the trick:
>>> a = [-0.5, 0.2, 0.9]
>>> foo(a)
[0.0, 0.5, 0.2, 0.0, 0.9, 0.0] # [positive component, negative component, ...]
>>> b = [1.0, 0.0, -0.3] # notice the behavior of 0.0
>>> foo(b)
[1.0, 0.0, 0.0, 0.0, 0.0, 0.3]
Use slicing to assign the min/max to different parts of the output array
In [33]: a = np.around(np.random.random((2,2,3))-0.5, 1)
In [34]: a
Out[34]:
array([[[-0.1, 0.3, 0.3],
[ 0.3, -0.2, -0.1]],
[[-0. , -0.2, 0.3],
[-0.1, -0. , 0.1]]])
In [35]: out = np.zeros((2,2,6))
In [36]: out[:,:,::2] = np.maximum(a, 0)
In [37]: out[:,:,1::2] = np.maximum(-a, 0)
In [38]: out
Out[38]:
array([[[ 0. , 0.1, 0.3, 0. , 0.3, 0. ],
[ 0.3, 0. , 0. , 0.2, 0. , 0.1]],
[[-0. , 0. , 0. , 0.2, 0.3, 0. ],
[ 0. , 0.1, -0. , 0. , 0.1, 0. ]]])
