[FIXED] what does numpy.apply_along_axis perform exactly?

Issue

I have come across the numpy.apply_along_axis function in some code. And I don't understand the documentation about it.

This is an example of the documentation:

>>> def new_func(a):
...     """Divide elements of a by 2."""
...     return a * 0.5
>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> np.apply_along_axis(new_func, 0, b)
array([[ 0.5,  1. ,  1.5],
       [ 2. ,  2.5,  3. ],
       [ 3.5,  4. ,  4.5]])

As far I as thought I understood the documentation, I would have expected:

array([[ 0.5,  1. ,  1.5],
       [ 4  ,  5  ,  6  ],
       [ 7  ,  8  ,  9  ]])

i.e. having applied the function along the axis [1,2,3] which is axis 0 in [[1,2,3], [4,5,6], [7,8,9]]

Obviously I am wrong. Could you correct me ?

Solution

apply_along_axis applies the supplied function along 1D slices of the input array, with the slices taken along the axis you specify. So in your example, new_func is applied over each slice of the array along the first axis. It becomes clearer if you use a vector valued function, rather than a scalar, like this:

In [20]: b = np.array([[1,2,3], [4,5,6], [7,8,9]])

In [21]: np.apply_along_axis(np.diff,0,b)
Out[21]: 
array([[3, 3, 3],
       [3, 3, 3]])

In [22]: np.apply_along_axis(np.diff,1,b)
Out[22]: 
array([[1, 1],
       [1, 1],
       [1, 1]])

Here, numpy.diff (i.e. the arithmetic difference of adjacent array elements) is applied along each slice of either the first or second axis (dimension) of the input array.

Answered By - talonmies