numpy.ma.apply_along_axis

numpy.ma.apply_along_axis(func1d, axis, arr, *args, **kwargs)

Apply a function to 1-D slices along the given axis.

Execute func1d(a, *args) where func1d operates on 1-D arrays and a is a 1-D slice of arr along axis.

Parameters :

func1d : function

This function should accept 1-D arrays. It is applied to 1-D slices of arr along the specified axis.

axis : integer

Axis along which arr is sliced.

arr : ndarray

Input array.

args : any

Additional arguments to func1d.

Returns :

outarr : ndarray

The output array. The shape of outarr is identical to the shape of arr, except along the axis dimension, where the length of outarr is equal to the size of the return value of func1d. If func1d returns a scalar outarr will have one fewer dimensions than arr.

See also

apply_over_axes

Apply a function repeatedly over multiple axes.

Examples

>>> def my_func(a):
...     """Average first and last element of a 1-D array"""
...     return (a[0] + a[-1]) * 0.5
>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> np.apply_along_axis(my_func, 0, b)
array([ 4.,  5.,  6.])
>>> np.apply_along_axis(my_func, 1, b)
array([ 2.,  5.,  8.])

For a function that doesn’t return a scalar, the number of dimensions in outarr is the same as arr.

>>> 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]])