numpy.ma.apply_along_axis¶
- numpy.ma.apply_along_axis(func1d, axis, arr, *args, **kwargs)[source]¶
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.
kwargs: any
Additional named arguments to func1d.
New in version 1.9.0.
Returns: apply_along_axis : 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.
>>> b = np.array([[8,1,7], [4,3,9], [5,2,6]]) >>> np.apply_along_axis(sorted, 1, b) array([[1, 7, 8], [3, 4, 9], [2, 5, 6]])