apply_along_axis(func1d, axis, arr, *args, **kwargs)
Execute :None:None:`func1d(a, *args, **kwargs)` where :None:None:`func1d` operates on 1-D arrays and a is a 1-D slice of :None:None:`arr` along :None:None:`axis`.
This is equivalent to (but faster than) the following use of ndindex
and :None:None:`s_`, which sets each of ii
, jj
, and kk
to a tuple of indices:
Ni, Nk = a.shape[:axis], a.shape[axis+1:]
for ii in ndindex(Ni):
for kk in ndindex(Nk):
f = func1d(arr[ii + s_[:,] + kk])
Nj = f.shape
for jj in ndindex(Nj):
out[ii + jj + kk] = f[jj]
Equivalently, eliminating the inner loop, this can be expressed as:
Ni, Nk = a.shape[:axis], a.shape[axis+1:]
for ii in ndindex(Ni):
for kk in ndindex(Nk):
out[ii + s_[...,] + kk] = func1d(arr[ii + s_[:,] + kk])
This function should accept 1-D arrays. It is applied to 1-D slices of :None:None:`arr` along the specified axis.
Axis along which :None:None:`arr` is sliced.
Input array.
Additional arguments to :None:None:`func1d`.
Additional named arguments to :None:None:`func1d`.
The output array. The shape of :None:None:`out` is identical to the shape of :None:None:`arr`, except along the :None:None:`axis` dimension. This axis is removed, and replaced with new dimensions equal to the shape of the return value of :None:None:`func1d`. So if :None:None:`func1d` returns a scalar :None:None:`out` will have one fewer dimensions than :None:None:`arr`.
Apply a function to 1-D slices along the given axis.
apply_over_axes
Apply a function repeatedly over multiple axes.
>>> 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 returns a 1D array, the number of dimensions in :None:None:`outarr` is the same as :None:None:`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]])
For a function that returns a higher dimensional array, those dimensions are inserted in place of the :None:None:`axis` dimension.
>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])See :
... np.apply_along_axis(np.diag, -1, b) array([[[1, 0, 0], [0, 2, 0], [0, 0, 3]], [[4, 0, 0], [0, 5, 0], [0, 0, 6]], [[7, 0, 0], [0, 8, 0], [0, 0, 9]]])
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