diff options
Diffstat (limited to 'numpy/lib/shape_base.py')
| -rw-r--r-- | numpy/lib/shape_base.py | 28 |
1 files changed, 23 insertions, 5 deletions
diff --git a/numpy/lib/shape_base.py b/numpy/lib/shape_base.py index a8977bd4c..fcd5028f0 100644 --- a/numpy/lib/shape_base.py +++ b/numpy/lib/shape_base.py @@ -27,14 +27,32 @@ def apply_along_axis(func1d, axis, arr, *args, **kwargs): Execute `func1d(a, *args)` where `func1d` operates on 1-D arrays and `a` is a 1-D slice of `arr` along `axis`. + This is equivalent to (but faster than) the following use of `ndindex` and + `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]) + Parameters ---------- - func1d : function + func1d : function (M,) -> (Nj...) 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 + arr : ndarray (Ni..., M, Nk...) Input array. args : any Additional arguments to `func1d`. @@ -46,11 +64,11 @@ def apply_along_axis(func1d, axis, arr, *args, **kwargs): Returns ------- - apply_along_axis : ndarray - The output array. The shape of `outarr` is identical to the shape of + out : ndarray (Ni..., Nj..., Nk...) + The output array. The shape of `out` is identical to the shape of `arr`, except along the `axis` dimension. This axis is removed, and replaced with new dimensions equal to the shape of the return value - of `func1d`. So if `func1d` returns a scalar `outarr` will have one + of `func1d`. So if `func1d` returns a scalar `out` will have one fewer dimensions than `arr`. See Also |