diff options
Diffstat (limited to 'numpy/lib/shape_base.py')
| -rw-r--r-- | numpy/lib/shape_base.py | 32 |
1 files changed, 23 insertions, 9 deletions
diff --git a/numpy/lib/shape_base.py b/numpy/lib/shape_base.py index 4fdaba349..a6d391728 100644 --- a/numpy/lib/shape_base.py +++ b/numpy/lib/shape_base.py @@ -12,7 +12,7 @@ from numpy.core.numeric import asarray, zeros, newaxis, outer, \ from numpy.core.fromnumeric import product, reshape from numpy.core import hstack, vstack, atleast_3d -def apply_along_axis(func1d,axis,arr,*args): +def apply_along_axis(func1d, axis, arr, *args, **kwargs): """ Apply a function to 1-D slices along the given axis. @@ -30,6 +30,11 @@ def apply_along_axis(func1d,axis,arr,*args): Input array. args : any Additional arguments to `func1d`. + kwargs: any + Additional named arguments to `func1d`. + + .. versionadded:: 1.9.0 + Returns ------- @@ -78,7 +83,7 @@ def apply_along_axis(func1d,axis,arr,*args): i[axis] = slice(None, None) outshape = asarray(arr.shape).take(indlist) i.put(indlist, ind) - res = func1d(arr[tuple(i.tolist())],*args) + res = func1d(arr[tuple(i.tolist())], *args, **kwargs) # if res is a number, then we have a smaller output array if isscalar(res): outarr = zeros(outshape, asarray(res).dtype) @@ -94,7 +99,7 @@ def apply_along_axis(func1d,axis,arr,*args): ind[n] = 0 n -= 1 i.put(indlist, ind) - res = func1d(arr[tuple(i.tolist())],*args) + res = func1d(arr[tuple(i.tolist())], *args, **kwargs) outarr[tuple(ind)] = res k += 1 return outarr @@ -115,7 +120,7 @@ def apply_along_axis(func1d,axis,arr,*args): ind[n] = 0 n -= 1 i.put(indlist, ind) - res = func1d(arr[tuple(i.tolist())],*args) + res = func1d(arr[tuple(i.tolist())], *args, **kwargs) outarr[tuple(i.tolist())] = res k += 1 return outarr @@ -153,6 +158,12 @@ def apply_over_axes(func, a, axes): apply_along_axis : Apply a function to 1-D slices of an array along the given axis. + Notes + ------ + This function is equivalent to tuple axis arguments to reorderable ufuncs + with keepdims=True. Tuple axis arguments to ufuncs have been availabe since + version 1.7.0. + Examples -------- >>> a = np.arange(24).reshape(2,3,4) @@ -172,6 +183,13 @@ def apply_over_axes(func, a, axes): [ 92], [124]]]) + Tuple axis arguments to ufuncs are equivalent: + + >>> np.sum(a, axis=(0,2), keepdims=True) + array([[[ 60], + [ 92], + [124]]]) + """ val = asarray(a) N = a.ndim @@ -274,10 +292,6 @@ def column_stack(tup): -------- hstack, vstack, concatenate - Notes - ----- - This function is equivalent to ``np.vstack(tup).T``. - Examples -------- >>> a = np.array((1,2,3)) @@ -638,7 +652,7 @@ def dsplit(ary, indices_or_sections): """ if len(_nx.shape(ary)) < 3: - raise ValueError('vsplit only works on arrays of 3 or more dimensions') + raise ValueError('dsplit only works on arrays of 3 or more dimensions') return split(ary, indices_or_sections, 2) def get_array_prepare(*args): |