apply_over_axes(func, a, axes)
:None:None:`func` is called as :None:None:`res = func(a, axis)`, where :None:None:`axis` is the first element of :None:None:`axes`. The result :None:None:`res` of the function call must have either the same dimensions as a or one less dimension. If :None:None:`res` has one less dimension than a, a dimension is inserted before :None:None:`axis`. The call to :None:None:`func` is then repeated for each axis in :None:None:`axes`, with :None:None:`res` as the first argument.
This function must take two arguments, :None:None:`func(a, axis)`.
Input array.
Axes over which :None:None:`func` is applied; the elements must be integers.
The output array. The number of dimensions is the same as a, but the shape can be different. This depends on whether :None:None:`func` changes the shape of its output with respect to its input.
Apply a function repeatedly over multiple axes.
apply_along_axis
Apply a function to 1-D slices of an array along the given axis.
>>> a = np.ma.arange(24).reshape(2,3,4)
... a[:,0,1] = np.ma.masked
... a[:,1,:] = np.ma.masked
... a masked_array( data=[[[0, --, 2, 3], [--, --, --, --], [8, 9, 10, 11]], [[12, --, 14, 15], [--, --, --, --], [20, 21, 22, 23]]], mask=[[[False, True, False, False], [ True, True, True, True], [False, False, False, False]], [[False, True, False, False], [ True, True, True, True], [False, False, False, False]]], fill_value=999999)
>>> np.ma.apply_over_axes(np.ma.sum, a, [0,2]) masked_array( data=[[[46], [--], [124]]], mask=[[[False], [ True], [False]]], fill_value=999999)
Tuple axis arguments to ufuncs are equivalent:
>>> np.ma.sum(a, axis=(0,2)).reshape((1,-1,1)) masked_array( data=[[[46], [--], [124]]], mask=[[[False], [ True], [False]]], fill_value=999999)See :
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