NumPy: Apply an index selecting function to multiple axes

I'm writing code to optimize quantities that depend on a variable number of parameters. For the optimization I would like to apply index selecting functions such as numpy.argmax and numpy.argmin across multiple axes at once. Below is the code I'm using right now. Is there a more built-in or efficient approach to perform this task across an arbitrary number of axes that may or may not be sequential?

def nd_arg_axes(func, array, start):
    """Applies an index selecting function over trailing axes from start."""

    n_trail = len(array.shape[start:])  # Number of trailing axes to apply to.

    indices = np.zeros((n_trail,)+array.shape[:start], dtype=np.intp)
    for i in np.ndindex(array.shape[:start]):
        indices[(Ellipsis,)+i] = np.unravel_index(func(array[i]),
                                               array.shape[start:])
    return tupl开发者_如何学JAVAe(indices)

# Test showing nd_arg_axes does indeed return the correct indices.
array = np.arange(27).reshape(3,3,3)
max_js = nd_arg_axes(np.argmax, array, 1)

(array[tuple(np.indices(array))+max_js] ==
np.squeeze(np.apply_over_axes(np.amax, array, axes=[1,2])))

If you are selecting over trailing axes, you can reshape the trailing axes to -1, and apply func to axis=-1:

def f(func, array, start):
    shape = array.shape
    tmp = array.reshape(shape[:start] + (-1,))
    indices = func(tmp, axis=-1)
    return np.unravel_index(indices, shape[start:])