Shear a numpy array

I'd like to 'shear' a numpy array. I'm not sure I'm using the term 'shear' correctly; by shear, I mean something like:

Shift the first column by 0 places
Shift the second column by 1 place
Shift the third colum by 2 places
etc...

So this array:

array([[11, 12, 13],
       [17, 18, 19],
       [35, 36, 37]])

would turn into either this array:

array([[11, 36, 19],
       [17, 12, 37],
       [35, 18, 13]])

or something like this array:

array([[11,  0,  0],
       [17, 12,  0],
       [35, 18, 13]])

depending on how we handle the edges. I'm not too particular about edge behavior.

Here's my attempt at a function that does this:

import numpy

def shear(a, strength=1, shift_axis=0, increase_axis=1, edges='clip'):
    strength = int(strength)
    shift_axis = int(shift_axis)
    increase_axis = int(increase_axis)
    if shift_axis == increase_axis:
        raise UserWarning("Shear can't shift in the direction it increases")
    temp = numpy.zeros(a.shape, dtype=int)
    indices = []
    for d, num in enumerate(a.shape):
        coords = numpy.arange(num)
        shape = [1] * len(a.shape)
        shape[d] = num
        coords = coords.reshape(shape) + temp
        indices.append(coords)
    indices[shift_axis] -= strength * indices[increase_axis]
    if edges == 'clip':
        indices[shift_axis][indices[shift_axis] < 0] = -1
        indices[shift_axis][indices[shift_axis] >= a.shape[shift_axis]] = -1
        res = a[indices]
        res[indices[shift_axis] == -1] = 0
    elif edges == 'roll':
        indices[shift_axis] %= a.shape[shift_axis]
        res = a[indices]
    return res

if __name__ == '__main__':
    a = numpy.random.random((3,4))
    print a
    print shear(a)

It seems to work. Please tell me if it doesn't!

It also seems clunky and inelegant. Am I overlooking a builtin nu