'Test smoothing of an image with a single positive pixel
Uses a simple, small kernel'

@pytest.mark.parametrize((u'shape', u'width'), list(itertools.product(SHAPES_ODD, WIDTHS)))
def test_uniform_smallkernel(self, shape, width):

    if ((width % 2) == 0):
        return
    kernel = np.ones([width, width])
    x = np.zeros(shape)
    xslice = [slice((sh // 2), ((sh // 2) + 1)) for sh in shape]
    x[xslice] = 1.0
    c2 = convolve_fft(x, kernel, boundary=u'fill')
    c1 = convolve(x, kernel, boundary=u'fill')
    assert_almost_equal(c1, c2, decimal=12)
