# make a kernel

def gaussian2d(grid, s):
    m = [0., 0.]
    cov = [[s*s, 0], [0, s*s]]
    var = multivariate_normal(mean=m, cov=cov)
    return var.pdf(grid)

kernelSize = 15
x = np.arange(-kernelSize+1, kernelSize, 1)
y = x.copy()
x0, y0 = np.meshgrid(x, y)
grid = np.dstack((y0, x0))

# use a kernel for convolution
kernel = gaussian2d(grid,s=sig)
 ndimage.filters.convolve(image, kernel, mode='constant', cval=0)