Is there a filter similar to ndimage's generic_filter that supports vector output? I did not manage to make scipy.ndimage.filters.generic_filter return more than a scalar. Uncomment the line in the code below to get the error: TypeError: only length-1 arrays can be converted to Python scalars.

I'm looking for a generic filter that process 2D or 3D arrays and returns a vector at each point. Thus the output would have one added dimension. For the example below I'd expect something like this:

m.shape    # (10,10)
res.shape  # (10,10,2)

Example Code

import numpy as np
from scipy import ndimagea = np.ones((10, 10)) * np.arange(10)footprint = np.array([[1,1,1],[1,0,1],[1,1,1]])def myfunc(x):r = sum(x)#r = np.array([1,1])  # uncomment thisreturn rres = ndimage.generic_filter(a, myfunc, footprint=footprint)

The generic_filter expects myfunc to return a scalar, never a vector. However, there is nothing that precludes myfunc from also adding information to, say, a list which is passed to myfunc as an extra argument.

Instead of using the array returned by generic_filter, we can generate our vector-valued array by reshaping this list.

For example,

import numpy as np
from scipy import ndimagea = np.ones((10, 10)) * np.arange(10)footprint = np.array([[1,1,1],[1,0,1],[1,1,1]])ndim = 2
def myfunc(x, out):r = np.arange(ndim, dtype='float64')out.extend(r)return 0result = []
ndimage.generic_filter(a, myfunc, footprint=footprint, extra_arguments=(result,))
result = np.array(result).reshape(a.shape+(ndim,))