# Is it possible to pass a np.recarray to a guvectorized function?

I’m trying to write a function decorated with `@guvectorize` which accepts numpy recarrays. It does work successfully with `@njit`, but I would like to use `@guvectorize` since it plays nicely with Dask. I’ve tried the following:

``````import numba as nb
import numpy as np

size = 10
arr = np.rec.fromarrays([np.arange(size), np.random.uniform(size=size), np.random.uniform(size=size)], names=['x', 'y', 'z'])
ty = nb.from_dtype(arr.dtype)

@nb.njit
def test_njit(a, b, out):
for i in range(len(a)):
# ...dummy example
out[i]['x'] = a[i]['x']
out[i]['y'] = a[i]['y'] * b
out[i]['z'] = a[i]['z'] / b

@nb.guvectorize([(ty[:], nb.float64, ty[:])], '(n),()->(n)')
def test_guvec(a, b, out):
for i in range(len(a)):
# ...dummy example
out[i]['x'] = a[i]['x']
out[i]['y'] = a[i]['y'] * b
out[i]['z'] = a[i]['z'] / b

out1 = np.zeros_like(arr)
test_njit(arr, 2., out1)  # This runs successfully
print(out1)

out2 = np.zeros_like(arr)
test_guvec(arr, 2., out2)  # This fails
print(out2)
``````

For `test_guvec`, I get the following error: `ufunc 'test_guvec' output (typecode 'V') could not be coerced to provided output parameter (typecode 'V') according to the casting rule ''same_kind''`

this seems like it might possibly be related to this issue