np.vectorize giving me IndexError: invalid index to scalar variable

Question

trying out something simple and it's frustratingly not working:

def myfunc(a,b):
  return a+b[0]

v = np.vectorize(myfunc, exclude=['b'])

a = np.array([1,2,3])
b = [0]

v(a,b)

This gives me "IndexError: invalid index to scalar variable." Upon printing b, it appears that the b taken in by the function is always 0, instead of [0]. Can I specify which arguments should be vectorized and which should remain constant?

Answer 1

When you use excluded=['b'] the keyword parameter b is excluded. Therefore, you must call v with keyword arguments, e.g. v(a=a, b=b) instead of v(a, b).

If you wish to call v with positional arguments with the second positional argument excluded, then use

v = np.vectorize(myfunc)
v.excluded.add(1)

---

For example,

import numpy as np

def myfunc(a, b):
    return a+b[0]

a = np.array([1,2,3])
b = [0, 1]

v = np.vectorize(myfunc, excluded=['b'])
print(v(a=a, b=b))
# [1 2 3]

v = np.vectorize(myfunc)
v.excluded.add(1)
print(v(a, b))
# [1 2 3]

Answer 2

Well here is the answer:

v.excluded.add(1) works, though passing exclude=['b'] does not, for some reason.