Is it possible to numpy.vectorize an instance method?

Question

I\'ve found that the numpy.vectorize allows one to convert \'ordinary\' functions which expect a single number as input to a function which can also convert a list of inputs

Answer 1

Here's a generic decorator that works with instance methods as well as functions (refer to Numpy's documentation for otypes and signature):

from functools import wraps

import numpy as np

def vectorize(otypes=None, signature=None):
    """Numpy vectorization wrapper that works with instance methods."""
    def decorator(fn):
        vectorized = np.vectorize(fn, otypes=otypes, signature=signature)
        @wraps(fn)
        def wrapper(*args):
            return vectorized(*args)
        return wrapper
    return decorator

You may use it to vectorize your method as follows:

class Dummy(object):
    def __init__(self, val=1):
        self.val = val

    @vectorize(signature="(),()->()")
    def f(self, x):
        if x == 0:
            return self.val
        else:
            return 2


def test_3():
    assert list(Dummy().f([0, 1, 2])) == [1, 2, 2]

The key is to make use of the signature kwarg. Parenthesized values to the left of -> specify input parameters and values to the right specify output values. () represents a scalar (0-dimensional vector); (n) represents a 1-dimensional vector; (m,n) represents a 2-dimensional vector; (m,n,p) represents a 3-dimensional vector; etc. Here, signature="(),()->()" specifies to Numpy that the first parameter (self) is a scalar, the second (x) is also a scalar, and the method returns a scalar (either self.val or 2, depending on x).

$ pytest /tmp/instance_vectorize.py
======================= test session starts ========================
platform linux -- Python 3.6.5, pytest-3.5.1, py-1.5.3, pluggy-0.6.0
rootdir: /tmp, inifile:
collected 1 item

../../tmp/instance_vectorize.py .                                                                                                                                                     [100%]

==================== 1 passed in 0.08 seconds ======================