Python numpy array of functions

Question

I've been looking for ways to avoid long "for" loops as I'll be dealing with mesh operations and was wondering if there was a way to make an array of functions. Something like the following would be nice.

x=np.array([1,2,3,4,5])
funcs=np.array([func1,func2,func3,func4],dtype=function)

output=funcs(x)

Does this answer your question? Calling functions by array index in Python — Bando
– Bando, Commented Mar 10, 2020 at 11:45
This really looks like an XY problem meta.stackexchange.com/questions/66377/what-is-the-xy-problem — ChatterOne
– ChatterOne, Commented Mar 10, 2020 at 11:48
Hey Bandoleras, that would still involve using a for loop, which is what I'm trying to avoid! — Cam K
– Cam K, Commented Mar 10, 2020 at 11:53
'avoiding loops' in a numpy context really means 'do the loops in compiled code'. There's a lot you can do with numeric dtype arrays. But your funcs is object dtype (look at it!). Most math on object arrays uses python level iteration (explicit or hidden). In any case, there's no numpy functionality that will do [func1(1), func2(2), ....] — hpaulj
– hpaulj, Commented Mar 10, 2020 at 16:35

a_guest · Accepted Answer · 2020-03-10 12:01:52Z

3

You can just create a list of functions and then use a list comprehension for evaluating them:

x = np.arange(5) + 1
funcs = [np.min, np.mean, np.std]
output = [f(x) for f in funcs]

If you really think that funcs(x) reads nicer in your code, you can create a custom class that wraps the above logic:

class Functions:
    def __init__(self, *funcs):
        self.funcs = funcs

    def __call__(self, *args, **kwargs):
        return [f(*args, **kwargs) for f in self.funcs]

funcs = Functions(np.min, np.mean, np.std)
output = funcs(x)

answered Mar 10, 2020 at 12:01

a_guest

36.7k15 gold badges75 silver badges137 bronze badges

Sign up to request clarification or add additional context in comments.

3 Comments

Cam K Over a year ago

Hey, this looks amazing! It still uses a for loop though, which is what I was trying to avoid! Is there any way to avoid using a for loop? In Python, for loops really slow down my code especially with lots of mesh elements.

a_guest Over a year ago

@CamK No, they don't slow down your code. A list comprehension is as fast as it can get. The required memory is known from the start and the loop itself runs in C. These function calls happen in Python so you can't get around that. I suppose you're not calling that many functions anyway?

Cam K Over a year ago

I was thinking this might be handy if I wanted to treat each element/cell in a mesh differently. A function for each object type wouldn't be so bad. A function for each mesh element would be in the thousands! I've found a way around using another for loop, but this is good to have! Thank you!

Collectives™ on Stack Overflow

Python numpy array of functions

1 Answer 1

3 Comments

Your Answer

Linked

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

3 Comments

Your Answer

Sign up or log in

Post as a guest

Linked

Related