Python Passing List or Numpy Array

Question

I wonder if anyone has an elegant solution to being able to pass a python list, a numpy vector (shape(n,)) or a numpy vector (shape(n,1)) to a function. The idea would be to generalize a function such that any of the three would be valid without adding complexity.

Initial thoughts:

1) Use a type checking decorator function and cast to a standard representation.
2) Add type checking logic inline (significantly less ideal than #1).
3) ?

I do not generally use python builtin array types, but suspect a solution to this question would also support those.

You realise you can just do it? Python is dynamically typed, you don't have to specify parameter types. — jonrsharpe
– jonrsharpe, Commented Jul 7, 2014 at 20:04
Since all of the types support iterating and getting items by index, I would just use Duck typing. You can simply treat it as a list. — Wolph
– Wolph, Commented Jul 7, 2014 at 20:07
@Wolph if he uses numpy arrays, then he most likely wants to avoid explicitly iterating over the arrays — shx2
– shx2, Commented Jul 7, 2014 at 20:09
@shx2, I suppose your right. In that case I would like to quote Knuth: "Premature optimization is the root of all evil (or at least most of it) in programming.". If he still needs it, indeed... option 1. — Wolph
– Wolph, Commented Jul 7, 2014 at 20:12

tbekolay · Accepted Answer · 2014-07-07 20:15:31Z

3

I think the simplest thing to do is to start off your function with numpy.atleast_2d. Then, all 3 of your possibilities will be converted to the x.shape == (n, 1) case, and you can use that to simplify your function.

For example,

def sum(x):
    x = np.atleast_2d(x)
    return np.dot(x, np.ones((x.shape[0], 1)))

atleast_2d returns a view on that array, so there won't be much overhead if you pass in something that's already an ndarray. However, if you plan to modify x and therefore want to make a copy instead, you can do x = np.atleast_2d(np.array(x)).

edited Jul 7, 2014 at 20:15

answered Jul 7, 2014 at 20:08

tbekolay

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1 Comment

Jzl5325 Over a year ago

This works, but it is messy in the case of many functions - quite a bit of code duplication that a decorator avoids.

shx2 · Accepted Answer · 2014-07-07 20:07:40Z

2

You can convert the three types to a "canonical" type, which is a 1dim array, using:

arr = np.asarray(arr).ravel()

Put in a decorator:

import numpy as np
import functools

def takes_1dim_array(func):
    @functools.wraps(func)
    def f(arr, *a, **kw):
        arr = np.asarray(arr).ravel()
        return func(arr, *a, **kw)
    return f

Then:

@takes_1dim_arr
def func(arr):
   print arr.shape

answered Jul 7, 2014 at 20:07

shx2

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1 Comment

Jzl5325 Over a year ago

very nice. Spot on re: not wanting to iterate as well. I was leaning in the decorator direction, but did not want to miss a trivial solutions with something that I convinced myself was clean.

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