weight/window functions with constant sum for infinite discrete sampling, like triangle functions Imagine a function $w_n(x) : R \rightarrow R , n \in N$ such that:
$w_n(x) = 0  , \forall x \notin [-nL, nL], L \in R $
$ 1 = \sum\limits_{k=-\infty}^{\infty} w_n(x - kL), k \in N, \forall x \in R$
I know there are many possible $w_n(x)$ functions. For example, for $ n = 1 $, the triangle function simply works , but I'm looking for smoother functions which work for any n (you can make it work for any n with triangle functions but it's just not smooth).
Also, with the aim of adding a context, I'm doing this because I want to implement a lightfield renderer using this paper . You can see some example functions like the triangle function in page 3, Figure 6:

On that picture, you can see that functions $ w_2 $ and $w_3 $ but unfortunately I just don't know which functions are, although they look similar to a variation of $ {sin (\pi x) \over \pi x } {1 \over 1 + |x|^2 } $ 
What kind of functions $w_n$ do you know and what is their name so that I can look them up? 
 A: I've been thinking about this and I think I have at least an answer on how to build $ w_n (x) $ functions. 
First, I have to argue why the $ sinc(x) $ function and its variations are quite nice. The following will assume L = 1. Let's define a sample function:
$ sample(x, func) = \sum\limits_{k=-\infty}^{\infty} w_n(x - k) $
If we use the bare $ sinc(x) $, without limiting it with $ rect(x/M) $, then:
$ sample(x, sinc) = 1, \forall x \in R $
So, in the limit ($ n \rightarrow \infty$), the $ sinc(x) $ function is what we want. But what happens if we limit it with $ f(x) = sinc(x) rect(x /6)$?
Then $ sample(x, f) \neq 1, \forall x \in R$ , but it's somewhat close, as you can see:
sample(x,f)
So the sample function oscillates between 1 and 1.1. Now the idea is simply to "normalize" f by dividing it by the sample function!
$ f(x) = sinc(x) rect(x /6)$
$ g(x) = { f(x) \over sample(x, f) } $
In this picture you can see the difference between f and g:
enter image description here
EDIT: I've found out that while the previous explanation works, a smoother and well-known function is the Lanczos kernel:
$ k(x) = sinc(x) sinc({x \over a}) rect({x \over 2a}) $
The lanczos kernel also has the property $ 1 = sample(x, k) $
