How To Nicely Handle [:-0] Slicing?

In implementing an autocorrelation function I have a term like for k in range(start,N): c[k] = np.sum(f[:-k] * f[k:])/(N-k) Now everything works fine if start = 1 but I'd like

Solution 1:

Don't use negative indices in this case

f[:len(f)-k]

For k == 0 it returns the whole array. For any other positive k it's equivalent to f[:-k]

Solution 2:

If k is zero use None for the slice, like so:

for k inrange(start,N):
    c[k] = np.sum(f[:-k if k elseNone] * f[k:])/(N-k)

Solution 3:

There are several ways of doing it, by testing if k==0 before applying the formula. It's up to you to find the only that looks nicer.

for k inrange(start,N):
    c[k] = np.sum(f[:-k] * f[k:])/(N-k) if k !=0else np.sum(f * f[k:])/(N-k)

for k inrange(start,N):
    end_in_list =-k if k !=0elseNone
    start_in_list = k
    c[k] = np.sum(f[:end_in_list] * f[start_in_list:])/(N-k)